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Research paper

How to Write a Research Paper | A Beginner's Guide

A research paper is a piece of academic writing that provides analysis, interpretation, and argument based on in-depth independent research.

Research papers are similar to academic essays , but they are usually longer and more detailed assignments, designed to assess not only your writing skills but also your skills in scholarly research. Writing a research paper requires you to demonstrate a strong knowledge of your topic, engage with a variety of sources, and make an original contribution to the debate.

This step-by-step guide takes you through the entire writing process, from understanding your assignment to proofreading your final draft.

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Understand the assignment, choose a research paper topic, conduct preliminary research, develop a thesis statement, create a research paper outline, write a first draft of the research paper, write the introduction, write a compelling body of text, write the conclusion, the second draft, the revision process, research paper checklist, free lecture slides.

Completing a research paper successfully means accomplishing the specific tasks set out for you. Before you start, make sure you thoroughly understanding the assignment task sheet:

Read it carefully, looking for anything confusing you might need to clarify with your professor.
Identify the assignment goal, deadline, length specifications, formatting, and submission method.
Make a bulleted list of the key points, then go back and cross completed items off as you’re writing.

Carefully consider your timeframe and word limit: be realistic, and plan enough time to research, write, and edit.

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these are the basic building blocks of a research report

There are many ways to generate an idea for a research paper, from brainstorming with pen and paper to talking it through with a fellow student or professor.

You can try free writing, which involves taking a broad topic and writing continuously for two or three minutes to identify absolutely anything relevant that could be interesting.

You can also gain inspiration from other research. The discussion or recommendations sections of research papers often include ideas for other specific topics that require further examination.

Once you have a broad subject area, narrow it down to choose a topic that interests you, m eets the criteria of your assignment, and i s possible to research. Aim for ideas that are both original and specific:

A paper following the chronology of World War II would not be original or specific enough.
A paper on the experience of Danish citizens living close to the German border during World War II would be specific and could be original enough.

Note any discussions that seem important to the topic, and try to find an issue that you can focus your paper around. Use a variety of sources , including journals, books, and reliable websites, to ensure you do not miss anything glaring.

Do not only verify the ideas you have in mind, but look for sources that contradict your point of view.

Is there anything people seem to overlook in the sources you research?
Are there any heated debates you can address?
Do you have a unique take on your topic?
Have there been some recent developments that build on the extant research?

In this stage, you might find it helpful to formulate some research questions to help guide you. To write research questions, try to finish the following sentence: “I want to know how/what/why…”

A thesis statement is a statement of your central argument — it establishes the purpose and position of your paper. If you started with a research question, the thesis statement should answer it. It should also show what evidence and reasoning you’ll use to support that answer.

The thesis statement should be concise, contentious, and coherent. That means it should briefly summarize your argument in a sentence or two, make a claim that requires further evidence or analysis, and make a coherent point that relates to every part of the paper.

You will probably revise and refine the thesis statement as you do more research, but it can serve as a guide throughout the writing process. Every paragraph should aim to support and develop this central claim.

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A research paper outline is essentially a list of the key topics, arguments, and evidence you want to include, divided into sections with headings so that you know roughly what the paper will look like before you start writing.

A structure outline can help make the writing process much more efficient, so it’s worth dedicating some time to create one.

Your first draft won’t be perfect — you can polish later on. Your priorities at this stage are as follows:

Maintaining forward momentum — write now, perfect later.
Paying attention to clear organization and logical ordering of paragraphs and sentences, which will help when you come to the second draft.
Expressing your ideas as clearly as possible, so you know what you were trying to say when you come back to the text.

You do not need to start by writing the introduction. Begin where it feels most natural for you — some prefer to finish the most difficult sections first, while others choose to start with the easiest part. If you created an outline, use it as a map while you work.

Do not delete large sections of text. If you begin to dislike something you have written or find it doesn’t quite fit, move it to a different document, but don’t lose it completely — you never know if it might come in useful later.

Paragraph structure

Paragraphs are the basic building blocks of research papers. Each one should focus on a single claim or idea that helps to establish the overall argument or purpose of the paper.

Example paragraph

George Orwell’s 1946 essay “Politics and the English Language” has had an enduring impact on thought about the relationship between politics and language. This impact is particularly obvious in light of the various critical review articles that have recently referenced the essay. For example, consider Mark Falcoff’s 2009 article in The National Review Online, “The Perversion of Language; or, Orwell Revisited,” in which he analyzes several common words (“activist,” “civil-rights leader,” “diversity,” and more). Falcoff’s close analysis of the ambiguity built into political language intentionally mirrors Orwell’s own point-by-point analysis of the political language of his day. Even 63 years after its publication, Orwell’s essay is emulated by contemporary thinkers.

Citing sources

It’s also important to keep track of citations at this stage to avoid accidental plagiarism . Each time you use a source, make sure to take note of where the information came from.

You can use our free citation generators to automatically create citations and save your reference list as you go.

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The research paper introduction should address three questions: What, why, and how? After finishing the introduction, the reader should know what the paper is about, why it is worth reading, and how you’ll build your arguments.

What? Be specific about the topic of the paper, introduce the background, and define key terms or concepts.

Why? This is the most important, but also the most difficult, part of the introduction. Try to provide brief answers to the following questions: What new material or insight are you offering? What important issues does your essay help define or answer?

How? To let the reader know what to expect from the rest of the paper, the introduction should include a “map” of what will be discussed, briefly presenting the key elements of the paper in chronological order.

The major struggle faced by most writers is how to organize the information presented in the paper, which is one reason an outline is so useful. However, remember that the outline is only a guide and, when writing, you can be flexible with the order in which the information and arguments are presented.

One way to stay on track is to use your thesis statement and topic sentences . Check:

topic sentences against the thesis statement;
topic sentences against each other, for similarities and logical ordering;
and each sentence against the topic sentence of that paragraph.

Be aware of paragraphs that seem to cover the same things. If two paragraphs discuss something similar, they must approach that topic in different ways. Aim to create smooth transitions between sentences, paragraphs, and sections.

The research paper conclusion is designed to help your reader out of the paper’s argument, giving them a sense of finality.

Trace the course of the paper, emphasizing how it all comes together to prove your thesis statement. Give the paper a sense of finality by making sure the reader understands how you’ve settled the issues raised in the introduction.

You might also discuss the more general consequences of the argument, outline what the paper offers to future students of the topic, and suggest any questions the paper’s argument raises but cannot or does not try to answer.

