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Psychological Research Methods: Types and Tips
Categories Research Methods
Psychological research methods are the techniques used by scientists and researchers to study human behavior and mental processes. These methods are used to gather empirical evidence.
The goal of psychological research methods is to obtain objective and verifiable data collected through scientific experimentation and observation.
The research methods that are used in psychology are crucial for understanding how and why people behave the way they do, as well as for developing and testing theories about human behavior.
Table of Contents
Reasons to Learn More About Psychological Research Methods
One of the key goals of psychological research is to make sure that the data collected is reliable and valid.
- Reliability means that the data is consistent and can be replicated
- Validity refers to the accuracy of the data collected
Researchers must take great care to ensure that their research methods are reliable and valid, as this is essential for drawing accurate conclusions and making valid claims about human behavior.
High school and college students who are interested in psychology can benefit greatly from learning about research methods. Understanding how psychologists study human behavior and mental processes can help students develop critical thinking skills and a deeper appreciation for the complexity of human behavior.
Having an understanding of these research methods can prepare students for future coursework in psychology, as well as for potential careers in the field.
Quantitative vs. Qualitative Psychological Research Methods
Psychological research methods can be broadly divided into two main types: quantitative and qualitative. These two methods differ in their approach to data collection and analysis.
Quantitative Research Methods
Quantitative research methods involve collecting numerical data through controlled experiments, surveys, and other objective measures.
The goal of quantitative research is to identify patterns and relationships in the data that can be analyzed statistically.
Researchers use statistical methods to test hypotheses, identify significant differences between groups, and make predictions about future behavior.
Qualitative Research Methods
Qualitative research methods, on the other hand, involve collecting non-numerical data through open-ended interviews, observations, and other subjective measures.
Qualitative research aims to understand the subjective experiences and perspectives of individuals and groups.
Researchers use methods such as content analysis and thematic analysis to identify themes and patterns in the data and to develop rich descriptions of the phenomenon under study.
How Quantitative and Qualitative Methods Are Used
While quantitative and qualitative research methods differ in their approach to data collection and analysis, they are often used together to gain a more complete understanding of complex phenomena.
For example, a researcher studying the impact of social media on mental health might use a quantitative survey to gather numerical data on social media use and a qualitative interview to gain insight into participants’ subjective experiences with social media.
Types of Psychological Research Methods
There are several types of research methods used in psychology, including experiments, surveys, case studies, and observational studies. Each method has its strengths and weaknesses, and researchers must choose the most appropriate method based on their research question and the data they hope to collect.
Case Studies
A case study is a research method used in psychology to investigate an individual, group, or event in great detail. In a case study, the researcher gathers information from a variety of sources, including:
- Observation
- Document analysis
These methods allow researchers to gain an in-depth understanding of the case being studied.
Case studies are particularly useful when the phenomenon under investigation is rare or complex, and when it is difficult to replicate in a laboratory setting.
Surveys are a commonly used research method in psychology that involve gathering data from a large number of people about their thoughts, feelings, behaviors, and attitudes.
Surveys can be conducted in a variety of ways, including:
- In-person interviews
- Online questionnaires
- Paper-and-pencil surveys
Surveys are particularly useful when researchers want to study attitudes or behaviors that are difficult to observe directly or when they want to generalize their findings to a larger population.
Experimental Psychological Research Methods
Experimental studies are a research method commonly used in psychology to investigate cause-and-effect relationships between variables. In an experimental study, the researcher manipulates one or more variables to see how they affect another variable, while controlling for other factors that may influence the outcome.
Experimental studies are considered the gold standard for establishing cause-and-effect relationships, as they allow researchers to control for potential confounding variables and to manipulate variables in a systematic way.
Correlational Psychological Research Methods
Correlational research is a research method used in psychology to investigate the relationship between two or more variables without manipulating them. The goal of correlational research is to determine the extent to which changes in one variable are associated with changes in another variable.
In other words, correlational research aims to establish the direction and strength of the relationship between two or more variables.
Naturalistic Observation
Naturalistic observation is a research method used in psychology to study behavior in natural settings, without any interference or manipulation from the researcher.
The goal of naturalistic observation is to gain insight into how people or animals behave in their natural environment without the influence of laboratory conditions.
Meta-Analysis
A meta-analysis is a research method commonly used in psychology to combine and analyze the results of multiple studies on a particular topic.
The goal of a meta-analysis is to provide a comprehensive and quantitative summary of the existing research on a topic, in order to identify patterns and relationships that may not be apparent in individual studies.
Tips for Using Psychological Research Methods
Here are some tips for high school and college students who are interested in using psychological research methods:
Understand the different types of research methods:
Before conducting any research, it is important to understand the different types of research methods that are available, such as surveys, case studies, experiments, and naturalistic observation.
Each method has its strengths and limitations, and selecting the appropriate method depends on the research question and variables being investigated.
Develop a clear research question:
A good research question is essential for guiding the research process. It should be specific, clear, and relevant to the field of psychology. It is also important to consider ethical considerations when developing a research question.
Use proper sampling techniques:
Sampling is the process of selecting participants for a study. It is important to use proper sampling techniques to ensure that the sample is representative of the population being studied.
Random sampling is considered the gold standard for sampling, but other techniques, such as convenience sampling, may also be used depending on the research question.
Use reliable and valid measures:
It is important to use reliable and valid measures to ensure the data collected is accurate and meaningful. This may involve using established measures or developing new measures and testing their reliability and validity.
Consider ethical issues:
It is important to consider ethical considerations when conducting psychological research, such as obtaining informed consent from participants, maintaining confidentiality, and minimizing any potential harm to participants.
In many cases, you will need to submit your study proposal to your school’s institutional review board for approval.
Analyze and interpret the data appropriately :
After collecting the data, it is important to analyze and interpret the data appropriately. This may involve using statistical techniques to identify patterns and relationships between variables, and using appropriate software tools for analysis.
Communicate findings clearly:
Finally, it is important to communicate the findings clearly in a way that is understandable to others. This may involve writing a research report, giving a presentation, or publishing a paper in a scholarly journal.
Clear communication is essential for advancing the field of psychology and informing future research.
Frequently Asked Questions
What are the 5 methods of psychological research.
The five main methods of psychological research are:
- Experimental research : This method involves manipulating one or more independent variables to observe their effect on one or more dependent variables while controlling for other variables. The goal is to establish cause-and-effect relationships between variables.
- Correlational research : This method involves examining the relationship between two or more variables, without manipulating them. The goal is to determine whether there is a relationship between the variables and the strength and direction of that relationship.
- Survey research : This method involves gathering information from a sample of participants using questionnaires or interviews. The goal is to collect data on attitudes, opinions, behaviors, or other variables of interest.
- Case study research : This method involves an in-depth analysis of a single individual, group, or event. The goal is to gain insight into specific behaviors, attitudes, or phenomena.
- Naturalistic observation research : This method involves observing and recording behavior in natural settings without any manipulation or interference from the researcher. The goal is to gain insight into how people or animals behave in their natural environment.
What is the most commonly used psychological research method?
The most common research method used in psychology varies depending on the research question and the variables being investigated. However, correlational research is one of the most frequently used methods in psychology.
This is likely because correlational research is useful in studying a wide range of psychological phenomena, and it can be used to examine the relationships between variables that cannot be manipulated or controlled, such as age, gender, and personality traits.
Experimental research is also a widely used method in psychology, particularly in the areas of cognitive psychology , social psychology , and developmental psychology .
Other methods, such as survey research, case study research, and naturalistic observation, are also commonly used in psychology research, depending on the research question and the variables being studied.
How do you know which research method to use?
Deciding which type of research method to use depends on the research question, the variables being studied, and the practical considerations involved. Here are some general guidelines to help students decide which research method to use:
- Identify the research question : The first step is to clearly define the research question. What are you trying to study? What is the hypothesis you want to test? Answering these questions will help you determine which research method is best suited for your study.
- Choose your variables : Identify the independent and dependent variables involved in your research question. This will help you determine whether an experimental or correlational research method is most appropriate.
- Consider your resources : Think about the time, resources, and ethical considerations involved in conducting the research. For example, if you are working on a tight budget, a survey or correlational research method may be more feasible than an experimental study.
- Review existing literature : Conducting a literature review of previous studies on the topic can help you identify the most appropriate research method. This can also help you identify gaps in the literature that your study can fill.
- Consult with a mentor or advisor : If you are still unsure which research method to use, consult with a mentor or advisor who has experience in conducting research in your area of interest. They can provide guidance and help you make an informed decision.
Scholtz SE, de Klerk W, de Beer LT. The use of research methods in psychological research: A systematised review . Front Res Metr Anal . 2020;5:1. doi:10.3389/frma.2020.00001
Palinkas LA. Qualitative and mixed methods in mental health services and implementation research . J Clin Child Adolesc Psychol . 2014;43(6):851-861. doi:10.1080/15374416.2014.910791
Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011;11(1):100. doi:10.1186/1471-2288-11-100
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Research Methods in Psychology - 4th American Edition
(40 reviews)
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Carrie Cuttler, Washington State University
Rajiv S. Jhangiani, Kwantlen Polytechnic University
Dana C. Leighton, Texas A&M University, Texarkana
Copyright Year: 2019
ISBN 13: 9781999198107
Publisher: Kwantlen Polytechnic University
Language: English
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Learn more about reviews.
Reviewed by Beth Mechlin, Associate Professor of Psychology & Neuroscience, Earlham College on 3/19/24
This is an extremely comprehensive text for an undergraduate psychology course about research methods. It does an excellent job covering the basics of a variety of types of research design. It also includes important topics related to research... read more
Comprehensiveness rating: 5 see less
This is an extremely comprehensive text for an undergraduate psychology course about research methods. It does an excellent job covering the basics of a variety of types of research design. It also includes important topics related to research such as ethics, finding journal articles, and writing reports in APA format.
Content Accuracy rating: 5
I did not notice any errors in this text.
Relevance/Longevity rating: 5
The content is very relevant. It will likely need to be updated over time in order to keep research examples relevant. Additionally, APA formatting guidelines may need to be updated when a new publication manual is released. However, these should be easy updates for the authors to make when the time comes.
Clarity rating: 5
This text is very clear and easy to follow. The explanations are easy for college students to understand. The authors use a lot of examples to help illustrate specific concepts. They also incorporate a variety of relevant outside sources (such as videos) to provide additional examples.
Consistency rating: 5
The text is consistent and flows well from one section to the next. At the end of each large section (similar to a chapter) the authors provide key takeaways and exercises.
Modularity rating: 5
This text is very modular. It is easy to pick and choose which sections you want to use in your course when. Each section can stand alone fairly easily.
Organization/Structure/Flow rating: 5
The text is very well organized. Information flows smoothly from one topic to the next.
Interface rating: 5
The interface is great. The text is easy to navigate and the images display well (I only noticed 1 image in which the formatting was a tad off).
Grammatical Errors rating: 5
I did not notice any grammatical errors.
Cultural Relevance rating: 5
The text is culturally relevant.
This is an excellent text for an undergraduate research methods course in the field of Psychology. I have been using the text for my Research Methods and Statistics course for a few years now. This text focuses on research methods, so I do use another text to cover statistical information. I do highly recommend this text for research methods. It is comprehensive, clear, and easy for students to use.
Reviewed by William Johnson, Lecturer, Old Dominion University on 1/12/24
This textbook covers every topic that I teach in my Research Methods course aside from psychology careers (which I would not really expect it to cover). read more
This textbook covers every topic that I teach in my Research Methods course aside from psychology careers (which I would not really expect it to cover).
I have not noticed any inaccurate information (other than directed students to read Malcolm Gladwell). I appreciate that the textbook includes information on research errors that have not been supported by replication efforts, such as embodied cognition.
Many of the basic concepts of research methods are rather timeless, but I appreciate that the text includes newer research as examples while also including "classic" studies that exemplify different methods.
The writing is clear and simple. The keywords are bolded and reveal a definition when clicked, which students often find very helpful. Many of the figures are very helpful in helping students understand various methods (I really like the ones in the single-subject design subchapter).
The book is very consistent in its terminology and writing style, which I see as a positive compared to other open psychology textbooks where each chapter is written by subject matter experts (such as the NOBA intro textbook).
Modularity rating: 4
I teach this textbook almost entirely in order (except for moving chapters 12 & 13 earlier in the semester to aid students in writing Results sections in their final papers). I think that the organization and consistency of the book reduces its modularity, in that earlier chapters are genuinely helpful for later chapters.
Organization/Structure/Flow rating: 4
I preferred the organization of previous editions, which had "Theory in Research" as its own chapter. If I were organizing the textbook, I am not sure that I would have out descriptive or inferential statistics as the final two chapters (I would have likely put Chapter 11: Presenting Your Research as the final chapter). I also would not have put information about replicability and open science in the inferential statistics section.
The text is easy to read and the formatting is attractive. My only minor complaint is that some of the longer subchapters can be a pretty long scroll, but I understand the desire for their only to be one page per subchapter/topic.
I have not noticed any grammatical errors.
Cultural Relevance rating: 3
I do not think the textbook is insensitive, but there is not much thought given to adapting research instruments across cultures. For instance, talking about how different constructs might have different underlying distributions in different cultures would be useful for students. In the survey methods section, a discussion of back translation or emic personality trait measurement/development for example might be a nice addition.
I choose to use this textbook in my methods classes, but I do miss the organization of the previous American editions. Overall, I recommend this textbook to my colleagues.
Reviewed by Brianna Ewert, Psychology Instructor, Salish Kootenai College on 12/30/22
This text includes the majority of content included in our undergraduate Research Methods in Psychology course. The glossary provides concise definitions of key terms. This text includes most of the background knowledge we expect our students to... read more
Comprehensiveness rating: 4 see less
This text includes the majority of content included in our undergraduate Research Methods in Psychology course. The glossary provides concise definitions of key terms. This text includes most of the background knowledge we expect our students to have as well as skill-based sections that will support them in developing their own research projects.
The content I have read is accurate and error-free.
The content is relevant and up-to-date.
The text is clear and concise. I find it pleasantly readable and anticipate undergraduate students will find it readable and understandable as well.
The terminology appears to be consistent throughout the text.
The modular sections stand alone and lend themselves to alignment with the syllabus of a particular course. I anticipate readily selecting relevant modules to assign in my course.
The book is logically organized with clear and section headings and subheadings. Content on a particular topic is easy to locate.
The text is easy to navigate and the format/design are clean and clear. There are not interface issues, distortions or distracting format in the pdf or online versions.
The text is grammatically correct.
Cultural Relevance rating: 4
I have not found culturally insensitive and offensive language or content in the text. For my courses, I would add examples and supplemental materials that are relevant for students at a Tribal College.
This textbook includes supplemental instructor materials, included slides and worksheets. I plan to adopt this text this year in our Research Methods in Psychology course. I expect it to be a benefit to the course and students.
Reviewed by Sara Peters, Associate Professor of Psychology, Newberry College on 11/3/22
This text serves as an excellent resource for introducing survey research methods topics to undergraduate students. It begins with a background of the science of psychology, the scientific method, and research ethics, before moving into the main... read more
This text serves as an excellent resource for introducing survey research methods topics to undergraduate students. It begins with a background of the science of psychology, the scientific method, and research ethics, before moving into the main types of research. This text covers experimental, non-experimental, survey, and quasi-experimental approaches, among others. It extends to factorial and single subject research, and within each topic is a subset (such as observational research, field studies, etc.) depending on the section.
I could find no accuracy issues with the text, and appreciated the discussions of research and cited studies.
There are revised editions of this textbook (this being the 4th), and the examples are up to date and clear. The inclusion of exercises at the end of each chapter offer potential for students to continue working with material in meaningful ways as they move through the book and (and course).
The prose for this text is well aimed at the undergraduate population. This book can easily be utilized for freshman/sophomore level students. It introduces the scientific terminology surrounding research methods and experimental design in a clear way, and the authors provide extensive examples of different studies and applications.
Terminology is consistent throughout the text. Aligns well with other research methods and statistics sources, so the vocabulary is transferrable beyond the text itself.
Navigating this book is a breeze. There are 13 chapters, and each have subsections that can be assigned. Within each chapter subsection, there is a set of learning objectives, and paragraphs are mixed in with tables and figures for students to have different visuals. Different application assignments within each chapter are highlighted with boxes, so students can think more deeply given a set of constructs as they consider different information. The last subsection in each chapter has key summaries and exercises.
The sections and topics in this text are very straightforward. The authors begin with an introduction of psychology as a science, and move into the scientific method, research ethics, and psychological measurement. They then present multiple different research methodologies that are well known and heavily utilized within the social sciences, before concluding with information on how to present your research, and also analyze your data. The text even provides links throughout to other free resources for a reader.
This book can be navigated either online (using a drop-down menu), or as a pdf download, so students can have an electronic copy if needed. All pictures and text display properly on screen, with no distortions. Very easy to use.
There were no grammatical errors, and nothing distracting within the text.
This book includes inclusive material in the discussion of research ethics, as well as when giving examples of the different types of research approaches. While there is always room for improvement in terms of examples, I was satisfied with the breadth of research the authors presented.
This text provides an overview of both research methods, and a nice introduction to statistics for a social science student. It would be a good choice for a survey research methods class, and if looking to change a statistics class into an open resource class, could also serve as a great resource.
Reviewed by Sharlene Fedorowicz, Adjunct Professor, Bridgewater State University on 6/23/21
The comprehensiveness of this book was appropriate for an introductory undergraduate psychology course. Critical topics are covered that are necessary for psychology students to obtain foundational learning concepts for research. Sections within... read more
The comprehensiveness of this book was appropriate for an introductory undergraduate psychology course. Critical topics are covered that are necessary for psychology students to obtain foundational learning concepts for research. Sections within the text and each chapter provide areas for class discussion with students to dive deeper into key concepts for better learning comprehension. The text covered APA format along with examples of research studies to supplement the learning. The text segues appropriately by introducing the science of psychology, followed by scientific method and ethics before getting into the core of scientific research in the field of psychology. Details are provided in quantitative and qualitative research, correlations, surveys, and research design. Overall, the text is fully comprehensive and necessary introductory research concepts.
The text appears to be accurate with no issues related to content.
Relevance/Longevity rating: 4
The text provided relevant research information to support the learning. The content was up-to-date with a variety of different examples related to the different fields of psychology. However, some topics such as in the pseudoscience section were not very relevant and bordered the line of beliefs. Here, more current or relevant solid examples would provide more relevancy in this part of the text. Bringing in more solid or concrete examples that are more current for students may have been more appropriate such as lack of connection between information found on social media versus real science.
The language and flow of the chapters accompanied by the terms, concepts, and examples of applied research allows for clarity of learning content. Terms were introduced at the appropriate time with the support of concepts and current or classic research. The writing style flows nicely and segues easily from concept to concept. The text is easy for students to understand and grasp the details related to psychological research and science.
The text provides consistency in the outline of each chapter. The beginning section chapter starts objectives as an overview to help students unpack the learning content. Key terms are consistently bolded followed by concept or definition and relevant examples. Research examples are pertinent and provide students with an opportunity to understand application of the contents. Practice exercises are provided with in the chapter and at the and in order for students to integrate learning concepts from within the text.
Sections and subsections are clearly organized and divided appropriately for ease-of-use. The topics are easily discernible and follow the flow of ideal learning routines for students. The sections and subsections are consistently outlined for each concept module. The modularity provides consistency allowing for students to focus on content rather than trying to discern how to pull out the information differently from each chapter or section. In addition, each section and subsection allow for flexibility in learning or expanding concepts within the content area.
The organization of the textbook was easy to follow and each major topic was outlined clearly. However, the chapter on presenting research may be more appropriately placed toward the end of the book rather than in the middle of the chapters related to research and research design. In addition, more information could have been provided upfront around APA format so that students could identify the format of citations within the text as practice for students throughout the book.
The interface of the book lends itself to a nice layout with appropriate examples and links to break up the different sections in the chapters. Examples where appropriate and provided engagement opportunities for the students for each learning module. Images and QR codes or easily viewed and used. Key terms are highlighted in relevant figures, graphs, and tables were appropriately placed. Overall, the interface of the text assisted with the organization and flow of learning material.
No grammatical errors were detected in this book.
The text appears to be culturally sensitive and not offensive. A variety of current and classic research examples are relevant. However, more examples of research from women, minorities, and ethnicities would strengthen the culture of this textbook. Instructors may need to supplement some research in this area to provide additional inclusivity.
Overall, I was impressed by the layout of the textbook and the ease of use. The layout provides a set of expectations for students related to the routine of how the book is laid out and how students will be able to unpack the information. Research examples were relevant, although I see areas where I will supplement information. The book provides opportunities for students to dive deeper into the learning and have rich conversations in the classroom. I plan to start using the psychology textbook for my students starting next year.
Reviewed by Anna Behler, Assistant Professo, North Carolina State University on 6/1/21
The text is very thorough and covers all of the necessary topics for an undergraduate psychology research methods course. There is even coverage of qualitative research, case studies, and the replication crisis which I have not seen in some other... read more
The text is very thorough and covers all of the necessary topics for an undergraduate psychology research methods course. There is even coverage of qualitative research, case studies, and the replication crisis which I have not seen in some other texts.
There were no issues with the accuracy of the text.
The content is very up to date and relevant for a research methods course. The only updates that will likely be necessary in the coming years are updates to examples and modifications to the section on APA formatting.
The clarity of the writing was good, and the chapters were written in a way that was accessible and easy to follow.
I did not note any issues with consistency.
Each chapter is divided into multiple subsections. This makes the chapters even easier to read, as they are broken down into short and easy to navigate sections. These sections make it easy to assign readings as needed depending on which topics are being covered in class.
Organization/Structure/Flow rating: 3
The organization was one of the few areas of weakness, and I felt that the chapters were ordered somewhat oddly. However, this is something that is easily fixed, as chapters (and even subsections) can be assigned in whatever order is needed.
There were no issues of note with the interface, and the PDF of the text was easy to navigate.
The text was well written and there were no grammatical/writing errors of note.
Overall, the book did not contain any notable instances of bias. However, it would probably be appropriate to offer a more thorough discussion of the WEIRD problem in psychology research.
Reviewed by Seth Surgan, Professor, Worcester State University on 5/24/21
Pitched very well for a 200-level Research Methods course. This text provided students with solid basis for class discussion and the further development of their understanding of fundamental concepts. read more
Pitched very well for a 200-level Research Methods course. This text provided students with solid basis for class discussion and the further development of their understanding of fundamental concepts.
No issues with accuracy.
Coverage was on target, relevant, and applicable, with good examples from a variety of subfields within Psychology.
Clearly written -- students often struggle with the dry, technical nature of concepts in Research Methods. Part of the reason I chose this text in the first place was how favorably it compared to other options in terms of clarity.
No problems with inconsistent of shifting language. This is extremely important in Research Methods, where there are many closely related terms. Language was consistent and compatible with other textbook options that were available to my students.
Chapters are broken down into sections that are reasonably sized and conceptually appropriate.
The organization of this textbook fit perfectly with the syllabus I've been using (in one form or another) for 15+ years.
This textbook was easy to navigate and available in a variety of formats.
No problems at all.
Examples show an eye toward inclusivity. I did not detect any insensitive or offensive examples or undertones.
I have used this textbook for a 200-level Research Methods course run over a single summer session. This was my first experience using an OER textbook and I don't plan on going back.
Reviewed by Laura Getz, Assistant Professor, University of San Diego on 4/29/21
The topics covered seemed to be at an appropriate level for beginner undergraduate psychology students; the learning objectives for each subsection and the key takeaways and exercises for each chapter are also very helpful in guiding students’... read more
The topics covered seemed to be at an appropriate level for beginner undergraduate psychology students; the learning objectives for each subsection and the key takeaways and exercises for each chapter are also very helpful in guiding students’ attention to what is most relevant. The glossary is also thorough and a good resource for clear definitions. I would like to see a final chapter on a “big picture” or integrating key ideas of replication, meta-analysis, and open science.
Content Accuracy rating: 4
For the most part, I like the way information is presented. I had a few specific issues with definitions for ordinal variables being quantitative (1st, 2nd, 3rd aren’t really numbers as much as ranked categories), the lack of specificity about different forms of validity (face, content, criterion, and discriminant all just labeled “validity” whereas internal and external validity appear in different sections), and the lack of clear distinction between correlational and quasi-experimental variables (e.g., in some places, country of origin is listed as making a design quasi-experimental, but in other chapters it is defined as correlational).
Some of the specific studies/experiments mentioned do not seem like the best or most relevant for students to learn about the topics, but for the most part, content is up-to-date and can definitely be updated with new studies to illustrate concepts with relative ease.
Besides the few concepts I listed above in “accuracy”, I feel the text was very accessible, provides clear definitions, and many examples to illustrate any potential technical/jargon terms.
I did not notice any issues with inconsistent terms except for terms that do have more than one way of describing the same concept (e.g., 2-sample vs. independent samples t-test)
I assigned the chapters out of order with relative ease, and students did not comment about it being burdensome to navigate.
The order of chapters sometimes did not make sense to me (e.g., Experimental before Non-experimental designs, Quasi-experimental designs separate from other non-experimental designs, waiting until Chapter 11 to talk about writing), but for the most part, the chapter subsections were logical and clear.
Interface rating: 4
I had no issues navigating the online version of the textbook other than taking a while to figure out how to move forward and back within the text itself rather than going back to the table of contents (this might just be a browser issue, but is still worth considering).
No grammatical errors of note.
There was nothing explicitly insensitive or offensive about the text, but there were many places where I felt like more focus on diversity and individual differences could be helpful. For example, ethics and history of psychological testing would definitely be a place to bring in issues of systemic racism and/or sexism and a focus on WEIRD samples (which is mentioned briefly at another point).
I was very satisfied with this free resource overall, and I recommend it for beginning level undergraduate psychology research methods courses.
Reviewed by Laura Stull, Associate Professor, Anderson University on 4/23/21
This book covers essential topics and areas related to conducting introductory psychological research. It covers all critical topics, including the scientific method, research ethics, research designs, and basic descriptive and inferential... read more
This book covers essential topics and areas related to conducting introductory psychological research. It covers all critical topics, including the scientific method, research ethics, research designs, and basic descriptive and inferential statistics. It even goes beyond other texts in terms of offering specific guidance in areas like how to conduct research literature searches and psychological measurement development. The only area that appears slightly lacking is detailed guidance in the mechanics of writing in APA style (though excellent basic information is provided in chapter 11).
All content appears accurate. For example, experimental designs discussed, descriptive and inferential statistical guidance, and critical ethical issues are all accurately addressed, See comment on relevance below regarding some outdated information.
Relevance/Longevity rating: 3
Chapter 11 on APA style does not appear to cover the most current version of the APA style guide (7th edition). While much of the information in Chapter 11 is still current, there are specifics that did change from 6th to 7th edition of the APA manual and so, in order to be current, this information would have to be supplemented with external sources.
The book is extremely well organized, written in language and terms that should be easily understood by undergraduate freshmen, and explains all necessary technical jargon.
The text is consistent throughout in terms of terminology and the organizational framework (which aids in the readability of the text).
The text is divided into intuitive and common units based on basic psychological research methodology. It is clear and easy to follow and is divided in a way that would allow omission of some information if necessary (such as "single subject research") or reorganization of information (such as presenting survey research before experimental research) without disruption to the course as a whole.
As stated previously, the book is organized in a clear and logical fashion. Not only are the chapters presented in a logical order (starting with basic and critical information like overviews of the scientific method and research ethics and progressing to more complex topics like statistical analyses).
No issues with interface were noted. Helpful images/charts/web resources (e.g., Youtube videos) are embedded throughout and are even easy to follow in a print version of the text.
No grammatical issues were noted.
No issues with cultural bias are noted. Examples are included that address topics that are culturally sensitive in nature.
I ordered a print version of the text so that I could also view it as students would who prefer a print version. I am extremely impressed with what is offered. It covers all of the key content that I am currently covering with a (non-open source) textbook in an introduction to research methods course. The only concern I have is that APA style is not completely current and would need to be supplemented with a style guide. However, I consider this a minimal issue given all of the many strengths of the book.
Reviewed by Anika Gearhart, Instructor (TT), Leeward Community College on 4/22/21
Includes the majority of elements you expect from a textbook covering research methods. Some topics that could have been covered in a bit more depth were factorial research designs (no coverage of 3 or more independent variables) and external... read more
Includes the majority of elements you expect from a textbook covering research methods. Some topics that could have been covered in a bit more depth were factorial research designs (no coverage of 3 or more independent variables) and external validity (or the validities in general).
Nothing found that was inaccurate.
Looks like a few updates could be made to chapter 11 to bring it up to date with APA 7. Otherwise, most examples are current.
Very clear, a great fit for those very new to the topic.
The framework is clear and logical, and the learning objectives are very helpful for orienting the reader immediately to the main goals of each section.
Subsections are well-organized and clear. Titles for sections and subsections are clear.
Though I think the flow of this textbook for the most part is excellent, I would make two changes: move chapter 5 down with the other chapters on experimental research and move chapter 11 to the very end. I feel that this would allow for a more logical presentation of content.
The webpage navigation is easy to use and intuitive, the ebook download works as designed, and the page can be embedded directly into a variety of LMS sites or used with a variety of devices.
I found no grammatical errors in this book.
While there were some examples of studies that included participants from several cultures, the book does not touch on ecological validity, an important external validity issue tied to cultural psychology, and there is no mention of the WEIRD culture issue in psychology, which seems somewhat necessary when orienting new psychology students to research methods today.
I currently use and enjoy this textbook in my research methods class. Overall, it has been a great addition to the course, and I am easily able to supplement any areas that I feel aren't covered with enough breadth.
Reviewed by Amy Foley, Instructor/Field & Clinical Placement Coordinator, University of Indianapolis on 3/11/21
This text provides a comprehensive overview of the research process from ideation to proposal. It covers research designs common to psychology and related fields. read more
This text provides a comprehensive overview of the research process from ideation to proposal. It covers research designs common to psychology and related fields.
Accurate information!
This book is current and lines up well with the music therapy research course I teach as a supplemental text for students to understand research designs.
Clear language for psychology and related fields.
The format of the text is consistent. I appreciate the examples, different colored boxes, questions, and links to external sources such as video clips.
It is easy to navigate this text by chapters and smaller units within each chapter. The only confusion that has come from using this text includes the fact that the larger units have roman numerals and the individual chapters have numbers. I have told students to "read unit six" and they only read the small chapter 6, not the entire unit for example.
Flows well!
I have not experienced any interface issues.
I have not found any grammar errors.
Book appears culturally relevant.
This is a great resource for research methods courses in psychology or related fields. I am glad to have used several chapters of this text within the music therapy research course I teach where students learn about research design and then create their own research proposal.
Reviewed by Veronica Howard, Associate Professor, University of Alaska Anchorage on 1/11/21, updated 1/11/21
VERY impressed by the coverage of single subject designs. I would recommend this content to colleagues. read more
VERY impressed by the coverage of single subject designs. I would recommend this content to colleagues.
Content appears accurate.
By expanding to include more contemporary research perspectives, the authors have created a wonderful dynamic that permits the text to be the foundation for many courses as well as revision and remixing for other authors.
Book easy to read, follow.
Consistency rating: 4
Content overall consistent. Only mild inconsistency in writing style between chapters.
Exceptionally modular. All content neatly divided into units with smaller portions. This would be a great book to use in a course that meets bi-weekly, or adapted into other formats.
Content organized in a clear and logical fashion, and would guide students through a semester-long course on research methods, starting with review content, broad overview of procedures (including limitations), then highlighting less common (though relevant) procedures.
Rich variety of formats for use.
No errors found.
I would appreciate more cultural examples.
Reviewed by Greg Mullin, Associate Professor, Bunker Hill Community College on 12/30/20, updated 1/6/21
I was VERY pleased with the comprehensiveness of the text. I believe it actually has an edge over the publisher-based text that I've been using for years. Each major topic was thoroughly covered with more than enough detail on individual concepts. read more
I was VERY pleased with the comprehensiveness of the text. I believe it actually has an edge over the publisher-based text that I've been using for years. Each major topic was thoroughly covered with more than enough detail on individual concepts.
I did not find any errors within the text. The authors provided an unbiased representation of research methods in psychology.
The content connects to classic, timeless examples in the field, but also mixes in a fair amount of more current, relatable examples. I feel like I'll be able to use this version of the text for many years without its age showing.
The authors present a clear and efficient writing style throughout that is rich with relatable examples. The only area that may be a bit much for undergraduate-level student understanding is the topic of statistics. I personally scale back my discussion of statistics in my Intro to Research Methods course, but for those that prefer a deeper dive, the higher-level elements are there.
I did not notice any shifts with the use of terminology or with the structural framework of the text. The text is very consistent and organized in an easily digestible way.
The authors do a fantastic job breaking complex topics down into manageable chunks both as a whole and within chapters. As I was reading, I could easily see how I could align my current approach of teaching Intro to Research Methods with their modulated presentation of the material.
I effortlessly moved through the text given the structural organization. All topics are presented in a logical fashion that allowed each message to be delivered to the reader with ease.
I read the text through the PDF version and found no issue with the interface. All image and text-based material was presented clearly.
I cannot recall coming across any grammatical errors. The text is very well written.
I did not find the text to be culturally insensitive in any way. The authors use inclusive language and even encourage that style of writing in the chapter on Presenting Your Research. I would have liked to see more cross-cultural research examples and more of an extended effort to include the theme of diversity throughout, but at no point did I find the text to be offensive.
This is a fantastic text and I look forward to adopting it for my Intro to Research Methods course in the Spring. :)
Reviewed by Maureen O'Connell, Adjunct Professor, Bunker Hill Community College on 12/15/20, updated 12/18/20
This text edition has covered all ideas and areas of research methods in psychology. It has provided a glossary of terms, sample APA format, and sample research papers. read more
This text edition has covered all ideas and areas of research methods in psychology. It has provided a glossary of terms, sample APA format, and sample research papers.
The content is unbiased, accurate, and I did not find any errors in the text.
The content is current and up-to-date. I found that the text can be added to should material change, the arrangement of the text/content makes it easily accessible to add material, if necessary.
The text is clear, easy to understand, simplistic writing at times, but I find this text easy for students to comprehend. All text is relevant to the content of behavioral research.
The text and terminology is consistent.
The text is organized well and sectioned appropriately. The information is presented in an easy-to-read format, with sections that can be assigned at various points during the semester and the reader can easily locate this.
The topics in the text are organized in a logical and clear manner. It flows really well.
The text is presented well, including charts, diagrams, and images. There did not appear to be any confusion with this text.
The text contains no grammatical errors.