You should not :

Offer new arguments or essential information
Take up any more space than necessary
Begin with stock phrases that signal you are ending the paper (e.g. “In conclusion”)

There are four main considerations when it comes to the second draft.

Check how your vision of the paper lines up with the first draft and, more importantly, that your paper still answers the assignment.
Identify any assumptions that might require (more substantial) justification, keeping your reader’s perspective foremost in mind. Remove these points if you cannot substantiate them further.
Be open to rearranging your ideas. Check whether any sections feel out of place and whether your ideas could be better organized.
If you find that old ideas do not fit as well as you anticipated, you should cut them out or condense them. You might also find that new and well-suited ideas occurred to you during the writing of the first draft — now is the time to make them part of the paper.

The goal during the revision and proofreading process is to ensure you have completed all the necessary tasks and that the paper is as well-articulated as possible. You can speed up the proofreading process by using the AI proofreader .

Global concerns

Confirm that your paper completes every task specified in your assignment sheet.
Check for logical organization and flow of paragraphs.
Check paragraphs against the introduction and thesis statement.

Fine-grained details

Check the content of each paragraph, making sure that:

each sentence helps support the topic sentence.
no unnecessary or irrelevant information is present.
all technical terms your audience might not know are identified.

Next, think about sentence structure , grammatical errors, and formatting . Check that you have correctly used transition words and phrases to show the connections between your ideas. Look for typos, cut unnecessary words, and check for consistency in aspects such as heading formatting and spellings .

Finally, you need to make sure your paper is correctly formatted according to the rules of the citation style you are using. For example, you might need to include an MLA heading or create an APA title page .

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Checklist: Research paper

I have followed all instructions in the assignment sheet.

My introduction presents my topic in an engaging way and provides necessary background information.

My introduction presents a clear, focused research problem and/or thesis statement .

My paper is logically organized using paragraphs and (if relevant) section headings .

Each paragraph is clearly focused on one central idea, expressed in a clear topic sentence .

Each paragraph is relevant to my research problem or thesis statement.

I have used appropriate transitions to clarify the connections between sections, paragraphs, and sentences.

My conclusion provides a concise answer to the research question or emphasizes how the thesis has been supported.

My conclusion shows how my research has contributed to knowledge or understanding of my topic.

My conclusion does not present any new points or information essential to my argument.

I have provided an in-text citation every time I refer to ideas or information from a source.

I have included a reference list at the end of my paper, consistently formatted according to a specific citation style .

I have thoroughly revised my paper and addressed any feedback from my professor or supervisor.

I have followed all formatting guidelines (page numbers, headers, spacing, etc.).

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Organizing Your Social Sciences Research Paper

Types of Research Designs
Purpose of Guide
Design Flaws to Avoid
Independent and Dependent Variables
Glossary of Research Terms
Reading Research Effectively
Narrowing a Topic Idea
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Extending the Timeliness of a Topic Idea
Academic Writing Style
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Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
Review and synthesize previously published literature associated with the research problem,
Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE: Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

This is a collaborative and adaptive research design that lends itself to use in work or community situations.
Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
Action research studies often have direct and obvious relevance to improving practice and advocating for change.
There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
Personal over-involvement of the researcher may bias research results.
The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA: Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
Design can extend experience or add strength to what is already known through previous research.
Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
The design can provide detailed descriptions of specific and rare cases.
A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
Design does not facilitate assessment of cause and effect relationships.
Vital information may be missing, making the case hard to interpret.
The case may not be representative or typical of the larger problem being investigated.
If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
Replication is possible.
There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
Either original data or secondary data can be used in this design.
In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
Studies cannot be utilized to establish cause and effect relationships.
This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
If the limitations are understood, they can be a useful tool in developing a more focused study.
Descriptive studies can yield rich data that lead to important recommendations in practice.
Appoach collects a large amount of data for detailed analysis.
The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
Approach provides the highest level of evidence for single studies.
The design is artificial, and results may not generalize well to the real world.
The artificial settings of experiments may alter the behaviors or responses of participants.
Experimental designs can be costly if special equipment or facilities are needed.
Some research problems cannot be studied using an experiment because of ethical or technical reasons.
Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

Familiarity with basic details, settings, and concerns.
Well grounded picture of the situation being developed.
Generation of new ideas and assumptions.
Development of tentative theories or hypotheses.
Determination about whether a study is feasible in the future.
Issues get refined for more systematic investigation and formulation of new research questions.
Direction for future research and techniques get developed.
Design is a useful approach for gaining background information on a particular topic.
Exploratory research is flexible and can address research questions of all types (what, why, how).
Provides an opportunity to define new terms and clarify existing concepts.
Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field [i.e., the act of triangulating the data].
Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

The historical research design is unobtrusive; the act of research does not affect the results of the study.
The historical approach is well suited for trend analysis.
Historical records can add important contextual background required to more fully understand and interpret a research problem.
There is often no possibility of researcher-subject interaction that could affect the findings.
Historical sources can be used over and over to study different research problems or to replicate a previous study.
The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
Interpreting historical sources can be very time consuming.
The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58; Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
The data collection method may change over time.
Maintaining the integrity of the original sample can be difficult over an extended period of time.
It can be difficult to show more than one variable at a time.
This design often needs qualitative research data to explain fluctuations in the results.
A longitudinal research design assumes present trends will continue unchanged.
It can take a long period of time to gather results.
There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
A well-reasoned and well-documented justification for identification and selection of the studies;
Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
Justification of the techniques used to evaluate the studies.
Can be an effective strategy for determining gaps in the literature.
Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
Can be used to generate new hypotheses or highlight research problems for future studies.
Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
A large sample size can yield reliable, but not necessarily valid, results.
A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
The strengths of one method can be used to overcome the inherent weaknesses of another method.
Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
The researcher is able to collect in-depth information about a particular behavior.
Can reveal interrelationships among multifaceted dimensions of group interactions.
You can generalize your results to real life situations.
Observational research is useful for discovering what variables may be important before applying other methods like experiments.
Observation research designs account for the complexity of group behaviors.
Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
There can be problems with bias as the researcher may only "see what they want to see."
There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
Sources or subjects may not all be equally credible.
Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
Can provide a basis for applying ethical decision-making to practice.
Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
Brings clarity to general guiding practices and principles of an individual or group.
Philosophy informs methodology.
Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
Limited application to specific research problems [answering the "So What?" question in social science research].
Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
There are limitations in the use of metaphor as a vehicle of philosophical analysis.
There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

The researcher has a limitless option when it comes to sample size and the sampling schedule.
Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
This is a useful design for exploratory studies.
There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods . David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research." Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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Uncomplicated Reviews of Educational Research Methods

Writing a Research Report

.pdf version of this page

This review covers the basic elements of a research report. This is a general guide for what you will see in journal articles or dissertations. This format assumes a mixed methods study, but you can leave out either quantitative or qualitative sections if you only used a single methodology.

This review is divided into sections for easy reference. There are five MAJOR parts of a Research Report:

1. Introduction 2. Review of Literature 3. Methods 4. Results 5. Discussion

As a general guide, the Introduction, Review of Literature, and Methods should be about 1/3 of your paper, Discussion 1/3, then Results 1/3.

Section 1 : Cover Sheet (APA format cover sheet) optional, if required.

Section 2: Abstract (a basic summary of the report, including sample, treatment, design, results, and implications) (≤ 150 words) optional, if required.

Section 3 : Introduction (1-3 paragraphs) • Basic introduction • Supportive statistics (can be from periodicals) • Statement of Purpose • Statement of Significance

Section 4 : Research question(s) or hypotheses • An overall research question (optional) • A quantitative-based (hypotheses) • A qualitative-based (research questions) Note: You will generally have more than one, especially if using hypotheses.

Section 5: Review of Literature ▪ Should be organized by subheadings ▪ Should adequately support your study using supporting, related, and/or refuting evidence ▪ Is a synthesis, not a collection of individual summaries

Section 6: Methods ▪ Procedure: Describe data gathering or participant recruitment, including IRB approval ▪ Sample: Describe the sample or dataset, including basic demographics ▪ Setting: Describe the setting, if applicable (generally only in qualitative designs) ▪ Treatment: If applicable, describe, in detail, how you implemented the treatment ▪ Instrument: Describe, in detail, how you implemented the instrument; Describe the reliability and validity associated with the instrument ▪ Data Analysis: Describe type of procedure (t-test, interviews, etc.) and software (if used)

Section 7: Results ▪ Restate Research Question 1 (Quantitative) ▪ Describe results ▪ Restate Research Question 2 (Qualitative) ▪ Describe results

Section 8: Discussion ▪ Restate Overall Research Question ▪ Describe how the results, when taken together, answer the overall question ▪ ***Describe how the results confirm or contrast the literature you reviewed

Section 9: Recommendations (if applicable, generally related to practice)

Section 10: Limitations ▪ Discuss, in several sentences, the limitations of this study. ▪ Research Design (overall, then info about the limitations of each separately) ▪ Sample ▪ Instrument/s ▪ Other limitations

Section 11: Conclusion (A brief closing summary)

Section 12: References (APA format)

Section I: The Building Blocks of Writing: Introduction

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Section i: the building blocks of writing: introduction.

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Writing uses words. There are two things you can do with words—choose them and arrange them. The first chapter of this book deals with choosing words. Most of the rest of the book deals with arranging words. The arranging is in increasingly larger units of thought—sentences, paragraphs, sections of a biomedical research paper, and the research paper as a whole.

Words, sentences, and paragraphs are the building blocks of writing. In later chapters of this book, the principles of word choice, sentence structure, and paragraph structure will be expanded to apply to the sections of a biomedical research paper and to the research paper as a whole.

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Research Paper – Structure, Examples and Writing Guide

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Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

How often do you use social media per day?
Less than 30 minutes
30 minutes to 1 hour
1 to 2 hours
2 to 4 hours
More than 4 hours
Which social media platforms do you use?
Others (Please specify)
How often do you experience the following on social media?
Social comparison (comparing yourself to others)
Cyberbullying
Fear of Missing Out (FOMO)
Have you ever experienced any of the following mental health problems in the past month?
Do you think social media use has a positive or negative impact on your mental health?
Very positive
Somewhat positive
Somewhat negative
Very negative
In your opinion, which factors contribute to the negative impact of social media on mental health?
Social comparison
In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
Education on healthy social media use
Counseling for mental health problems caused by social media
Social media detox programs
Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

Table of Contents – Types, Formats, Examples

Literature Review – Types Writing Guide and...

Implications in Research – Types, Examples and...

Figures in Research Paper – Examples and Guide

Research Approach – Types Methods and Examples

Data Verification – Process, Types and Examples

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Research methods--quantitative, qualitative, and more: overview.

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Qualitative Research
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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley.

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more. This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question.

Suggestions for changes and additions to this guide are welcome!

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods. HERE IS THE ONLINE GUIDE to this one-stop shopping collection, and some helpful links are below:

SAGE Research Methods
Little Green Books (Quantitative Methods)
Little Blue Books (Qualitative Methods)
Dictionaries and Encyclopedias
Case studies of real research projects
Sample datasets for hands-on practice
Streaming video--see methods come to life
Methodspace- -a community for researchers
SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools ! From this link, check out pages for each of the following topics: discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
Wiley Stats Ref for background information on statistics topics
Survey Documentation and Analysis (SDA) . Program for easy web-based analysis of survey data.

Consultants

D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
Sponsored Projects Sponsored projects works with researchers applying for major external grants.
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13.2 The Research Process: How to Create Sources

Learning outcomes.

By the end of this section, you will be able to:

Locate and create primary research materials.
Apply methods and technologies used for research and communication in various fields.

Introducing Research as Evidence and Compiling Sources for an Annotated Bibliography explain the difference between primary and secondary sources in the research process. Almost all of the source-gathering information in this chapter thus far has focused on gathering secondary sources. However, your research assignment may require that you use primary sources—not only those you find in the library but also those you create yourself, outside the library, by doing field research , also called fieldwork .

Conducting Field Research

If not applicable for this class, you may be asked at some point in your college career to conduct fieldwork, which is considered primary research (the collection or development of primary sources). You may already have engaged in primary research by performing experiments in a laboratory for a science course or by documenting your observation of a musical or artistic performance.

Fieldwork is the kind of research done when a person goes out into the “field” to collect data. This term is often used by both biologists, who may observe nature to understand plant growth, and anthropologists, who observe people to understand human cultural habits. Social scientists interested in language use and development, for example, have borrowed the term fieldwork to describe the ways they collect data about how people learn or use language. In fact, fieldwork is common to subjects such as education, medicine, engineering, sociology, journalism, criminology, and advertising.

Different disciplines—and different rhetorical situations—require different kinds of fieldwork. If your task, for instance, is to find out what consumers think of a new product, you may need to create and distribute a survey or questionnaire. If you want to find out how many people stop at a certain fast-food restaurant along the highway just to use the restroom, as compared with the number of customers who purchase food, you will have to spend some time observing the location.