The text was culturally appropriate and not offensive. Clear examples of potential biases were outlined in this text which I found quite helpful for the reader.
Overall, I found this to be a great edition. Much of the time I spend researching outside material for students has been included in this text. I enjoyed the format, easier to navigate, helpful to students by providing an updated version of discussions and practice assignments, and visually more appealing.
Reviewed by Brittany Jeye, Assistant Professor of Psychology, Worcester State University on 6/26/20
All of the main topics in a Research Methods course are covered in this textbook (e.g., scientific method, ethics, measurement, experimental design, hypothesis testing, APA style, etc.). Some of these topics are not covered as in-depth as in other... read more
All of the main topics in a Research Methods course are covered in this textbook (e.g., scientific method, ethics, measurement, experimental design, hypothesis testing, APA style, etc.). Some of these topics are not covered as in-depth as in other Research Method textbooks I have used previously, but this actually may be a positive depending on the students and course level (that is, students may only need a solid overview of certain topics without getting overwhelmed with too many details). It also gives the instructor the ability to add content as needed, which helps with flexibility in course design.
I did not note any errors or inaccurate/biasing statements in the text.
For the most part, everything was up to date. There was a good mix of classic research and newer studies presented and/or used as examples, which kept the chapters interesting, topical and relevant. I only noted the section on APA Style in the chapter “Presenting Your Research” which may need some updating to be in line with the new APA 7th edition. However, there should be only minor edits needed (the chapter itself was great overview and introduction to the main points of APA style) and it looks like they should be relatively easy to implement.
The text was very well-written and was presented at an accessible level for undergraduates new to Research Methods. Terms were well-defined with a helpful glossary at the end of the textbook.
The consistent structure of the textbook is huge positive. Each chapter begins with learning objectives and ends with bulleted key takeaways. There are also good exercises and learning activities for students at the end of each chapter. Instructors may need to add their own activities for chapters that do not go into a lot of depth (there are also instructor resources online, which may have more options available).
This is one of the biggest strengths of this textbook, in my opinion. I appreciate how each chapter is broken down into clearly defined subsections. The chapters and the subsections, in particular, are not lengthy, which is great for students’ learning. These subsections could be reorganized and used in a variety of ways to suit the needs of a particular course (or even as standalone subsections).
The topics were presented in a logical manner. As mentioned above, since the textbook is very modular, I feel that you could easily rearrange the chapters to fit your needs (for example, presenting survey design before experimental research or making the presenting your research section a standalone unit).
I downloaded the textbook as an ebook, which was very easy to use/navigate. There were no problems reading any of the text or figures/tables. I also appreciated that you could open the ebook using a variety of apps (Kindle, iBook, etc.) depending on your preference (and this is good for students who have a variety of technical needs).
There were no grammatical errors noted.
The examples were inclusive of races, ethnicity and background and there were not any examples that were culturally insensitive or offensive in any way. In future iterations of the replicability section, it may be beneficial to touch upon the “weird” phenomena in psychology research (that many studies use participants who are western, educated and from industrialized, rich and democratic countries) as a point to engage students in improving psychological practices.
I will definitely consider switching to this textbook in the future for Research Methods.
Reviewed by Alice Frye, Associate Teaching Professor, University of Massachusetts Lowell on 6/22/20
Hits all the necessary marks from ways of knowing to measurement, research designs, and presentation. Comparable in detail and content to other Research Methods texts I have used for teaching. read more
Hits all the necessary marks from ways of knowing to measurement, research designs, and presentation. Comparable in detail and content to other Research Methods texts I have used for teaching.
Correct and to the point. Complex ideas such as internal consistency reliability and discriminant validity are well handled--correct descriptions that are also succinct and articulated simply and with clear examples that are easy for a student reader to grasp.
Seems likely to have good staying power. One area that has changed quickly in the past is the usefulness of various research data bases. So it is possible that portion could become more quickly outdated, but there is no predicting that. The current descriptions are useful.
Very clearly written without being condescending, overly casual or clunky.
Excellent consistency throughout in terms of organization, language usage, level of detail and tone.
Imho this is one of the particular strengths of the text. Chapters are well divided into discrete parts, which seems likely to be a benefit in cohorts of students who are increasingly accustomed to digesting small amounts of information.
Well organized, straightforward structure that is maintained throughout.
No problems with the interface.
The grammar level is another notable strength. Ideas are articulated clearly, and with sophistication, but in a syntactically very straightforward manner.
The text isn't biased or offensive. I wish that to illustrate various points and research designs it had drawn more frequently on research studies that incorporate a specific focus on race and ethnicity.
This is a very good text. As good as any for profit text I have used to teach a research methods course, if not better.
Reviewed by Lauren Mathieu-Frasier, Adjunct Instructor, University of Indianapolis on 1/13/20
As other reviews have mentioned, this textbook provides a comprehensive look at multiple concepts for an introductory course in research methods in psychology. Some of the concepts (i.e., variables, external validity) are briefly described and... read more
As other reviews have mentioned, this textbook provides a comprehensive look at multiple concepts for an introductory course in research methods in psychology. Some of the concepts (i.e., variables, external validity) are briefly described and glossed over that it will take additional information, examples, and reinforcement from instructors in the classroom. Other sections and concepts, like ethics or reporting of research were well-described and thorough.
It appeared that the information was accurate, error-free, and unbiased.
The information is up-to-date. In the section on APA presentation, it looks like the minor adjustments to the APA Publication Manual 7th Edition would need to be included. However, this section gives a good foundation and the instructor can easily implement the changes.
Clarity rating: 4
The text is clearly written written and provides an appropriate context when terminology is used.
There aren't any issues with consistency in the textbook.
The division of smaller sections can be beneficial when reading it and assigning it to classes. The sections are clearly organized based on learning objectives.
The textbook is organized in a logical, clear manner. There may be topics that instructors choose to present in a different manner (non-experimental and survey research prior to experimental). However, this doesn't generally impact the organization and flow of the book.
While reading and utilizing the book, there weren't any navigation issues that could impact the readability of the book. Students could find this textbook easy to use.
Grammatical errors were not noted.
There weren't any issues with cultural sensitivity in the examples of studies used in the textbook.
Reviewed by Tiffany Kindratt, Assistant Professor, University of Texas at Arlington on 1/1/20
The text is comprehensive with an effective glossary of terms at the end. It would be beneficial to include additional examples and exercises for students to better understand concepts covered in Chapter II, Overview of the Scientific Method,... read more
The text is comprehensive with an effective glossary of terms at the end. It would be beneficial to include additional examples and exercises for students to better understand concepts covered in Chapter II, Overview of the Scientific Method, Chapter IV, Psychological Measurement, and Chapter XII Descriptive Statistics.
The text is accurate and there are minimal type/grammatical errors throughout. The verbiage is written in an unbiased manner consistently throughout the textbook.
The content is up-to-date, and examples can be easily updated for future versions. As a public health instructor, I would be interested in seeing examples of community-based examples in future versions. The current examples provided are relevant for undergraduate public health students as well as psychology students.
The text is written in a clear manner. The studies used can be easily understood by undergraduate students in other social science fields, such as public health. More examples and exercises using inferential statistics would be helpful for students to better grasp the concepts.
The framework for each chapter and terminology used are consistent. It is helpful that each section within each chapter begins with learning objectives and the chapter ends with key takeaways and exercises.
The text is clearly divided into sections within each chapter. When I first started reviewing this textbook, I thought each section was actually a very short chapter. I would recommend including a listing of all of the objectives covered in each chapter at the beginning to improve the modularity of the text.
Some of the topics do not follow a logical order. For example, it would be more appropriate to discuss ethics before providing the overview of the scientific method. It would be better to discuss statistics used to determine results before describing how to write manuscripts. However, the text is written in a way that that the chapters could be assigned to students in a different order without impacting the students’ comprehension of the concepts.
I did not encounter any interface issues when reviewing this text. All links worked and there were no distortions of the images or charts that may confuse the reader. There are several data tables throughout the text which are left-aligned and there is a large amount of empty white space next it. I would rearrange the text in future versions to make better use of this space.
The text contains minimal grammatical errors.
The examples are culturally relevant. I did not see any examples that may be considered culturally insensitive or offensive in any way.
As an instructor for an undergraduate public health sciences and methods course, I will consider using some of the content in this text to supplement the current textbook in the future.
Reviewed by Mickey White, Assistant Professor, East Tennessee State University on 10/23/19
The table of contents is well-formatted and comprehensive. Easy to navigate and find exactly what is needed, students would be able to quickly find needed subjects. read more
The table of contents is well-formatted and comprehensive. Easy to navigate and find exactly what is needed, students would be able to quickly find needed subjects.
Content appears to be accurate and up-to-date.
This text is useful and relevant, particularly with regard to expressing and reporting descriptive statistics and results. As APA updates, the text will be easy to edit, as the sections are separated.
Easy to read and engaging.
Chapters were laid out in a consistent manner, which allows readers to know what is coming. The subsections contained a brief overview and terminology was consistent throughout. The glossary added additional information.
Sections and subsections are delineated in a usable format.
The key takeaways were useful, including the exercises at the end of each chapter.
Reading the book online is a little difficult to navigate page-by-page, but e-pub and PDF formats are easy to navigate.
No errors noted.
Would be helpful to have a clearer exploration of cultural factors impacting research, including historical bias in assessment and research outside of research ethics.
Reviewed by Robert Michael, Assistant Professor, University of Louisiana at Lafayette on 10/14/19
Successfully spans the gamut of topics expected in a Research Methods textbook. Some topics are covered in-depth, while others are addressed only at a surface level. Instructors may therefore need to carefully arrange class material for topics in... read more
Successfully spans the gamut of topics expected in a Research Methods textbook. Some topics are covered in-depth, while others are addressed only at a surface level. Instructors may therefore need to carefully arrange class material for topics in which depth of knowledge is an important learning outcome.
The factual content was error-free, according to my reading. I did spot a few grammatical and typographical errors, but they were infrequent and minor.
Great to see nuanced—although limited—discussion of issues with Null Hypothesis Significance Testing, Reproducibility in Psychological Science, and so forth. I expect that these areas are likely to grow in future editions, perhaps supplementing or even replacing more traditional material.
Extremely easy to read with multiple examples throughout to illustrate the principles being covered. Many of these examples are "classics" that students can easily relate to. Plus, who doesn't like XKCD comics?
The textbook is structured sensibly. At times, certain authors' "voices" seemed apparent in the writing, but I suspect this variability is unlikely to be noticed by or even bothersome to the vast majority of readers.
The topics are easily divisible and seem to follow routine expectations. Instructors might find it beneficial and/or necessary to incorporate some of the statistical thinking and learning into various earlier chapters to facilitate student understanding in-the-moment, rather than trying to leave all the statistics to the end.
Sensible and easy-to-follow structure. As per "Modularity", the Statistical sections may benefit from instructors folding in such learning throughout, rather than only at the end.
Beautifully presented, crisp, easy-to-read and navigate. Caveat: I read this online, in a web-browser, on only one device. I haven't tested across multiple platforms.
High quality writing throughout. Only a few minor slip-ups that could be easily fixed.
Includes limited culturally relevant material where appropriate.
Reviewed by Matthew DeCarlo, Assistant Professor, Radford University on 6/26/19
The authors do a great job of simplifying the concepts of research methods and presenting them in a way that is understandable. There is a tradeoff between brevity and depth here. Faculty who adopt this textbook may need to spend more time in... read more
The authors do a great job of simplifying the concepts of research methods and presenting them in a way that is understandable. There is a tradeoff between brevity and depth here. Faculty who adopt this textbook may need to spend more time in class going in depth into concepts, rather than relying on the textbook for all of the information related to key concepts. The text does not cover qualitative methods in detail.
The textbook provides an accurate picture of research methods. The tone is objective and without bias.
The textbook is highly relevant and up to date. Examples are drawn from modern theories and articles.
The writing is a fantastic mix of objective and authoritative while also being approachable.
The book coheres well together. Each chapter and section are uniform.
This book fits very well within a traditional 16 week semester, covering roughly a chapter per week. One could take out specific chapters and assign them individually if research methods is taught in a different way from a standard research textbook.
Content is very well organized. The table of contents is easy to navigate and each chapter is presented in a clear and consistent manner. The use of a two-tier table of contents is particularly helpful.
Standard pressbooks interface, which is great. Uses all of the standard components of Pressbooks well, though the lack of H5P and interactive content is a drawback.
I did not notice any grammar errors.
Cultural Relevance rating: 2
The book does not deal with cultural competence and humility in the research process. Integration of action research and decolonization perspectives would be helpful.
Reviewed by Christopher Garris, Associate Professor, Metropolitan State University of Denver on 5/24/19
Most content areas in this textbook were covered appropriately extensively. Notably, this textbook included some content that is commonly missing in other textbooks (e.g. presenting your research). There were some areas where more elaboration... read more
Most content areas in this textbook were covered appropriately extensively. Notably, this textbook included some content that is commonly missing in other textbooks (e.g. presenting your research). There were some areas where more elaboration and more examples were needed. For example, the section covering measurement validities included all the important concepts, but needed more guidance for student comprehension. Also, the beginning chapters on 'common sense' reasoning and pseudoscience seemed a little too brief.
Overall, this textbook appeared to be free from glaring errors. There were a couple of instances of concern, but were not errors, per se. For example, the cut-off for Cronbach's alpha was stated definitively at .80, while this value likely would be debated among researchers.
This textbook was presented in such a way that seemed protect it from becoming obsolete within the next few years. This is important for continued, consistent use of the book. The authors have revised this book, and those revisions are clearly summarized in the text. Importantly, the APA section of the textbook appears to be up-to-date. Also, the use of QR codes throughout the text is a nice touch that students may appreciate.
Connected to comprehensiveness, there are some important content areas that I felt were lacking in elaboration and examples (e.g. testing the validity of measurement; introduction of experimental design), which inhibits clarity. Overall, however, the topics seemed to be presented in a straightforward, accessible manner. The textbook includes links to informative videos and walk-throughs where appropriate, which seem to be potentially beneficial for student comprehension. The textbook includes tools designed to aid learning, namely "Key Takeaways" and "Exercises" sections at the end of most modules, but not all. "Key Takeaways" seemed valuable, as they were a nice bookend to the learning objectives stated at the beginning of each module. "Exercises" did not appear to be as valuable, especially for the less-motivated student. On their face, these seemed to be more designed for instructors to use as class activities/active learning. Lastly, many modules of the textbook were text-heavy and visually unappealing. While this is superficial, the inclusion of additional graphics, example boxes, or figures in these text-heavy modules might be beneficial.
The textbook appeared to be internally consistent with its approach and use of terminology.
The textbook had a tendency to 'throw out' big concepts very briefly in earlier modules (e.g. sampling, experimental/non-experimental design), and then cover them in more detail in later modules. This would have been less problematic if the text would explicitly inform the student that these concepts would be elaborated upon later. Beyond this issue, the textbook seems to lend itself to being divided up and used on module-by-module basis.
The organization of the chapters did not make intuitive sense to me. The fact that correlation followed experimental research, and that descriptive research was the second-to-last module in the sequence was confusing. That said, textbook is written in such a way that an instructor easily assign the modules in the order that works best for their class.
Overall, the interface worked smoothly and there were few technical issues. Where there were issues (e.g. inconsistent spacing between lines and words), they were negligible.
The text seemed to be free from glaring grammatical problems.
Because this is a methodology textbook, it does not lend itself to too much cultural criticism. That said, the book did not rely on overly controversial examples, but also didn't shy away from important cultural topics (e.g. gender stereotypes, vaccines).
Reviewed by Michel Heijnen, Assistant Professor, University of North Carolina Wilmington on 3/27/18
The book covers all areas related to research methods, not only for the field of psychology, but also to other related fields like exercise science. Topics include ethics, developing a research questions, experimental designs, non-experimental... read more
The book covers all areas related to research methods, not only for the field of psychology, but also to other related fields like exercise science. Topics include ethics, developing a research questions, experimental designs, non-experimental designs, and basic statistics, making this book a great resource for undergraduate research methods classes.
Reviewed content is accurate and seems free of any personal bias.
The topic of research methods in general is not expected to change quickly. It is not expected that this text will become obsolete in the near future. Furthermore, for both the field of psychology as well as other related fields, the examples will continue to have an application to explain certain concepts and will not be outdated soon, even with new research emerging every day.
The text is written so an undergraduate student should be able to understand the concepts. The examples provided in the text greatly contribute to the understanding of the topics and the proposed exercises at the end of each chapter will further apply the knowledge.
The layout and writing style are consistent throughout the text.
Layout of the text is clear, with multiple subsection within each chapter. Each chapter can easily be split into multiple subsection to assign to students. No evidence of self-refers was observed, and individual chapters could be assigned to students without needed to read all preceding chapters. For example, Chapter 4 may not be particularly useful to students outside of psychology, but an instructor can easily reorganize the text and skip this chapter while students can still understand following chapters.
Topics are addressed in a logical manner. Overall, an introduction to research is provided first (including ethics to research), which is followed by different types of research, and concludes with types of analysis.
No images or tables are distorted, making the text easy to read.
No grammatical errors observed in text.
Text is not offensive and does not appear to be culturally insensitive.
I believe that this book is a great resource and, as mentioned previously, can be used for a wider audience than just psychology as the basics of research methods can be applied to various fields, including exercise science.
Reviewed by Chris Koch, Professor of Psychology, George Fox University on 3/27/18
All appropriate areas and topics are covered in the text. In that sense, this book is equivalent to other top texts dealing with research methods in psychology. The appeal of this book is the brevity and clarity. Therefore, some may find that,... read more
All appropriate areas and topics are covered in the text. In that sense, this book is equivalent to other top texts dealing with research methods in psychology. The appeal of this book is the brevity and clarity. Therefore, some may find that, although the topics are covered, topics may not be covered as thoroughly they might like. Overall, the coverage is solid for an introductory course in research methods.
In terms of presentation, this book could be more comprehensive. Each chapter does start with a set of learning objectives and ends with "takeaways" and a short set of exercises. However, it lacks detailed chapter outlines, summaries, and glossaries. Furthermore, an index does not accompany the text.
I found the book to be accurate with content being fairly presented. There was no underlying bias throughout the book.
This is an introductory text for research methods. The basics of research methods have been consistent for some time. The examples used in the text fit the concepts well. Therefore, it should not be quickly dated. It is organized in such a way that sections could be easily modified with more current examples as needed.
The text is easy to read. It is succinct yet engaging. Examples are clear and terminology is adequately defined.
New terms and concepts are dealt with chapter by chapter. However, those things which go across chapters are consistently presented.
The material for each chapter is presented in subsections with each subsection being tied to a particular learning objective. It is possible to use the book by subsection instead of by chapter. In fact, I did that during class by discussing the majority of one chapter, discussing another chapter, and then covering what I previously skipped,
In general, the book follows a "traditional" organization, matching the organization of many competing books. As mentioned in regard to modularity, I did not follow the organization of the book exactly as it was laid out. This may not necessarily reflect poorly on the book, however, since I have never followed the order of any research methods book. My three exams covered chapter 1 through 4, chapters 5, 6, part of 8, and chapters 7, the remainder of 8, 9, and 10. Once we collected data I covered chapters 11 through 13.
Interface rating: 3
The text and images are clear and distortion free. The text is available in several formats including epub, pdf, mobi, odt, and wxr. Unfortunately, the electronic format is not taken full advantage of. The text could be more interactive. As it is, it is just text and images. Therefore, the interface could be improved.
The book appeared to be well written and edited.
I did not find anything in the book that was culturally insensitive or offensive. However, more examples of cross-cultural research could be included.
I was, honestly, surprised by how much I liked the text. The material was presented in easy to follow format that is consistent with how I think about research methods. That made the text extremely easy to use. Students also thought the book was highly accessible Each chapter was relatively short but informative and easy to read.
Reviewed by Kevin White, Assistant Professor, East Carolina University on 2/1/18
This book covers all relevant topics for an introduction to research methods course in the social sciences, including measurement, sampling, basic research design, and ethics. The chapters were long enough to be somewhat comprehensive, but short... read more
This book covers all relevant topics for an introduction to research methods course in the social sciences, including measurement, sampling, basic research design, and ethics. The chapters were long enough to be somewhat comprehensive, but short enough to be digestible for students in an introductory-level class. Student reviews of the book have so far been very positive. The only section of the text for which more detail may be helpful is 2.3 (Reviewing the Research Literature), in which more specific instructions related to literature searches may be helpful to students.
I did not notice any issues related to accuracy. Content appeared to be accurate, error-free, and unbiased.
One advantage of this book is that it is relevant to other applied fields outside of psychology (e.g., social work, counseling, etc.). Also, the exercises at the end of chapter sections are helpful.
The clarity of the text provides students with succinct definitions for research-related concepts, without unnecessary discipline-specific jargon. One suggestion for future editions would be to make the distinctions between different types of non-experimental research a bit more clear for students in introductory classes (e.g., "Correlational Research" in Section 7.2).
Formatting and terminology was consistent throughout this text.
A nice feature of this book is that instructors can select individual sections within chapters, or even jump between sections within chapters. For example, Section 1.4 may not fit for a class that is less clinically-oriented in nature.
The flow of the text was appropriate, with ethics close to the beginning of the book (and an entire chapter devoted to it), and descriptive/inferential statistics at the end.
I did not notice any problems related to interface. I had no trouble accessing or reading the text, and the images were clear.
The text contained no discernible grammatical errors.
The book does not appear to be culturally insensitive in any discernible way, and explicitly addresses prejudice in research (e.g., Section 5.2). However, I think that continuing to add more examples that relate to specific marginalized groups would help improve the text (and especially exercises).
Overall, this book is very useful for an introductory research methods course in psychology or social work, and I highly recommend.
Reviewed by Elizabeth Do, Instructor, Virginia Commonwealth University on 2/1/18
Although this textbook does provide good information regarding introductory concepts necessary for the understanding of correlational designs, and is presented in a logical order. It does not, however, cover qualitative methodologies, or research... read more
Although this textbook does provide good information regarding introductory concepts necessary for the understanding of correlational designs, and is presented in a logical order. It does not, however, cover qualitative methodologies, or research ethics as it relates to other countries outside of the US.
There does not seem to be any errors within the text.
Since this textbook covers a topic that is unlikely to change over the years and it's content is up-to-date, it remains relevant to the field.
The textbook is written at an appropriate level for undergraduate students and is useful in that it does explain important terminology.
There does not seem to be any major inconsistencies within the text.
Overall, the text is very well organized - it is separated into chapters that are divided up into modules and within each module, there are clear learning objectives. It is also helpful that the textbook includes useful exercises for students to practice what they've read about from the text.
The topics covered by this textbook are presented in an order that is logical. The writing is clear and the examples are very useful. However, more information could be provided in some of the chapters and it would be useful to include a table of contents that links to the different chapters within the PDF copy, for reader's ease in navigation when looking for specific terms and/or topics.
Overall, the PDF copy of the textbook made it easy to read; however, there did seem to be a few links that were missing. Additionally, it would be helpful to have some of the graphs printed in color to help with ease of following explanations provided by the text. The inclusion of a table of contents would also be useful for greater ease with navigation.
There does not seem to be any grammatical errors in the textbook. Also, the textbook is written in a clear way, and the information flows nicely.
This textbook focuses primarily on examples from the United States. It does not seem to be culturally insensitive or offensive in anyway and I liked that it included content regarding the avoidance of biased language (chapter 11).
This textbook makes the material very accessible, and it is easy to read/follow examples.
![empty star examples of research methods psychology](https://open.umn.edu/assets/common/stars/star-empty-f5137ed2268385bc46b68192c47ae317500ee42864da811ee418c7e5a421d488.webp)
Reviewed by Eric Lindsey, Professor, Penn State University Berks Campus on 2/1/18
The content of the Research Methods in Psychology textbook was very thorough and covered what I would consider to be the important concepts and issues pertaining to research methods. I would judge that the textbook has a comparable coverage of... read more
The content of the Research Methods in Psychology textbook was very thorough and covered what I would consider to be the important concepts and issues pertaining to research methods. I would judge that the textbook has a comparable coverage of information to other textbooks I have reviewed, including the current textbook I am using. The range of scholarly sources included in the textbook was good, with an appropriate balance between older and classic research examples and newer more cutting edge research information. Overall, the textbook provides substantive coverage of the science of conducting research in the field of psychology, supported by good examples, and thoughtful questions.
The textbook adopts a coherent and student-friendly format, and offers a precise introduction to psychological research methodology that includes consideration of a broad range of qualitative and quantitative methods to help students identify and evaluate the best approach for their research needs. The textbook offers a detailed review of the way that psychological researchers approach their craft. The author guides the reader through all aspects of the research process including formulating objectives, choosing research methods, securing research participants, as well as advice on how to effectively collect, analyze and interpret data and disseminate those findings to others through a variety of presentation and publication venues. The textbook offers relevant supplemental information in textboxes that is highly relevant to the material in the accompanying text and should prove helpful to learners. Likewise the graphics and figures that are included are highly relevant and clearly linked to the material presented in the text. The information covered by the textbook reflects an accurate summary of current techniques and methods used in research in the field of psychology. The presentation of information addresses the pros and cons of different research strategies in an objective and evenhanded way.
The range of scholarly sources included in the textbook was good, with an appropriate balance between older, classic research evidence and newer, cutting edge research. Overall, the textbook provides substantive coverage of the science on most topics in research methods of psychology, supported by good case studies, and thoughtful questions. The book is generally up to date, with adequate coverage of basic data collection methods and statistical techniques. Likewise the review of APA style guidelines is reflects the current manual and I like the way the author summarizes changes from the older version of the APA manual. The organization of the textbook does appear to lend itself to editing and adding new information with updates in the future.
I found the textbook chapters to be well written, in a straightforward yet conversational manner. It gives the reader an impression of being taught by a knowledgeable yet approachable expert. The writing style gives the learner a feeling of being guided through the lessons and supported in a very conversational approach. The experience of reading the textbook is less like being taught and more like a colleague sharing information. Furthermore, the style keeps the reader engaged but doesn't detract from its educational purpose. I also appreciate that the writing is appropriately concise. No explanations are so wordy as to overwhelm or lull the reader to sleep, but at the same time the information is not so vague that the reader can't understand the point at all.
The book’s main aim is to enable students to develop their own skills as researchers, so they can generate and advance common knowledge on a variety of psychological topics. The book achieves this objective by introducing its readers, step-by-step, to psychological research design, while maintaining an excellent balance between substance and attention grabbing examples that is uncommon in other research methods textbooks. Its accessible language and easy-to-follow structure and examples lend themselves to encouraging readers to move away from the mere memorization of facts, formulas and techniques towards a more critical evaluation of their own ideas and work – both inside and outside the classroom. The content of the chapters have a very good flow that help the reader to connect information in a progressive manner as they proceed through the textbook.
Each chapter goes into adequate depth in reviewing both past and current research related to the topic that it covers for an undergraduate textbook on research methods in psychology. The information within each chapter flows well from point-to-point, so that the reader comes away feeling like there is a progression in the information presented. The only limitation that I see is that I felt the author could do a little more to let the reader know how information is connected from chapter to chapter. Rather than just drawing the reader’s attention to things that were mentioned in previous chapters, it would be nice to have brief comments about how issues in one chapter relate to topics covered in previous chapters.
In my opinion the chapters are arranged in easily digestible units that are manageable in 30-40 minute reading sessions. In fact, the author designed the chapters of the textbook in a way to make it easy to chunk information, and start and stop to easily pick up where one leaves off from one reading session to another. I also found the flow of information to be appropriate, with chapters containing just the right amount of detail for use in my introductory course in research methods in psychology.
The book is organized into thirteen chapters. The order of the chapters offers a logical progression from a broad overview of information about the principles and theory behind research in psychology, to more specific issues concerning the techniques and mechanics of conducting research. Each chapter ends with a summary of key takeaways from the chapter and exercises that do more than ask for content regurgitation. I find the organization of the textbook to be effective, and matches my approach to the course very well. I would not make any changes to the overall format with the exception of moving chapter 11 on presenting research to the end of the textbook, after the chapters on statistical analysis and interpretation.
I found the quality of the appearance of the textbook to be very good. The textbook features appropriate text and section/header font sizes that allow for an adequate zooming level to read large or smalls sections of text, that will give readers flexibility to match their personal preference. There are learning objectives at the start of each chapter to help students know what to expect. Key terms are highlighted in a separate color that are easily distinguishable in the body of the page. There are very useful visuals in every chapter, including tables, figures, and graphs. Relevant supplemental information is also highlighted in well formatted text boxes that are color coded to indicate what type of information is included. My only criticism is that the photographs included in the text are of low quality, and there are so few in the textbook that I feel it would have been better to just leave them out.
I found no grammatical errors in my review of the textbook. The textbook is generally well written, and the style of writing is at a level that is appropriate for an undergraduate class.
Although the textbook contains no instances of presenting information that is cultural insensitive or offensive, it does not offer an culturally inclusive review of information pertaining to research methods in psychology. I found no inclusion of examples of research conducting with non European American samples included in the summary of studies. Likewise the authors do place much attention on the issue of cultural sensitivity when conducing research. If there is one major weakness of the textbook I would say it is in this area, but based on my experience it is not an uncommon characteristic of textbooks on research methods in psychology.
Reviewed by zehra peynircioglu, Professor, American University on 2/1/18
Short and sweet in most areas. Covers the basic concepts, not very comprehensively but definitely adequately so for a general beginning-level research methods course. For instance, I would liked to have seen a "separate" chapter on correlational... read more
Comprehensiveness rating: 3 see less
Short and sweet in most areas. Covers the basic concepts, not very comprehensively but definitely adequately so for a general beginning-level research methods course. For instance, I would liked to have seen a "separate" chapter on correlational research (there is one on single subject research and one on survey research), a discussion of the importance of providing a theoretical rationale for "getting an idea" (most students are fine with finding interesting and feasible project ideas but cannot give a theoretical rationale) before or after Chapter 4 on Theory, or a chapter on neuroscientific methods, which are becoming more and more popular. Nevertheless, it touches on most traditional areas that are in other books.
I did not find any errors or biases
This is one area where there is not much danger of going obsolete any time soon. The examples might need to be updated periodically (my students tend to not like dated materials, however relevant), but that should be easy.
Very clear and accessible prose. Despite the brevity, the concepts are put forth quite clearly. I like the "not much fluff" mentality. There is also adequate explanations of jargon and technical terminology.
I could not find any inconsistencies. The style and exposition frameworks are also quite consistent.
Yes, the modularity is fine. The chapters follow a logical pattern, so there should not be too much of a need for jumping around. And even if jumping around is needed depending on teaching style, the sections are solid in terms of being able to stand alone (or as an accompaniment to lectures).
Yes, the contents is ordered logically and the high modularity helps with any reorganization that an instructor may favor. In my case, for instance, Ch. 1 is fine, but I would skip it because it's mostly a repetition of what most introductory psychology books also say. I would also discuss non-experimental methods before going into experimental design. But such changes are easy to do, and if someone followed the book's own organization, there would also be a logical flow.
As far as I could see, the text is free of significant interface issues, at least in the pdf version
I could not find any errors.
As far as I could see, the book was culturally relevant.
I loved the short and sweet learning objectives, key takeaway sections, and the exercises. They are not overwhelming and can be used in class discussions, too.
Reviewed by George Woodbury, Graduate Student, Miami University, Ohio on 6/20/17
This text covers the typical areas for an undergraduate psychology course in research design. There is no table of contents included with the downloadable version, although there is a table of contents on the website (which excludes sub-sections... read more
This text covers the typical areas for an undergraduate psychology course in research design. There is no table of contents included with the downloadable version, although there is a table of contents on the website (which excludes sub-sections of chapters). The sections on statistics are not extensive enough to be useful in and of themselves, but they are useful for transitions to a follow-up statistics course. There does not seem to be a glossary of terms, which made it difficult at times for my read through and I assume later for students who decide to print the text. The text is comprehensive without being wordy or tedious.
Relatively minor errors; There does not seem to be explicit cultural or methodological bias in the text.
The content is up-to-date, and examples from the psychology literature are generally within the last 25 years. Barring extensive restructuring in the fundamentals of methodology and design in psychology, any updates will be very easy to implement.
Text will be very clear and easy to read for students fluent in English. There is little jargon/technical terminology used, and the vocabulary that is provided in the text is contemporary
There do not seem to be obvious shifts in the terminology or the framework. The text is internally consistent in that regard.
The text is well divided into chapter and subsections. Each chapter is relatively self-contained, so there are little issues with referring to past material that may have been skipped. The learning objectives at the beginning of the chapter are very useful. Blocks of text are well divided with headings.
As mentioned above, the topics of the text follow the well-established trajectory of undergraduate psychology courses. This makes it very logical and clear.
The lack of a good table of contents made it difficult to navigate the text for my read through. There were links to an outside photo-hosting website (flickr) for some of the stock photos, which contained the photos of the original creators of the photos. This may be distracting or confusing to readers. However, the hyperlinks in general helped with navigation with the PDF.
No more grammatical errors than a standard, edited textbook.
Very few examples explicitly include other races, ethnicities, or backgrounds, however the examples seem to intentionally avoid cultural bias. Overall, the writing seems to be appropriately focused on avoiding culturally insensitive or offensive content.
After having examined several textbooks on research design and methodology related to psychology, this book stands out as superior.
Reviewed by Angela Curl, Assistant Professor, Miami University (Ohio) on 6/20/17
"Research Methods in Psychology" covers most research method topics comprehensively. The author does an excellent job explaining main concepts. The chapter on causation is very detailed and well-written as well as the chapter on research ethics.... read more
"Research Methods in Psychology" covers most research method topics comprehensively. The author does an excellent job explaining main concepts. The chapter on causation is very detailed and well-written as well as the chapter on research ethics. However, the explanations of data analysis seem to address upper level students rather than beginners. For example, in the “Describing Statistical Relationships” chapter, the author does not give detailed enough explanations for key terms. A reader who is not versed in research terminology, in my opinion, would struggle to understand the process. While most topics are covered, there are some large gaps. For example, this textbook has very little content related to qualitative research methods (five pages).
The content appears to be accurate and unbias.
The majority of the content will not become obsolete within a short time period-- many of the information can be used for the coming years, as the information provided is, overall, general in nature. The notably exceptions are the content on APA Code of Ethics and the APA Publication Manual, which both rely heavily on outdated versions, which limits the usefulness of these sections. In addition, it would be helpful to incorporate research studies that have been published after 2011.
The majority of the text is clear, with content that is easy for undergraduate students to read and understand. The key points included in the chapters are helpful, but some chapters seem to be missing key points (i.e., the key points do not accurately represent the overall chapter).