No matter what kind of data you collect, the way you represent your research in your written work requires careful attention to fairness and accuracy. These ideas are particularly important when you write about the people you interview or observe. The way you represent them, as well as their words and actions, may present challenges. Be aware of those challenges by considering personal biases as you write about your research participants. Finally, be considerate of interviewees’ time, and acknowledge their help by sending a follow-up email thanking them.

Planning Field Research

Unlike a library, which bundles millions of bits of every kind of information in a single location, “fields” are everywhere, including the campus of your university (academic departments, administrative offices, labs, libraries, dining and sports facilities, and dormitories) as well as the neighborhood beyond the campus (theaters, restaurants, malls, parks, playgrounds, farms, factories, schools, and so on). Field information is not cataloged, organized, indexed, or shelved for your convenience. Obtaining it requires diligence, energy, and careful planning. Some considerations are listed below:

Think about your research question. What field sources might strengthen your argument or add to your report?
Select your contacts and sites. Find the person, place, thing, or event that you would see as most helpful to you. Will you set up observations, conduct interviews, distribute surveys, or use a combination of methods?
Schedule research in advance. Interviews, trips, and events don’t always work out according to plans. Allow time for glitches, such as having to reschedule an interview or return for more information.
Do your homework. Visit the library or do a Google search before conducting extensive field research. No matter from whom or from where you intend to collect information, having background knowledge can help you make more insightful observations and formulate better interview questions.
Log what you find. Record visits, questions, phone calls, and conversations in a research log . From the very beginning of your research, enter information about topics, questions, methods, and answers into a journal, or use another record-keeping method. Record even dead-end searches to remind yourself that you tried them. This method of organizing information is the writing task for this chapter and will be discussed at length in Glance at the Research Process: Key Skills and Annotated Student Sample .

Observation

In general, fieldwork that relies on observation as a method of data collection involves taking notes while observing events, activities, people, places, animals, and so on. Observations can range from a single visit to one event or location to several visits over an extended period. Consider your research question and topic to determine whether you need to observe over time or just once to get the information you need. You can prepare for your observation by doing some of the preliminary work in your research log. At this time, consider the limitations of only one observation session, which may yield only partial information.

As you plan for your observation, and before you arrive, decide whether you will be a participant observer , which involves taking part in what you are observing. For example, if you observed a volleyball club meeting that you attend regularly, and thus know most of its members or join in activities, you would be a participant observer. You will need to consider, though, how well you will be able to focus on the observation tasks. How actively will you participate in the group/event? While participating, how frequently will you be able to jot down notes or otherwise document your experience? Might you become distracted and forget your observation tasks, and if so, how will you handle that possibility?

Another option is to be a nonparticipant observer. In this capacity, you try to let your presence go unnoticed. Although you are there and observing, you do not influence the situation in any way. If you sat in a corner of an art class at your university to observe what materials students use, for example, you would be an unobtrusive and nonparticipant observer.

When you observe, take detailed notes; without them, you may forget much of what you observed after you leave a site. After recording your notes in your research log, review and rewrite your observation notes as soon after your site visit as possible. Take precise notes, indicating the color, shape, size, texture, and arrangement of everything relevant as applicable. Pay special attention to any anomalies you notice in a situation. Visual images provide excellent memory aids, so consider sketching, photographing, or videotaping the site you visit. If you speak your notes into a recording device, you will also pick up the characteristic sounds of the site.

Your observation notes will become very important as you begin to analyze your data. When you have finished observing, review your notes to consider them for analysis. Ask yourself what kinds of questions or conclusions your observations raise. Record your questions and conclusions in the speculations section of your research log; this material serves as your tentative interpretation of observation notes. These questions and conclusions can help direct further analysis of what you have observed and what you write based on those observations.

For example, consider that you want to determine whether seating is an issue for students eating in on-campus restaurants. You might organize your observations, questions, and speculations as shown in Table 13.2 .


Student enters campus restaurant with friends. Friends look around for seating before rejoining student at the counter. Friends take over five minutes to find an empty table that will seat them. Both the students searching for tables and the student at the counter keep checking their watches. The time is between classes but later in the afternoon than normal lunch time: 2:30.	How many seats are available in the campus restaurant? Are students at the tables eating, studying, or both? The students appear concerned about time and the opportunity to eat as a group.

In the “Observations” column, the writer describes, whereas in the “Questions and Speculation” column, the writer evaluates the situation. The “Questions and Speculation” section is particularly important, as different observers usually will have different interpretations. Moreover, both columns might be different at different times of the day or on different days of the week. To increase the validity of your findings, you might get a second opinion on your interpretation by sharing your observations with a peer.

Researchers in the social sciences often use surveys to collect data from a large number of individuals or from groups of people. A survey is a structured interview in which respondents are all asked the same questions and their answers are tabulated and interpreted. A survey would be a good source of data if, for example, you were comparing the eating habits of students who eat off campus with those of students who eat in college dining facilities. Or you may want to conduct research that compares the overall eating habits of students in one on-campus dining facility with those of students in another. You might ask questions such as these:

How often do you eat at a campus facility? This question could provide several options to assist survey takers with some ranges.
When you eat on campus, which of these dining facilities do you choose? A list of the dining facilities you are comparing would follow this question to refamiliarize survey takers with the names of the venues.

For research purposes, respondents to surveys can be treated as experts because they are being asked for opinions or information about their own behavior. Design your survey questions to be answered quickly and to generate useful information about your topic. To get this kind of information, ask questions skillfully. What and who questions are easy for respondents to answer easily and accurately. Less valuable for a survey is a why question, which requires a more thorough and planned answer. Respondents are less likely to give the proper attention to a why question for this reason. Furthermore, wording that suggests a right or wrong answer reveals the researcher’s biases more than the subject’s candid responses.

The format of the questioning and the way the research is conducted also influence responses. For example, to get complete and honest answers about a sensitive or highly personal issue, the researcher would probably use anonymous written surveys to ensure confidentiality. Other survey techniques include oral interviews in which the researcher records each subject’s responses on a written form. Surveys are usually brief in order to gain the cooperation of a sufficiently large number of respondents. To enable the researcher to compare answers, the questions are usually closed, although researchers may sometimes ask open-ended questions to gain additional information or insights. Treated briefly here, surveys involve complex procedures for the designing of questions, distribution of the survey, and assessment of the results.