The text seems to be internally consistent in its terminology and organization.
Each chapter is broken into subsections that can be used alone. For example, section 5.2 covers reliability and validity of measurement. This could be extremely helpful for educators to select specific content for assigned readings.
The topics are presented in a logical matter for the most part. However, the PDF version of the book does not include a table of contents, and none of the formats has a glossary or index. This can make it difficult to quickly navigate to specific topics or terms, especially when explanations do not appear where expected. For example, the definitions of independent and dependent variables is provided under the heading “Correlation Does Not Imply Causation” (p. 22).
The text is consistent but needs more visual representations throughout the book, rather than heavily in some chapters and none at all in other chapters. Similarly, the text within the chapters is not easily readable due to the large sections of text with little to no graphics or breaks.
The interface of the text is adequate. However, the formatting of the PDF is sometimes weak. For example, the textbook has a number of pages with large blank spaces and other pages are taken up with large photos or graphics. The number of pages (and cost of printing) could have been reduced, or more graphics added to maximize utility.
I found no grammatical errors.
Text appears to be culturally sensitive. I appreciated the inclusion of the content about avoiding biased language (chapter 11).
Instructors who adopt this book would likely benefit from either selecting certain chapters/modules and/or integrating multiple texts together to address the shortcomings of this text. Further, the sole focus on psychology limits the use of this textbook for introductory research methods for other disciplines (e.g., social work, sociology).
Reviewed by Pramit Nadpara, Assistant Professor, Virginia Commonwealth University on 4/11/17
The text book provides good information in certain areas, while not comprehensive information in other areas. The text provides practical information, especially the section on survey development was good. Additional information on sampling... read more
The text book provides good information in certain areas, while not comprehensive information in other areas. The text provides practical information, especially the section on survey development was good. Additional information on sampling strategies would have been beneficial for the readers.
There are no errors.
Research method is a common topic and the fundamentals of it will not change over the years. Therefore, the book is relevant and will not become obsolete.
Clarity rating: 3
The text in the book is clear. Certain aspects of the text could have been presented more clearly. For example, the section on main effects and interactions are some concepts that students may have difficulty understanding. Those areas could be explained more clearly with an example.
Consistency rating: 3
Graphs in the book lacks titles and variable names. Also, the format of chapter title page needs to be consistent.
At times there were related topics spread across several chapters. This could be corrected for a better read by the audience..
The book text is very clear, and the flow from one topic to the next was adequate. However, having a outline would help the reader.
The PDF copy of the book was a easy read. There were few links that were missing though.
There were no grammatical errors.
The text is not offensive and examples in it are mostly based on historical US based experiments.
I would start of by saying that I am a supporter of the Open Textbook concept. In this day and age, there are a variety of Research Methods book/text available on the market. While this book covers research methods basics, it cannot be recommended in its current form as an acceptable alternative to the standard text. Modifications to the text as recommended by myself and other reviewers might improve the quality of this book in the future.
Reviewed by Meghan Babcock, Instructor, University of Texas at Arlington on 4/11/17
This text includes all important areas that are featured in other Research Methods textbooks and are presented in a logical order. The text includes great examples and provides the references which can be assigned as supplemental readings. In... read more
This text includes all important areas that are featured in other Research Methods textbooks and are presented in a logical order. The text includes great examples and provides the references which can be assigned as supplemental readings. In addition, the chapters end with exercises that can be completed in class or as part of a laboratory assignment. This text would be a great addition to a Research Methods course or an Introductory Statistics course for Psychology majors.
The content is accurate. I did not find any errors and the material is unbiased.
Yes - the content is up to date and would be easy to update if/when necessary.
The text is written at an appropriate level for undergraduate students and explains important terminology. The research studies that the author references are ones that undergraduate psychology majors should be familiar with. The only section that was questionable to me was that on multiple regression in section 8.3 (Complex Correlational Designs). I am unaware of other introductory Research Methods textbooks that cover this analysis, especially without describing simple regression first.
The text is consistent in terms of terminology. The framework is also consistent - the chapters begin with Learning Objectives and ends with Key Takeaways and Exercises.
The text is divisible into smaller reading sections - possibly too many. The sections are brief, and in some instances too brief (e.g., the section on qualitative research). I think that the section headers are helpful for instructors who plan on using this text in conjunction with another text in their course.
The topics were presented in a logical fashion and are similar to other published Research Methods texts. The writing is very clear and great examples are provided. I think that some of the sections are rather brief and more information and examples could be provided.
I did not see any interface issues. All of the links worked properly and the tables and figures were accurate and free of errors. I particularly liked the figures in section 5.2 on reliability of measurement.
There are three comments that I have about the interface, however. First, I was expecting the keywords in blue font to be linked to a glossary, but they were not. I would have appreciated this feature. Second, I read this text as a PDF on an iPad and this version lacking was the Table of Contents (TOC) feature. Although I was able to view the TOC in different versions, I would have appreciated it in the PDF version. Also, it would be nice if the TOC was clickable (i.e., you could click on a section and it automatically directed you to that section). Third, I think the reader of this text would benefit from a glossary at the end of each chapter and/or an index at the end of the text. The "Key Takeaways" sections at the end of each chapter were helpful, but I think that a glossary would be a nice addition as well.
I did not notice any grammatical errors of any kind. The text was easy to read and I think that undergraduate students would agree.
The text was not insensitive or offensive to any races, ethnicities, or backgrounds. I appreciated the section on avoiding biased language when writing manuscripts (e.g., using 'children with learning disabilities' instead of 'special children' or using 'African American' instead of 'minority').
I think that this text would be a nice addition to a Research Methods & Statistics course in psychology. There are some sections that I found particularly helpful: (1) 2.2 and 2.3 - the author gives detailed information about generating research questions and reviewing the literature; (2) 9.2 - this section focuses on constructing survey questionnaires; (3) 11.2 and 11.3 - the author talks about writing a research report and about presenting at conferences. These sections will be great additions to an undergraduate Research Methods course. The brief introduction to APA style was also helpful, but should be supplemented with the most recent APA style manual.
Reviewed by Shannon Layman, Lecturer, University of Texas at Arlington on 4/11/17
The sections in this textbook are overall more brief than in previous Methods texts that I have used. Sometimes this brevity is helpful in terms of getting to the point of the text and moving on. In other cases, some topics could use a bit more... read more
The sections in this textbook are overall more brief than in previous Methods texts that I have used. Sometimes this brevity is helpful in terms of getting to the point of the text and moving on. In other cases, some topics could use a bit more detail to establish a better foundation of the content before moving on to examples and/or the next topic.
I did not find any incorrect information or gross language issues.
Basic statistical and/or methodological texts tend to stay current and up-to-date because the topics in this field have not changed over the decades. Any updated methodologies would be found in a more advanced methods text.
The text is very clear and the ideas are easy to follow/ presented in a logical manner. The most helpful thing about this textbook is that the author arrives at the point of the topic very quickly. Another helpful point about this textbook is the relevancy of the examples used. The examples appear to be accessible to a wide audience and do not require specialization or previous knowledge of other fields of psychology.
I feel this text is very consistent throughout. The ideas build on each other and no terms are discussed in later chapters without being established in previous chapters.
Each chapter had multiple subsections which would allow for smaller reading sections throughout the course. The amount of content in each section and chapter appeared to be less than what I have encountered in other Methods texts.
The organization of the topics in this textbook follows the same or similar organization that I see in other textbooks. As I mentioned previously, the ideas build very well throughout the text.
I did not find any issues with navigation or distortion of the figures in the text.
There were not any obvious and/or egregious grammatical errors that I encountered in this text.
This topic is not really an issue with a Methods textbook as the topics are more so conceptual as opposed to topical. That being said, I did not see an issue with any examples used.
I have no other comments than what I addressed previously.
Reviewed by Sarah Allred, Associate Professor, Rutgers University, Camden on 2/8/17
Mixed. For some topics, there is more (and more practical) information than in most textbooks. I appreciated the very practical advice to students about how to plot data (in statistics chapters). Similarly, there is practical advice about how... read more
Mixed. For some topics, there is more (and more practical) information than in most textbooks. I appreciated the very practical advice to students about how to plot data (in statistics chapters). Similarly, there is practical advice about how to comply with ethical guidelines. The section on item development in surveys was very good.
On the other hand, there is far too little information about some subjects. For example, independent and dependent variables are introduced in passing in an early chapter and then referred to only much later in the text. In my experience, students have a surprisingly difficult time grasping this concept. Another important example is sampling; I would have preferred much more information on types of samples and sampling techniques, and the problems that arise from poor sampling. A third example is the introduction to basic experimental design. Variables, measurement, validity, and reliability are all introduced in one chapter.
I did not see an index or glossary.
I found no errors.
The fundamentals of research methods do not change much. Given the current replication crisis in psychology, it might be helpful to have something about replicability.
Mixed. The text itself is spare and clear. The style of the book is to explain a concept in very few words. There are some excellent aspects of this, but on the other hand, there are some concepts that students have a very difficult time undersatnding if they are not embedded in concrete examples. For example, the section on main effects and interactions shows bar graphs of interactions, but this is presented without variable names or axis titles, and separate from any specific experiment.
Sometimes the chapter stucture is laid out on the title page, and other times it is not. Some graphs lack titles and variable names.
The chapters can be stand alone, but sometimes I found conceptually similar pieces spread across several chapters, and conceptually different pieces in the same chapters.
The individual sentences and paragraphs are always very clear. However, I felt that more tables/outlines of major concepts would have been helpful. For example, perhaps a flow chart of different kinds of experimental designs would be useful. (See section on comprehensiveness for more about organization).
The flow from one topic to the next was adequate.
I read the pdf. Perhaps the interface is more pleasant on other devices, but I found the different formats and fonts in image/captions/main text/figure labels distracting. Many if the instances of apparently hyperlinked (blue) text to do not link to anything.
I found no grammatical errors, and prose is standard academic English.
Like most psychology textbooks available in the US, examples are focused on important experiments in US history.
I really wanted to be happy with this text. I am a supporter of the Open Textbook concept, and I wanted to find this book an acceptable alternative to the variety of Research Methods texts I’ve used. Unfortunately, I cannot recommend this book as superior in quality.
Reviewed by Joel Malin, Assistant Professor, Miami University on 8/21/16
This textbook covers all or nearly all of what I believe are important topics to provide an introduction to research methods in psychology. One minor issue is that the pdf version, which I reviewed, does not include an index or a glossary. As... read more
This textbook covers all or nearly all of what I believe are important topics to provide an introduction to research methods in psychology. One minor issue is that the pdf version, which I reviewed, does not include an index or a glossary. As such, it may be difficult for readers to zero in on material that they need, and/or to get a full sense of what will be covered and in what order.
I did not notice errors.
The book provides a solid overview of key issues related to introductory research methods, many of which are nearly timeless.
The writing is clear and accessible. It was easy and pleasing to read.
Terms are clearly defined and build upon each other as the book progresses.
I believe the text is organized in such a way that it could be easily divided into smaller sections.
The order in which material is presented seems to be well thought out and sensible.
I did not notice any issues with the interface. I reviewed the pdf version and thought the images were very helpful.
The book is written in a culturally relevant manner.
Reviewed by Abbey Dvorak, Assistant Professor, University of Kansas on 8/21/16
The text includes basic, essential information needed for students in an introductory research methods course. In addition, the text includes three chapters (i.e., research ethics, theory, and APA style) that are typically absent from or... read more
The text includes basic, essential information needed for students in an introductory research methods course. In addition, the text includes three chapters (i.e., research ethics, theory, and APA style) that are typically absent from or inadequately covered in similar texts. However, I did have some areas of concern regarding the coverage of qualitative and mixed methods approaches, and nonparametric tests. Although the author advocates for the research question to guide the choice of approach and design, minimal attention is given to the various qualitative designs (e.g., phenomenology, narrative, participatory action, etc.) beyond grounded theory and case studies, with no mention of the different types of mixed methods designs (e.g., concurrent, explanatory, exploratory) that are prevalent today. In addition, common nonparametric tests (e.g., Wilcoxon, Mann-Whitney, etc.) and parametric tests for categorical data (e.g., chi-square, Fisher’s exact, etc.) are not mentioned.
The text overall is accurate and free of errors. I noticed in the qualitative research sub-section, the author describes qualitative research in general, but does not mention common practices associated with qualitative research, such as transcribing interviews, coding data (e.g., different approaches to coding, different types of codes), and data analysis procedures. The information that is included appears accurate.
The text appears up-to-date and includes basic research information and classic examples that rarely change, which may allow the text to be used for many years. However, the author may want to add information about mixed methods research, a growing research approach, in order for the text to stay relevant across time.
The text includes clear, accessible, straightforward language with minimal jargon. When the author introduces a new term, the term is immediately defined and described. The author also provides interesting examples to clarify and expand understanding of terms and concepts throughout the text.
The text is internally consistent and uses similar language and vocabulary throughout. The author uses real-life examples across chapters in order to provide depth and insight into the information. In addition, the vocabulary, concepts, and organization are consistent with other research methods textbooks.
The modules are short, concise, and manageable for students; the material within each module is logically focused and related to each other. I may move the modules and the sub-topics within them into a slightly different order for my class, and add the information mentioned above, but overall, this is very good.
The author presents topics and structures chapters in a logical and organized manner. The epub and online version do not include page numbers in the text, but the pdf does; this may be confusing when referencing the text or answering student questions. The book ends somewhat abruptly after the chapter on inferential statistics; the text may benefit from a concluding chapter to bring everything together, perhaps with a culminating example that walks the reader through creating the research question, choosing a research approach/design, etc., all the way to writing the research report.
I used and compared the pdf, epub, and online versions of the text. The epub and online versions include a clickable table of contents, but the pdf does not. The table format is inconsistent across the three versions; in the epub version (viewed through ibooks), the table data does not always line up correctly, making it difficult to interpret quickly. In the pdf and online versions, the table format looks different, but the data are lined up. No index made it difficult to quickly find areas of interest in the text; however, I could use the Find/Search functions in all three versions to search and find needed items.
As I read through this text, I did not detect any glaring grammatical errors. Overall, I think the text is written quite well in a style that is accessible to students.
The author uses inclusive, person-first language, and the text does not seem to be offensive or insensitive. As I read, I did notice that topics such as diversity and cultural competency are absent.
I enjoyed reading this text and am very excited to have a free research methods text for my students that I may supplement as needed. I wish there was a test question bank and/or flashcards for my students to help them study, but perhaps that could be added in the future. Overall, this is a great resource!
Reviewed by Karen Pikula, Psychology Instructor PhD, Central Lakes College on 1/7/16
The text covers all the areas and ideas of the subject of research methods in psychology for the learner that is just entering the field. The authors cover all of the content of an introductory research methods textbook and use exemplary examples... read more
The text covers all the areas and ideas of the subject of research methods in psychology for the learner that is just entering the field. The authors cover all of the content of an introductory research methods textbook and use exemplary examples that make those concepts relevent to a beginning researcher. As the authors state, the material is presented in such a manner as to encourage learners to not only be effective consumers of current research but also engage as critical thinkers in the many diverse situations one encounters in everyday life.
The content is accurate, error free, and unbiased. It explains both quantiative and qualitative methods in an unbiased manner. It is a bit slim on qualitative. It would be nice to have a bit more information on, for example, creating interview questions, coding, and qualitative data anaylisis.
The text is up to date, having just been revised. This revision was authored by Rajiv Jhangiani (Capilano University, North Vancouver) and includes the addition of a table of contents and cover page that the original text did not have, changes to Chapter 3 (Research Ethics) to include a contemporary example of an ethical breach and to reflect Canadian ethical guidelines and privacy laws, additional information regarding online data collection in Chapter 9 (Survey Research). Jhangiani has correcte of errors in the text and formulae, as well as changing spelling from US to Canadian conventions. The text is also now available in a inexpensive hard copy which students can purchase online or college bookstores can stock. This makes the text current and updates should be minimal.
The text is very easy to read and also very interesting as the authors supplement content with amazing real life examples.
The text is internally consistent in terms of terminology and framework.
This text is easily and readily divisible into smaller reading sections that can be assigned at different points within a course. I am going to use this text in conjunction with the OER OpenStax Psychology text for my Honors Psychology course. I currently use the OER Openstax Psychology textbook for my Positive Psychology course as well as my General Psychology course,
The topics in the text are presented in logical and clear fashion. The way they are presented allows the text to be used in conjuction with other textbooks as a secondary resource.
The text is free of significant interface issues. It is written in a manner that follows the natural process of doing research.
The text contained no noted grammatical errors.
The text is not culturally insensitive or offensive and actually has been revised to accomodate Canadian ethical guidelines as well as those of the APA.
I have to say that I am excited to have found this revised edition. My students will be so happy that there is also a reasonable priced hard coopy for them to purchase. They love the OpenStax Psychology text with the hard copy available from our bookstore. I do wish there were PowerPoints available for the text as well as a test bank. That is always a bonus!
Reviewed by Alyssa Gibbons, Instructor, Colorado State University on 1/7/16
This text covers everything I would consider essential for a first course in research methods, including some areas that are not consistently found in introductory texts (e.g., qualitative research, criticisms of null hypothesis significance... read more
This text covers everything I would consider essential for a first course in research methods, including some areas that are not consistently found in introductory texts (e.g., qualitative research, criticisms of null hypothesis significance testing). The chapters on ethics (Ch. 3) and theory (Ch. 4) are more comprehensive than most I have seen at this level, but not to the extent of information overload; rather, they anticipate and address many questions that undergraduates often have about these issues.
There is no index or table of contents provided in the PDF, and the table of contents on the website is very broad, but the material is well organized and it would not be hard for an instructor to create such a table. Chapter 2.1 is intended to be an introduction to several key terms and ideas (e.g., variable, correlation) that could serve as a sort of glossary.
I found the text to be highly accurate throughout; terms are defined precisely and correctly.
Where there are controversies or differences of opinion in the field, the author presents both sides of the argument in a respectful and unbiased manner. He explicitly discourages students from dismissing any one approach as inherently flawed, discussing not only the advantages and disadvantages of all methods (including nonexperimental ones) but also ways researchers address the disadvantages.
In several places, the textbook explicitly addresses the history and development of various methods (e.g., qualitative research, null hypothesis significance testing) and the ways in which researchers' views have changed. This allows the author to present current thinking and debate in these areas yet still expose students to older ideas they are likely to encounter as they read the research literature. I think this approach sets students up well to encounter future methodological advances; as a field, we refine our methods over time. I think the author could easily integrate new developments in future editions, or instructors could introduce such developments as supplementary material without creating confusion by contradicting the test.
The examples are generally drawn from classic psychological studies that have held up well over time; I think they will appeal to students for some time to come and not appear dated.
The only area in which I did not feel the content was entirely up to date was in the area of psychological measurement; Chapter 5.2 is based on the traditional view and not the more comprehensive modern or holistic view as presented in the 1999 AERA/APA Standards for Educational and Psychological Measurement. However, a comprehensive treatment of measurement validity is probably not necessary for most undergraduates at this stage, and they will certainly encounter the older framework in the research literature.
The textbook does an excellent job of presenting concepts in simple, accessible language without introducing error by oversimplification. The author consistently anticipates common points of confusion, clarifies terms, and even suggests ways for students to remember key distinctions. Terms are clearly and concretely defined when they are introduced. In contrast to many texts I have used, the terms that are highlighted in the text are actually the terms I would want my students to remember and study; the author refrains from using psychological jargon that is not central to the concepts he is discussing.
I noticed no major inconsistencies or gaps.
The division of sections within each chapter is useful; although I liked the overall organization of the text, there were points at which I would likely assign sections in a slightly different order and I felt I could do this easily without loss of continuity. The one place I would have liked more modularity was in the discussion of inferential statistics: t-tests, ANOVA, and Pearson's r are all covered within Chapter 13.2. On the one hand, this enables students to see the relationships and similarities among these tests, but on the other, this is a lot for students to take in at once.
I found the overall organization of the book to be quite logical, mirroring the sequence of steps a researcher would use to develop a research question, design a study, etc. As discussed above, the modularity of the book makes it easy to reorder sections to suit the structure of a particular class (for example, I might have students read the section on APA writing earlier in the semester as they begin drafting their own research proposals). I like the inclusion of ethics very early on in the text, establishing the importance of this topic for all research design choices.
One organizational feature I particularly appreciated was the consistent integration of conceptual and practical ideas; for example, in the discussion of psychological measurement, reliability and validity are discussed alongside the importance of giving clear instructions and making sure participants cannot be identified by their writing implements. This gives students an accurate and honest picture of the research process - some of the choices we make are driven by scientific ideals and some are driven by practical lessons learned. Students often have questions related to these mundane aspects of conducting research and it is helpful to have them so clearly addressed.
Although I didn't encounter any problems per se with the interface, I do think it could be made more user-friendly. For example, references to figures and tables are highlighted in blue, appearing to be hyperlinks, but they were not. Having such links, as well as a linked, easily-navigable and detailed table of contents, would also be helpful (and useful to students who use assistive technology).
I noticed no grammatical errors.
Where necessary, the author uses inclusive language and there is nothing that seems clearly offensive. The examples generally reflect American psychology research, but the focus is on the methods used and not the participants or cultural context. The text could be more intentionally or proactively inclusive, but it is not insensitive or exclusive.
I am generally hard to please when it comes to textbooks, but I found very little to quibble with in this one. It is a very well-written and accessible introduction to research methods that meets students where they are, addressing their common questions, misconceptions, and concerns. Although it's not flashy, the figures, graphics, and extra resources provided are clear, helpful, and relevant.
Reviewed by Moin Syed, Assistant Professor, University of Minnesota on 6/10/15
The text is thorough in terms of covering introductory concepts that are central to experimental and correlational/association designs. I find the general exclusion of qualitative and mixed methods designs hard to defend (despite some researchers’... read more
The text is thorough in terms of covering introductory concepts that are central to experimental and correlational/association designs. I find the general exclusion of qualitative and mixed methods designs hard to defend (despite some researchers’ distaste for the methods). While these approaches were less commonly used in the recent past, they are prevalent in the early years of psychology and are ascending once again. It strikes me as odd to just ignore two whole families of methods that are used within the practice of psychology—definitely not a sustainable approach.
I do very much appreciate the emphasis on those who will both practice and consume psychology, given the wide variety of undergraduate career paths.
One glaring omission is a Table of Contents within the PDF. It would be nice to make this a linked PDF, so that clicking on the entry in a TOC (or cross-references) would jump the reader to the relevant section.
I did not see an errors. The chapter on theory is not as clear as it could be. The section “what is theory” is not very clear, and these are difficulte concepts (difference between theory, hypothesis, etc.). A bit more time spent here could have been good. Also, the discussion of functional, mechanistic, and typological theories leaves out the fourth of Pepper’s metaphors: contextualism. I’m not sure that was intentional and accidental, but it is noticeable!
This is a research methods text focused on experimental and association designs. The basics of these designs do not change a whole lot over time, so there is little likelihood that the main content will become obsolete anytime soon. Some of the examples used are a bit dated, but then again most of them are considered “classics” in the field, which I think are important to retain (and there is at least one “new classic” included in the ethics section, namely the fraudulent research linking autism to the MMR vaccine).
The text is extremely clear and accessible. In fact, it may even be *too* simple for undergraduate use. Then again, students often struggle with methods, so simplicity is good, and the simplicity can also make the book marketable to high school courses (although I doubt many high schools have methods courses).
Yes, quite consistent throughout. Carrying through the same examples into different chapters is a major strength of the text.
I don’ anticipate any problems here.
The book flows well, with brief sections. I do wonder if maybe the sections are too brief? Perhaps too many check-ins? The “key take-aways” usually come after only a few pages. As mentioned above, the book is written at a very basic level, so this brevity is consistent with that approach. It is not a problem, per se, but those considering adopting the text should be aware of this aspect.
No problems here.
I did not detect any grammatical errors. The text flows very well.
The book is fairly typical of American research methods books in that it only focuses on the U.S. context and draws its examples from “mainstream” psychology (e.g., little inclusion of ethnic minority or cross-cultural psychology). However, the text is certainly not insensitive or offensive in any way.
Nice book, thanks for writing it!
Reviewed by Rajiv Jhangiani, Instructor, Capilano University on 10/9/13
The text is well organized and written, integrates excellent pedagogical features, and covers all of the traditional areas of the topic admirably. The final two chapters provide a good bridge between the research methods course and the follow-up... read more
The text is well organized and written, integrates excellent pedagogical features, and covers all of the traditional areas of the topic admirably. The final two chapters provide a good bridge between the research methods course and the follow-up course on behavioural statistics. The text integrates real psychological measures, harnesses students' existing knowledge from introductory psychology, includes well-chosen examples from real life and research, and even includes a very practical chapter on the use of APA style for writing and referencing. On the other hand, it does not include a table of contents or an index, both of which are highly desirable. The one chapter that requires significant revision is Chapter 3 (Research Ethics), which is based on the US codes of ethics (e.g., Federal policy & APA code) and does not include any mention of the Canadian Tri-Council Policy Statement.
The very few errors I found include the following: 1. The text should read "The fact that his F score…" instead of "The fact that his t score…" on page 364 2. Some formulae are missing the line that separates the numerator from the denominator. See pages 306, 311, 315, and 361 3. Table 12.3 on page 310 lists the variance as 288 when it is 28.8
The text is up-to-date and will not soon lose relevance. The only things I would add are a brief discussion of the contemporary case of Diederik Stapel's research fraud in the chapter on Research Ethics, as well as some research concerning the external validity of web-based studies (e.g., Gosling et al.'s 2004 article in American Psychologist).
Overall, the style of writing makes this text highly accessible. The writing flows well, is well organized, and includes excellent, detailed, and clear examples and explanations for concepts. The examples often build on concepts or theories students would have covered in their introductory psychology course. Some constructive criticism: 1. When discussing z scores on page 311 it might have been helpful to point out that the mean and SD for a set of calculated z scores are 0 and 1 respectively. Good students will come to this realization themselves, but it is not a bad thing to point it out nonetheless. 2. The introduction of the concept of multiple regression might be difficult for some students to grasp. 3. The only place where I felt short of an explanation was in the use of a research example to demonstrate the use of a line graph on page 318. In this case the explanation in question does not pertain to the line graph itself but the result of the study used, which is so fascinating that students will wish for the researchers' explanation for it.
The text is internally consistent.
The text is organized very well into chapters, modules within each chapter, and learning objectives within each module. Each module also includes useful exercises that help consolidate learning.
As mentioned earlier, the style of writing makes this text highly accessible. The writing flows well, is well organized, and includes excellent, detailed, and clear examples and explanations for concepts. The examples often build on concepts or theories students would have covered in their introductory psychology course. Only rarely did I feel that the author could have assisted the student by demonstrating the set-by-step calculation of a statistic (e.g., on page 322 for the calculation of Pearson's r)
The images, graphs, and charts are clear. The only serious issues that hamper navigation are the lack of a table of contents and an index. Many of the graphs will need to be printed in colour (or otherwise modified) for the students to follow the explanations provided in the text.
The text is written rather well and is free from grammatical errors. Of course, spellings are in the US convention.
The text is not culturally insensitive or offensive. Of course, it is not a Canadian edition and so many of the examples (all of which are easy to comprehend) come from a US context.
I have covered most of these issues in my earlier comments. The only things left to mention are that the author should have clearly distinguished between mundane and psychological realism, and that, in my opinion, the threats to internal validity could have been grouped together and might have been closer to an exhaustive list. This review originated in the BC Open Textbook Collection and is licensed under CC BY-ND.
Table of Contents
- Chapter 1: The Science of Psychology
- Chapter 2: Overview of the Scientific Method
- Chapter 3: Research Ethics
- Chapter 4: Psychological Measurement
- Chapter 5: Experimental Research
- Chapter 6: Non-experimental Research
- Chapter 7: Survey Research
- Chapter 8: Quasi-Experimental Research
- Chapter 9: Factorial Designs
- Chapter 10: Single-Subject Research
- Chapter 11: Presenting Your Research
- Chapter 12: Descriptive Statistics
- Chapter 13: Inferential Statistics
Ancillary Material
- Kwantlen Polytechnic University
About the Book
This fourth edition (published in 2019) was co-authored by Rajiv S. Jhangiani (Kwantlen Polytechnic University), Carrie Cuttler (Washington State University), and Dana C. Leighton (Texas A&M University—Texarkana) and is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Revisions throughout the current edition include changing the chapter and section numbering system to better accommodate adaptions that remove or reorder chapters; continued reversion from the Canadian edition; general grammatical edits; replacement of “he/she” to “they” and “his/her” to “their”; removal or update of dead links; embedded videos that were not embedded; moved key takeaways and exercises from the end of each chapter section to the end of each chapter; a new cover design.
About the Contributors
Dr. Carrie Cuttler received her Ph.D. in Psychology from the University of British Columbia. She has been teaching research methods and statistics for over a decade. She is currently an Assistant Professor in the Department of Psychology at Washington State University, where she primarily studies the acute and chronic effects of cannabis on cognition, mental health, and physical health. Dr. Cuttler was also an OER Research Fellow with the Center for Open Education and she conducts research on open educational resources. She has over 50 publications including the following two published books: A Student Guide for SPSS (1st and 2nd edition) and Research Methods in Psychology: Student Lab Guide. Finally, she edited another OER entitled Essentials of Abnormal Psychology. In her spare time, she likes to travel, hike, bike, run, and watch movies with her husband and son. You can find her online at @carriecuttler or carriecuttler.com.
Dr. Rajiv Jhangiani is the Associate Vice Provost, Open Education at Kwantlen Polytechnic University in British Columbia. He is an internationally known advocate for open education whose research and practice focuses on open educational resources, student-centered pedagogies, and the scholarship of teaching and learning. Rajiv is a co-founder of the Open Pedagogy Notebook, an Ambassador for the Center for Open Science, and serves on the BC Open Education Advisory Committee. He formerly served as an Open Education Advisor and Senior Open Education Research & Advocacy Fellow with BCcampus, an OER Research Fellow with the Open Education Group, a Faculty Workshop Facilitator with the Open Textbook Network, and a Faculty Fellow with the BC Open Textbook Project. A co-author of three open textbooks in Psychology, his most recent book is Open: The Philosophy and Practices that are Revolutionizing Education and Science (2017). You can find him online at @thatpsychprof or thatpsychprof.com.
Dr. Dana C. Leighton is Assistant Professor of Psychology in the College of Arts, Science, and Education at Texas A&M University—Texarkana. He earned his Ph.D. from the University of Arkansas, and has 15 years experience teaching across the psychology curriculum at community colleges, liberal arts colleges, and research universities. Dr. Leighton’s social psychology research lab studies intergroup relations, and routinely includes undergraduate students as researchers. He is also Chair of the university’s Institutional Review Board. Recently he has been researching and writing about the use of open science research practices by undergraduate researchers to increase diversity, justice, and sustainability in psychological science. He has published on his teaching methods in eBooks from the Society for the Teaching of Psychology, presented his methods at regional and national conferences, and received grants to develop new teaching methods. His teaching interests are in undergraduate research, writing skills, and online student engagement. For more about Dr. Leighton see http://www.danaleighton.net and http://danaleighton.edublogs.org
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APA Handbook of Research Methods in Psychology
Available formats.
- Table of contents
- Contributor bios
- Book details
With significant new and updated content across dozens of chapters, this second edition presents the most exhaustive treatment available of the techniques psychologists and others have developed to help them pursue a shared understanding of why humans think, feel, and behave the way they do.
The initial chapters in this indispensable three-volume handbook address broad, crosscutting issues faced by researchers: the philosophical, ethical, and societal underpinnings of psychological research. Next, chapters detail the research planning process, describe the range of measurement techniques that psychologists most often use to collect data, consider how to determine the best measurement techniques for a particular purpose, and examine ways to assess the trustworthiness of measures.
Additional chapters cover various aspects of quantitative, qualitative, neuropsychological, and biological research designs, presenting an array of options and their nuanced distinctions. Chapters on techniques for data analysis follow, and important issues in writing up research to share with the community of psychologists are discussed in the handbook’s concluding chapters.
Among the newly written chapters in the second edition, the handbook’s stellar roster of authors cover literature searching, workflow and reproducibility, research funding, neuroimaging, various facets of a wide range of research designs and data analysis methods, and updated information on the publication process, including research data management and sharing, questionable practices in statistical analysis, and ethical issues in manuscript preparation and authorship.