A complication arises when a survey requests sensitive information, such as personal experience with drugs, alcohol, or sex, from identifiable subjects. Colleges and universities have “human subject” boards or committees that need to approve any research that could compromise the privacy of students, staff, or faculty. Consequently, consult your instructor before launching any survey on or off campus. However, the simple, informal polling of people to request opinions takes place quite often in daily college life, such as every time a class takes a vote or an instructor asks the class for opinions or interpretations of texts.

In one case, a student who was writing a self-profile wanted to find out how others perceived her. First, she listed 10 people who knew her in different ways—her mother, father, older sister, roommate, best friend, favorite teacher, and so on. Next, she invited each to list five words that best characterized her. Finally, she asked each to call her answering machine on a day when she knew she would not be home and name those five words. ln this way, she was able to collect original outside opinion (fieldwork research) in a nonthreatening manner. She then wove those opinions into her profile paper, combining them with her own self-assessment. The external points of view added an interesting (and sometimes surprising) view of herself as well as other voices to her paper.

Conducting interviews is another method of gathering information. Consulting, interviewing, and using information gathered from professionals in specific fields can offer authoritative perspectives. Other possibilities include interviewing people who have direct experience with your research topic.

If you are comfortable talking to people you don’t know, then you have a head start as a successful interviewer. In many respects, a good interview is simply a good conversation. If you consider yourself shy, don’t worry; you can still learn how to ask insightful interview questions that will elicit useful answers. Before conducting an interview, determine whether it is more appropriate to use a formal question-and-answer session, an informal exchange of ideas, or something in between.

Your chances of gathering helpful interview material increase dramatically when you prepare ahead of time and formulate the questions you want to ask. Consider the following guidelines:

what information you need;
why you need it;
who is likely to have it; and
how you might gain it.

Most research projects benefit from more than one perspective, so plan on more than one interview. For example, to research Lake Erie pollution, you could interview someone who lives on the shore, a chemist who knows about pesticide decomposition, a vice president of a nearby paper company, and people who frequent the waterfront.

Know your subject. Before you talk to an expert about your topic, make sure you know something about it yourself. Be prepared to explain your interest in it, know the general issues, and learn what your interview subject has already said about it in books, articles, or interviews. ln this way, you will ask sharper questions, get to the point faster, and be more interesting for your subject to talk with.
Create a working script. A good interview doesn’t follow a script, but it usually starts with one. Before you begin an interview, write the questions you plan to ask, and arrange them so that they build on each other—general questions first, specific ones later. Your written questions can remind you to get back on track, should you or your subject digress.
Ask both open and closed questions. Different kinds of questions elicit different kinds of information. Open questions place few limits on the answers given: Why did you decide to major in business? What are your plans for the future? Closed questions specify the information you want and usually elicit brief responses: When did you receive your degree? From what college? Open questions usually provide general information; closed questions supply details.
Ask follow-up questions. Listen closely to the answers you receive. When the information is incomplete or confusing, ask follow-up questions to request clarification. Such questions are seldom scripted, so plan on using your wits to direct your subject toward the information you consider most important.
Use silence. If you don’t get an immediate response to a question, wait a bit before asking another one. ln some cases, your question may not have been clear, and you will need to rephrase it. But in many cases, your interview subject is simply collecting their thoughts, not ignoring you. After a slight pause, you may hear answers worth waiting for.
Read body language. Be aware of what your subject is doing while answering questions. Does the subject look you in the eye? Fidget and squirm? Look distracted or bored? Smile? From these visual cues, you may be able to infer when your subject is speaking most frankly, doesn’t want to give more information, or is tired of answering questions.
Take content notes. Many interviewers take notes on a pad that is spiral bound on top and thus allows for quick page flipping. Don’t try to write down everything, just major ideas and telling statements in the subject’s own words that you might want to use as quotations in your paper. Omitting small words, focusing on the most distinctive and precise language, and using common abbreviations are all techniques to make taking notes more efficient.
Take context notes. Note your subject’s physical appearance, facial expressions, and clothing as well as the interview setting itself. These details will be useful later when you reconstruct the interview, helping you represent it more vividly in your paper.
Record audio with permission only. If you plan to record the interview, ask for permission in advance. The advantage of recording is that you have a complete record of the conversation. Sometimes, when hearing the interview subject a second time, you notice important things you missed earlier. However, recording devices may make subjects nervous. Be aware, too, that transcribing a recording is time consuming. It’s a good idea to have a pen in hand to catch highlights or jot down additional questions.
Confirm important assertions. When your subject says something especially important or controversial, read back your notes aloud to check for accuracy and to allow your subject to elaborate. Some interviewers do this during the interview, while others do it at the end.
Review your notes. Notes taken during an interview are brief reminders of what your subject has said, not complete quotations. Write out the complete information as soon after the interview as you can, certainly within 24 hours. Supplement the notes with other remembered details while they are still fresh, recording them in your research log or directly into a computer file that you can refer to as you write your paper.
Interview electronically. It is possible, and useful, to contact individuals electronically. Phone interviews are quick and obvious ways of finding out information on short notice. Even better is asking questions via email, which is less intrusive than telephoning, as your subject can answer quickly, specifically, and in writing at a convenient time. Other electronic media for interviews include Skype, Zoom, Google Meet, and Microsoft Teams. All of these tools allow you to see the interviewee physically without having to travel for an in-person interview.

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Chapter 4 Theories in Scientific Research

As we know from previous chapters, science is knowledge represented as a collection of “theories” derived using the scientific method. In this chapter, we will examine what is a theory, why do we need theories in research, what are the building blocks of a theory, how to evaluate theories, how can we apply theories in research, and also presents illustrative examples of five theories frequently used in social science research.

Theories are explanations of a natural or social behavior, event, or phenomenon. More formally, a scientific theory is a system of constructs (concepts) and propositions (relationships between those constructs) that collectively presents a logical, systematic, and coherent explanation of a phenomenon of interest within some assumptions and boundary conditions (Bacharach 1989). [1]

Theories should explain why things happen, rather than just describe or predict. Note that it is possible to predict events or behaviors using a set of predictors, without necessarily explaining why such events are taking place. For instance, market analysts predict fluctuations in the stock market based on market announcements, earnings reports of major companies, and new data from the Federal Reserve and other agencies, based on previously observed correlations . Prediction requires only correlations. In contrast, explanations require causations , or understanding of cause-effect relationships. Establishing causation requires three conditions: (1) correlations between two constructs, (2) temporal precedence (the cause must precede the effect in time), and (3) rejection of alternative hypotheses (through testing). Scientific theories are different from theological, philosophical, or other explanations in that scientific theories can be empirically tested using scientific methods.

Explanations can be idiographic or nomothetic. Idiographic explanations are those that explain a single situation or event in idiosyncratic detail. For example, you did poorly on an exam because: (1) you forgot that you had an exam on that day, (2) you arrived late to the exam due to a traffic jam, (3) you panicked midway through the exam, (4) you had to work late the previous evening and could not study for the exam, or even (5) your dog ate your text book. The explanations may be detailed, accurate, and valid, but they may not apply to other similar situations, even involving the same person, and are hence not generalizable. In contrast, nomothetic explanations seek to explain a class of situations or events rather than a specific situation or event. For example, students who do poorly in exams do so because they did not spend adequate time preparing for exams or that they suffer from nervousness, attention-deficit, or some other medical disorder. Because nomothetic explanations are designed to be generalizable across situations, events, or people, they tend to be less precise, less complete, and less detailed. However, they explain economically, using only a few explanatory variables. Because theories are also intended to serve as generalized explanations for patterns of events, behaviors, or phenomena, theoretical explanations are generally nomothetic in nature.

While understanding theories, it is also important to understand what theory is not. Theory is not data, facts, typologies, taxonomies, or empirical findings. A collection of facts is not a theory, just as a pile of stones is not a house. Likewise, a collection of constructs (e.g., a typology of constructs) is not a theory, because theories must go well beyond constructs to include propositions, explanations, and boundary conditions. Data, facts, and findings operate at the empirical or observational level, while theories operate at a conceptual level and are based on logic rather than observations.

There are many benefits to using theories in research. First, theories provide the underlying logic of the occurrence of natural or social phenomenon by explaining what are the key drivers and key outcomes of the target phenomenon and why, and what underlying processes are responsible driving that phenomenon. Second, they aid in sense-making by helping us synthesize prior empirical findings within a theoretical framework and reconcile contradictory findings by discovering contingent factors influencing the relationship between two constructs in different studies. Third, theories provide guidance for future research by helping identify constructs and relationships that are worthy of further research. Fourth, theories can contribute to cumulative knowledge building by bridging gaps between other theories and by causing existing theories to be reevaluated in a new light.

However, theories can also have their own share of limitations. As simplified explanations of reality, theories may not always provide adequate explanations of the phenomenon of interest based on a limited set of constructs and relationships. Theories are designed to be simple and parsimonious explanations, while reality may be significantly more complex. Furthermore, theories may impose blinders or limit researchers’ “range of vision,” causing them to miss out on important concepts that are not defined by the theory.

Building Blocks of a Theory

David Whetten (1989) suggests that there are four building blocks of a theory: constructs, propositions, logic, and boundary conditions/assumptions. Constructs capture the “what” of theories (i.e., what concepts are important for explaining a phenomenon), propositions capture the “how” (i.e., how are these concepts related to each other), logic represents the “why” (i.e., why are these concepts related), and boundary conditions/assumptions examines the “who, when, and where” (i.e., under what circumstances will these concepts and relationships work). Though constructs and propositions were previously discussed in Chapter 2, we describe them again here for the sake of completeness.

Constructs are abstract concepts specified at a high level of abstraction that are chosen specifically to explain the phenomenon of interest. Recall from Chapter 2 that constructs may be unidimensional (i.e., embody a single concept), such as weight or age, or multi-dimensional (i.e., embody multiple underlying concepts), such as personality or culture. While some constructs, such as age, education, and firm size, are easy to understand, others, such as creativity, prejudice, and organizational agility, may be more complex and abstruse, and still others such as trust, attitude, and learning, may represent temporal tendencies rather than steady states. Nevertheless, all constructs must have clear and unambiguous operational definition that should specify exactly how the construct will be measured and at what level of analysis (individual, group, organizational, etc.). Measurable representations of abstract constructs are called variables . For instance, intelligence quotient (IQ score) is a variable that is purported to measure an abstract construct called intelligence. As noted earlier, scientific research proceeds along two planes: a theoretical plane and an empirical plane. Constructs are conceptualized at the theoretical plane, while variables are operationalized and measured at the empirical (observational) plane. Furthermore, variables may be independent, dependent, mediating, or moderating, as discussed in Chapter 2. The distinction between constructs (conceptualized at the theoretical level) and variables (measured at the empirical level) is shown in Figure 4.1.

Flowchart showing the theoretical plane with construct A leading to a proposition of construct B, then the emprical plane below with the independent variable leading to a hypothesis about the dependent variable.

Figure 4.1. Distinction between theoretical and empirical concepts

Propositions are associations postulated between constructs based on deductive logic. Propositions are stated in declarative form and should ideally indicate a cause-effect relationship (e.g., if X occurs, then Y will follow). Note that propositions may be conjectural but MUST be testable, and should be rejected if they are not supported by empirical observations. However, like constructs, propositions are stated at the theoretical level, and they can only be tested by examining the corresponding relationship between measurable variables of those constructs. The empirical formulation of propositions, stated as relationships between variables, is called hypotheses . The distinction between propositions (formulated at the theoretical level) and hypotheses (tested at the empirical level) is depicted in Figure 4.1.

The third building block of a theory is the logic that provides the basis for justifying the propositions as postulated. Logic acts like a “glue” that connects the theoretical constructs and provides meaning and relevance to the relationships between these constructs. Logic also represents the “explanation” that lies at the core of a theory. Without logic, propositions will be ad hoc, arbitrary, and meaningless, and cannot be tied into a cohesive “system of propositions” that is the heart of any theory.

Finally, all theories are constrained by assumptions about values, time, and space, and boundary conditions that govern where the theory can be applied and where it cannot be applied. For example, many economic theories assume that human beings are rational (or boundedly rational) and employ utility maximization based on cost and benefit expectations as a way of understand human behavior. In contrast, political science theories assume that people are more political than rational, and try to position themselves in their professional or personal environment in a way that maximizes their power and control over others. Given the nature of their underlying assumptions, economic and political theories are not directly comparable, and researchers should not use economic theories if their objective is to understand the power structure or its evolution in a organization. Likewise, theories may have implicit cultural assumptions (e.g., whether they apply to individualistic or collective cultures), temporal assumptions (e.g., whether they apply to early stages or later stages of human behavior), and spatial assumptions (e.g., whether they apply to certain localities but not to others). If a theory is to be properly used or tested, all of its implicit assumptions that form the boundaries of that theory must be properly understood. Unfortunately, theorists rarely state their implicit assumptions clearly, which leads to frequent misapplications of theories to problem situations in research.