Volume 1. Foundations, Planning, Measures, and Psychometrics
Editorial Board
About the Editors
Contributors
A Note from the Publisher
Introduction: Objectives of Psychological Research and Their Relations to Research Methods
Part I. Philosophical, Ethical, and Societal Underpinnings of Psychological Research
- Chapter 1. Perspectives on the Epistemological Bases for Qualitative Research Carla Willig
- Chapter 2. Frameworks for Causal Inference in Psychological Science Peter M. Steiner, William R. Shadish, and Kristynn J. Sullivan
- Chapter 3. Ethics in Psychological Research: Guidelines and Regulations Adam L. Fried and Kate L. Jansen
- Chapter 4. Ethics and Regulation of Research With Nonhuman Animals Sangeeta Panicker, Chana K. Akins, and Beth Ann Rice
- Chapter 5. Cross-Cultural Research Methods David Masumoto and Fons J. R. van de Vijver
- Chapter 6.Research With Populations that Experience Marginalization George P. Knight, Rebecca M. B. White, Stefanie Martinez-Fuentes, Mark W. Roosa, and Adriana J. Umaña-Taylor
Part II. Planning Research
- Chapter 7. Developing Testable and Important Research Questions Frederick T. L. Leong, Neal Schmitt, and Brent J. Lyons
- Chapter 8. Searching With a Purpose: How to Use Literature Searching to Support Your Research Diana Ramirez and Margaret J. Foster
- Chapter 9. Psychological Measurement: Scaling and Analysis Heather Hayes and Susan E. Embretson
- Chapter 10. Sample Size Planning Ken Kelley, Samantha F. Anderson, and Scott E. Maxwell
- Chapter 11. Workflow and Reproducibility Oliver Kirchkamp
- Chapter 12. Obtaining and Evaluating Research Funding Jonathan S. Comer and Amanda L. Sanchez
Part III. Measurement Methods
- Chapter 13. Behavioral Observation Roger Bakeman and Vicenç Quera
- Chapter 14. Question Order Effects Lisa Lee, Parvati Krishnamurty, and Struther Van Horn
- Chapter 15. Interviews and Interviewing Techniques Anna Madill
- Chapter 16. Using Intensive Longitudinal Methods in Psychological Research Masumi Iida, Patrick E. Shrout, Jean-Philippe Laurenceau, and Niall Bolger
- Chapter 17. Automated Analyses of Natural Language in Psychological Research Laura K. Allen, Arthur C. Graesser, and Danielle S. McNamara
- Chapter 18. Objective Tests as Instruments of Psychological Theory and Research David Watson
- Chapter 19. Norm- and Criterion-Referenced Testing Kurt F. Geisinger
- Chapter 20. The Current Status of "Projective" "Tests" Robert E. McGrath, Alec Twibell, and Elizabeth J. Carroll
- Chapter 21. Brief Instruments and Short Forms Emily A. Atkinson, Carolyn M. Pearson Carter, Jessica L. Combs Rohr, and Gregory T. Smith
- Chapter 22. Eye Movements, Pupillometry, and Cognitive Processes Simon P. Liversedge, Sara V. Milledge, and Hazel I. Blythe
- Chapter 23. Response Times Roger Ratcliff
- Chapter 24. Psychophysics: Concepts, Methods, and Frontiers Allie C. Hexley, Takuma Morimoto, and Manuel Spitschan
- Chapter 25. The Perimetric Physiological Measurement of Psychological Constructs Louis G. Tassinary, Ursula Hess, Luis M. Carcoba, and Joseph M. Orr
- Chapter 26. Salivary Hormone Assays Linda Becker, Nicholas Rohleder, and Oliver C. Schultheiss
- Chapter 27. Electro- and Magnetoencephalographic Methods in Psychology Eddie Harmon-Jones, David M. Amodio, Philip A. Gable, and Suzanne Dikker
- Chapter 28. Event-Related Potentials Steven J. Luck
- Chapter 29. Functional Neuroimaging Megan T. deBettencourt, Wilma A. Bainbridge, Monica D. Rosenberg
- Chapter 30. Noninvasive Stimulation of the Cerebral Cortex Dennis J. L. G. Schutter
- Chapter 31. Combined Neuroimaging Methods Marius Moisa and Christian C. Ruff
- Chapter 32. Neuroimaging Analysis Methods Yanyu Xiong and Sharlene D. Newman
Part IV. Psychometrics
- Chapter 33. Reliability Sean P. Lane, Elizabeth N. Aslinger, and Patrick E. Shrout
- Chapter 34. Generalizability Theory Xiaohong Gao and Deborah J. Harris
- Chapter 35. Construct Validity Kevin J. Grimm and Keith F. Widaman
- Chapter 36. Item-Level Factor Nisha C. Gottfredson, Brian D. Stucky, and A. T. Panter
- Chapter 37. Item Response Theory Steven P. Reise and Tyler M. Moore
- Chapter 38. Measuring Test Performance With Signal Detection Theory Techniques Teresa A. Treat and Richard J. Viken
Volume 2. Research Designs: Quantitative, Qualitative, Neuropsychological, and Biological
Part I. Qualitative Research Methods
- Chapter 1. Developments in Qualitative Inquiry Sarah Riley and Andrea LaMarre
- Chapter 2. Metasynthesis of Qualitative Research Sally Thorne
- Chapter 3. Grounded Theory and Psychological Research Robert Thornberg, Elaine Keane, and Malgorzata Wójcik
- Chapter 4. Thematic Analysis Virginia Braun and Victoria Clarke
- Chapter 5. Phenomenological Methodology, Methods, and Procedures for Research in Psychology Frederick J. Wertz
- Chapter 6. Narrative Analysis Javier Monforte and Brett Smith
- Chapter 7. Ethnomethodology and Conversation Analysis Paul ten Have
- Chapter 8. Discourse Analysis and Discursive Psychology Chris McVittie and Andy McKinlay
- Chapter 9. Ethnography in Psychological Research Elizabeth Fein and Jonathan Yahalom
- Chapter 10. Visual Research in Psychology Paula Reavey, Jon Prosser, and Steven D. Brown
- Chapter 11. Researching the Temporal Karen Henwood and Fiona Shirani
Part II. Working Across Epistemologies, Methodologies, and Methods
- Chapter 12. Mixed Methods Research in Psychology Timothy C. Guetterman and Analay Perez
- Chapter 13. The "Cases Within Trials" (CWT) Method: An Example of a Mixed-Methods Research Design Daniel B. Fishman
- Chapter 14. Researching With American Indian and Alaska Native Communities: Pursuing Partnerships for Psychological Inquiry in Service to Indigenous Futurity Joseph P. Gone
- Chapter 15. Participatory Action Research as Movement Toward Radical Relationality, Epistemic Justice, and Transformative Intervention: A Multivocal Reflection Urmitapa Dutta, Jesica Siham Fernández, Anne Galletta, and Regina Day Langhout
Part III. Sampling Across People and Time
- Chapter 16. Introduction to Survey Sampling Roger Tourangeau and Ting Yan
- Chapter 17. Epidemiology Rumi Kato Price and Heidi H. Tastet
- Chapter 18. Collecting Longitudinal Data: Present Issues and Future Challenges Simran K. Johal, Rohit Batra, and Emilio Ferrer
- Chapter 19. Using the Internet to Collect Data Ulf-Dietrich Reips
Part IV. Building and Testing Models
- Chapter 20. Statistical Mediation Analysis David P. MacKinnon, Jeewon Cheong, Angela G. Pirlott, and Heather L. Smyth
- Chapter 21. Structural Equation Modeling with Latent Variables Rick H. Hoyle and Nisha C. Gottfredson
- Chapter 22. Mathematical Psychology Parker Smith, Yanjun Liu, James T. Townsend, and Trisha Van Zandt
- Chapter 23. Computational Modeling Adele Diederich
- Chapter 24. Fundamentals of Bootstrapping and Monte Carlo Methods William Howard Beasley, Patrick O'Keefe, and Joseph Lee Rodgers
- Chapter 25. Designing Simulation Studies Xitao Fan
- Chapter 26. Bayesian Modeling for Psychologists: An Applied Approach Fred M. Feinberg and Richard Gonzalez
Part V. Designs Involving Experimental Manipulations
- Chapter 27. Randomized Designs in Psychological Research Larry Christensen, Lisa A. Turner, and R. Burke Johnson
- Chapter 28. Nonequivalent Comparison Group Designs Henry May and Zachary K. Collier
- Chapter 29. Regression Discontinuity Designs Charles S. Reichardt and Gary T. Henry
- Chapter 30. Treatment Validity for Intervention Studies Dianne L. Chambless and Steven D. Hollon
- Chapter 31. Translational Research Michael T. Bardo, Christopher Cappelli, and Mary Ann Pentz
- Chapter 32. Program Evaluation: Outcomes and Costs of Putting Psychology to Work Brian T. Yates
Part VI. Quantitative Research Designs Involving Single Participants or Units
- Chapter 33. Single-Case Experimental Design John M. Ferron, Megan Kirby, and Lodi Lipien
- Chapter 34. Time Series Designs Bradley J. Bartos, Richard McCleary, and David McDowall
Part VII. Designs in Neuropsychology and Biological Psychology
- Chapter 35. Case Studies in Neuropsychology Randi C. Martin, Simon Fischer-Baum, and Corinne M. Pettigrew
- Chapter 36. Group Studies in Experimental Neuropsychology Avinash R Vaidya, Maia Pujara, and Lesley K. Fellows
- Chapter 37. Genetic Methods in Psychology Terrell A. Hicks, Daniel Bustamante, Karestan C. Koenen, Nicole R. Nugent, and Ananda B. Amstadter
- Chapter 38. Human Genetic Epidemiology Floris Huider, Lannie Ligthart, Yuri Milaneschi, Brenda W. J. H. Penninx, and Dorret I. Boomsma
Volume 3. Data Analysis and Research Publication
Part I. Quantitative Data Analysis
- Chapter 1. Methods for Dealing With Bad Data and Inadequate Models: Distributions, Linear Models, and Beyond Rand R. Wilcox and Guillaume A. Rousselet
- Chapter 2. Maximum Likelihood and Multiple Imputation Missing Data Handling: How They Work, and How to Make Them Work in Practice Timothy Hayes and Craig K. Enders
- Chapter 3. Exploratory Data Analysis Paul F. Velleman and David C. Hoaglin
- Chapter 4. Graphic Displays of Data Leland Wilkinson
- Chapter 5. Estimating and Visualizing Interactions in Moderated Multiple Regression Connor J. McCabe and Kevin M. King
- Chapter 6. Effect Size Estimation Michael Borenstein
- Chapter 7. Measures of Clinically Significant Change Russell J. Bailey, Benjamin M. Ogles, and Michael J. Lambert
- Chapter 8. Analysis of Variance and the General Linear Model James Jaccard and Ai Bo
- Chapter 9. Generalized Linear Models David Rindskopf
- Chapter 10. Multilevel Modeling for Psychologists John B. Nezlek
- Chapter 11. Longitudinal Data Analysis Andrew K. Littlefield
- Chapter 12. Event History Analysis Fetene B. Tekle and Jeroen K. Vermunt
- Chapter 13. Latent State-Trait Models Rolf Steyer, Christian Geiser, and Christiane Loßnitzer
- Chapter 14. Latent Variable Modeling of Continuous Growth David A. Cole, Jeffrey A. Ciesla, and Qimin Liu
- Chapter 15. Dynamical Systems and Differential Equation Models of Change Steven M. Boker and Robert G. Moulder
- Chapter 16. A Multivariate Growth Curve Model for Three-Level Data Patrick J. Curran, Chris L. Strauss, Ethan M. McCormick, and James S. McGinley
- Chapter 17. Exploratory Factor Analysis and Confirmatory Factor Analysis Keith F. Widaman and Jonathan Lee Helm
- Chapter 18. Latent Class and Latent Profile Models Brian P. Flaherty, Liying Wang, and Cara J. Kiff
- Chapter 19. Decision Trees and Ensemble Methods in the Behavioral Sciences Kevin J. Grimm, Ross Jacobucci, and John J. McArdle
- Chapter 20. Using the Social Relations Model to Understand Interpersonal Perception and Behavior P. Niels Christensen, Deborah A. Kashy, and Katelin E. Leahy
- Chapter 21. Dyadic Data Analysis Richard Gonzalez and Dale Griffin
- Chapter 22. The Data of Others: New and Old Faces of Archival Research Sophie Pychlau and David T. Wagner
- Chapter 23. Social Network Analysis in Psychology: Recent Breakthroughs in Methods and Theories Wei Wang, Tobias Stark, James D. Westaby, Adam K. Parr, and Daniel A. Newman
- Chapter 24. Meta-Analysis Jeffrey C. Valentine, Therese D. Pigott, and Joseph Morris
Part II. Publishing and the Publication Process
- Chapter 25. Research Data Management and Sharing Katherine G. Akers and John A. Borghi
- Chapter 26. Questionable Practices in Statistical Analysis Rex B. Kline
- Chapter 27. Ethical Issues in Manuscript Preparation and Authorship Jennifer Crocker
Harris Cooper, PhD, is the Hugo L. Blomquist professor, emeritus, in the Department of Psychology and Neuroscience at Duke University. His research interests concern research synthesis and research methodology, and he also studies the application of social and developmental psychology to education policy. His book Research Synthesis and Meta-Analysis: A Step-by-Step Approach (2017) is in its fifth edition. He is the coeditor of the Handbook of Research Synthesis and Meta-Analysis (3 rd ed. 2019).
In 2007, Dr. Cooper was the recipient of the Frederick Mosteller Award for Contributions to Research Synthesis Methodology, and in 2008 he received the Ingram Olkin Award for Distinguished Lifetime Contribution to Research Synthesis from the Society for Research Synthesis Methodology.
He served as the chair of the Department of Psychology and Neuroscience at Duke University from 2009 to 2014, and from 2017 to 2018 he served as the dean of social science at Duke. Dr. Cooper chaired the first APA committee that developed guidelines for information about research that should be included in manuscripts submitted to APA journals. He currently serves as the editor of American Psychologist, the flagship journal of APA.
Marc N. Coutanche, PhD, is an associate professor of psychology and research scientist in the Learning Research and Development Center at the University of Pittsburgh. Dr. Coutanche directs a program of cognitive neuroscience research and develops and tests new computational techniques to identify and understand the neural information present within neuroimaging data.
His work has been funded by the National Institutes of Health, National Science Foundation, American Psychological Foundation, and other organizations, and he has published in a variety of journals.
Dr. Coutanche received his PhD from the University of Pennsylvania, and conducted postdoctoral training at Yale University. He received a Howard Hughes Medical Institute International Student Research Fellowship and Ruth L. Kirschstein Postdoctoral National Research Service Award, and was named a 2019 Rising Star by the Association for Psychological Science.
Linda M. McMullen, PhD, is professor emerita of psychology at the University of Saskatchewan, Canada. Over her career, she has contributed to the development of qualitative inquiry in psychology through teaching, curriculum development, and pedagogical scholarship; original research; and service to the qualitative research community.
Dr. McMullen introduced qualitative inquiry into both the graduate and undergraduate curriculum in her home department, taught courses at both levels for many years, and has published articles, coedited special issues, and written a book ( Essentials of Discursive Psychology ) that is part of APA’s series on qualitative methodologies, among other works. She has been engaged with building the Society for Qualitative Inquiry in Psychology (SQIP; a section of Division 5 of the APA) into a vibrant scholarly society since its earliest days, and took on many leadership roles while working as a university professor.
Dr. McMullen’s contributions have been recognized by Division 5 of the APA, the Canadian Psychological Association, and the Saskatchewan Psychological Association.
Abigail Panter, PhD, is the senior associate dean for undergraduate education and a professor of psychology in the L. L. Thurstone Psychometric Laboratory at University of North Carolina at Chapel Hill. She is past president of APA’s Division 5, Quantitative and Qualitative Methods.
As a quantitative psychologist, she develops instruments, research designs and data-analytic strategies for applied research questions in higher education, personality, and health. She serves as a program evaluator for UNC’s Chancellor’s Science Scholars Program, and was also principal investigator for The Finish Line Project, a $3 million grant from the U.S. Department of Education that systematically investigated new supports and academic initiatives, especially for first-generation college students.
Her books include the APA Dictionary of Statistics and Research Methods (2014), the APA Handbook of Research Methods in Psychology (first edition; 2012), the Handbook of Ethics in Quantitative Methodology (2011), and the SAGE Handbook of Methods in Social Psychology (2004), among others.
David Rindskopf, PhD, is distinguished professor at the City University of New York Graduate Center, specializing in research methodology and statistics. His main interests are in Bayesian statistics, causal inference, categorical data analysis, meta-analysis, and latent variable models.
He is a fellow of the American Statistical Association and the American Educational Research Association, and is past president of the Society of Multivariate Experimental Psychology and the New York Chapter of the American Statistical Association.
Kenneth J. Sher, PhD, is chancellor’s professor and curators’ distinguished professor of psychological sciences, emeritus, at the University of Missouri. He received his PhD in clinical psychology from Indiana University (1980) and his clinical internship training at Brown University (1981).
His primary areas of research focus on etiological processes in the development of alcohol dependence, factors that affect the course of drinking and alcohol use disorders throughout adulthood, longitudinal research methodology, psychiatric comorbidity, and nosology. At the University of Missouri he directed the predoctoral and postdoctoral training program in alcohol studies, and his research has been continually funded by the National Institute on Alcohol Abuse and Alcoholism for more than 35 years.
Dr. Sher’s research contributions have been recognized by professional societies including the Research Society on Alcoholism and APA, and throughout his career, he has been heavily involved in service to professional societies and scholarly publications.
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The Use of Research Methods in Psychological Research: A Systematised Review
Salomé elizabeth scholtz.
1 Community Psychosocial Research (COMPRES), School of Psychosocial Health, North-West University, Potchefstroom, South Africa
Werner de Klerk
Leon t. de beer.
2 WorkWell Research Institute, North-West University, Potchefstroom, South Africa
Research methods play an imperative role in research quality as well as educating young researchers, however, the application thereof is unclear which can be detrimental to the field of psychology. Therefore, this systematised review aimed to determine what research methods are being used, how these methods are being used and for what topics in the field. Our review of 999 articles from five journals over a period of 5 years indicated that psychology research is conducted in 10 topics via predominantly quantitative research methods. Of these 10 topics, social psychology was the most popular. The remainder of the conducted methodology is described. It was also found that articles lacked rigour and transparency in the used methodology which has implications for replicability. In conclusion this article, provides an overview of all reported methodologies used in a sample of psychology journals. It highlights the popularity and application of methods and designs throughout the article sample as well as an unexpected lack of rigour with regard to most aspects of methodology. Possible sample bias should be considered when interpreting the results of this study. It is recommended that future research should utilise the results of this study to determine the possible impact on the field of psychology as a science and to further investigation into the use of research methods. Results should prompt the following future research into: a lack or rigour and its implication on replication, the use of certain methods above others, publication bias and choice of sampling method.
Introduction
Psychology is an ever-growing and popular field (Gough and Lyons, 2016 ; Clay, 2017 ). Due to this growth and the need for science-based research to base health decisions on (Perestelo-Pérez, 2013 ), the use of research methods in the broad field of psychology is an essential point of investigation (Stangor, 2011 ; Aanstoos, 2014 ). Research methods are therefore viewed as important tools used by researchers to collect data (Nieuwenhuis, 2016 ) and include the following: quantitative, qualitative, mixed method and multi method (Maree, 2016 ). Additionally, researchers also employ various types of literature reviews to address research questions (Grant and Booth, 2009 ). According to literature, what research method is used and why a certain research method is used is complex as it depends on various factors that may include paradigm (O'Neil and Koekemoer, 2016 ), research question (Grix, 2002 ), or the skill and exposure of the researcher (Nind et al., 2015 ). How these research methods are employed is also difficult to discern as research methods are often depicted as having fixed boundaries that are continuously crossed in research (Johnson et al., 2001 ; Sandelowski, 2011 ). Examples of this crossing include adding quantitative aspects to qualitative studies (Sandelowski et al., 2009 ), or stating that a study used a mixed-method design without the study having any characteristics of this design (Truscott et al., 2010 ).
The inappropriate use of research methods affects how students and researchers improve and utilise their research skills (Scott Jones and Goldring, 2015 ), how theories are developed (Ngulube, 2013 ), and the credibility of research results (Levitt et al., 2017 ). This, in turn, can be detrimental to the field (Nind et al., 2015 ), journal publication (Ketchen et al., 2008 ; Ezeh et al., 2010 ), and attempts to address public social issues through psychological research (Dweck, 2017 ). This is especially important given the now well-known replication crisis the field is facing (Earp and Trafimow, 2015 ; Hengartner, 2018 ).
Due to this lack of clarity on method use and the potential impact of inept use of research methods, the aim of this study was to explore the use of research methods in the field of psychology through a review of journal publications. Chaichanasakul et al. ( 2011 ) identify reviewing articles as the opportunity to examine the development, growth and progress of a research area and overall quality of a journal. Studies such as Lee et al. ( 1999 ) as well as Bluhm et al. ( 2011 ) review of qualitative methods has attempted to synthesis the use of research methods and indicated the growth of qualitative research in American and European journals. Research has also focused on the use of research methods in specific sub-disciplines of psychology, for example, in the field of Industrial and Organisational psychology Coetzee and Van Zyl ( 2014 ) found that South African publications tend to consist of cross-sectional quantitative research methods with underrepresented longitudinal studies. Qualitative studies were found to make up 21% of the articles published from 1995 to 2015 in a similar study by O'Neil and Koekemoer ( 2016 ). Other methods in health psychology, such as Mixed methods research have also been reportedly growing in popularity (O'Cathain, 2009 ).
A broad overview of the use of research methods in the field of psychology as a whole is however, not available in the literature. Therefore, our research focused on answering what research methods are being used, how these methods are being used and for what topics in practice (i.e., journal publications) in order to provide a general perspective of method used in psychology publication. We synthesised the collected data into the following format: research topic [areas of scientific discourse in a field or the current needs of a population (Bittermann and Fischer, 2018 )], method [data-gathering tools (Nieuwenhuis, 2016 )], sampling [elements chosen from a population to partake in research (Ritchie et al., 2009 )], data collection [techniques and research strategy (Maree, 2016 )], and data analysis [discovering information by examining bodies of data (Ktepi, 2016 )]. A systematised review of recent articles (2013 to 2017) collected from five different journals in the field of psychological research was conducted.
Grant and Booth ( 2009 ) describe systematised reviews as the review of choice for post-graduate studies, which is employed using some elements of a systematic review and seldom more than one or two databases to catalogue studies after a comprehensive literature search. The aspects used in this systematised review that are similar to that of a systematic review were a full search within the chosen database and data produced in tabular form (Grant and Booth, 2009 ).
Sample sizes and timelines vary in systematised reviews (see Lowe and Moore, 2014 ; Pericall and Taylor, 2014 ; Barr-Walker, 2017 ). With no clear parameters identified in the literature (see Grant and Booth, 2009 ), the sample size of this study was determined by the purpose of the sample (Strydom, 2011 ), and time and cost constraints (Maree and Pietersen, 2016 ). Thus, a non-probability purposive sample (Ritchie et al., 2009 ) of the top five psychology journals from 2013 to 2017 was included in this research study. Per Lee ( 2015 ) American Psychological Association (APA) recommends the use of the most up-to-date sources for data collection with consideration of the context of the research study. As this research study focused on the most recent trends in research methods used in the broad field of psychology, the identified time frame was deemed appropriate.
Psychology journals were only included if they formed part of the top five English journals in the miscellaneous psychology domain of the Scimago Journal and Country Rank (Scimago Journal & Country Rank, 2017 ). The Scimago Journal and Country Rank provides a yearly updated list of publicly accessible journal and country-specific indicators derived from the Scopus® database (Scopus, 2017b ) by means of the Scimago Journal Rank (SJR) indicator developed by Scimago from the algorithm Google PageRank™ (Scimago Journal & Country Rank, 2017 ). Scopus is the largest global database of abstracts and citations from peer-reviewed journals (Scopus, 2017a ). Reasons for the development of the Scimago Journal and Country Rank list was to allow researchers to assess scientific domains, compare country rankings, and compare and analyse journals (Scimago Journal & Country Rank, 2017 ), which supported the aim of this research study. Additionally, the goals of the journals had to focus on topics in psychology in general with no preference to specific research methods and have full-text access to articles.
The following list of top five journals in 2018 fell within the abovementioned inclusion criteria (1) Australian Journal of Psychology, (2) British Journal of Psychology, (3) Europe's Journal of Psychology, (4) International Journal of Psychology and lastly the (5) Journal of Psychology Applied and Interdisciplinary.
Journals were excluded from this systematised review if no full-text versions of their articles were available, if journals explicitly stated a publication preference for certain research methods, or if the journal only published articles in a specific discipline of psychological research (for example, industrial psychology, clinical psychology etc.).
The researchers followed a procedure (see Figure 1 ) adapted from that of Ferreira et al. ( 2016 ) for systematised reviews. Data collection and categorisation commenced on 4 December 2017 and continued until 30 June 2019. All the data was systematically collected and coded manually (Grant and Booth, 2009 ) with an independent person acting as co-coder. Codes of interest included the research topic, method used, the design used, sampling method, and methodology (the method used for data collection and data analysis). These codes were derived from the wording in each article. Themes were created based on the derived codes and checked by the co-coder. Lastly, these themes were catalogued into a table as per the systematised review design.
![Click on image to zoom An external file that holds a picture, illustration, etc.
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Systematised review procedure.
According to Johnston et al. ( 2019 ), “literature screening, selection, and data extraction/analyses” (p. 7) are specifically tailored to the aim of a review. Therefore, the steps followed in a systematic review must be reported in a comprehensive and transparent manner. The chosen systematised design adhered to the rigour expected from systematic reviews with regard to full search and data produced in tabular form (Grant and Booth, 2009 ). The rigorous application of the systematic review is, therefore discussed in relation to these two elements.
Firstly, to ensure a comprehensive search, this research study promoted review transparency by following a clear protocol outlined according to each review stage before collecting data (Johnston et al., 2019 ). This protocol was similar to that of Ferreira et al. ( 2016 ) and approved by three research committees/stakeholders and the researchers (Johnston et al., 2019 ). The eligibility criteria for article inclusion was based on the research question and clearly stated, and the process of inclusion was recorded on an electronic spreadsheet to create an evidence trail (Bandara et al., 2015 ; Johnston et al., 2019 ). Microsoft Excel spreadsheets are a popular tool for review studies and can increase the rigour of the review process (Bandara et al., 2015 ). Screening for appropriate articles for inclusion forms an integral part of a systematic review process (Johnston et al., 2019 ). This step was applied to two aspects of this research study: the choice of eligible journals and articles to be included. Suitable journals were selected by the first author and reviewed by the second and third authors. Initially, all articles from the chosen journals were included. Then, by process of elimination, those irrelevant to the research aim, i.e., interview articles or discussions etc., were excluded.
To ensure rigourous data extraction, data was first extracted by one reviewer, and an independent person verified the results for completeness and accuracy (Johnston et al., 2019 ). The research question served as a guide for efficient, organised data extraction (Johnston et al., 2019 ). Data was categorised according to the codes of interest, along with article identifiers for audit trails such as authors, title and aims of articles. The categorised data was based on the aim of the review (Johnston et al., 2019 ) and synthesised in tabular form under methods used, how these methods were used, and for what topics in the field of psychology.
The initial search produced a total of 1,145 articles from the 5 journals identified. Inclusion and exclusion criteria resulted in a final sample of 999 articles ( Figure 2 ). Articles were co-coded into 84 codes, from which 10 themes were derived ( Table 1 ).
![Click on image to zoom An external file that holds a picture, illustration, etc.
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Journal article frequency.
Codes used to form themes (research topics).
Social Psychology | 31 | Aggression SP, Attitude SP, Belief SP, Child abuse SP, Conflict SP, Culture SP, Discrimination SP, Economic, Family illness, Family, Group, Help, Immigration, Intergeneration, Judgement, Law, Leadership, Marriage SP, Media, Optimism, Organisational and Social justice, Parenting SP, Politics, Prejudice, Relationships, Religion, Romantic Relationships SP, Sex and attraction, Stereotype, Violence, Work |
Experimental Psychology | 17 | Anxiety, stress and PTSD, Coping, Depression, Emotion, Empathy, Facial research, Fear and threat, Happiness, Humor, Mindfulness, Mortality, Motivation and Achievement, Perception, Rumination, Self, Self-efficacy |
Cognitive Psychology | 12 | Attention, Cognition, Decision making, Impulse, Intelligence, Language, Math, Memory, Mental, Number, Problem solving, Reading |
Health Psychology | 7 | Addiction, Body, Burnout, Health, Illness (Health Psychology), Sleep (Health Psychology), Suicide and Self-harm |
Physiological Psychology | 6 | Gender, Health (Physiological psychology), Illness (Physiological psychology), Mood disorders, Sleep (Physiological psychology), Visual research |
Developmental Psychology | 3 | Attachment, Development, Old age |
Personality | 3 | Machiavellian, Narcissism, Personality |
Psychological Psychology | 3 | Programme, Psychology practice, Theory |
Education and Learning | 1 | Education and Learning |
Psychometrics | 1 | Measure |
Code Total | 84 |
These 10 themes represent the topic section of our research question ( Figure 3 ). All these topics except, for the final one, psychological practice , were found to concur with the research areas in psychology as identified by Weiten ( 2010 ). These research areas were chosen to represent the derived codes as they provided broad definitions that allowed for clear, concise categorisation of the vast amount of data. Article codes were categorised under particular themes/topics if they adhered to the research area definitions created by Weiten ( 2010 ). It is important to note that these areas of research do not refer to specific disciplines in psychology, such as industrial psychology; but to broader fields that may encompass sub-interests of these disciplines.
![Click on image to zoom An external file that holds a picture, illustration, etc.
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Topic frequency (international sample).
In the case of developmental psychology , researchers conduct research into human development from childhood to old age. Social psychology includes research on behaviour governed by social drivers. Researchers in the field of educational psychology study how people learn and the best way to teach them. Health psychology aims to determine the effect of psychological factors on physiological health. Physiological psychology , on the other hand, looks at the influence of physiological aspects on behaviour. Experimental psychology is not the only theme that uses experimental research and focuses on the traditional core topics of psychology (for example, sensation). Cognitive psychology studies the higher mental processes. Psychometrics is concerned with measuring capacity or behaviour. Personality research aims to assess and describe consistency in human behaviour (Weiten, 2010 ). The final theme of psychological practice refers to the experiences, techniques, and interventions employed by practitioners, researchers, and academia in the field of psychology.
Articles under these themes were further subdivided into methodologies: method, sampling, design, data collection, and data analysis. The categorisation was based on information stated in the articles and not inferred by the researchers. Data were compiled into two sets of results presented in this article. The first set addresses the aim of this study from the perspective of the topics identified. The second set of results represents a broad overview of the results from the perspective of the methodology employed. The second set of results are discussed in this article, while the first set is presented in table format. The discussion thus provides a broad overview of methods use in psychology (across all themes), while the table format provides readers with in-depth insight into methods used in the individual themes identified. We believe that presenting the data from both perspectives allow readers a broad understanding of the results. Due a large amount of information that made up our results, we followed Cichocka and Jost ( 2014 ) in simplifying our results. Please note that the numbers indicated in the table in terms of methodology differ from the total number of articles. Some articles employed more than one method/sampling technique/design/data collection method/data analysis in their studies.
What follows is the results for what methods are used, how these methods are used, and which topics in psychology they are applied to . Percentages are reported to the second decimal in order to highlight small differences in the occurrence of methodology.
Firstly, with regard to the research methods used, our results show that researchers are more likely to use quantitative research methods (90.22%) compared to all other research methods. Qualitative research was the second most common research method but only made up about 4.79% of the general method usage. Reviews occurred almost as much as qualitative studies (3.91%), as the third most popular method. Mixed-methods research studies (0.98%) occurred across most themes, whereas multi-method research was indicated in only one study and amounted to 0.10% of the methods identified. The specific use of each method in the topics identified is shown in Table 2 and Figure 4 .
Research methods in psychology.
Quantitative | 401 | 162 | 69 | 60 | 52 | 52 | 48 | 28 | 38 | 13 |
Qualitative | 28 | 4 | 1 | 0 | 5 | 2 | 3 | 5 | 0 | 1 |
Review | 11 | 5 | 2 | 0 | 3 | 4 | 1 | 13 | 0 | 1 |
Mixed Methods | 7 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
Multi-method | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Total | 447 | 171 | 72 | 60 | 61 | 58 | 53 | 47 | 39 | 15 |
![Click on image to zoom An external file that holds a picture, illustration, etc.
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Research method frequency in topics.
Secondly, in the case of how these research methods are employed , our study indicated the following.
Sampling −78.34% of the studies in the collected articles did not specify a sampling method. From the remainder of the studies, 13 types of sampling methods were identified. These sampling methods included broad categorisation of a sample as, for example, a probability or non-probability sample. General samples of convenience were the methods most likely to be applied (10.34%), followed by random sampling (3.51%), snowball sampling (2.73%), and purposive (1.37%) and cluster sampling (1.27%). The remainder of the sampling methods occurred to a more limited extent (0–1.0%). See Table 3 and Figure 5 for sampling methods employed in each topic.
Sampling use in the field of psychology.
Not stated | 331 | 153 | 45 | 57 | 49 | 43 | 43 | 38 | 31 | 14 |
Convenience sampling | 55 | 8 | 10 | 1 | 6 | 8 | 9 | 2 | 6 | 1 |
Random sampling | 15 | 3 | 9 | 1 | 2 | 2 | 0 | 2 | 1 | 1 |
Snowball sampling | 14 | 4 | 4 | 1 | 2 | 0 | 0 | 3 | 0 | 0 |
Purposive sampling | 6 | 0 | 2 | 0 | 0 | 2 | 0 | 3 | 1 | 0 |
Cluster sampling | 8 | 1 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
Stratified sampling | 4 | 1 | 2 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
Non-probability sampling | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Probability sampling | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Quota sampling | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Criterion sampling | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Self-selection sampling | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Unsystematic sampling | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Total | 443 | 172 | 76 | 60 | 60 | 58 | 52 | 48 | 40 | 16 |
![Click on image to zoom An external file that holds a picture, illustration, etc.
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Sampling method frequency in topics.
Designs were categorised based on the articles' statement thereof. Therefore, it is important to note that, in the case of quantitative studies, non-experimental designs (25.55%) were often indicated due to a lack of experiments and any other indication of design, which, according to Laher ( 2016 ), is a reasonable categorisation. Non-experimental designs should thus be compared with experimental designs only in the description of data, as it could include the use of correlational/cross-sectional designs, which were not overtly stated by the authors. For the remainder of the research methods, “not stated” (7.12%) was assigned to articles without design types indicated.
From the 36 identified designs the most popular designs were cross-sectional (23.17%) and experimental (25.64%), which concurred with the high number of quantitative studies. Longitudinal studies (3.80%), the third most popular design, was used in both quantitative and qualitative studies. Qualitative designs consisted of ethnography (0.38%), interpretative phenomenological designs/phenomenology (0.28%), as well as narrative designs (0.28%). Studies that employed the review method were mostly categorised as “not stated,” with the most often stated review designs being systematic reviews (0.57%). The few mixed method studies employed exploratory, explanatory (0.09%), and concurrent designs (0.19%), with some studies referring to separate designs for the qualitative and quantitative methods. The one study that identified itself as a multi-method study used a longitudinal design. Please see how these designs were employed in each specific topic in Table 4 , Figure 6 .
Design use in the field of psychology.
Experimental design | 82 | 82 | 3 | 60 | 10 | 12 | 8 | 6 | 4 | 3 |
Non-experimental design | 115 | 30 | 51 | 0 | 13 | 17 | 13 | 13 | 14 | 3 |
Cross-sectional design | 123 | 31 | 12 | 1 | 19 | 17 | 21 | 5 | 13 | 2 |
Correlational design | 56 | 12 | 3 | 0 | 10 | 2 | 2 | 0 | 4 | 2 |
Not stated | 37 | 7 | 3 | 0 | 4 | 2 | 4 | 14 | 1 | 3 |
Longitudinal design | 21 | 6 | 2 | 1 | 1 | 2 | 2 | 0 | 2 | 3 |
Quasi-experimental design | 4 | 1 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 |
Systematic review | 3 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 |
Cross-cultural design | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
Descriptive design | 2 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
Ethnography | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Literature review | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
Interpretative Phenomenological Analysis (IPA) | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Narrative design | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
Case-control research design | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
Concurrent data collection design | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Grounded Theory | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Narrative review | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Auto-ethnography | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Case series evaluation | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Case study | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Comprehensive review | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Descriptive-inferential | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Explanatory sequential design | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Exploratory mixed-method | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
Grounded ethnographic design | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Historical cohort design | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Historical research | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
interpretivist approach | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Meta-review | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Prospective design | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Qualitative review | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Qualitative systematic review | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Short-term prospective design | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Total | 461 | 175 | 74 | 63 | 63 | 58 | 56 | 48 | 39 | 16 |
![Click on image to zoom An external file that holds a picture, illustration, etc.
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Design frequency in topics.
Data collection and analysis —data collection included 30 methods, with the data collection method most often employed being questionnaires (57.84%). The experimental task (16.56%) was the second most preferred collection method, which included established or unique tasks designed by the researchers. Cognitive ability tests (6.84%) were also regularly used along with various forms of interviewing (7.66%). Table 5 and Figure 7 represent data collection use in the various topics. Data analysis consisted of 3,857 occurrences of data analysis categorised into ±188 various data analysis techniques shown in Table 6 and Figures 1 – 7 . Descriptive statistics were the most commonly used (23.49%) along with correlational analysis (17.19%). When using a qualitative method, researchers generally employed thematic analysis (0.52%) or different forms of analysis that led to coding and the creation of themes. Review studies presented few data analysis methods, with most studies categorising their results. Mixed method and multi-method studies followed the analysis methods identified for the qualitative and quantitative studies included.
Data collection in the field of psychology.
Questionnaire | 364 | 113 | 65 | 42 | 40 | 51 | 39 | 24 | 37 | 11 |
Experimental task | 68 | 66 | 3 | 52 | 9 | 5 | 11 | 5 | 5 | 1 |
Cognitive ability test | 9 | 57 | 1 | 12 | 6 | 1 | 5 | 1 | 1 | 0 |
Physiological measure | 3 | 12 | 1 | 6 | 2 | 5 | 3 | 0 | 1 | 0 |
Interview | 19 | 3 | 0 | 1 | 3 | 0 | 2 | 2 | 0 | 1 |
Online scholarly literature | 10 | 4 | 0 | 0 | 3 | 4 | 0 | 10 | 0 | 0 |
Open-ended questions | 15 | 3 | 0 | 1 | 3 | 1 | 2 | 3 | 0 | 0 |
Semi-structured interviews | 10 | 3 | 0 | 0 | 3 | 2 | 1 | 2 | 0 | 1 |
Observation | 10 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
Documents | 5 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 |
Focus group | 6 | 1 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Not stated | 2 | 1 | 1 | 0 | 0 | 0 | 1 | 4 | 0 | 1 |
Public data | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 |
Drawing task | 0 | 2 | 0 | 1 | 1 | 1 | 0 | 2 | 0 | 0 |
In-depth interview | 6 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Structured interview | 0 | 2 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 0 |
Writing task | 1 | 0 | 0 | 0 | 4 | 0 | 0 | 1 | 0 | 0 |
Questionnaire interviews | 1 | 0 | 1 | 0 | 2 | 0 | 1 | 0 | 0 | 0 |
Non-experimental task | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Tests | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Group accounts | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Open-ended prompts | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Field notes | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Open-ended interview | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Qualitative questions | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
Social media | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Assessment procedure | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
Closed-ended questions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Open discussions | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Qualitative descriptions | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Total | 551 | 273 | 75 | 116 | 79 | 73 | 65 | 60 | 50 | 17 |
![Click on image to zoom An external file that holds a picture, illustration, etc.
Object name is frma-05-00001-g0007.jpg](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8028395/bin/frma-05-00001-g0007.jpg)
Data collection frequency in topics.
Data analysis in the field of psychology.
Not stated | 5 | 1 | 2 | 0 | 0 | 1 | 1 | 5 | 0 | 1 |
Actor-Partner Interdependence Model (APIM) | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Analysis of Covariance (ANCOVA) | 17 | 8 | 1 | 3 | 4 | 2 | 1 | 0 | 0 | 1 |
Analysis of Variance (ANOVA) | 112 | 60 | 16 | 29 | 15 | 17 | 15 | 6 | 5 | 3 |
Auto-regressive path coefficients | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Average variance extracted (AVE) | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Bartholomew's classification system | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Bayesian analysis | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Bibliometric analysis | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Binary logistic regression | 1 | 1 | 0 | 0 | 1 | 4 | 1 | 0 | 0 | 0 |
Binary multilevel regression | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
Binomial and Bernoulli regression models | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Binomial mixed effects model | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Bivariate Correlations | 32 | 10 | 3 | 0 | 4 | 3 | 5 | 1 | 1 | 1 |
Bivariate logistic correlations | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Bootstrapping | 39 | 16 | 2 | 3 | 5 | 1 | 6 | 1 | 2 | 1 |
Canonical correlations | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
Cartesian diagram | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Case-wise diagnostics | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Casual network analysis | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
Categorisation | 5 | 2 | 0 | 0 | 1 | 1 | 0 | 4 | 0 | 0 |
Categorisation of responses | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Category codes | 3 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Cattell's scree-test | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Chi-square tests | 52 | 20 | 17 | 5 | 6 | 11 | 8 | 7 | 4 | 3 |
Classic Parallel Analysis (PA) | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 |
Cluster analysis | 7 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 |
Coded | 15 | 3 | 1 | 2 | 1 | 1 | 1 | 2 | 1 | 0 |
Cohen d effect size | 14 | 5 | 2 | 1 | 3 | 2 | 3 | 1 | 0 | 1 |
Common method variance (CMV) | 5 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Comprehensive Meta-Analysis (CMA) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Confidence Interval (CI) | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Confirmatory Factor Analysis (CFA) | 57 | 13 | 40 | 0 | 2 | 4 | 7 | 1 | 3 | 1 |
Content analysis | 9 | 1 | 0 | 0 | 2 | 1 | 0 | 1 | 0 | 0 |
Convergent validity | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Cook's distance | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Correlated-trait-correlated-method minus one model | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Correlational analysis | 259 | 85 | 44 | 18 | 27 | 31 | 34 | 8 | 33 | 8 |
Covariance matrix | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Covariance modelling | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Covariance structure analyses | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Cronbach's alpha | 61 | 14 | 18 | 6 | 5 | 10 | 8 | 3 | 7 | 5 |
Cross-validation | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Cross-lagged analyses | 1 | 2 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Dependent t-test | 1 | 2 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 |
Descriptive statistics | 324 | 132 | 43 | 49 | 41 | 43 | 36 | 28 | 29 | 10 |
Differentiated analysis | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Discriminate analysis | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Discursive psychology | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Dominance analysis | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Expectation maximisation | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Exploratory data Analysis | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
Exploratory Factor Analysis (EFA) | 14 | 5 | 24 | 0 | 1 | 1 | 4 | 0 | 4 | 0 |
Exploratory structural equation modelling (ESEM) | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Factor analysis | 12 | 4 | 16 | 0 | 2 | 1 | 5 | 0 | 2 | 0 |
Measurement invariance testing | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Four-way mixed ANOVA | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
Frequency rate | 20 | 1 | 4 | 2 | 1 | 2 | 2 | 2 | 0 | 0 |
Friedman test | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Games-Howell | 2 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
General linear model analysis | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
Greenhouse-Geisser correction | 2 | 5 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 |
Grounded theory method | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Grounded theory methodology using open and axial coding | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Guttman split-half | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Harman's one-factor test | 13 | 2 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 |
Herman's criteria of experience categorisation | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Hierarchical CFA (HCFA) | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Hierarchical cluster analysis | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Hierarchical Linear Modelling (HLM) | 76 | 22 | 2 | 3 | 7 | 6 | 7 | 4 | 4 | 1 |
Huynh-Felt correction | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Identified themes | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Independent samples t-test | 38 | 9 | 4 | 4 | 4 | 8 | 3 | 3 | 1 | 1 |
Inductive open coding | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Inferential statistics | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Interclass correlation | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Internal consistency | 3 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Interpreted and defined | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Interpretive Phenomenological Analysis (IPA) | 2 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Item fit analysis | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
K-means clustering | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Kaiser-meyer-Olkin measure of sampling adequacy | 2 | 0 | 8 | 0 | 0 | 0 | 2 | 0 | 2 | 0 |
Kendall's coefficients | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Kolmogorov-Smirnov test | 1 | 2 | 1 | 1 | 2 | 2 | 0 | 0 | 1 | 0 |
Lagged-effects multilevel modelling | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Latent class differentiation (LCD) | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Latent cluster analysis | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Latent growth curve modelling (LGCM) | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
Latent means | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Latent Profile Analysis (LPA) | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Linear regressions | 69 | 19 | 4 | 10 | 3 | 12 | 5 | 3 | 13 | 0 |
Linguistic Inquiry and Word Count | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Listwise deletion method | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Log-likelihood ratios | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Logistic mixed-effects model | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Logistic regression analyses | 17 | 0 | 1 | 0 | 4 | 2 | 1 | 0 | 0 | 1 |
Loglinear Model | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Mahalanobis distances | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Mann-Whitney U tests | 6 | 4 | 2 | 1 | 2 | 0 | 2 | 4 | 0 | 0 |
Mauchly's test | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 1 |
Maximum likelihood method | 11 | 3 | 9 | 0 | 1 | 3 | 2 | 3 | 1 | 0 |
Maximum-likelihood factor analysis with promax rotation | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Measurement invariance testing | 4 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Mediation analysis | 29 | 7 | 1 | 2 | 4 | 3 | 5 | 0 | 3 | 0 |
Meta-analysis | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Microanalysis | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Minimum significant difference (MSD) comparison | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Mixed ANOVAs | 19 | 6 | 0 | 10 | 1 | 2 | 1 | 4 | 1 | 0 |
Mixed linear model | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
Mixed-design ANCOVA | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Mixed-effects multiple regression models | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Moderated hierarchical regression model | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Moderated regression analysis | 8 | 4 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 |
Monte Carlo Markov Chains | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multi-group analysis | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multidimensional Random Coefficient Multinomial Logit (MRCML) | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multidimensional Scaling | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multiple-Group Confirmatory Factor Analysis (MGCFA) | 3 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
Multilevel latent class analysis | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Multilevel modelling | 7 | 2 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 0 |
Multilevel Structural Equation Modelling (MSEM) | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multinominal logistic regression (MLR) | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multinominal regression analysis | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
Multiple Indicators Multiple Causes (MIMIC) | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
Multiple mediation analysis | 2 | 6 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 |
Multiple regression | 34 | 15 | 3 | 0 | 3 | 4 | 5 | 0 | 7 | 2 |
Multivariate analysis of co-variance (MANCOVA) | 12 | 2 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 |
Multivariate Analysis of Variance (MANOVA) | 38 | 8 | 4 | 5 | 5 | 6 | 9 | 1 | 1 | 2 |
Multivariate hierarchical linear regression | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multivariate linear regression | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Multivariate logistic regression analyses | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multivariate regressions | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Nagelkerke's R square | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Narrative analysis | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Negative binominal regression with log link | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Newman-Keuls | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Nomological Validity Analysis | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
One sample t-test | 8 | 10 | 1 | 7 | 4 | 6 | 4 | 0 | 1 | 0 |
Ordinary Least-Square regression (OLS) | 2 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
Pairwise deletion method | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Pairwise parameter comparison | 4 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 |
Parametric Analysis | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
Partial Least Squares regression method (PLS) | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Path analysis | 21 | 9 | 0 | 1 | 2 | 4 | 5 | 1 | 2 | 0 |
Path-analytic model test | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Phenomenological analysis | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Polynomial regression analyses | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Fisher LSD | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Principal axis factoring | 2 | 1 | 4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Principal component analysis (PCA) | 8 | 1 | 12 | 1 | 1 | 0 | 3 | 2 | 5 | 1 |
Pseudo-panel regression | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Quantitative content analysis | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Receiver operating characteristic (ROC) curve analysis | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
Relative weight analysis | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Repeated measures analyses of variances (rANOVA) | 18 | 22 | 1 | 7 | 5 | 2 | 1 | 1 | 1 | 1 |
Ryan-Einot-Gabriel-Welsch multiple F test | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Satorra-Bentler scaled chi-square statistic | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Scheffe's test | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Sequential multiple mediation analysis | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Shapiro-Wilk test | 2 | 3 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 |
Sobel Test | 13 | 5 | 0 | 1 | 0 | 2 | 4 | 0 | 0 | 0 |
Squared multiple correlations | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Squared semi-partial correlations (sr2) | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Stepwise regression analysis | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 |
Structural Equation Modelling (SEM) | 56 | 22 | 3 | 3 | 3 | 5 | 5 | 0 | 5 | 3 |
Structure analysis | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Subsequent t-test | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Systematic coding- Gemeinschaft-oriented | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Task analysis | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Thematic analysis | 11 | 2 | 0 | 0 | 3 | 0 | 2 | 2 | 0 | 0 |
Three (condition)-way ANOVA | 0 | 4 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
Three-way hierarchical loglinear analysis | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Tukey-Kramer corrections | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
Two-paired sample t-test | 7 | 6 | 1 | 1 | 0 | 3 | 1 | 1 | 0 | 1 |
Two-tailed related t-test | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Unadjusted Logistic regression analysis | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Univariate generalized linear models (GLM) | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Variance inflation factor (VIF) | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Variance-covariance matrix | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Wald test | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Ward's hierarchical cluster method | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Weighted least squares with corrections to means and variances (WLSMV) | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Welch and Brown-Forsythe F-ratios | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Wilcoxon signed-rank test | 3 | 3 | 0 | 2 | 0 | 0 | 0 | 2 | 0 | 1 |
Wilks' Lamba | 6 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Word analysis | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Word Association Analysis | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
scores | 5 | 6 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 |
Total | 1738 | 635 | 329 | 192 | 198 | 237 | 225 | 117 | 152 | 55 |
Results of the topics researched in psychology can be seen in the tables, as previously stated in this article. It is noteworthy that, of the 10 topics, social psychology accounted for 43.54% of the studies, with cognitive psychology the second most popular research topic at 16.92%. The remainder of the topics only occurred in 4.0–7.0% of the articles considered. A list of the included 999 articles is available under the section “View Articles” on the following website: https://methodgarden.xtrapolate.io/ . This website was created by Scholtz et al. ( 2019 ) to visually present a research framework based on this Article's results.
This systematised review categorised full-length articles from five international journals across the span of 5 years to provide insight into the use of research methods in the field of psychology. Results indicated what methods are used how these methods are being used and for what topics (why) in the included sample of articles. The results should be seen as providing insight into method use and by no means a comprehensive representation of the aforementioned aim due to the limited sample. To our knowledge, this is the first research study to address this topic in this manner. Our discussion attempts to promote a productive way forward in terms of the key results for method use in psychology, especially in the field of academia (Holloway, 2008 ).
With regard to the methods used, our data stayed true to literature, finding only common research methods (Grant and Booth, 2009 ; Maree, 2016 ) that varied in the degree to which they were employed. Quantitative research was found to be the most popular method, as indicated by literature (Breen and Darlaston-Jones, 2010 ; Counsell and Harlow, 2017 ) and previous studies in specific areas of psychology (see Coetzee and Van Zyl, 2014 ). Its long history as the first research method (Leech et al., 2007 ) in the field of psychology as well as researchers' current application of mathematical approaches in their studies (Toomela, 2010 ) might contribute to its popularity today. Whatever the case may be, our results show that, despite the growth in qualitative research (Demuth, 2015 ; Smith and McGannon, 2018 ), quantitative research remains the first choice for article publication in these journals. Despite the included journals indicating openness to articles that apply any research methods. This finding may be due to qualitative research still being seen as a new method (Burman and Whelan, 2011 ) or reviewers' standards being higher for qualitative studies (Bluhm et al., 2011 ). Future research is encouraged into the possible biasness in publication of research methods, additionally further investigation with a different sample into the proclaimed growth of qualitative research may also provide different results.
Review studies were found to surpass that of multi-method and mixed method studies. To this effect Grant and Booth ( 2009 ), state that the increased awareness, journal contribution calls as well as its efficiency in procuring research funds all promote the popularity of reviews. The low frequency of mixed method studies contradicts the view in literature that it's the third most utilised research method (Tashakkori and Teddlie's, 2003 ). Its' low occurrence in this sample could be due to opposing views on mixing methods (Gunasekare, 2015 ) or that authors prefer publishing in mixed method journals, when using this method, or its relative novelty (Ivankova et al., 2016 ). Despite its low occurrence, the application of the mixed methods design in articles was methodologically clear in all cases which were not the case for the remainder of research methods.
Additionally, a substantial number of studies used a combination of methodologies that are not mixed or multi-method studies. Perceived fixed boundaries are according to literature often set aside, as confirmed by this result, in order to investigate the aim of a study, which could create a new and helpful way of understanding the world (Gunasekare, 2015 ). According to Toomela ( 2010 ), this is not unheard of and could be considered a form of “structural systemic science,” as in the case of qualitative methodology (observation) applied in quantitative studies (experimental design) for example. Based on this result, further research into this phenomenon as well as its implications for research methods such as multi and mixed methods is recommended.
Discerning how these research methods were applied, presented some difficulty. In the case of sampling, most studies—regardless of method—did mention some form of inclusion and exclusion criteria, but no definite sampling method. This result, along with the fact that samples often consisted of students from the researchers' own academic institutions, can contribute to literature and debates among academics (Peterson and Merunka, 2014 ; Laher, 2016 ). Samples of convenience and students as participants especially raise questions about the generalisability and applicability of results (Peterson and Merunka, 2014 ). This is because attention to sampling is important as inappropriate sampling can debilitate the legitimacy of interpretations (Onwuegbuzie and Collins, 2017 ). Future investigation into the possible implications of this reported popular use of convenience samples for the field of psychology as well as the reason for this use could provide interesting insight, and is encouraged by this study.
Additionally, and this is indicated in Table 6 , articles seldom report the research designs used, which highlights the pressing aspect of the lack of rigour in the included sample. Rigour with regards to the applied empirical method is imperative in promoting psychology as a science (American Psychological Association, 2020 ). Omitting parts of the research process in publication when it could have been used to inform others' research skills should be questioned, and the influence on the process of replicating results should be considered. Publications are often rejected due to a lack of rigour in the applied method and designs (Fonseca, 2013 ; Laher, 2016 ), calling for increased clarity and knowledge of method application. Replication is a critical part of any field of scientific research and requires the “complete articulation” of the study methods used (Drotar, 2010 , p. 804). The lack of thorough description could be explained by the requirements of certain journals to only report on certain aspects of a research process, especially with regard to the applied design (Laher, 20). However, naming aspects such as sampling and designs, is a requirement according to the APA's Journal Article Reporting Standards (JARS-Quant) (Appelbaum et al., 2018 ). With very little information on how a study was conducted, authors lose a valuable opportunity to enhance research validity, enrich the knowledge of others, and contribute to the growth of psychology and methodology as a whole. In the case of this research study, it also restricted our results to only reported samples and designs, which indicated a preference for certain designs, such as cross-sectional designs for quantitative studies.
Data collection and analysis were for the most part clearly stated. A key result was the versatile use of questionnaires. Researchers would apply a questionnaire in various ways, for example in questionnaire interviews, online surveys, and written questionnaires across most research methods. This may highlight a trend for future research.
With regard to the topics these methods were employed for, our research study found a new field named “psychological practice.” This result may show the growing consciousness of researchers as part of the research process (Denzin and Lincoln, 2003 ), psychological practice, and knowledge generation. The most popular of these topics was social psychology, which is generously covered in journals and by learning societies, as testaments of the institutional support and richness social psychology has in the field of psychology (Chryssochoou, 2015 ). The APA's perspective on 2018 trends in psychology also identifies an increased amount of psychology focus on how social determinants are influencing people's health (Deangelis, 2017 ).
This study was not without limitations and the following should be taken into account. Firstly, this study used a sample of five specific journals to address the aim of the research study, despite general journal aims (as stated on journal websites), this inclusion signified a bias towards the research methods published in these specific journals only and limited generalisability. A broader sample of journals over a different period of time, or a single journal over a longer period of time might provide different results. A second limitation is the use of Excel spreadsheets and an electronic system to log articles, which was a manual process and therefore left room for error (Bandara et al., 2015 ). To address this potential issue, co-coding was performed to reduce error. Lastly, this article categorised data based on the information presented in the article sample; there was no interpretation of what methodology could have been applied or whether the methods stated adhered to the criteria for the methods used. Thus, a large number of articles that did not clearly indicate a research method or design could influence the results of this review. However, this in itself was also a noteworthy result. Future research could review research methods of a broader sample of journals with an interpretive review tool that increases rigour. Additionally, the authors also encourage the future use of systematised review designs as a way to promote a concise procedure in applying this design.
Our research study presented the use of research methods for published articles in the field of psychology as well as recommendations for future research based on these results. Insight into the complex questions identified in literature, regarding what methods are used how these methods are being used and for what topics (why) was gained. This sample preferred quantitative methods, used convenience sampling and presented a lack of rigorous accounts for the remaining methodologies. All methodologies that were clearly indicated in the sample were tabulated to allow researchers insight into the general use of methods and not only the most frequently used methods. The lack of rigorous account of research methods in articles was represented in-depth for each step in the research process and can be of vital importance to address the current replication crisis within the field of psychology. Recommendations for future research aimed to motivate research into the practical implications of the results for psychology, for example, publication bias and the use of convenience samples.
Ethics Statement
This study was cleared by the North-West University Health Research Ethics Committee: NWU-00115-17-S1.
Author Contributions
All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Ch 2: Psychological Research Methods
![examples of research methods psychology Children sit in front of a bank of television screens. A sign on the wall says, “Some content may not be suitable for children.”](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224457/CNX_Psych_02_00_childrentv.jpg)
Have you ever wondered whether the violence you see on television affects your behavior? Are you more likely to behave aggressively in real life after watching people behave violently in dramatic situations on the screen? Or, could seeing fictional violence actually get aggression out of your system, causing you to be more peaceful? How are children influenced by the media they are exposed to? A psychologist interested in the relationship between behavior and exposure to violent images might ask these very questions.
The topic of violence in the media today is contentious. Since ancient times, humans have been concerned about the effects of new technologies on our behaviors and thinking processes. The Greek philosopher Socrates, for example, worried that writing—a new technology at that time—would diminish people’s ability to remember because they could rely on written records rather than committing information to memory. In our world of quickly changing technologies, questions about the effects of media continue to emerge. Is it okay to talk on a cell phone while driving? Are headphones good to use in a car? What impact does text messaging have on reaction time while driving? These are types of questions that psychologist David Strayer asks in his lab.
Watch this short video to see how Strayer utilizes the scientific method to reach important conclusions regarding technology and driving safety.
You can view the transcript for “Understanding driver distraction” here (opens in new window) .
How can we go about finding answers that are supported not by mere opinion, but by evidence that we can all agree on? The findings of psychological research can help us navigate issues like this.
Introduction to the Scientific Method
Learning objectives.
- Explain the steps of the scientific method
- Describe why the scientific method is important to psychology
- Summarize the processes of informed consent and debriefing
- Explain how research involving humans or animals is regulated
![examples of research methods psychology photograph of the word "research" from a dictionary with a pen pointing at the word.](https://s3-us-west-2.amazonaws.com/courses-images/wp-content/uploads/sites/855/2016/10/17193458/17664002728_acb9354acf_z.jpg)
Scientists are engaged in explaining and understanding how the world around them works, and they are able to do so by coming up with theories that generate hypotheses that are testable and falsifiable. Theories that stand up to their tests are retained and refined, while those that do not are discarded or modified. In this way, research enables scientists to separate fact from simple opinion. Having good information generated from research aids in making wise decisions both in public policy and in our personal lives. In this section, you’ll see how psychologists use the scientific method to study and understand behavior.
The Scientific Process
![examples of research methods psychology A skull has a large hole bored through the forehead.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224458/CNX_Psych_02_01_trephining.jpg)
The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.
While behavior is observable, the mind is not. If someone is crying, we can see the behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This module explores how scientific knowledge is generated, and how important that knowledge is in forming decisions in our personal lives and in the public domain.
Process of Scientific Research
![examples of research methods psychology Flowchart of the scientific method. It begins with make an observation, then ask a question, form a hypothesis that answers the question, make a prediction based on the hypothesis, do an experiment to test the prediction, analyze the results, prove the hypothesis correct or incorrect, then report the results.](https://s3-us-west-2.amazonaws.com/courses-images/wp-content/uploads/sites/855/2016/10/13033743/scimethod.png)
Scientific knowledge is advanced through a process known as the scientific method. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on.
The basic steps in the scientific method are:
- Observe a natural phenomenon and define a question about it
- Make a hypothesis, or potential solution to the question
- Test the hypothesis
- If the hypothesis is true, find more evidence or find counter-evidence
- If the hypothesis is false, create a new hypothesis or try again
- Draw conclusions and repeat–the scientific method is never-ending, and no result is ever considered perfect
In order to ask an important question that may improve our understanding of the world, a researcher must first observe natural phenomena. By making observations, a researcher can define a useful question. After finding a question to answer, the researcher can then make a prediction (a hypothesis) about what he or she thinks the answer will be. This prediction is usually a statement about the relationship between two or more variables. After making a hypothesis, the researcher will then design an experiment to test his or her hypothesis and evaluate the data gathered. These data will either support or refute the hypothesis. Based on the conclusions drawn from the data, the researcher will then find more evidence to support the hypothesis, look for counter-evidence to further strengthen the hypothesis, revise the hypothesis and create a new experiment, or continue to incorporate the information gathered to answer the research question.
Basic Principles of the Scientific Method
Two key concepts in the scientific approach are theory and hypothesis. A theory is a well-developed set of ideas that propose an explanation for observed phenomena that can be used to make predictions about future observations. A hypothesis is a testable prediction that is arrived at logically from a theory. It is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests.
![examples of research methods psychology A diagram has four boxes: the top is labeled “theory,” the right is labeled “hypothesis,” the bottom is labeled “research,” and the left is labeled “observation.” Arrows flow in the direction from top to right to bottom to left and back to the top, clockwise. The top right arrow is labeled “use the hypothesis to form a theory,” the bottom right arrow is labeled “design a study to test the hypothesis,” the bottom left arrow is labeled “perform the research,” and the top left arrow is labeled “create or modify the theory.”](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224503/CNX_Psych_02_01_Method.jpg)
Other key components in following the scientific method include verifiability, predictability, falsifiability, and fairness. Verifiability means that an experiment must be replicable by another researcher. To achieve verifiability, researchers must make sure to document their methods and clearly explain how their experiment is structured and why it produces certain results.
Predictability in a scientific theory implies that the theory should enable us to make predictions about future events. The precision of these predictions is a measure of the strength of the theory.
Falsifiability refers to whether a hypothesis can be disproved. For a hypothesis to be falsifiable, it must be logically possible to make an observation or do a physical experiment that would show that there is no support for the hypothesis. Even when a hypothesis cannot be shown to be false, that does not necessarily mean it is not valid. Future testing may disprove the hypothesis. This does not mean that a hypothesis has to be shown to be false, just that it can be tested.
To determine whether a hypothesis is supported or not supported, psychological researchers must conduct hypothesis testing using statistics. Hypothesis testing is a type of statistics that determines the probability of a hypothesis being true or false. If hypothesis testing reveals that results were “statistically significant,” this means that there was support for the hypothesis and that the researchers can be reasonably confident that their result was not due to random chance. If the results are not statistically significant, this means that the researchers’ hypothesis was not supported.
Fairness implies that all data must be considered when evaluating a hypothesis. A researcher cannot pick and choose what data to keep and what to discard or focus specifically on data that support or do not support a particular hypothesis. All data must be accounted for, even if they invalidate the hypothesis.
Applying the Scientific Method
To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later module, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.
Remember that a good scientific hypothesis is falsifiable, or capable of being shown to be incorrect. Recall from the introductory module that Sigmund Freud had lots of interesting ideas to explain various human behaviors (Figure 5). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.
![examples of research methods psychology (a)A photograph shows Freud holding a cigar. (b) The mind’s conscious and unconscious states are illustrated as an iceberg floating in water. Beneath the water’s surface in the “unconscious” area are the id, ego, and superego. The area just below the water’s surface is labeled “preconscious.” The area above the water’s surface is labeled “conscious.”](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224504/CNX_Psych_02_01_freud.jpg)
In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).
Link to Learning
Why the scientific method is important for psychology.
The use of the scientific method is one of the main features that separates modern psychology from earlier philosophical inquiries about the mind. Compared to chemistry, physics, and other “natural sciences,” psychology has long been considered one of the “social sciences” because of the subjective nature of the things it seeks to study. Many of the concepts that psychologists are interested in—such as aspects of the human mind, behavior, and emotions—are subjective and cannot be directly measured. Psychologists often rely instead on behavioral observations and self-reported data, which are considered by some to be illegitimate or lacking in methodological rigor. Applying the scientific method to psychology, therefore, helps to standardize the approach to understanding its very different types of information.
The scientific method allows psychological data to be replicated and confirmed in many instances, under different circumstances, and by a variety of researchers. Through replication of experiments, new generations of psychologists can reduce errors and broaden the applicability of theories. It also allows theories to be tested and validated instead of simply being conjectures that could never be verified or falsified. All of this allows psychologists to gain a stronger understanding of how the human mind works.
Scientific articles published in journals and psychology papers written in the style of the American Psychological Association (i.e., in “APA style”) are structured around the scientific method. These papers include an Introduction, which introduces the background information and outlines the hypotheses; a Methods section, which outlines the specifics of how the experiment was conducted to test the hypothesis; a Results section, which includes the statistics that tested the hypothesis and state whether it was supported or not supported, and a Discussion and Conclusion, which state the implications of finding support for, or no support for, the hypothesis. Writing articles and papers that adhere to the scientific method makes it easy for future researchers to repeat the study and attempt to replicate the results.
Ethics in Research
Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, as you will read in the Tuskegee Syphilis Study, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound. This section presents how ethical considerations affect the design and implementation of research conducted today.
Research Involving Human Participants
Any experiment involving the participation of human subjects is governed by extensive, strict guidelines designed to ensure that the experiment does not result in harm. Any research institution that receives federal support for research involving human participants must have access to an institutional review board (IRB) . The IRB is a committee of individuals often made up of members of the institution’s administration, scientists, and community members (Figure 6). The purpose of the IRB is to review proposals for research that involves human participants. The IRB reviews these proposals with the principles mentioned above in mind, and generally, approval from the IRB is required in order for the experiment to proceed.
![examples of research methods psychology A photograph shows a group of people seated around tables in a meeting room.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224526/CNX_Psych_02_06_irb.jpg)
An institution’s IRB requires several components in any experiment it approves. For one, each participant must sign an informed consent form before they can participate in the experiment. An informed consent form provides a written description of what participants can expect during the experiment, including potential risks and implications of the research. It also lets participants know that their involvement is completely voluntary and can be discontinued without penalty at any time. Furthermore, the informed consent guarantees that any data collected in the experiment will remain completely confidential. In cases where research participants are under the age of 18, the parents or legal guardians are required to sign the informed consent form.
While the informed consent form should be as honest as possible in describing exactly what participants will be doing, sometimes deception is necessary to prevent participants’ knowledge of the exact research question from affecting the results of the study. Deception involves purposely misleading experiment participants in order to maintain the integrity of the experiment, but not to the point where the deception could be considered harmful. For example, if we are interested in how our opinion of someone is affected by their attire, we might use deception in describing the experiment to prevent that knowledge from affecting participants’ responses. In cases where deception is involved, participants must receive a full debriefing upon conclusion of the study—complete, honest information about the purpose of the experiment, how the data collected will be used, the reasons why deception was necessary, and information about how to obtain additional information about the study.
Dig Deeper: Ethics and the Tuskegee Syphilis Study
Unfortunately, the ethical guidelines that exist for research today were not always applied in the past. In 1932, poor, rural, black, male sharecroppers from Tuskegee, Alabama, were recruited to participate in an experiment conducted by the U.S. Public Health Service, with the aim of studying syphilis in black men (Figure 7). In exchange for free medical care, meals, and burial insurance, 600 men agreed to participate in the study. A little more than half of the men tested positive for syphilis, and they served as the experimental group (given that the researchers could not randomly assign participants to groups, this represents a quasi-experiment). The remaining syphilis-free individuals served as the control group. However, those individuals that tested positive for syphilis were never informed that they had the disease.
While there was no treatment for syphilis when the study began, by 1947 penicillin was recognized as an effective treatment for the disease. Despite this, no penicillin was administered to the participants in this study, and the participants were not allowed to seek treatment at any other facilities if they continued in the study. Over the course of 40 years, many of the participants unknowingly spread syphilis to their wives (and subsequently their children born from their wives) and eventually died because they never received treatment for the disease. This study was discontinued in 1972 when the experiment was discovered by the national press (Tuskegee University, n.d.). The resulting outrage over the experiment led directly to the National Research Act of 1974 and the strict ethical guidelines for research on humans described in this chapter. Why is this study unethical? How were the men who participated and their families harmed as a function of this research?
![examples of research methods psychology A photograph shows a person administering an injection.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224528/CNX_Psych_02_06_tuskegee.jpg)
Learn more about the Tuskegee Syphilis Study on the CDC website .
Research Involving Animal Subjects
![examples of research methods psychology A photograph shows a rat.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224529/CNX_Psych_02_06_rat.jpg)
This does not mean that animal researchers are immune to ethical concerns. Indeed, the humane and ethical treatment of animal research subjects is a critical aspect of this type of research. Researchers must design their experiments to minimize any pain or distress experienced by animals serving as research subjects.
Whereas IRBs review research proposals that involve human participants, animal experimental proposals are reviewed by an Institutional Animal Care and Use Committee (IACUC) . An IACUC consists of institutional administrators, scientists, veterinarians, and community members. This committee is charged with ensuring that all experimental proposals require the humane treatment of animal research subjects. It also conducts semi-annual inspections of all animal facilities to ensure that the research protocols are being followed. No animal research project can proceed without the committee’s approval.
Introduction to Approaches to Research
- Differentiate between descriptive, correlational, and experimental research
- Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys
- Describe the strength and weaknesses of archival research
- Compare longitudinal and cross-sectional approaches to research
- Explain what a correlation coefficient tells us about the relationship between variables
- Describe why correlation does not mean causation
- Describe the experimental process, including ways to control for bias
- Identify and differentiate between independent and dependent variables
![examples of research methods psychology Three researchers review data while talking around a microscope.](https://s3-us-west-2.amazonaws.com/courses-images/wp-content/uploads/sites/855/2016/10/17210224/734px-Researchers_review_documents.jpg)
Psychologists use descriptive, experimental, and correlational methods to conduct research. Descriptive, or qualitative, methods include the case study, naturalistic observation, surveys, archival research, longitudinal research, and cross-sectional research.