Attributes of a Good Theory

Theories are simplified and often partial explanations of complex social reality. As such, there can be good explanations or poor explanations, and consequently, there can be good theories or poor theories. How can we evaluate the “goodness” of a given theory? Different criteria have been proposed by different researchers, the more important of which are listed below:

Logical consistency : Are the theoretical constructs, propositions, boundary conditions, and assumptions logically consistent with each other? If some of these “building blocks” of a theory are inconsistent with each other (e.g., a theory assumes rationality, but some constructs represent non-rational concepts), then the theory is a poor theory.
Explanatory power : How much does a given theory explain (or predict) reality? Good theories obviously explain the target phenomenon better than rival theories, as often measured by variance explained (R-square) value in regression equations.
Falsifiability : British philosopher Karl Popper stated in the 1940’s that for theories to be valid, they must be falsifiable. Falsifiability ensures that the theory is potentially disprovable, if empirical data does not match with theoretical propositions, which allows for their empirical testing by researchers. In other words, theories cannot be theories unless they can be empirically testable. Tautological statements, such as “a day with high temperatures is a hot day” are not empirically testable because a hot day is defined (and measured) as a day with high temperatures, and hence, such statements cannot be viewed as a theoretical proposition. Falsifiability requires presence of rival explanations it ensures that the constructs are adequately measurable, and so forth. However, note that saying that a theory is falsifiable is not the same as saying that a theory should be falsified. If a theory is indeed falsified based on empirical evidence, then it was probably a poor theory to begin with!
Parsimony : Parsimony examines how much of a phenomenon is explained with how few variables. The concept is attributed to 14 th century English logician Father William of Ockham (and hence called “Ockham’s razor” or “Occam’s razor), which states that among competing explanations that sufficiently explain the observed evidence, the simplest theory (i.e., one that uses the smallest number of variables or makes the fewest assumptions) is the best. Explanation of a complex social phenomenon can always be increased by adding more and more constructs. However, such approach defeats the purpose of having a theory, which are intended to be “simplified” and generalizable explanations of reality. Parsimony relates to the degrees of freedom in a given theory. Parsimonious theories have higher degrees of freedom, which allow them to be more easily generalized to other contexts, settings, and populations.

Approaches to Theorizing

How do researchers build theories? Steinfeld and Fulk (1990) [2] recommend four such approaches. The first approach is to build theories inductively based on observed patterns of events or behaviors. Such approach is often called “grounded theory building”, because the theory is grounded in empirical observations. This technique is heavily dependent on the observational and interpretive abilities of the researcher, and the resulting theory may be subjective and non -confirmable. Furthermore, observing certain patterns of events will not necessarily make a theory, unless the researcher is able to provide consistent explanations for the observed patterns. We will discuss the grounded theory approach in a later chapter on qualitative research.

The second approach to theory building is to conduct a bottom-up conceptual analysis to identify different sets of predictors relevant to the phenomenon of interest using a predefined framework. One such framework may be a simple input-process-output framework, where the researcher may look for different categories of inputs, such as individual, organizational, and/or technological factors potentially related to the phenomenon of interest (the output), and describe the underlying processes that link these factors to the target phenomenon. This is also an inductive approach that relies heavily on the inductive abilities of the researcher, and interpretation may be biased by researcher’s prior knowledge of the phenomenon being studied.

The third approach to theorizing is to extend or modify existing theories to explain a new context, such as by extending theories of individual learning to explain organizational learning. While making such an extension, certain concepts, propositions, and/or boundary conditions of the old theory may be retained and others modified to fit the new context. This deductive approach leverages the rich inventory of social science theories developed by prior theoreticians, and is an efficient way of building new theories by building on existing ones.

The fourth approach is to apply existing theories in entirely new contexts by drawing upon the structural similarities between the two contexts. This approach relies on reasoning by analogy, and is probably the most creative way of theorizing using a deductive approach. For instance, Markus (1987) [3] used analogic similarities between a nuclear explosion and uncontrolled growth of networks or network-based businesses to propose a critical mass theory of network growth. Just as a nuclear explosion requires a critical mass of radioactive material to sustain a nuclear explosion, Markus suggested that a network requires a critical mass of users to sustain its growth, and without such critical mass, users may leave the network, causing an eventual demise of the network.

Examples of Social Science Theories

In this section, we present brief overviews of a few illustrative theories from different social science disciplines. These theories explain different types of social behaviors, using a set of constructs, propositions, boundary conditions, assumptions, and underlying logic. Note that the following represents just a simplistic introduction to these theories; readers are advised to consult the original sources of these theories for more details and insights on each theory.

Agency Theory. Agency theory (also called principal-agent theory), a classic theory in the organizational economics literature, was originally proposed by Ross (1973) [4] to explain two-party relationships (such as those between an employer and its employees, between organizational executives and shareholders, and between buyers and sellers) whose goals are not congruent with each other. The goal of agency theory is to specify optimal contracts and the conditions under which such contracts may help minimize the effect of goal incongruence. The core assumptions of this theory are that human beings are self-interested individuals, boundedly rational, and risk-averse, and the theory can be applied at the individual or organizational level.

The two parties in this theory are the principal and the agent; the principal employs the agent to perform certain tasks on its behalf. While the principal’s goal is quick and effective completion of the assigned task, the agent’s goal may be working at its own pace, avoiding risks, and seeking self-interest (such as personal pay) over corporate interests. Hence, the goal incongruence. Compounding the nature of the problem may be information asymmetry problems caused by the principal’s inability to adequately observe the agent’s behavior or accurately evaluate the agent’s skill sets. Such asymmetry may lead to agency problems where the agent may not put forth the effort needed to get the task done (the moral hazard problem) or may misrepresent its expertise or skills to get the job but not perform as expected (the adverse selection problem). Typical contracts that are behavior-based, such as a monthly salary, cannot overcome these problems. Hence, agency theory recommends using outcome-based contracts, such as a commissions or a fee payable upon task completion, or mixed contracts that combine behavior-based and outcome-based incentives. An employee stock option plans are is an example of an outcome-based contract while employee pay is a behavior-based contract. Agency theory also recommends tools that principals may employ to improve the efficacy of behavior-based contracts, such as investing in monitoring mechanisms (such as hiring supervisors) to counter the information asymmetry caused by moral hazard, designing renewable contracts contingent on agent’s performance (performance assessment makes the contract partially outcome-based), or by improving the structure of the assigned task to make it more programmable and therefore more observable.