Experiments are conducted in order to determine cause-and-effect relationships. In ideal experimental design, the only difference between the experimental and control groups is whether participants are exposed to the experimental manipulation. Each group goes through all phases of the experiment, but each group will experience a different level of the independent variable: the experimental group is exposed to the experimental manipulation, and the control group is not exposed to the experimental manipulation. The researcher then measures the changes that are produced in the dependent variable in each group. Once data is collected from both groups, it is analyzed statistically to determine if there are meaningful differences between the groups.
When scientists passively observe and measure phenomena it is called correlational research. Here, psychologists do not intervene and change behavior, as they do in experiments. In correlational research, they identify patterns of relationships, but usually cannot infer what causes what. Importantly, with correlational research, you can examine only two variables at a time, no more and no less.
Watch It: More on Research
If you enjoy learning through lectures and want an interesting and comprehensive summary of this section, then click on the Youtube link to watch a lecture given by MIT Professor John Gabrieli . Start at the 30:45 minute mark and watch through the end to hear examples of actual psychological studies and how they were analyzed. Listen for references to independent and dependent variables, experimenter bias, and double-blind studies. In the lecture, you’ll learn about breaking social norms, “WEIRD” research, why expectations matter, how a warm cup of coffee might make you nicer, why you should change your answer on a multiple choice test, and why praise for intelligence won’t make you any smarter.
You can view the transcript for “Lec 2 | MIT 9.00SC Introduction to Psychology, Spring 2011” here (opens in new window) .
Descriptive Research
There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.
The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.
Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.
Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.
The three main types of descriptive studies are, naturalistic observation, case studies, and surveys.
Naturalistic Observation
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?
This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.
Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).
![examples of research methods psychology A photograph shows two police cars driving, one with its lights flashing.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224506/CNX_Psych_02_02_policecar.jpg)
It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 9).
It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 10). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).
![examples of research methods psychology (a) A photograph shows Jane Goodall speaking from a lectern. (b) A photograph shows a chimpanzee’s face.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224508/CNX_Psych_02_02_goodall.jpg)
The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.
The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.
Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.
Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.
Case Studies
In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.
The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.
These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).
In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.
If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.
Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 11). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.
![examples of research methods psychology A sample online survey reads, “Dear visitor, your opinion is important to us. We would like to invite you to participate in a short survey to gather your opinions and feedback on your news consumption habits. The survey will take approximately 10-15 minutes. Simply click the “Yes” button below to launch the survey. Would you like to participate?” Two buttons are labeled “yes” and “no.”](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224509/CNX_Psych_02_03_surveyn.jpg)
There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.
Another potential weakness of surveys is something we touched on earlier in this chapter: people don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.
Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).
Think It Over
Archival research.
![examples of research methods psychology (a) A photograph shows stacks of paper files on shelves. (b) A photograph shows a computer.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224511/CNX_Psych_02_03_records.jpg)
In comparing archival research to other research methods, there are several important distinctions. For one, the researcher employing archival research never directly interacts with research participants. Therefore, the investment of time and money to collect data is considerably less with archival research. Additionally, researchers have no control over what information was originally collected. Therefore, research questions have to be tailored so they can be answered within the structure of the existing data sets. There is also no guarantee of consistency between the records from one source to another, which might make comparing and contrasting different data sets problematic.
Longitudinal and Cross-Sectional Research
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.
Another approach is cross-sectional research . In cross-sectional research, a researcher compares multiple segments of the population at the same time. Using the dietary habits example above, the researcher might directly compare different groups of people by age. Instead of observing a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old individuals. While cross-sectional research requires a shorter-term investment, it is also limited by differences that exist between the different generations (or cohorts) that have nothing to do with age per se, but rather reflect the social and cultural experiences of different generations of individuals make them different from one another.
To illustrate this concept, consider the following survey findings. In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.
Often longitudinal studies are employed when researching various diseases in an effort to understand particular risk factors. Such studies often involve tens of thousands of individuals who are followed for several decades. Given the enormous number of people involved in these studies, researchers can feel confident that their findings can be generalized to the larger population. The Cancer Prevention Study-3 (CPS-3) is one of a series of longitudinal studies sponsored by the American Cancer Society aimed at determining predictive risk factors associated with cancer. When participants enter the study, they complete a survey about their lives and family histories, providing information on factors that might cause or prevent the development of cancer. Then every few years the participants receive additional surveys to complete. In the end, hundreds of thousands of participants will be tracked over 20 years to determine which of them develop cancer and which do not.
Clearly, this type of research is important and potentially very informative. For instance, earlier longitudinal studies sponsored by the American Cancer Society provided some of the first scientific demonstrations of the now well-established links between increased rates of cancer and smoking (American Cancer Society, n.d.) (Figure 13).
![examples of research methods psychology A photograph shows pack of cigarettes and cigarettes in an ashtray. The pack of cigarettes reads, “Surgeon general’s warning: smoking causes lung cancer, heart disease, emphysema, and may complicate pregnancy.”](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224513/CNX_Psych_02_03_cigarettes.jpg)
As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.
Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increases over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.
Correlational Research
Did you know that as sales in ice cream increase, so does the overall rate of crime? Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone? There is no question that a relationship exists between ice cream and crime (e.g., Harper, 2013), but it would be pretty foolish to decide that one thing actually caused the other to occur.
It is much more likely that both ice cream sales and crime rates are related to the temperature outside. When the temperature is warm, there are lots of people out of their houses, interacting with each other, getting annoyed with one another, and sometimes committing crimes. Also, when it is warm outside, we are more likely to seek a cool treat like ice cream. How do we determine if there is indeed a relationship between two things? And when there is a relationship, how can we discern whether it is attributable to coincidence or causation?
![examples of research methods psychology Three scatterplots are shown. Scatterplot (a) is labeled “positive correlation” and shows scattered dots forming a rough line from the bottom left to the top right; the x-axis is labeled “weight” and the y-axis is labeled “height.” Scatterplot (b) is labeled “negative correlation” and shows scattered dots forming a rough line from the top left to the bottom right; the x-axis is labeled “tiredness” and the y-axis is labeled “hours of sleep.” Scatterplot (c) is labeled “no correlation” and shows scattered dots having no pattern; the x-axis is labeled “shoe size” and the y-axis is labeled “hours of sleep.”](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224515/CNX_Psych_02_04_scatter.jpg)
Correlation Does Not Indicate Causation
Correlational research is useful because it allows us to discover the strength and direction of relationships that exist between two variables. However, correlation is limited because establishing the existence of a relationship tells us little about cause and effect . While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable , is actually causing the systematic movement in our variables of interest. In the ice cream/crime rate example mentioned earlier, temperature is a confounding variable that could account for the relationship between the two variables.
Even when we cannot point to clear confounding variables, we should not assume that a correlation between two variables implies that one variable causes changes in another. This can be frustrating when a cause-and-effect relationship seems clear and intuitive. Think back to our discussion of the research done by the American Cancer Society and how their research projects were some of the first demonstrations of the link between smoking and cancer. It seems reasonable to assume that smoking causes cancer, but if we were limited to correlational research , we would be overstepping our bounds by making this assumption.
![examples of research methods psychology A photograph shows a bowl of cereal.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224516/CNX_Psych_02_04_cereal.jpg)
Unfortunately, people mistakenly make claims of causation as a function of correlations all the time. Such claims are especially common in advertisements and news stories. For example, recent research found that people who eat cereal on a regular basis achieve healthier weights than those who rarely eat cereal (Frantzen, Treviño, Echon, Garcia-Dominic, & DiMarco, 2013; Barton et al., 2005). Guess how the cereal companies report this finding. Does eating cereal really cause an individual to maintain a healthy weight, or are there other possible explanations, such as, someone at a healthy weight is more likely to regularly eat a healthy breakfast than someone who is obese or someone who avoids meals in an attempt to diet (Figure 15)? While correlational research is invaluable in identifying relationships among variables, a major limitation is the inability to establish causality. Psychologists want to make statements about cause and effect, but the only way to do that is to conduct an experiment to answer a research question. The next section describes how scientific experiments incorporate methods that eliminate, or control for, alternative explanations, which allow researchers to explore how changes in one variable cause changes in another variable.
Watch this clip from Freakonomics for an example of how correlation does not indicate causation.
You can view the transcript for “Correlation vs. Causality: Freakonomics Movie” here (opens in new window) .
Illusory Correlations
The temptation to make erroneous cause-and-effect statements based on correlational research is not the only way we tend to misinterpret data. We also tend to make the mistake of illusory correlations, especially with unsystematic observations. Illusory correlations , or false correlations, occur when people believe that relationships exist between two things when no such relationship exists. One well-known illusory correlation is the supposed effect that the moon’s phases have on human behavior. Many people passionately assert that human behavior is affected by the phase of the moon, and specifically, that people act strangely when the moon is full (Figure 16).
![examples of research methods psychology A photograph shows the moon.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224518/CNX_Psych_02_04_moon.jpg)
There is no denying that the moon exerts a powerful influence on our planet. The ebb and flow of the ocean’s tides are tightly tied to the gravitational forces of the moon. Many people believe, therefore, that it is logical that we are affected by the moon as well. After all, our bodies are largely made up of water. A meta-analysis of nearly 40 studies consistently demonstrated, however, that the relationship between the moon and our behavior does not exist (Rotton & Kelly, 1985). While we may pay more attention to odd behavior during the full phase of the moon, the rates of odd behavior remain constant throughout the lunar cycle.
Why are we so apt to believe in illusory correlations like this? Often we read or hear about them and simply accept the information as valid. Or, we have a hunch about how something works and then look for evidence to support that hunch, ignoring evidence that would tell us our hunch is false; this is known as confirmation bias . Other times, we find illusory correlations based on the information that comes most easily to mind, even if that information is severely limited. And while we may feel confident that we can use these relationships to better understand and predict the world around us, illusory correlations can have significant drawbacks. For example, research suggests that illusory correlations—in which certain behaviors are inaccurately attributed to certain groups—are involved in the formation of prejudicial attitudes that can ultimately lead to discriminatory behavior (Fiedler, 2004).
We all have a tendency to make illusory correlations from time to time. Try to think of an illusory correlation that is held by you, a family member, or a close friend. How do you think this illusory correlation came about and what can be done in the future to combat them?
Experiments
Causality: conducting experiments and using the data, experimental hypothesis.
In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that children should not be allowed to watch violent programming on television because doing so would cause them to behave more violently, then you have basically formulated a hypothesis—namely, that watching violent television programs causes children to behave more violently. How might you have arrived at this particular hypothesis? You may have younger relatives who watch cartoons featuring characters using martial arts to save the world from evildoers, with an impressive array of punching, kicking, and defensive postures. You notice that after watching these programs for a while, your young relatives mimic the fighting behavior of the characters portrayed in the cartoon (Figure 17).
![examples of research methods psychology A photograph shows a child pointing a toy gun.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224519/CNX_Psych_02_05_toygun.jpg)
These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to rigorously test our hypothesis. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.
Designing an Experiment
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested (in this case, violent TV images)—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.
In our example of how violent television programming might affect violent behavior in children, we have the experimental group view violent television programming for a specified time and then measure their violent behavior. We measure the violent behavior in our control group after they watch nonviolent television programming for the same amount of time. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation. Therefore, we have the control group watch non-violent television programming for the same amount of time as the experimental group.
We also need to precisely define, or operationalize, what is considered violent and nonviolent. An operational definition is a description of how we will measure our variables, and it is important in allowing others understand exactly how and what a researcher measures in a particular experiment. In operationalizing violent behavior, we might choose to count only physical acts like kicking or punching as instances of this behavior, or we also may choose to include angry verbal exchanges. Whatever we determine, it is important that we operationalize violent behavior in such a way that anyone who hears about our study for the first time knows exactly what we mean by violence. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.
Once we have operationalized what is considered violent television programming and what is considered violent behavior from our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants watch a 30-minute television program (either violent or nonviolent, depending on their group membership) before sending them out to a playground for an hour where their behavior is observed and the number and type of violent acts is recorded.
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how much attention they paid to each child’s behavior as well as how they interpreted that behavior. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study , meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.
![examples of research methods psychology A photograph shows three glass bottles of pills labeled as placebos.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224520/CNX_Psych_02_05_placebo.jpg)
In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect, you already have some idea as to why this is an important consideration. The placebo effect occurs when people’s expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.
The placebo effect is commonly described in terms of testing the effectiveness of a new medication. Imagine that you work in a pharmaceutical company, and you think you have a new drug that is effective in treating depression. To demonstrate that your medication is effective, you run an experiment with two groups: The experimental group receives the medication, and the control group does not. But you don’t want participants to know whether they received the drug or not.
Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.
To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case a sugar pill). Now everyone gets a pill, and once again neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations (Figure 18).
Independent and Dependent Variables
In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how violent television programs affect children’s display of violent behavior, the independent variable is the type of program—violent or nonviolent—viewed by participants in the study (Figure 19). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the number of violent acts displayed by the experimental participants.
![examples of research methods psychology A box labeled “independent variable: type of television programming viewed” contains a photograph of a person shooting an automatic weapon. An arrow labeled “influences change in the…” leads to a second box. The second box is labeled “dependent variable: violent behavior displayed” and has a photograph of a child pointing a toy gun.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224522/CNX_Psych_02_05_variables.jpg)
We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable depends on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what effect does watching a half hour of violent television programming or nonviolent television programming have on the number of incidents of physical aggression displayed on the playground?
Selecting and Assigning Experimental Participants
Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants so we need to determine who to include. Participants are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.
Our hypothetical experiment involves children, and we must first generate a sample of child participants. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment (Figure 20). If possible, we should use a random sample (there are other types of samples, but for the purposes of this section, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.
In our example, let’s say we decide our population of interest is fourth graders. But all fourth graders is a very large population, so we need to be more specific; instead we might say our population of interest is all fourth graders in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 fourth graders who we want to participate in our experiment.
In summary, because we cannot test all of the fourth graders in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.
![examples of research methods psychology (a) A photograph shows an aerial view of crowds on a street. (b) A photograph shows s small group of children.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224523/CNX_Psych_02_05_sample.jpg)
Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the fourth graders in the sample to either the experimental or the control group.
Random assignment is critical for sound experimental design. With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be very unlikely that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.
Issues to Consider
While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot directly control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.
Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.
Introduction to Statistical Thinking
Psychologists use statistics to assist them in analyzing data, and also to give more precise measurements to describe whether something is statistically significant. Analyzing data using statistics enables researchers to find patterns, make claims, and share their results with others. In this section, you’ll learn about some of the tools that psychologists use in statistical analysis.
- Define reliability and validity
- Describe the importance of distributional thinking and the role of p-values in statistical inference
- Describe the role of random sampling and random assignment in drawing cause-and-effect conclusions
- Describe the basic structure of a psychological research article
Interpreting Experimental Findings
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this experiment 100 times, we would expect to find the same results at least 95 times out of 100.
The greatest strength of experiments is the ability to assert that any significant differences in the findings are caused by the independent variable. This occurs because random selection, random assignment, and a design that limits the effects of both experimenter bias and participant expectancy should create groups that are similar in composition and treatment. Therefore, any difference between the groups is attributable to the independent variable, and now we can finally make a causal statement. If we find that watching a violent television program results in more violent behavior than watching a nonviolent program, we can safely say that watching violent television programs causes an increase in the display of violent behavior.
Reporting Research
When psychologists complete a research project, they generally want to share their findings with other scientists. The American Psychological Association (APA) publishes a manual detailing how to write a paper for submission to scientific journals. Unlike an article that might be published in a magazine like Psychology Today, which targets a general audience with an interest in psychology, scientific journals generally publish peer-reviewed journal articles aimed at an audience of professionals and scholars who are actively involved in research themselves.
A peer-reviewed journal article is read by several other scientists (generally anonymously) with expertise in the subject matter. These peer reviewers provide feedback—to both the author and the journal editor—regarding the quality of the draft. Peer reviewers look for a strong rationale for the research being described, a clear description of how the research was conducted, and evidence that the research was conducted in an ethical manner. They also look for flaws in the study’s design, methods, and statistical analyses. They check that the conclusions drawn by the authors seem reasonable given the observations made during the research. Peer reviewers also comment on how valuable the research is in advancing the discipline’s knowledge. This helps prevent unnecessary duplication of research findings in the scientific literature and, to some extent, ensures that each research article provides new information. Ultimately, the journal editor will compile all of the peer reviewer feedback and determine whether the article will be published in its current state (a rare occurrence), published with revisions, or not accepted for publication.
Peer review provides some degree of quality control for psychological research. Poorly conceived or executed studies can be weeded out, and even well-designed research can be improved by the revisions suggested. Peer review also ensures that the research is described clearly enough to allow other scientists to replicate it, meaning they can repeat the experiment using different samples to determine reliability. Sometimes replications involve additional measures that expand on the original finding. In any case, each replication serves to provide more evidence to support the original research findings. Successful replications of published research make scientists more apt to adopt those findings, while repeated failures tend to cast doubt on the legitimacy of the original article and lead scientists to look elsewhere. For example, it would be a major advancement in the medical field if a published study indicated that taking a new drug helped individuals achieve a healthy weight without changing their diet. But if other scientists could not replicate the results, the original study’s claims would be questioned.
Dig Deeper: The Vaccine-Autism Myth and the Retraction of Published Studies
Some scientists have claimed that routine childhood vaccines cause some children to develop autism, and, in fact, several peer-reviewed publications published research making these claims. Since the initial reports, large-scale epidemiological research has suggested that vaccinations are not responsible for causing autism and that it is much safer to have your child vaccinated than not. Furthermore, several of the original studies making this claim have since been retracted.
A published piece of work can be rescinded when data is called into question because of falsification, fabrication, or serious research design problems. Once rescinded, the scientific community is informed that there are serious problems with the original publication. Retractions can be initiated by the researcher who led the study, by research collaborators, by the institution that employed the researcher, or by the editorial board of the journal in which the article was originally published. In the vaccine-autism case, the retraction was made because of a significant conflict of interest in which the leading researcher had a financial interest in establishing a link between childhood vaccines and autism (Offit, 2008). Unfortunately, the initial studies received so much media attention that many parents around the world became hesitant to have their children vaccinated (Figure 21). For more information about how the vaccine/autism story unfolded, as well as the repercussions of this story, take a look at Paul Offit’s book, Autism’s False Prophets: Bad Science, Risky Medicine, and the Search for a Cure.
![examples of research methods psychology A photograph shows a child being given an oral vaccine.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224525/CNX_Psych_02_05_vaccine.jpg)
Reliability and Validity
Dig deeper: everyday connection: how valid is the sat.
Standardized tests like the SAT are supposed to measure an individual’s aptitude for a college education, but how reliable and valid are such tests? Research conducted by the College Board suggests that scores on the SAT have high predictive validity for first-year college students’ GPA (Kobrin, Patterson, Shaw, Mattern, & Barbuti, 2008). In this context, predictive validity refers to the test’s ability to effectively predict the GPA of college freshmen. Given that many institutions of higher education require the SAT for admission, this high degree of predictive validity might be comforting.
However, the emphasis placed on SAT scores in college admissions has generated some controversy on a number of fronts. For one, some researchers assert that the SAT is a biased test that places minority students at a disadvantage and unfairly reduces the likelihood of being admitted into a college (Santelices & Wilson, 2010). Additionally, some research has suggested that the predictive validity of the SAT is grossly exaggerated in how well it is able to predict the GPA of first-year college students. In fact, it has been suggested that the SAT’s predictive validity may be overestimated by as much as 150% (Rothstein, 2004). Many institutions of higher education are beginning to consider de-emphasizing the significance of SAT scores in making admission decisions (Rimer, 2008).
In 2014, College Board president David Coleman expressed his awareness of these problems, recognizing that college success is more accurately predicted by high school grades than by SAT scores. To address these concerns, he has called for significant changes to the SAT exam (Lewin, 2014).
Statistical Significance
![examples of research methods psychology Coffee cup with heart shaped cream inside.](https://s3-us-west-2.amazonaws.com/courses-images/wp-content/uploads/sites/855/2016/10/17151911/coffee.jpg)
Does drinking coffee actually increase your life expectancy? A recent study (Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012) found that men who drank at least six cups of coffee a day also had a 10% lower chance of dying (women’s chances were 15% lower) than those who drank none. Does this mean you should pick up or increase your own coffee habit? We will explore these results in more depth in the next section about drawing conclusions from statistics. Modern society has become awash in studies such as this; you can read about several such studies in the news every day.
Conducting such a study well, and interpreting the results of such studies requires understanding basic ideas of statistics , the science of gaining insight from data. Key components to a statistical investigation are:
- Planning the study: Start by asking a testable research question and deciding how to collect data. For example, how long was the study period of the coffee study? How many people were recruited for the study, how were they recruited, and from where? How old were they? What other variables were recorded about the individuals? Were changes made to the participants’ coffee habits during the course of the study?
- Examining the data: What are appropriate ways to examine the data? What graphs are relevant, and what do they reveal? What descriptive statistics can be calculated to summarize relevant aspects of the data, and what do they reveal? What patterns do you see in the data? Are there any individual observations that deviate from the overall pattern, and what do they reveal? For example, in the coffee study, did the proportions differ when we compared the smokers to the non-smokers?
- Inferring from the data: What are valid statistical methods for drawing inferences “beyond” the data you collected? In the coffee study, is the 10%–15% reduction in risk of death something that could have happened just by chance?
- Drawing conclusions: Based on what you learned from your data, what conclusions can you draw? Who do you think these conclusions apply to? (Were the people in the coffee study older? Healthy? Living in cities?) Can you draw a cause-and-effect conclusion about your treatments? (Are scientists now saying that the coffee drinking is the cause of the decreased risk of death?)
Notice that the numerical analysis (“crunching numbers” on the computer) comprises only a small part of overall statistical investigation. In this section, you will see how we can answer some of these questions and what questions you should be asking about any statistical investigation you read about.
Distributional Thinking
When data are collected to address a particular question, an important first step is to think of meaningful ways to organize and examine the data. Let’s take a look at an example.
Example 1 : Researchers investigated whether cancer pamphlets are written at an appropriate level to be read and understood by cancer patients (Short, Moriarty, & Cooley, 1995). Tests of reading ability were given to 63 patients. In addition, readability level was determined for a sample of 30 pamphlets, based on characteristics such as the lengths of words and sentences in the pamphlet. The results, reported in terms of grade levels, are displayed in Figure 23.
![examples of research methods psychology Table showing patients' reading levels and pahmphlet's reading levels.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2016/06/10212647/000001456original.jpg)
- Data vary . More specifically, values of a variable (such as reading level of a cancer patient or readability level of a cancer pamphlet) vary.
- Analyzing the pattern of variation, called the distribution of the variable, often reveals insights.
Addressing the research question of whether the cancer pamphlets are written at appropriate levels for the cancer patients requires comparing the two distributions. A naïve comparison might focus only on the centers of the distributions. Both medians turn out to be ninth grade, but considering only medians ignores the variability and the overall distributions of these data. A more illuminating approach is to compare the entire distributions, for example with a graph, as in Figure 24.
![examples of research methods psychology Bar graph showing that the reading level of pamphlets is typically higher than the reading level of the patients.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2016/06/10212639/000001457original.jpg)
Figure 24 makes clear that the two distributions are not well aligned at all. The most glaring discrepancy is that many patients (17/63, or 27%, to be precise) have a reading level below that of the most readable pamphlet. These patients will need help to understand the information provided in the cancer pamphlets. Notice that this conclusion follows from considering the distributions as a whole, not simply measures of center or variability, and that the graph contrasts those distributions more immediately than the frequency tables.
Finding Significance in Data
Even when we find patterns in data, often there is still uncertainty in various aspects of the data. For example, there may be potential for measurement errors (even your own body temperature can fluctuate by almost 1°F over the course of the day). Or we may only have a “snapshot” of observations from a more long-term process or only a small subset of individuals from the population of interest. In such cases, how can we determine whether patterns we see in our small set of data is convincing evidence of a systematic phenomenon in the larger process or population? Let’s take a look at another example.
Example 2 : In a study reported in the November 2007 issue of Nature , researchers investigated whether pre-verbal infants take into account an individual’s actions toward others in evaluating that individual as appealing or aversive (Hamlin, Wynn, & Bloom, 2007). In one component of the study, 10-month-old infants were shown a “climber” character (a piece of wood with “googly” eyes glued onto it) that could not make it up a hill in two tries. Then the infants were shown two scenarios for the climber’s next try, one where the climber was pushed to the top of the hill by another character (“helper”), and one where the climber was pushed back down the hill by another character (“hinderer”). The infant was alternately shown these two scenarios several times. Then the infant was presented with two pieces of wood (representing the helper and the hinderer characters) and asked to pick one to play with.
The researchers found that of the 16 infants who made a clear choice, 14 chose to play with the helper toy. One possible explanation for this clear majority result is that the helping behavior of the one toy increases the infants’ likelihood of choosing that toy. But are there other possible explanations? What about the color of the toy? Well, prior to collecting the data, the researchers arranged so that each color and shape (red square and blue circle) would be seen by the same number of infants. Or maybe the infants had right-handed tendencies and so picked whichever toy was closer to their right hand?
Well, prior to collecting the data, the researchers arranged it so half the infants saw the helper toy on the right and half on the left. Or, maybe the shapes of these wooden characters (square, triangle, circle) had an effect? Perhaps, but again, the researchers controlled for this by rotating which shape was the helper toy, the hinderer toy, and the climber. When designing experiments, it is important to control for as many variables as might affect the responses as possible. It is beginning to appear that the researchers accounted for all the other plausible explanations. But there is one more important consideration that cannot be controlled—if we did the study again with these 16 infants, they might not make the same choices. In other words, there is some randomness inherent in their selection process.
Maybe each infant had no genuine preference at all, and it was simply “random luck” that led to 14 infants picking the helper toy. Although this random component cannot be controlled, we can apply a probability model to investigate the pattern of results that would occur in the long run if random chance were the only factor.
If the infants were equally likely to pick between the two toys, then each infant had a 50% chance of picking the helper toy. It’s like each infant tossed a coin, and if it landed heads, the infant picked the helper toy. So if we tossed a coin 16 times, could it land heads 14 times? Sure, it’s possible, but it turns out to be very unlikely. Getting 14 (or more) heads in 16 tosses is about as likely as tossing a coin and getting 9 heads in a row. This probability is referred to as a p-value . The p-value represents the likelihood that experimental results happened by chance. Within psychology, the most common standard for p-values is “p < .05”. What this means is that there is less than a 5% probability that the results happened just by random chance, and therefore a 95% probability that the results reflect a meaningful pattern in human psychology. We call this statistical significance .
So, in the study above, if we assume that each infant was choosing equally, then the probability that 14 or more out of 16 infants would choose the helper toy is found to be 0.0021. We have only two logical possibilities: either the infants have a genuine preference for the helper toy, or the infants have no preference (50/50) and an outcome that would occur only 2 times in 1,000 iterations happened in this study. Because this p-value of 0.0021 is quite small, we conclude that the study provides very strong evidence that these infants have a genuine preference for the helper toy.
If we compare the p-value to some cut-off value, like 0.05, we see that the p=value is smaller. Because the p-value is smaller than that cut-off value, then we reject the hypothesis that only random chance was at play here. In this case, these researchers would conclude that significantly more than half of the infants in the study chose the helper toy, giving strong evidence of a genuine preference for the toy with the helping behavior.
Drawing Conclusions from Statistics
Generalizability.
![examples of research methods psychology Photo of a diverse group of college-aged students.](https://s3-us-west-2.amazonaws.com/courses-images/wp-content/uploads/sites/855/2016/10/17150423/generalizability.jpg)
One limitation to the study mentioned previously about the babies choosing the “helper” toy is that the conclusion only applies to the 16 infants in the study. We don’t know much about how those 16 infants were selected. Suppose we want to select a subset of individuals (a sample ) from a much larger group of individuals (the population ) in such a way that conclusions from the sample can be generalized to the larger population. This is the question faced by pollsters every day.
Example 3 : The General Social Survey (GSS) is a survey on societal trends conducted every other year in the United States. Based on a sample of about 2,000 adult Americans, researchers make claims about what percentage of the U.S. population consider themselves to be “liberal,” what percentage consider themselves “happy,” what percentage feel “rushed” in their daily lives, and many other issues. The key to making these claims about the larger population of all American adults lies in how the sample is selected. The goal is to select a sample that is representative of the population, and a common way to achieve this goal is to select a r andom sample that gives every member of the population an equal chance of being selected for the sample. In its simplest form, random sampling involves numbering every member of the population and then using a computer to randomly select the subset to be surveyed. Most polls don’t operate exactly like this, but they do use probability-based sampling methods to select individuals from nationally representative panels.
In 2004, the GSS reported that 817 of 977 respondents (or 83.6%) indicated that they always or sometimes feel rushed. This is a clear majority, but we again need to consider variation due to random sampling . Fortunately, we can use the same probability model we did in the previous example to investigate the probable size of this error. (Note, we can use the coin-tossing model when the actual population size is much, much larger than the sample size, as then we can still consider the probability to be the same for every individual in the sample.) This probability model predicts that the sample result will be within 3 percentage points of the population value (roughly 1 over the square root of the sample size, the margin of error. A statistician would conclude, with 95% confidence, that between 80.6% and 86.6% of all adult Americans in 2004 would have responded that they sometimes or always feel rushed.
The key to the margin of error is that when we use a probability sampling method, we can make claims about how often (in the long run, with repeated random sampling) the sample result would fall within a certain distance from the unknown population value by chance (meaning by random sampling variation) alone. Conversely, non-random samples are often suspect to bias, meaning the sampling method systematically over-represents some segments of the population and under-represents others. We also still need to consider other sources of bias, such as individuals not responding honestly. These sources of error are not measured by the margin of error.
Cause and Effect
In many research studies, the primary question of interest concerns differences between groups. Then the question becomes how were the groups formed (e.g., selecting people who already drink coffee vs. those who don’t). In some studies, the researchers actively form the groups themselves. But then we have a similar question—could any differences we observe in the groups be an artifact of that group-formation process? Or maybe the difference we observe in the groups is so large that we can discount a “fluke” in the group-formation process as a reasonable explanation for what we find?
Example 4 : A psychology study investigated whether people tend to display more creativity when they are thinking about intrinsic (internal) or extrinsic (external) motivations (Ramsey & Schafer, 2002, based on a study by Amabile, 1985). The subjects were 47 people with extensive experience with creative writing. Subjects began by answering survey questions about either intrinsic motivations for writing (such as the pleasure of self-expression) or extrinsic motivations (such as public recognition). Then all subjects were instructed to write a haiku, and those poems were evaluated for creativity by a panel of judges. The researchers conjectured beforehand that subjects who were thinking about intrinsic motivations would display more creativity than subjects who were thinking about extrinsic motivations. The creativity scores from the 47 subjects in this study are displayed in Figure 26, where higher scores indicate more creativity.
![examples of research methods psychology Image showing a dot for creativity scores, which vary between 5 and 27, and the types of motivation each person was given as a motivator, either extrinsic or intrinsic.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2016/06/10212632/000001452original.jpg)
In this example, the key question is whether the type of motivation affects creativity scores. In particular, do subjects who were asked about intrinsic motivations tend to have higher creativity scores than subjects who were asked about extrinsic motivations?
Figure 26 reveals that both motivation groups saw considerable variability in creativity scores, and these scores have considerable overlap between the groups. In other words, it’s certainly not always the case that those with extrinsic motivations have higher creativity than those with intrinsic motivations, but there may still be a statistical tendency in this direction. (Psychologist Keith Stanovich (2013) refers to people’s difficulties with thinking about such probabilistic tendencies as “the Achilles heel of human cognition.”)
The mean creativity score is 19.88 for the intrinsic group, compared to 15.74 for the extrinsic group, which supports the researchers’ conjecture. Yet comparing only the means of the two groups fails to consider the variability of creativity scores in the groups. We can measure variability with statistics using, for instance, the standard deviation: 5.25 for the extrinsic group and 4.40 for the intrinsic group. The standard deviations tell us that most of the creativity scores are within about 5 points of the mean score in each group. We see that the mean score for the intrinsic group lies within one standard deviation of the mean score for extrinsic group. So, although there is a tendency for the creativity scores to be higher in the intrinsic group, on average, the difference is not extremely large.
We again want to consider possible explanations for this difference. The study only involved individuals with extensive creative writing experience. Although this limits the population to which we can generalize, it does not explain why the mean creativity score was a bit larger for the intrinsic group than for the extrinsic group. Maybe women tend to receive higher creativity scores? Here is where we need to focus on how the individuals were assigned to the motivation groups. If only women were in the intrinsic motivation group and only men in the extrinsic group, then this would present a problem because we wouldn’t know if the intrinsic group did better because of the different type of motivation or because they were women. However, the researchers guarded against such a problem by randomly assigning the individuals to the motivation groups. Like flipping a coin, each individual was just as likely to be assigned to either type of motivation. Why is this helpful? Because this random assignment tends to balance out all the variables related to creativity we can think of, and even those we don’t think of in advance, between the two groups. So we should have a similar male/female split between the two groups; we should have a similar age distribution between the two groups; we should have a similar distribution of educational background between the two groups; and so on. Random assignment should produce groups that are as similar as possible except for the type of motivation, which presumably eliminates all those other variables as possible explanations for the observed tendency for higher scores in the intrinsic group.
But does this always work? No, so by “luck of the draw” the groups may be a little different prior to answering the motivation survey. So then the question is, is it possible that an unlucky random assignment is responsible for the observed difference in creativity scores between the groups? In other words, suppose each individual’s poem was going to get the same creativity score no matter which group they were assigned to, that the type of motivation in no way impacted their score. Then how often would the random-assignment process alone lead to a difference in mean creativity scores as large (or larger) than 19.88 – 15.74 = 4.14 points?
We again want to apply to a probability model to approximate a p-value , but this time the model will be a bit different. Think of writing everyone’s creativity scores on an index card, shuffling up the index cards, and then dealing out 23 to the extrinsic motivation group and 24 to the intrinsic motivation group, and finding the difference in the group means. We (better yet, the computer) can repeat this process over and over to see how often, when the scores don’t change, random assignment leads to a difference in means at least as large as 4.41. Figure 27 shows the results from 1,000 such hypothetical random assignments for these scores.
![examples of research methods psychology Standard distribution in a typical bell curve.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2016/06/10212623/000001454original.jpg)
Only 2 of the 1,000 simulated random assignments produced a difference in group means of 4.41 or larger. In other words, the approximate p-value is 2/1000 = 0.002. This small p-value indicates that it would be very surprising for the random assignment process alone to produce such a large difference in group means. Therefore, as with Example 2, we have strong evidence that focusing on intrinsic motivations tends to increase creativity scores, as compared to thinking about extrinsic motivations.
Notice that the previous statement implies a cause-and-effect relationship between motivation and creativity score; is such a strong conclusion justified? Yes, because of the random assignment used in the study. That should have balanced out any other variables between the two groups, so now that the small p-value convinces us that the higher mean in the intrinsic group wasn’t just a coincidence, the only reasonable explanation left is the difference in the type of motivation. Can we generalize this conclusion to everyone? Not necessarily—we could cautiously generalize this conclusion to individuals with extensive experience in creative writing similar the individuals in this study, but we would still want to know more about how these individuals were selected to participate.
![examples of research methods psychology Close-up photo of mathematical equations.](https://s3-us-west-2.amazonaws.com/courses-images/wp-content/uploads/sites/855/2016/10/17150557/conclusion.jpg)
Statistical thinking involves the careful design of a study to collect meaningful data to answer a focused research question, detailed analysis of patterns in the data, and drawing conclusions that go beyond the observed data. Random sampling is paramount to generalizing results from our sample to a larger population, and random assignment is key to drawing cause-and-effect conclusions. With both kinds of randomness, probability models help us assess how much random variation we can expect in our results, in order to determine whether our results could happen by chance alone and to estimate a margin of error.
So where does this leave us with regard to the coffee study mentioned previously (the Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012 found that men who drank at least six cups of coffee a day had a 10% lower chance of dying (women 15% lower) than those who drank none)? We can answer many of the questions:
- This was a 14-year study conducted by researchers at the National Cancer Institute.
- The results were published in the June issue of the New England Journal of Medicine , a respected, peer-reviewed journal.
- The study reviewed coffee habits of more than 402,000 people ages 50 to 71 from six states and two metropolitan areas. Those with cancer, heart disease, and stroke were excluded at the start of the study. Coffee consumption was assessed once at the start of the study.
- About 52,000 people died during the course of the study.
- People who drank between two and five cups of coffee daily showed a lower risk as well, but the amount of reduction increased for those drinking six or more cups.
- The sample sizes were fairly large and so the p-values are quite small, even though percent reduction in risk was not extremely large (dropping from a 12% chance to about 10%–11%).
- Whether coffee was caffeinated or decaffeinated did not appear to affect the results.
- This was an observational study, so no cause-and-effect conclusions can be drawn between coffee drinking and increased longevity, contrary to the impression conveyed by many news headlines about this study. In particular, it’s possible that those with chronic diseases don’t tend to drink coffee.
This study needs to be reviewed in the larger context of similar studies and consistency of results across studies, with the constant caution that this was not a randomized experiment. Whereas a statistical analysis can still “adjust” for other potential confounding variables, we are not yet convinced that researchers have identified them all or completely isolated why this decrease in death risk is evident. Researchers can now take the findings of this study and develop more focused studies that address new questions.
Explore these outside resources to learn more about applied statistics:
- Video about p-values: P-Value Extravaganza
- Interactive web applets for teaching and learning statistics
- Inter-university Consortium for Political and Social Research where you can find and analyze data.
- The Consortium for the Advancement of Undergraduate Statistics
- Find a recent research article in your field and answer the following: What was the primary research question? How were individuals selected to participate in the study? Were summary results provided? How strong is the evidence presented in favor or against the research question? Was random assignment used? Summarize the main conclusions from the study, addressing the issues of statistical significance, statistical confidence, generalizability, and cause and effect. Do you agree with the conclusions drawn from this study, based on the study design and the results presented?
- Is it reasonable to use a random sample of 1,000 individuals to draw conclusions about all U.S. adults? Explain why or why not.
How to Read Research
In this course and throughout your academic career, you’ll be reading journal articles (meaning they were published by experts in a peer-reviewed journal) and reports that explain psychological research. It’s important to understand the format of these articles so that you can read them strategically and understand the information presented. Scientific articles vary in content or structure, depending on the type of journal to which they will be submitted. Psychological articles and many papers in the social sciences follow the writing guidelines and format dictated by the American Psychological Association (APA). In general, the structure follows: abstract, introduction, methods, results, discussion, and references.
- Abstract : the abstract is the concise summary of the article. It summarizes the most important features of the manuscript, providing the reader with a global first impression on the article. It is generally just one paragraph that explains the experiment as well as a short synopsis of the results.
- Introduction : this section provides background information about the origin and purpose of performing the experiment or study. It reviews previous research and presents existing theories on the topic.
- Method : this section covers the methodologies used to investigate the research question, including the identification of participants , procedures , and materials as well as a description of the actual procedure . It should be sufficiently detailed to allow for replication.
- Results : the results section presents key findings of the research, including reference to indicators of statistical significance.
- Discussion : this section provides an interpretation of the findings, states their significance for current research, and derives implications for theory and practice. Alternative interpretations for findings are also provided, particularly when it is not possible to conclude for the directionality of the effects. In the discussion, authors also acknowledge the strengths and limitations/weaknesses of the study and offer concrete directions about for future research.
Watch this 3-minute video for an explanation on how to read scholarly articles. Look closely at the example article shared just before the two minute mark.
https://digitalcommons.coastal.edu/kimbel-library-instructional-videos/9/
Practice identifying these key components in the following experiment: Food-Induced Emotional Resonance Improves Emotion Recognition.
In this chapter, you learned to
- define and apply the scientific method to psychology
- describe the strengths and weaknesses of descriptive, experimental, and correlational research
- define the basic elements of a statistical investigation
Putting It Together: Psychological Research
Psychologists use the scientific method to examine human behavior and mental processes. Some of the methods you learned about include descriptive, experimental, and correlational research designs.
Watch the CrashCourse video to review the material you learned, then read through the following examples and see if you can come up with your own design for each type of study.
You can view the transcript for “Psychological Research: Crash Course Psychology #2” here (opens in new window).
Case Study: a detailed analysis of a particular person, group, business, event, etc. This approach is commonly used to to learn more about rare examples with the goal of describing that particular thing.
- Ted Bundy was one of America’s most notorious serial killers who murdered at least 30 women and was executed in 1989. Dr. Al Carlisle evaluated Bundy when he was first arrested and conducted a psychological analysis of Bundy’s development of his sexual fantasies merging into reality (Ramsland, 2012). Carlisle believes that there was a gradual evolution of three processes that guided his actions: fantasy, dissociation, and compartmentalization (Ramsland, 2012). Read Imagining Ted Bundy (http://goo.gl/rGqcUv) for more information on this case study.
Naturalistic Observation : a researcher unobtrusively collects information without the participant’s awareness.
- Drain and Engelhardt (2013) observed six nonverbal children with autism’s evoked and spontaneous communicative acts. Each of the children attended a school for children with autism and were in different classes. They were observed for 30 minutes of each school day. By observing these children without them knowing, they were able to see true communicative acts without any external influences.
Survey : participants are asked to provide information or responses to questions on a survey or structure assessment.
- Educational psychologists can ask students to report their grade point average and what, if anything, they eat for breakfast on an average day. A healthy breakfast has been associated with better academic performance (Digangi’s 1999).
- Anderson (1987) tried to find the relationship between uncomfortably hot temperatures and aggressive behavior, which was then looked at with two studies done on violent and nonviolent crime. Based on previous research that had been done by Anderson and Anderson (1984), it was predicted that violent crimes would be more prevalent during the hotter time of year and the years in which it was hotter weather in general. The study confirmed this prediction.
Longitudinal Study: researchers recruit a sample of participants and track them for an extended period of time.
- In a study of a representative sample of 856 children Eron and his colleagues (1972) found that a boy’s exposure to media violence at age eight was significantly related to his aggressive behavior ten years later, after he graduated from high school.
Cross-Sectional Study: researchers gather participants from different groups (commonly different ages) and look for differences between the groups.
- In 1996, Russell surveyed people of varying age groups and found that people in their 20s tend to report being more lonely than people in their 70s.
Correlational Design: two different variables are measured to determine whether there is a relationship between them.
- Thornhill et al. (2003) had people rate how physically attractive they found other people to be. They then had them separately smell t-shirts those people had worn (without knowing which clothes belonged to whom) and rate how good or bad their body oder was. They found that the more attractive someone was the more pleasant their body order was rated to be.
- Clinical psychologists can test a new pharmaceutical treatment for depression by giving some patients the new pill and others an already-tested one to see which is the more effective treatment.
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Arnett, J. (2008). The neglected 95%: Why American psychology needs to become less American. American Psychologist, 63(7), 602–614.
Barton, B. A., Eldridge, A. L., Thompson, D., Affenito, S. G., Striegel-Moore, R. H., Franko, D. L., . . . Crockett, S. J. (2005). The relationship of breakfast and cereal consumption to nutrient intake and body mass index: The national heart, lung, and blood institute growth and health study. Journal of the American Dietetic Association, 105(9), 1383–1389. Retrieved from http://dx.doi.org/10.1016/j.jada.2005.06.003
Chwalisz, K., Diener, E., & Gallagher, D. (1988). Autonomic arousal feedback and emotional experience: Evidence from the spinal cord injured. Journal of Personality and Social Psychology, 54, 820–828.
Dominus, S. (2011, May 25). Could conjoined twins share a mind? New York Times Sunday Magazine. Retrieved from http://www.nytimes.com/2011/05/29/magazine/could-conjoined-twins-share-a-mind.html?_r=5&hp&
Fanger, S. M., Frankel, L. A., & Hazen, N. (2012). Peer exclusion in preschool children’s play: Naturalistic observations in a playground setting. Merrill-Palmer Quarterly, 58, 224–254.
Fiedler, K. (2004). Illusory correlation. In R. F. Pohl (Ed.), Cognitive illusions: A handbook on fallacies and biases in thinking, judgment and memory (pp. 97–114). New York, NY: Psychology Press.
Frantzen, L. B., Treviño, R. P., Echon, R. M., Garcia-Dominic, O., & DiMarco, N. (2013). Association between frequency of ready-to-eat cereal consumption, nutrient intakes, and body mass index in fourth- to sixth-grade low-income minority children. Journal of the Academy of Nutrition and Dietetics, 113(4), 511–519.
Harper, J. (2013, July 5). Ice cream and crime: Where cold cuisine and hot disputes intersect. The Times-Picaune. Retrieved from http://www.nola.com/crime/index.ssf/2013/07/ice_cream_and_crime_where_hot.html
Jenkins, W. J., Ruppel, S. E., Kizer, J. B., Yehl, J. L., & Griffin, J. L. (2012). An examination of post 9-11 attitudes towards Arab Americans. North American Journal of Psychology, 14, 77–84.
Jones, J. M. (2013, May 13). Same-sex marriage support solidifies above 50% in U.S. Gallup Politics. Retrieved from http://www.gallup.com/poll/162398/sex-marriage-support-solidifies-above.aspx
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grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing
well-developed set of ideas that propose an explanation for observed phenomena
(plural: hypotheses) tentative and testable statement about the relationship between two or more variables
an experiment must be replicable by another researcher
implies that a theory should enable us to make predictions about future events
able to be disproven by experimental results
implies that all data must be considered when evaluating a hypothesis
committee of administrators, scientists, and community members that reviews proposals for research involving human participants
process of informing a research participant about what to expect during an experiment, any risks involved, and the implications of the research, and then obtaining the person’s consent to participate
purposely misleading experiment participants in order to maintain the integrity of the experiment
when an experiment involved deception, participants are told complete and truthful information about the experiment at its conclusion
committee of administrators, scientists, veterinarians, and community members that reviews proposals for research involving non-human animals
research studies that do not test specific relationships between variables
research investigating the relationship between two or more variables
research method that uses hypothesis testing to make inferences about how one variable impacts and causes another
observation of behavior in its natural setting
inferring that the results for a sample apply to the larger population
when observations may be skewed to align with observer expectations
measure of agreement among observers on how they record and classify a particular event
observational research study focusing on one or a few people
list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people
subset of individuals selected from the larger population
overall group of individuals that the researchers are interested in
method of research using past records or data sets to answer various research questions, or to search for interesting patterns or relationships
studies in which the same group of individuals is surveyed or measured repeatedly over an extended period of time
compares multiple segments of a population at a single time
reduction in number of research participants as some drop out of the study over time
relationship between two or more variables; when two variables are correlated, one variable changes as the other does
number from -1 to +1, indicating the strength and direction of the relationship between variables, and usually represented by r
two variables change in the same direction, both becoming either larger or smaller
two variables change in different directions, with one becoming larger as the other becomes smaller; a negative correlation is not the same thing as no correlation
changes in one variable cause the changes in the other variable; can be determined only through an experimental research design
unanticipated outside factor that affects both variables of interest, often giving the false impression that changes in one variable causes changes in the other variable, when, in actuality, the outside factor causes changes in both variables
seeing relationships between two things when in reality no such relationship exists
tendency to ignore evidence that disproves ideas or beliefs
group designed to answer the research question; experimental manipulation is the only difference between the experimental and control groups, so any differences between the two are due to experimental manipulation rather than chance
serves as a basis for comparison and controls for chance factors that might influence the results of the study—by holding such factors constant across groups so that the experimental manipulation is the only difference between groups
description of what actions and operations will be used to measure the dependent variables and manipulate the independent variables
researcher expectations skew the results of the study
experiment in which the researcher knows which participants are in the experimental group and which are in the control group
experiment in which both the researchers and the participants are blind to group assignments
people's expectations or beliefs influencing or determining their experience in a given situation
variable that is influenced or controlled by the experimenter; in a sound experimental study, the independent variable is the only important difference between the experimental and control group
variable that the researcher measures to see how much effect the independent variable had
subjects of psychological research
subset of a larger population in which every member of the population has an equal chance of being selected
method of experimental group assignment in which all participants have an equal chance of being assigned to either group
consistency and reproducibility of a given result
accuracy of a given result in measuring what it is designed to measure
determines how likely any difference between experimental groups is due to chance
statistical probability that represents the likelihood that experimental results happened by chance
Psychological Science is the scientific study of mind, brain, and behavior. We will explore what it means to be human in this class. It has never been more important for us to understand what makes people tick, how to evaluate information critically, and the importance of history. Psychology can also help you in your future career; indeed, there are very little jobs out there with no human interaction!
Because psychology is a science, we analyze human behavior through the scientific method. There are several ways to investigate human phenomena, such as observation, experiments, and more. We will discuss the basics, pros and cons of each! We will also dig deeper into the important ethical guidelines that psychologists must follow in order to do research. Lastly, we will briefly introduce ourselves to statistics, the language of scientific research. While reading the content in these chapters, try to find examples of material that can fit with the themes of the course.
To get us started:
- The study of the mind moved away Introspection to reaction time studies as we learned more about empiricism
- Psychologists work in careers outside of the typical "clinician" role. We advise in human factors, education, policy, and more!
- While completing an observation study, psychologists will work to aggregate common themes to explain the behavior of the group (sample) as a whole. In doing so, we still allow for normal variation from the group!
- The IRB and IACUC are important in ensuring ethics are maintained for both human and animal subjects
Psychological Science: Understanding Human Behavior Copyright © by Karenna Malavanti is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
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7 Important Methods in Psychology With Examples
Psychology is a scientific study of the human mind, mental processes, and behavior. It is called a scientific study because psychologists also do various systematic research and experiments to study and formulate psychological theories like other scientists. Psychological researches involve understanding complex mental processes, human behavior, and collecting different types of data (physiological, psychological, physical, and demographic data), psychologists use various research methods as it is difficult to obtain accurate and reliable results if we use a single research method for collecting research data. The type of method they use depends upon the type of research. Broadly, researches are divide into two types, i.e., experimental and non-experimental researches. Experimental researches involve two or more variables, and it studies the effect of the independent variable on the dependent variable (cause-effect relationship), whereas non-experimental researches do not involve the manipulations of variables. The concept of variables is briefly explained further in this article. Let’s get familiar with some widely used methods of collecting psychology research data.
1. Experimental Method
To understand the experimental method, firstly we need to be familiar with the term ‘variable.’ A variable is an event or stimulus that varies, and its values can be measured. It is to be noted that we can not regard any object as a variable; in fact, the attributes related to that object are called variables. For example, A person is not a variable, but the height of the person is a variable because different people may have different heights. In the experiment method of data collection, we mainly concern with two types of variables, i.e., independent variables and dependent variables. If the value of the variable is manipulated by the researcher to observe its effects, then it is called the independent variable, and the variable that is affected by the change in the independent variable is called the dependent variable. For example, if we want to study the influence of alcohol on the reaction time and driving abilities of the driver, then the amount of alcohol that the driver consumes is the independent variable, and the driving performance of the driver is called the dependent variable. Experimental methods are conducted to establish the relationship between the independent variable (cause) and dependent variables (effect). The experiments are conducted very carefully, and any variables other than the independent variable are kept constant or negligible so that an accurate relationship between the cause and effect can be established. In the above example, other factors like the driver’s stress, anxiety, or mood (extraneous variables) can interfere with the dependent variable (driving ability). It is difficult to avoid these extraneous variables; extraneous variables are the undesired variables that are not studied under the experiments, and their manipulation can alter the results of the study, but we should always try to make them constant or negligible for accurate results.
Control Group and Experimental Group
Experiments generally consist of several research groups that are broadly categorized into control groups and experimental groups. The group that undergoes the manipulation of the independent variable is called the experimental group, whereas the group that does not undergoes the independent variable manipulation, but its other factors or variables are kept the same as the experimental group, is called a control group. The control group basically acts as a comparison group as it is used to measure the changes caused by the independent variable on the experimental group. For example, if a researcher wants to study that how does the conduction of exams affects the learning ability of the student, then, here, the learning ability of the student is the dependent variable and exams are the independent variable. In this experiment, some lectures will be delivered to the students of the same class and of nearly the same learning abilities (based on their previous exam scores or other criteria), and then the students are divide into different groups, one group is not subjected to give the exams, while the other group has to give the exam of what they have learned in the lesson. The group of students that were not subjected to give the exams is called the control group, and the group of students that were subjected to give the exams is called the experimental group. The number of experimental groups can be more than one based on how often does the exams are conducted for each group. At the end of the experiments, the researcher can find the results by comparing the experimental group with the control group.
Types of Experimental Method
Some major types of the experimental method include,
1. Lab Experiments
It is difficult to conduct some experiments in natural settings as many extraneous variables can become a problem for the research. So, researchers conduct the experiments in a controlled manner in laboratories or research centers. It is easy to manage the independent and dependent variables in the controlled settings. For example, if the researcher wants to study the effect of different kinds of music like pop, classical, etc., on the health of the patients, then the researcher will conduct this study in a room rather than in a natural environment as it’s easy to keep extraneous variables constant in the closed settings. Here, music is the independent variable and health is the dependent variable. If the same experiment is conducted outside the lab, then extraneous variables like sunlight, weather, noise, etc., may interfere with the study and manipulate the results of the research.
2. Field Experiments
Sometimes, lab experiment results face criticism for their lack of generalizability as they are not conducted in real-life settings. Field experiments are conducted in the natural environment and real-life settings like schools, industries, hospitals, etc., so they are more ecologically valid than lab experiments. For example, if we want to study whether classroom learning or open environment learning is the best teaching method for students, the researcher would prefer the field experiment over the lab experiment. However, in field experiments, it is very difficult to control the undesired or extraneous variables, which makes it difficult to establish an accurate cause-effect relationship. Moreover, they consume more time than the lab experiments.
3. Quasi Experiments
In lab experiments or fields experiments, sometimes, it is difficult to manipulate some variables due to ethical issues or other constraints. Quasi-experiments are conducted in this situation. In quasi-experiments, the researcher studies that how does a single or many independent variables impact the dependent variable but without manipulating the independent variable. For instance, if the researcher wants to study the effect of terrorism or bomb blasts on the children who have lost their families, then it is difficult to create this situation artificially, so researchers use the quasi-experiments approach. Here, the researcher selects the independent variable instead of manipulating it and compare it with the dependent variable. The researchers will take a group of children who have lost their families (experimental group), and the children who suffered the bomb blast but did not lose their families (control group), and by comparing both these groups, the researcher can analyze the effect of terrorism on the children who lost their families.
2. Observational Method
The observational method is a non-experimental and qualitative research method in which the behavior of the subject under research is observed. An observational method is a great tool for data collection in psychology because the researcher does not require any special types of equipment to collect the research data. We observe several items throughout our day, but psychological researches are different from our daily observations as it involves some important steps such as selection of the area of interest, noting the observations, and analyzing the obtained data. Gathering the data through observation is itself a skill as an observer should be well aware of his actual area of research and he/she should have a clear picture in mind that what qualities or attributes he should observe, and what he should avoid. The researcher should have a good understanding of the correct methods of recording and analyzing the gathered data. The major problem of the observational method is the observer’s biases, there are high chances that the observer may judge the event according to his/her biases rather than interpreting the event in its natural form. We can relate it to a famous saying,
We see things as we are and not as things are”
So, it is the responsibility of the observer to make accurate observations by minimizing his/her biases.
Types of Observations
The observational methods are broadly categorized into the following types,
1. Naturalistic Observation
If the researcher has made the observations in real-life or natural settings such as schools, institutes, homes, open environments, etc., without interfering with the phenomena under observation, then it is known as naturalistic observation. In this type of observation, the researcher does not manipulate or control any situation, and he/she only records the spontaneous behavior of the subject (individual or event under investigation) in their natural environment. Naturalistic observations provide more generalized results because of the natural settings, but it’s difficult to manage the extraneous variables in natural observations and ethical issues of privacy interference and observer bias are some other major problems of naturalistic observations.
2. Controlled Observation
The observations that are conducted in the closed settings, i.e., their various conditions and variable are highly under control, are known as controlled observations. In these observations, variables are manipulated according to the need of the research. For example, if the researcher wants to study the effect of induced workload on the worker’s performance, the research should be conducted in a controlled setting as the researcher can control the independent variable (workload). However, due to the controlled settings approach, these observations are far less to ecological validity than the naturalistic observations, and the behavior of the participants or subjects that are being studied may change because of their awareness of being observed.
3. Participant Observation
The types of observation in which the observer or the researcher itself becomes part of the research are called participant observations. The other participants in the research may or may not be informed about the presence of the observer in the group. However, if the participants are not aware of the observer’s presence, then the results gathered will be more reliable and satisfy ecological validity. In participant observation as the researcher acts as an active member of the observed group, the observer has to be cautious about the fact that other members of the group won’t recognize him/her, and he/she should maintain the proper relationships and a good rapport with the participants under investigation. The strength of the participant observation is that it provides the researcher a holistic approach to understand the process not only from his/her own perspective but also from the participant’s perspective, which reduces the research biases. However, Participant observation is time-consuming, and the findings of this type of observation are usually not generalizable because of the small research groups.
4. Non-Participant Observation
In this type of research, the observer is not present in the research, but he/she uses other means to observe the spontaneous activities or behavior of the individual or group members, this may include installing the camera in the rooms that need to be observed. The main benefit of non-participants’ observation is that the actual behavior of the participants can be observed without making them aware of being under observation. An example of non-participation observation is a school principal who observes the classroom activities of the teacher and students through the CCTV cameras in his/her office.
3. Case Study
In the case study method, the researcher does qualitative research and in-depth analysis of a specific case (subject under investigation). The results obtained from this method are highly reliable; in fact, many famous theories such as the psychoanalytic theory of Sigmund Freud and Jean Piaget’s cognitive development theory are the results of well-structured and proper case studies of the subjects. The case study method allows the researcher to deeply study the psyche of the cases. The researcher does the case studies of the people or events that provide some critical information about the new or less discovered phenomena of the human mind. The number of cases can be one or more, or they are of different or same characteristics, for example, a patient suffering from a mental disorder, a group of people belonging to the same gender, class, or ethnicity, and effect on the people of various natural or man-made disasters such as flood, tsunami, terrorism, and industrialization. Case studies involve the multi-method approach as it uses various other research methods like unstructured interviews, psychological testings, and observations to get detailed information about the subjects. It is the best method to deeply understand and analyze the impact of certain traumatic events on the psychological health of the individual, and it is widely used by clinical psychologists to diagnose various psychological disorders of the patients.
4. Correlational Research
The researcher uses the correlational method if he/she wants to examine the relationship between the two variables. It is to be noted that here researcher does not vary the independent variable as he is only concerned about whether the two variables are linked to each other or not. For example, if you are interested in finding the relation between yoga and the psychological health of the person, then you simply try to find the relationship between these two factors rather than manipulating anything. The degree of the association between the variables is represented by the correlational coefficients ranges from +1.0 to -1.0. The correlation can be of three types, i.e., positive correlation, negative correlation, or zero correlation. If we increase or decrease the value of one variable, the value of another variable also increases or decreases respectively, then it is called a positive correlation, and the value of the correlation coefficient would be near +1.0. If we increase or decrease the value of one variable, the value of another variable decreases or increases respectively, then it is called the negative correlation, and the value of correlational coefficient would be near -1.0, and if the changes in the value of one variable do not affect the other variable, then there does not exist any relationship between the variables, and it is called zero correlation with the correlation value near or equal to zero.
5. Content Analysis
In content analysis research methods, the researcher analyses and quantifies various types of content pieces such as articles, texts, interviews, researches, and other important documents to get useful information about their area of research. Content analyses involve various steps that are data collection, examining the research data, and getting familiar with it, developing ṭhe set of rules for selecting coding units, making coding units (coding unit is the smallest parts of the content that is analyzed) as per the developed rules, and then, finally, analyzing the findings and drawing conclusions. Content analysis is generally of two types, i.e., conceptual analysis, and relational analysis. These are briefly discussed below.
Conceptual Analyses
It involves the selection of the concept (word, phrase, sentence), and then examining the occurrence of the selected concept in the available research data. In conceptual analyses, the researcher selects the sample according to the research question and divides the content into different categories, which makes it easier to focus on the specific data that gives useful information about the research, and then coding and analyzing the results.
Relational Analyses
The initial steps of the relational analyses are the same as the conceptual analyses like selecting the concept, but it’s different from the conceptual analyses because it involves finding the associations or relationships among the concepts. In conceptual analyses, we analyze every concept, but in relational analyses, the individual concepts do not have any importance, instead, the useful information is assessed by finding the associations among the concepts present in the research data.
6. Survey Research Method
Survey research is the most popular mean of data collection in almost every branch of social sciences. It finds its applications in election poll results (election surveys), literacy rate, and population rate analysis. The survey research methods help the researchers understand the actual ground reality of the event by analyzing the social views, attitudes, behavior, and opinions of the people. The researchers use various techniques of survey research methods, which are briefly discussed below.
1. Direct Interviews
An interview process involves direct communication between the interviewer/researcher (who asks the question) and the interviewee/respondent (who answers the questions). Interviews give better in-depth results than any other technique of data collection as the researcher gets first-hand information about the respondent’s mind through communication and observation of his/her behavior. Interviews may be structured or unstructured, when the researcher prepares the sequential list of the questions about when and what questions to be asked in the interview, it is called a structured interview, whereas if the questions to be asked in the interview are not pre-planned, and flexibility is provided to the interviewer to ask questions according to the situation, then it is called the unstructured interview. The responses to the questions in the case of structured interviews are also specified to some extent, such questions are called close-ended questions, while in the case of unstructured interviews, the respondent is free to answer the questions according to his/her desire, and these types of questions are called open-ended questions. For instance, if you ask the respondent whether he/she likes the coffee, then the answer would be either yes or no, i.e., a close-ended question. However, if you ask the respondents about their hobbies, then the respondent will answer it according to his/her will, hence it is an open-ended question. An interview can be of the following types, depending upon the number of interviewers and interviewees involved in the interview. For example,
- One to One Interview: When only the interviewer and one interviewee are present in the interview process.
- Individual to group Interview: When one interviewer interviews a group of people.
- Group to Individual: It is also called group panel interview, in this case, an individual is interviewed by a group of interviewers.
- Group to Group: When a group of interviewers, interviews a group of interviewees.
The most important thing in direct interviews is that the researcher/interviewer should have good interviewing skills, and the ability to build a good rapport with the respondent and making him/her comfortable enough to give accurate answers to the questions asked. The main purpose of conducting an interview is to gather the data about the subject, but the interviewer should be sensitive to the emotions and behavior of the respondent and should not pressurize him/her to give the answers to which he/she is not comfortable enough. The process of the interview is very time-consuming, so it is not much effective as in psychology researches, it would become tedious to take interviews of a large section of society, which is why it is usually preferred for some specific population that may include illiterate or blind people as the interviewer can verbally ask them questions and make sure that whether they understood the questions or not.
2. Telephonic or Digital Surveys
Telephonic surveys involve asking questions about the survey through direct calls or messages. Digital surveys through ‘Google forms’ are also commonly used these days. Telephone and digital surveys are easy to conduct, and they do not consume much time. However, they have many limitations such as the results obtained through them are not much reliable because in this method the researcher does not have proper evidence of certain factors like respondents’ age, gender, and qualifications, etc., and the respondents may have given the manipulative or vague answers.
3. Questionnaires
Questionnaires consist of a well-structured set of questions that are distributed to the people to mark or write the answers. The questions can be open-ended or close-ended, depending upon the type of survey. It is one of the most commonly used survey techniques as it is easy to conduct, less time-consuming, and a cost-effective method to collect research information. It is a better method than the interview for obtaining accurate answers because, in this method, the proper assurance of confidentiality is provided to the respondent, hence the respondent is more likely to mark the accurate answer. Earlier, only paper-based questionnaires were used, but due to the advancement of technology, digital questionnaires, which are sent to people through emails or google forms, are also used these days.
7. Psychological Testing
Psychological testing is also known as psychometrics. Psychological tests are scientifically proven and standardized tests that are constructed by psychologists. These are used to assess the various characteristics of humans such as attitude, aptitude, personality, intelligence quotient, and emotional quotient. There are many psychological tests available these days such as aptitude testing, mental health assessment, educational testing, personality assessment, etc., which are used for different purposes. The multiple-choice questions (MCQs) of the psychological tests are carefully designed, and the factors like gender, age, class, qualification, etc., are considered before conducting these tests. Psychological tests can be conducted offline (pen-paper-based) or online (digital format), depending upon the applicability and availability. The necessary part of the psychological tests is that the participants or the subjects, upon whom the test is conducted, should be properly informed about the testing procedure, and proper instructions about marking or filling the test, time durations of the test, should be verbally provided to them for their better understanding. These tests are constructed by following a systematic approach and three important factors, i.e., validity, reliability, and norms. These are briefly discussed below,
- Validity : The most obvious criterion of constructing the test is that it should be valid. The validity of the test implies that the test should measure what it is designed for. For example, the psychological health assessment test should measure the psychological health of the person rather than the physical health.
- Reliability : The results obtained by the psychological test should be reliable, i.e., there should be almost negligible variations in test scores if the same test is repeated upon the same subjects after some time.
- Norm : For every psychological test, norms are developed, these are the standard values that represent the average performance of the subject or the group of subjects in the tasks that are provided them. Norms enable psychologists to interpret and compare the results obtained by the psychological tests. There are various types of norms for different types of psychological tests such as descriptive norms, grade norms, age norms, and percentile norms.
Rorschach Psychological Test
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Thank you for sharing this very useful knowledge for a psychology student (in this case). Very explanatory, clear to understand. successes.
Well explained 👏 Thanks
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- Research Methods in Psychology
Psychology is such a vast topic, not only in terms of what is investigated but also in terms of how it can be researched. Research methods in psychology are the discipline's core; without them, we can't ensure that researched topics follow a standardised scientific protocol, but we'll get into this later.
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Why do researchers need to consider sampling methods when using observational research designs?
What are the different data recording methods in observational research designs?
Which of the following data recording methods produce quantitative data?
Which of the following data recording methods produce qualitative data?
What are the advantages of observational research designs?
What are the disadvantages of observational research designs?
What is overt research?
What is covert research?
Which observational designs involve no manipulation?
Which of the following are examples of the Hawthorne effect?
What is an observational study design?
- Approaches in Psychology
- Basic Psychology
- Biological Bases of Behavior
- Biopsychology
- Careers in Psychology
- Clinical Psychology
- Cognition and Development
- Cognitive Psychology
- Data Handling and Analysis
- Developmental Psychology
- Eating Behaviour
- Emotion and Motivation
- Famous Psychologists
- Forensic Psychology
- Health Psychology
- Individual Differences Psychology
- Issues and Debates in Psychology
- Personality in Psychology
- Psychological Treatment
- Relationships
- Aims and Hypotheses
- Causation in Psychology
- Coding Frame Psychology
- Correlational Studies
- Cross Cultural Research
- Cross Sectional Research
- Ethical Issues and Ways of Dealing with Them
- Experimental Designs
- Features of Science
- Field Experiment
- Independent Group Design
- Lab Experiment
- Longitudinal Research
- Matched Pairs Design
- Meta Analysis
- Natural Experiment
- Observational Design
- Online Research
- Paradigms and Falsifiability
- Peer Review and Economic Applications of Research
- Pilot Studies and the Aims of Piloting
- Quality Criteria
- Questionnaire Construction
- Repeated Measures Design
- Research Methods
- Sampling Frames
- Sampling Psychology
- Scientific Processes
- Scientific Report
- Scientific Research
- Self-Report Design
- Self-Report Techniques
- Semantic Differential Rating Scale
- Snowball Sampling
- Schizophrenia
- Scientific Foundations of Psychology
- Scientific Investigation
- Sensation and Perception
- Social Context of Behaviour
- Social Psychology
- We will start by exploring the hypothesis scientific method.
- Then, we will delve into the types of research methods in psychology .
- After, we will look at the scientific process in psychology.
- Moving on, we will be comparing research methods in psychology.
- Finally, we will identify research methods in psychology examples.
Hypothesis Scientific Method
Before we get into the different research methods used in psychology, let's go over the aims and purposes of research.
A researcher's goal in psychology is to support or negate existing theories or propose new ones via empirical research.
Empiricism in research refers to testing and measuring something observable through our five senses.
In scientific research, to test a theory, it must first be organised and written in the form of an operationalised hypothesis.
An operationalised hypothesis is a predictive statement that lists the variables investigated, how they are measured and the expected outcome of the study.
Let's take a look at an example of a good operationalised hypothesis.
Clients diagnosed with a major depressive disorder who receive CBT are more likely to score lower on Beck's depressive inventory scale than patients diagnosed with a major depressive disorder who receive no intervention for their symptoms.
The investigation of providing supporting or disproving hypotheses/ theories is where research methods in psychology come in.
Types of Research Methods in Psychology
When it comes to research methods in psychology, they can be subdivided into two categories; qualitative and quantitative.
Qualitative research is when the data generated from using the research method is non-numerical and quantitative research is when the data is numerical.
Not only do the two categories differ in how data is collected but also in how it is analysed. For instance, qualitative research typically uses statistical analyses, whereas qualitative research commonly uses the content or thematic analysis.
Thematic analysis keeps the data qualitative, but content analysis transforms it into quantitative data.
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Scientific Process: Psychology
Research in psychology must follow a standardised protocol to ensure the research is scientific. In essence, research should form a hypothesis based on existing theories, test them empirically and conclude if they support or negate the hypothesis. If the theory is disproved, then the research should be adapted, and the same steps described above should be repeated.
But why does research need to be scientific? Psychology tests important things, e.g. the effectiveness of interventions; if a researcher concludes it is effective when this is not the case, it can lead to severe consequences.
Quantitative and qualitative research differ in what makes research effective. For instance, quantitative research should be empirical, reliable, objective and valid. In contrast, qualitative research highlights the importance of transferability, credibility and confirmability.
Comparing Research Methods: Psychology
There are distinct approaches employed in psychological research under the two main categories. Let's discuss the five standard research methods used in psychology. These are the experimental methods, observational techniques, self-report techniques , correlational studies , and case studies.
Research Methods in Psychology: Experimental Methods
Experiments provide insight into cause-and-effect by demonstrating what outcome occurs when a particular variable is manipulated.
Experimental studies are quantitative research.
There are mainly four types of experiments in psychology:
- Laboratory experiments.
- Field experiments.
- Natural experiments.
- Quasi-experiments.
Each type of experiment has strengths and limitations.
The type of experiment depends on how participants are allocated into experimental conditions and whether or not the independent variable is naturally occurring or manipulated.
Research Methods in Psychology: Observational Techniques
Observational techniques are used when a researcher observes how people behave and act to learn more about their ideas, experiences, actions, and beliefs.
Observation studies are primarily categorised as qualitative . However, they may also be quantitative or both (mixed methods) .
The two main observation techniques are:
Participant observation.
Non-participant observation.
Observations can also be overt and covert (refers to whether the participant is aware they are being observed), naturalistic and controlled .
Research methods in Psychology: Self-Report Techniques
Self-report techniques refer to data collection approaches in which participants report information about themselves without interference from the experimenter. Ultimately, such methods require respondents to respond to pre-set questions.
Self-report techniques can provide researchers with quantitative and qualitative data, depending on the set-up of questions.
Self-report techniques can include:
Interviews.
Psychometric testing.
Questionnaires.
There are many established questionnaires in psychology; however, sometimes, these are not useful for precisely measuring what the researcher intends to measure. In that case, the researcher needs to construct a new questionnaire.
When constructing questionnaires, researchers need to ensure many things, e.g. the questions are logical and easy to understand. In addition, the questionnaire should have high internal reliability and validity; to ensure these questionnaires must be tested in a pilot study before being used in a full-scale experiment.
Research Methods in Psychology: Correlational Studies
Correlational Studies are a non-experimental quantitative research method. It is used to measure the strength and direction of two co-variables.
Correlations can be categorised as weak, moderate or strong and negative, no or positive correlations.
Positive correlations are where one variable increases the other also increases.
Umbrella sales increase as the rainy weather increases.
Negative correlations are where one variable increases and the other decreases.
Hot drink sales increase as the temperature decreases.
And no correlation is when there is no relationship between co-variables.
Research Methods in Psychology: Case Studies
Case studies belong to a qualitative research methodology. Case studies investigate persons, groups, communities, or events in-depth. They frequently employ a multi-methodological approach that includes participant interviews and observations.
A psychology case study typically gathers critical and influential biographical moments from a patient's past and salient details in the individual's daily life that may drive the development of particular behaviours or thinking.
A famous psychological case study is H.M. From his case study; we learned the effect of hippocampal damage on memory .
Research Methods in Psychology: Other Research Method Examples
Some other standard research methods in psychology are:
- Cross-cultural research compares the findings from countries that investigated similar concepts to identify cultural similarities and differences.
- Meta-analyses systematically consolidate the findings of multiple studies into a single result and are commonly used to identify the direction of the established research in a specific field. For instance, a meta-analysis can show whether current research suggests an effective intervention.
- Longitudinal research is a study conducted over an extended period, e.g. to investigate the long-term effects of something.
- Cross-sectional research is when researchers collect data from many people during a set time frame. The research method is typically used to measure the prevalence of illnesses.
Research Methods in Psychology Examples
Let's look at examples of psychology's five standard research methods that may be used to test hypotheses.
Research Method | Hypotheses |
Experimental Methods | People with a major depressive disorder who receive CBT will score lower on Beck's Depressive Inventory than those with a major depressive disorder who received no intervention. |
Observational Techniques | Victims of bullying are less likely to play and interact with others on the school playground. |
Self-Report Techniques | People who report higher education status are more likely to report higher incomes. |
Correlational Studies | There is a relationship between the amount of time spent exercising and muscle mass. |
Case Studies | Centaurians are more likely to come from blue-zone countries. |
Research Methods in Psychology - Key takeaways
- The scientific method suggests that before using research methods in psychology, an operationalised hypothesis must be formulated.
- Some types of research methods in psychology are experimental, observational and self-report techniques, as well as correlational and case studies.
- When comparing research methods: psychology, the research methods can be categorised into two; qualitative and quantitative.
- Some research methods in psychology examples are using experimental methods to identify if people with a major depressive disorder who receive CBT will score lower on Beck's Depressive Inventory than those with a major depressive disorder who received no intervention.
Flashcards in Research Methods in Psychology 1048
As researchers cannot collect data for too long, they need to try and gather data that is representative of the entirety of participants behaviour rather than of a specific time.
Behavioural categories.
- Recordings.
- Observer narrative.
- High ecological validity.
- Typically cost-effective.
- The researcher can get a deep, insightful understanding of a phenomenon.
- The Hawthorne effect.
- Potential ethical issues.
- Difficulties replicating research.
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Frequently Asked Questions about Research Methods in Psychology
What are the five research methods in psychology?
Some types of research methods in psychology are experimental, observational and self-report techniques, as well as correlational and case studies.
What are research methods in psychology?
Research methods in psychology refer to the various methods of testing different theories and obtaining results.
What are the types of research methods in psychology?
When comparing research methods: psychology, the research methods can be categorised into two; qualitative and quantitative.
Why are research methods important in psychology?
Research methods in psychology are important because psychology tests important things, e.g. the effectiveness of interventions; if a researcher concludes it is effective when this is not the case, it can lead to severe consequences.
What approach does psychology research take?
Inductive. theories/ hypotheses are proposed based on existing theories.
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Home » Research Methods – Types, Examples and Guide
Research Methods – Types, Examples and Guide
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Research Methods
Definition:
Research Methods refer to the techniques, procedures, and processes used by researchers to collect , analyze, and interpret data in order to answer research questions or test hypotheses. The methods used in research can vary depending on the research questions, the type of data that is being collected, and the research design.
Types of Research Methods
Types of Research Methods are as follows:
Qualitative research Method
Qualitative research methods are used to collect and analyze non-numerical data. This type of research is useful when the objective is to explore the meaning of phenomena, understand the experiences of individuals, or gain insights into complex social processes. Qualitative research methods include interviews, focus groups, ethnography, and content analysis.
Quantitative Research Method
Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.
Mixed Method Research
Mixed Method Research refers to the combination of both qualitative and quantitative research methods in a single study. This approach aims to overcome the limitations of each individual method and to provide a more comprehensive understanding of the research topic. This approach allows researchers to gather both quantitative data, which is often used to test hypotheses and make generalizations about a population, and qualitative data, which provides a more in-depth understanding of the experiences and perspectives of individuals.
Key Differences Between Research Methods
The following Table shows the key differences between Quantitative, Qualitative and Mixed Research Methods
Research Method | Quantitative | Qualitative | Mixed Methods |
---|---|---|---|
To measure and quantify variables | To understand the meaning and complexity of phenomena | To integrate both quantitative and qualitative approaches | |
Typically focused on testing hypotheses and determining cause and effect relationships | Typically exploratory and focused on understanding the subjective experiences and perspectives of participants | Can be either, depending on the research design | |
Usually involves standardized measures or surveys administered to large samples | Often involves in-depth interviews, observations, or analysis of texts or other forms of data | Usually involves a combination of quantitative and qualitative methods | |
Typically involves statistical analysis to identify patterns and relationships in the data | Typically involves thematic analysis or other qualitative methods to identify themes and patterns in the data | Usually involves both quantitative and qualitative analysis | |
Can provide precise, objective data that can be generalized to a larger population | Can provide rich, detailed data that can help understand complex phenomena in depth | Can combine the strengths of both quantitative and qualitative approaches | |
May not capture the full complexity of phenomena, and may be limited by the quality of the measures used | May be subjective and may not be generalizable to larger populations | Can be time-consuming and resource-intensive, and may require specialized skills | |
Typically focused on testing hypotheses and determining cause-and-effect relationships | Surveys, experiments, correlational studies | Interviews, focus groups, ethnography | Sequential explanatory design, convergent parallel design, explanatory sequential design |
Examples of Research Methods
Examples of Research Methods are as follows:
Qualitative Research Example:
A researcher wants to study the experience of cancer patients during their treatment. They conduct in-depth interviews with patients to gather data on their emotional state, coping mechanisms, and support systems.
Quantitative Research Example:
A company wants to determine the effectiveness of a new advertisement campaign. They survey a large group of people, asking them to rate their awareness of the product and their likelihood of purchasing it.
Mixed Research Example:
A university wants to evaluate the effectiveness of a new teaching method in improving student performance. They collect both quantitative data (such as test scores) and qualitative data (such as feedback from students and teachers) to get a complete picture of the impact of the new method.
Applications of Research Methods
Research methods are used in various fields to investigate, analyze, and answer research questions. Here are some examples of how research methods are applied in different fields:
- Psychology : Research methods are widely used in psychology to study human behavior, emotions, and mental processes. For example, researchers may use experiments, surveys, and observational studies to understand how people behave in different situations, how they respond to different stimuli, and how their brains process information.
- Sociology : Sociologists use research methods to study social phenomena, such as social inequality, social change, and social relationships. Researchers may use surveys, interviews, and observational studies to collect data on social attitudes, beliefs, and behaviors.
- Medicine : Research methods are essential in medical research to study diseases, test new treatments, and evaluate their effectiveness. Researchers may use clinical trials, case studies, and laboratory experiments to collect data on the efficacy and safety of different medical treatments.
- Education : Research methods are used in education to understand how students learn, how teachers teach, and how educational policies affect student outcomes. Researchers may use surveys, experiments, and observational studies to collect data on student performance, teacher effectiveness, and educational programs.
- Business : Research methods are used in business to understand consumer behavior, market trends, and business strategies. Researchers may use surveys, focus groups, and observational studies to collect data on consumer preferences, market trends, and industry competition.
- Environmental science : Research methods are used in environmental science to study the natural world and its ecosystems. Researchers may use field studies, laboratory experiments, and observational studies to collect data on environmental factors, such as air and water quality, and the impact of human activities on the environment.
- Political science : Research methods are used in political science to study political systems, institutions, and behavior. Researchers may use surveys, experiments, and observational studies to collect data on political attitudes, voting behavior, and the impact of policies on society.
Purpose of Research Methods
Research methods serve several purposes, including:
- Identify research problems: Research methods are used to identify research problems or questions that need to be addressed through empirical investigation.
- Develop hypotheses: Research methods help researchers develop hypotheses, which are tentative explanations for the observed phenomenon or relationship.
- Collect data: Research methods enable researchers to collect data in a systematic and objective way, which is necessary to test hypotheses and draw meaningful conclusions.
- Analyze data: Research methods provide tools and techniques for analyzing data, such as statistical analysis, content analysis, and discourse analysis.
- Test hypotheses: Research methods allow researchers to test hypotheses by examining the relationships between variables in a systematic and controlled manner.
- Draw conclusions : Research methods facilitate the drawing of conclusions based on empirical evidence and help researchers make generalizations about a population based on their sample data.
- Enhance understanding: Research methods contribute to the development of knowledge and enhance our understanding of various phenomena and relationships, which can inform policy, practice, and theory.
When to Use Research Methods
Research methods are used when you need to gather information or data to answer a question or to gain insights into a particular phenomenon.
Here are some situations when research methods may be appropriate:
- To investigate a problem : Research methods can be used to investigate a problem or a research question in a particular field. This can help in identifying the root cause of the problem and developing solutions.
- To gather data: Research methods can be used to collect data on a particular subject. This can be done through surveys, interviews, observations, experiments, and more.
- To evaluate programs : Research methods can be used to evaluate the effectiveness of a program, intervention, or policy. This can help in determining whether the program is meeting its goals and objectives.
- To explore new areas : Research methods can be used to explore new areas of inquiry or to test new hypotheses. This can help in advancing knowledge in a particular field.
- To make informed decisions : Research methods can be used to gather information and data to support informed decision-making. This can be useful in various fields such as healthcare, business, and education.
Advantages of Research Methods
Research methods provide several advantages, including:
- Objectivity : Research methods enable researchers to gather data in a systematic and objective manner, minimizing personal biases and subjectivity. This leads to more reliable and valid results.
- Replicability : A key advantage of research methods is that they allow for replication of studies by other researchers. This helps to confirm the validity of the findings and ensures that the results are not specific to the particular research team.
- Generalizability : Research methods enable researchers to gather data from a representative sample of the population, allowing for generalizability of the findings to a larger population. This increases the external validity of the research.
- Precision : Research methods enable researchers to gather data using standardized procedures, ensuring that the data is accurate and precise. This allows researchers to make accurate predictions and draw meaningful conclusions.
- Efficiency : Research methods enable researchers to gather data efficiently, saving time and resources. This is especially important when studying large populations or complex phenomena.
- Innovation : Research methods enable researchers to develop new techniques and tools for data collection and analysis, leading to innovation and advancement in the field.
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Experimental Method In Psychology
Saul McLeod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
Learn about our Editorial Process
Olivia Guy-Evans, MSc
Associate Editor for Simply Psychology
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
On This Page:
The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups .
What is an Experiment?
An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.
An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid and less biased.
There are three types of experiments you need to know:
1. Lab Experiment
A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions.
A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.
The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.
Participants are randomly allocated to each independent variable group.
Examples are Milgram’s experiment on obedience and Loftus and Palmer’s car crash study .
- Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
- Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
- Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
- Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .
2. Field Experiment
A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.
However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .
Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.
An example is Holfing’s hospital study on obedience .
- Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
- Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
- Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.
3. Natural Experiment
A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.
Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.
Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.
For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.
Here is a fictional example of a natural experiment in psychology:
Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.
In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.
- Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
- Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
- Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
- Limitation : They may be more expensive and time-consuming than lab experiments.
- Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.
Key Terminology
Ecological validity.
The degree to which an investigation represents real-life experiences.
Experimenter effects
These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.
Demand characteristics
The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).
Independent variable (IV)
The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.
Dependent variable (DV)
Variable the experimenter measures. This is the outcome (i.e., the result) of a study.
Extraneous variables (EV)
All variables which are not independent variables but could affect the results (DV) of the experiment. EVs should be controlled where possible.
Confounding variables
Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.
Random Allocation
Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.
The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.
Order effects
Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:
(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;
(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.
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What Is Educational Psychology?
Studying the Process of How People Learn Most Effectively
- Major Perspectives
- Topics of Study
Frequently Asked Questions
Educational psychology is the study of how people learn and retain information. It mainly focuses on the learning process of early childhood and adolescence; however, learning is a lifelong endeavor. People don't only learn at school; rather, they learn through all of their life experiences, including at home, with friends, at work, through social media, and through culture.
Educational psychologists study the biological, cognitive, emotional, and social factors involved in learning and deepen our understanding of instructional strategies, individual learning styles, and the importance of the environmental context. They may specialize in children with specific learning challenges and develop teaching methods that help students succeed in school.
Educational psychology incorporates several other psychology disciplines , including developmental psychology , behavioral psychology , and cognitive psychology . Over time, five main schools of thought have emerged, including behaviorism, cognitivism, constructivism, experientialism, and social contextual learning theories.
This article discusses some of the different perspectives taken within the field of educational psychology, topics that educational psychologists study, and career options in this field.
8 Things to Know About Educational Psychology
Perspectives in educational psychology.
As with other areas of psychology, researchers within educational psychology tend to take on different perspectives when considering a problem. These perspectives focus on specific factors that influence learning, including thoughts, emotions, behaviors, experiences, and more.
Five main schools of thought dominate this field of research, including behavioral, developmental, cognitive, constructivist, and experiential perspectives.
The Behavioral Perspective
This perspective suggests that all behaviors are learned through conditioning, such as positive reinforcement . Psychologists who take this perspective rely firmly on the principles of operant conditioning to explain how learning happens.
For example, teachers might reward learning by giving students tokens that can be exchanged for desirable items such as candy or toys. The behavioral perspective operates on the theory that students will learn when rewarded for desirable behavior and punished for bad behavior.
While such methods can be useful in some cases, the behavioral approach has been criticized for failing to account for internal processes such as attitudes , emotions, and intrinsic motivations for learning.
The Developmental Perspective
The developmental perspective includes studying biological, cognitive, emotional, and social development throughout the lifespan. This perspective focuses on how children acquire new skills and knowledge as they grow and develop.
For developmental psychologists, there is a balance between nature and nurture in the learning process. This means that as the brain develops, the capacity for learning, problem-solving, and memory increases, and at the same time, life experiences with family, playmates, teachers, and mentors facilitate the learning process and acquisition of knowledge.
Jean Piaget's stages of cognitive development are one example of an important developmental theory that examines how children grow intellectually.
By understanding how children think at different stages of development, educational psychologists can better understand what children are capable of at each point of their growth. This can help educators create instructional methods and materials aimed at specific age groups.
The Cognitive Perspective
The cognitive approach has become much more widespread, mainly because it accounts for factors such as thinking, attention, information processing, and memory formation.
Cognitive psychologists value constructs such as individual beliefs, perspectives, emotions , and motivations that contribute to the learning process. This theory supports the idea that a person learns because of their own intrinsic motivation , not because of external rewards as a behaviorist would view it.
Cognitive psychology aims to understand how people think, learn, remember, and process information.
Educational psychologists who take a cognitive perspective are interested in understanding how kids become motivated to learn, how they remember the things that they learn, and how they solve problems, among other topics.
The Constructivist Approach
This perspective focuses on how we actively construct our knowledge of the world. Constructivism accounts for the social and cultural influences that affect how we learn.
Those who take the constructivist approach believe that what a person already knows significantly influences how they learn new information. This means that new knowledge can only be added to and understood in terms of existing knowledge.
This perspective is heavily influenced by the work of psychologist Lev Vygotsky , who proposed ideas such as the zone of proximal development and instructional scaffolding.
Experiential Perspective
This perspective emphasizes that a person's life experiences influence how they understand new information. This method is similar to constructivist and cognitive perspectives in that it considers the learner's experiences, thoughts, and feelings.
This method allows someone to find personal meaning in what they learn instead of feeling that the information doesn't apply to them.
Different perspectives on human learning can be helpful when looking at topics within the field of educational psychology. Each school of thought provides a unique perspective that adds to our overall understanding of the learner and the learning environment.
Topics in Educational Psychology
From the materials teachers use to the individual needs of students, educational psychologists delve deep to more fully understand the learning process. Some of these topics of study in educational psychology include:
- Educational technology : Looking at how different types of technology can help students learn
- Instructional design : Designing effective learning materials
- Special education : Helping students who may need specialized instruction
- Curriculum development : Creating coursework that will maximize learning
- Organizational learning : Studying how people learn in organizational settings, such as workplaces
- Gifted learners : Helping students who are identified as gifted learners
Careers in Educational Psychology
Educational psychologists work with educators, administrators, teachers, and students to analyze how to help people learn best. This often involves identifying students who may need extra help, developing programs for struggling students, and even creating new learning methods.
- School system . Many educational psychologists work directly with schools . Some are teachers or professors, while others help teachers try new learning methods for their students and develop new course curricula.
- Counselor . An educational psychologist may even become a counselor, directly helping students cope with learning barriers.
- Research . Other educational psychologists work in research. For instance, they might work for a government organization such as the U.S. Department of Education, influencing decisions about the best resources and programs for kids to learn in schools nationwide.
- Administration . In addition, an educational psychologist may work in school or university administration. In each of these roles, they can influence educational methods and help students learn in a way that best suits them.
A bachelor's degree and master's degree are usually required for careers in this field; if you want to work at a university or in school administration, you may also need to complete a doctorate.
Educational psychologists often work in schools to help students and teachers improve the learning experience. Other professionals in this field conduct research to investigate the learning process and evaluate programs designed to foster learning.
History of Educational Psychology
Educational psychology is a relatively young subfield that has experienced tremendous growth. Psychology did not emerge as a separate science until the late 1800s, so earlier interest in educational psychology was largely fueled by educational philosophers.
Many regard philosopher Johann Herbart as the father of educational psychology.
- Johann Herbart. He believed that a student's interest in a topic had a tremendous influence on the learning outcome. He believed teachers should consider this when deciding which type of instruction is most appropriate.
- William James. Psychologist and philosopher William James made significant contributions to the field. His seminal 1899 text "Talks to Teachers on Psychology" is considered the first textbook on educational psychology.
- Alfred Binet. In the early 1900s, French psychologist Alfred Binet was developing his famous IQ tests. The tests were originally designed to help the French government identify children who had developmental delays and create special education programs.
- John Dewey. In the United States, John Dewey had a significant influence on education. Dewey's ideas were progressive; he believed schools should focus on students rather than on subjects. He advocated active learning, arguing that hands-on experience was an important part of the process.
- Benjamin Bloom. More recently, educational psychologist Benjamin Bloom developed an important taxonomy designed to categorize and describe different educational objectives. The three top-level domains he described were cognitive, affective, and psychomotor learning objectives.
Other Significant Figures
Throughout history, several additional figures have played an important role in the development of educational psychology. Some of these well-known individuals include:
- John Locke : Locke is an English philosopher who suggested the concept of tabula rasa , or the idea that the mind is essentially a blank slate at birth. This means that knowledge is developed through experience and learning.
- Jean Piaget : A Swiss psychologist best known for his highly influential theory of cognitive development, Piaget's influence on educational psychology is still evident today.
- B.F. Skinner : Skinner was an American psychologist who introduced the concept of operant conditioning, which influences behaviorist perspectives. His research on reinforcement and punishment continues to play an important role in education.
Educational psychology has been influenced by a number of philosophers, psychologists, and educators. Some thinkers who had a significant influence include William James, Alfred Binet, John Dewey, Jean Piaget, Benjamin Bloom, and many more.
Final Thoughts
Educational psychology offers valuable insights into how people learn and plays an important role in informing educational strategies and teaching methods. In addition to exploring the learning process itself, different areas of educational psychology explore the emotional, social, and cognitive factors that can influence how people learn. If you are interested in topics such as special education, curriculum design, and educational technology, then you might want to consider pursuing a career in the field of educational psychology.
A master's in educational psychology can prepare you for a career working in K-12 schools, colleges and universities, government agencies, community organizations, and counseling practices. A career as an educational psychologist involves working with children, families, schools, and other community and government agencies to create programs and resources that enhance learning.
The primary focus of educational psychology is the study of how people learn. This includes exploring the instructional processes, studying individual differences in how people learn, and developing teaching methods to help people learn more effectively.
Educational psychology is important because it has the potential to help both students and teachers. It provides important information for educators to help them create educational experiences, measure learning, and improve student motivation.
Educational psychology can aid teachers in better understanding the principles of learning in order to design more engaging and effective lesson plans and classroom experiences. It can also foster a better understanding of how learning environments, social factors, and student motivation can influence how students learn.
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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
9 Best Marketing Research Methods to Know Your Buyer Better [+ Examples]
![examples of research methods psychology Ramona Sukhraj](https://knowledge.hubspot.com/hs-fs/hubfs/ramona-sukhraj-headshot.jpeg?width=48&height=48&name=ramona-sukhraj-headshot.jpeg)
Published: August 08, 2024
One of the most underrated skills you can have as a marketer is marketing research — which is great news for this unapologetic cyber sleuth.
![examples of research methods psychology marketer using marketer research methods to better understand her buyer personas](https://www.hubspot.com/hs-fs/hubfs/marketing-research-methods-featured.png?width=595&height=400&name=marketing-research-methods-featured.png)
From brand design and product development to buyer personas and competitive analysis, I’ve researched a number of initiatives in my decade-long marketing career.
And let me tell you: having the right marketing research methods in your toolbox is a must.
Market research is the secret to crafting a strategy that will truly help you accomplish your goals. The good news is there is no shortage of options.
How to Choose a Marketing Research Method
Thanks to the Internet, we have more marketing research (or market research) methods at our fingertips than ever, but they’re not all created equal. Let’s quickly go over how to choose the right one.
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1. Identify your objective.
What are you researching? Do you need to understand your audience better? How about your competition? Or maybe you want to know more about your customer’s feelings about a specific product.
Before starting your research, take some time to identify precisely what you’re looking for. This could be a goal you want to reach, a problem you need to solve, or a question you need to answer.
For example, an objective may be as foundational as understanding your ideal customer better to create new buyer personas for your marketing agency (pause for flashbacks to my former life).
Or if you’re an organic sode company, it could be trying to learn what flavors people are craving.
2. Determine what type of data and research you need.
Next, determine what data type will best answer the problems or questions you identified. There are primarily two types: qualitative and quantitative. (Sound familiar, right?)
- Qualitative Data is non-numerical information, like subjective characteristics, opinions, and feelings. It’s pretty open to interpretation and descriptive, but it’s also harder to measure. This type of data can be collected through interviews, observations, and open-ended questions.
- Quantitative Data , on the other hand, is numerical information, such as quantities, sizes, amounts, or percentages. It’s measurable and usually pretty hard to argue with, coming from a reputable source. It can be derived through surveys, experiments, or statistical analysis.
Understanding the differences between qualitative and quantitative data will help you pinpoint which research methods will yield the desired results.
For instance, thinking of our earlier examples, qualitative data would usually be best suited for buyer personas, while quantitative data is more useful for the soda flavors.
However, truth be told, the two really work together.
Qualitative conclusions are usually drawn from quantitative, numerical data. So, you’ll likely need both to get the complete picture of your subject.
For example, if your quantitative data says 70% of people are Team Black and only 30% are Team Green — Shout out to my fellow House of the Dragon fans — your qualitative data will say people support Black more than Green.
(As they should.)
Primary Research vs Secondary Research
You’ll also want to understand the difference between primary and secondary research.
Primary research involves collecting new, original data directly from the source (say, your target market). In other words, it’s information gathered first-hand that wasn’t found elsewhere.
Some examples include conducting experiments, surveys, interviews, observations, or focus groups.
Meanwhile, secondary research is the analysis and interpretation of existing data collected from others. Think of this like what we used to do for school projects: We would read a book, scour the internet, or pull insights from others to work from.
So, which is better?
Personally, I say any research is good research, but if you have the time and resources, primary research is hard to top. With it, you don’t have to worry about your source's credibility or how relevant it is to your specific objective.
You are in full control and best equipped to get the reliable information you need.
3. Put it all together.
Once you know your objective and what kind of data you want, you’re ready to select your marketing research method.
For instance, let’s say you’re a restaurant trying to see how attendees felt about the Speed Dating event you hosted last week.
You shouldn’t run a field experiment or download a third-party report on speed dating events; those would be useless to you. You need to conduct a survey that allows you to ask pointed questions about the event.
This would yield both qualitative and quantitative data you can use to improve and bring together more love birds next time around.
Best Market Research Methods for 2024
Now that you know what you’re looking for in a marketing research method, let’s dive into the best options.
Note: According to HubSpot’s 2024 State of Marketing report, understanding customers and their needs is one of the biggest challenges facing marketers today. The options we discuss are great consumer research methodologies , but they can also be used for other areas.
Primary Research
1. interviews.
Interviews are a form of primary research where you ask people specific questions about a topic or theme. They typically deliver qualitative information.
I’ve conducted many interviews for marketing purposes, but I’ve also done many for journalistic purposes, like this profile on comedian Zarna Garg . There’s no better way to gather candid, open-ended insights in my book, but that doesn’t mean they’re a cure-all.
What I like: Real-time conversations allow you to ask different questions if you’re not getting the information you need. They also push interviewees to respond quickly, which can result in more authentic answers.
What I dislike: They can be time-consuming and harder to measure (read: get quantitative data) unless you ask pointed yes or no questions.
Best for: Creating buyer personas or getting feedback on customer experience, a product, or content.
2. Focus Groups
Focus groups are similar to conducting interviews but on a larger scale.
In marketing and business, this typically means getting a small group together in a room (or Zoom), asking them questions about various topics you are researching. You record and/or observe their responses to then take action.
They are ideal for collecting long-form, open-ended feedback, and subjective opinions.
One well-known focus group you may remember was run by Domino’s Pizza in 2009 .
After poor ratings and dropping over $100 million in revenue, the brand conducted focus groups with real customers to learn where they could have done better.
It was met with comments like “worst excuse for pizza I’ve ever had” and “the crust tastes like cardboard.” But rather than running from the tough love, it took the hit and completely overhauled its recipes.
The team admitted their missteps and returned to the market with better food and a campaign detailing their “Pizza Turn Around.”
The result? The brand won a ton of praise for its willingness to take feedback, efforts to do right by its consumers, and clever campaign. But, most importantly, revenue for Domino’s rose by 14.3% over the previous year.
The brand continues to conduct focus groups and share real footage from them in its promotion:
What I like: Similar to interviewing, you can dig deeper and pivot as needed due to the real-time nature. They’re personal and detailed.
What I dislike: Once again, they can be time-consuming and make it difficult to get quantitative data. There is also a chance some participants may overshadow others.
Best for: Product research or development
Pro tip: Need help planning your focus group? Our free Market Research Kit includes a handy template to start organizing your thoughts in addition to a SWOT Analysis Template, Survey Template, Focus Group Template, Presentation Template, Five Forces Industry Analysis Template, and an instructional guide for all of them. Download yours here now.
3. Surveys or Polls
Surveys are a form of primary research where individuals are asked a collection of questions. It can take many different forms.
They could be in person, over the phone or video call, by email, via an online form, or even on social media. Questions can be also open-ended or closed to deliver qualitative or quantitative information.
A great example of a close-ended survey is HubSpot’s annual State of Marketing .
In the State of Marketing, HubSpot asks marketing professionals from around the world a series of multiple-choice questions to gather data on the state of the marketing industry and to identify trends.
The survey covers various topics related to marketing strategies, tactics, tools, and challenges that marketers face. It aims to provide benchmarks to help you make informed decisions about your marketing.
It also helps us understand where our customers’ heads are so we can better evolve our products to meet their needs.
Apple is no stranger to surveys, either.
In 2011, the tech giant launched Apple Customer Pulse , which it described as “an online community of Apple product users who provide input on a variety of subjects and issues concerning Apple.”
![examples of research methods psychology Screenshot of Apple’s Consumer Pulse Website from 2011.](https://knowledge.hubspot.com/hubfs/Marketing-Research-Methods-1-20240801-9051477.webp)
"For example, we did a large voluntary survey of email subscribers and top readers a few years back."
While these readers gave us a long list of topics, formats, or content types they wanted to see, they sometimes engaged more with content types they didn’t select or favor as much on the surveys when we ran follow-up ‘in the wild’ tests, like A/B testing.”
Pepsi saw similar results when it ran its iconic field experiment, “The Pepsi Challenge” for the first time in 1975.
The beverage brand set up tables at malls, beaches, and other public locations and ran a blindfolded taste test. Shoppers were given two cups of soda, one containing Pepsi, the other Coca-Cola (Pepsi’s biggest competitor). They were then asked to taste both and report which they preferred.
People overwhelmingly preferred Pepsi, and the brand has repeated the experiment multiple times over the years to the same results.
What I like: It yields qualitative and quantitative data and can make for engaging marketing content, especially in the digital age.
What I dislike: It can be very time-consuming. And, if you’re not careful, there is a high risk for scientific error.
Best for: Product testing and competitive analysis
Pro tip: " Don’t make critical business decisions off of just one data set," advises Pamela Bump. "Use the survey, competitive intelligence, external data, or even a focus group to give you one layer of ideas or a short-list for improvements or solutions to test. Then gather your own fresh data to test in an experiment or trial and better refine your data-backed strategy."
Secondary Research
8. public domain or third-party research.
While original data is always a plus, there are plenty of external resources you can access online and even at a library when you’re limited on time or resources.
Some reputable resources you can use include:
- Pew Research Center
- McKinley Global Institute
- Relevant Global or Government Organizations (i.e United Nations or NASA)
It’s also smart to turn to reputable organizations that are specific to your industry or field. For instance, if you’re a gardening or landscaping company, you may want to pull statistics from the Environmental Protection Agency (EPA).
If you’re a digital marketing agency, you could look to Google Research or HubSpot Research . (Hey, I know them!)
What I like: You can save time on gathering data and spend more time on analyzing. You can also rest assured the data is from a source you trust.
What I dislike: You may not find data specific to your needs.
Best for: Companies under a time or resource crunch, adding factual support to content
Pro tip: Fellow HubSpotter Iskiev suggests using third-party data to inspire your original research. “Sometimes, I use public third-party data for ideas and inspiration. Once I have written my survey and gotten all my ideas out, I read similar reports from other sources and usually end up with useful additions for my own research.”
9. Buy Research
If the data you need isn’t available publicly and you can’t do your own market research, you can also buy some. There are many reputable analytics companies that offer subscriptions to access their data. Statista is one of my favorites, but there’s also Euromonitor , Mintel , and BCC Research .
What I like: Same as public domain research
What I dislike: You may not find data specific to your needs. It also adds to your expenses.
Best for: Companies under a time or resource crunch or adding factual support to content
Which marketing research method should you use?
You’re not going to like my answer, but “it depends.” The best marketing research method for you will depend on your objective and data needs, but also your budget and timeline.
My advice? Aim for a mix of quantitative and qualitative data. If you can do your own original research, awesome. But if not, don’t beat yourself up. Lean into free or low-cost tools . You could do primary research for qualitative data, then tap public sources for quantitative data. Or perhaps the reverse is best for you.
Whatever your marketing research method mix, take the time to think it through and ensure you’re left with information that will truly help you achieve your goals.
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Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.
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Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your research objectives. Methodology is the first step in planning a research project.
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