Theory of Planned Behavior. Postulated by Azjen (1991) [5] , the theory of planned behavior (TPB) is a generalized theory of human behavior in the social psychology literature that can be used to study a wide range of individual behaviors. It presumes that individual behavior represents conscious reasoned choice, and is shaped by cognitive thinking and social pressures. The theory postulates that behaviors are based on one’s intention regarding that behavior, which in turn is a function of the person’s attitude toward the behavior, subjective norm regarding that behavior, and perception of control over that behavior (see Figure 4.2). Attitude is defined as the individual’s overall positive or negative feelings about performing the behavior in question, which may be assessed as a summation of one’s beliefs regarding the different consequences of that behavior, weighted by the desirability of those consequences.

Subjective norm refers to one’s perception of whether people important to that person expect the person to perform the intended behavior, and represented as a weighted combination of the expected norms of different referent groups such as friends, colleagues, or supervisors at work. Behavioral control is one’s perception of internal or external controls constraining the behavior in question. Internal controls may include the person’s ability to perform the intended behavior (self-efficacy), while external control refers to the availability of external resources needed to perform that behavior (facilitating conditions). TPB also suggests that sometimes people may intend to perform a given behavior but lack the resources needed to do so, and therefore suggests that posits that behavioral control can have a direct effect on behavior, in addition to the indirect effect mediated by intention.

TPB is an extension of an earlier theory called the theory of reasoned action, which included attitude and subjective norm as key drivers of intention, but not behavioral control. The latter construct was added by Ajzen in TPB to account for circumstances when people may have incomplete control over their own behaviors (such as not having high-speed Internet access for web surfing).

Flowchart theory of planned behavior showing a consequence leading to attitude, a norm leading to subjective norms, control leading to behavioral control, and all of these things leading to the intention and then the behavior.

Figure 4.2. Theory of planned behavior

Innovation diffusion theory. Innovation diffusion theory (IDT) is a seminal theory in the communications literature that explains how innovations are adopted within a population of potential adopters. The concept was first studied by French sociologist Gabriel Tarde, but the theory was developed by Everett Rogers in 1962 based on observations of 508 diffusion studies. The four key elements in this theory are: innovation, communication channels, time, and social system. Innovations may include new technologies, new practices, or new ideas, and adopters may be individuals or organizations. At the macro (population) level, IDT views innovation diffusion as a process of communication where people in a social system learn about a new innovation and its potential benefits through communication channels (such as mass media or prior adopters) and are persuaded to adopt it. Diffusion is a temporal process; the diffusion process starts off slow among a few early adopters, then picks up speed as the innovation is adopted by the mainstream population, and finally slows down as the adopter population reaches saturation. The cumulative adoption pattern therefore an S-shaped curve, as shown in Figure 4.3, and the adopter distribution represents a normal distribution. All adopters are not identical, and adopters can be classified into innovators, early adopters, early majority, late majority, and laggards based on their time of their adoption. The rate of diffusion a lso depends on characteristics of the social system such as the presence of opinion leaders (experts whose opinions are valued by others) and change agents (people who influence others’ behaviors).

At the micro (adopter) level, Rogers (1995) [6] suggests that innovation adoption is a process consisting of five stages: (1) knowledge: when adopters first learn about an innovation from mass-media or interpersonal channels, (2) persuasion: when they are persuaded by prior adopters to try the innovation, (3) decision: their decision to accept or reject the innovation, (4) implementation: their initial utilization of the innovation, and (5) confirmation: their decision to continue using it to its fullest potential (see Figure 4.4). Five innovation characteristics are presumed to shape adopters’ innovation adoption decisions: (1) relative advantage: the expected benefits of an innovation relative to prior innovations, (2) compatibility: the extent to which the innovation fits with the adopter’s work habits, beliefs, and values, (3) complexity: the extent to which the innovation is difficult to learn and use, (4) trialability: the extent to which the innovation can be tested on a trial basis, and (5) observability: the extent to which the results of using the innovation can be clearly observed. The last two characteristics have since been dropped from many innovation studies. Complexity is negatively correlated to innovation adoption, while the other four factors are positively correlated. Innovation adoption also depends on personal factors such as the adopter’s risk- taking propensity, education level, cosmopolitanism, and communication influence. Early adopters are venturesome, well educated, and rely more on mass media for information about the innovation, while later adopters rely more on interpersonal sources (such as friends and family) as their primary source of information. IDT has been criticized for having a “pro-innovation bias,” that is for presuming that all innovations are beneficial and will be eventually diffused across the entire population, and because it does not allow for inefficient innovations such as fads or fashions to die off quickly without being adopted by the entire population or being replaced by better innovations.

S-shaped diffusion curve showing the comparison with the traditional bell-shaped curve with 2.5% as innovators, 13.5% as early adopters, 34% as early majority, 34% as the late majority, and 16% as laggards.

Figure 4.3. S-shaped diffusion curve

Innovation adoption process showing knowledge then persuasion then decision then implementation and then confirmation.

Figure 4.4. Innovation adoption process.

Elaboration Likelihood Model . Developed by Petty and Cacioppo (1986) [7] , the elaboration likelihood model (ELM) is a dual-process theory of attitude formation or change in the psychology literature. It explains how individuals can be influenced to change their attitude toward a certain object, events, or behavior and the relative efficacy of such change strategies. The ELM posits that one’s attitude may be shaped by two “routes” of influence, the central route and the peripheral route, which differ in the amount of thoughtful information processing or “elaboration” required of people (see Figure 4.5). The central route requires a person to think about issue-related arguments in an informational message and carefully scrutinize the merits and relevance of those arguments, before forming an informed judgment about the target object. In the peripheral route, subjects rely on external “cues” such as number of prior users, endorsements from experts, or likeability of the endorser, rather than on the quality of arguments, in framing their attitude towards the target object. The latter route is less cognitively demanding, and the routes of attitude change are typically operationalized in the ELM using the argument quality and peripheral cues constructs respectively.

Argument quality (central route), motivation and ability (elaboration likelihood) and source credibility (peripheral route) all lead to attitude change

Figure 4.5. Elaboration likelihood model

Whether people will be influenced by the central or peripheral routes depends upon their ability and motivation to elaborate the central merits of an argument. This ability and motivation to elaborate is called elaboration likelihood . People in a state of high elaboration likelihood (high ability and high motivation) are more likely to thoughtfully process the information presented and are therefore more influenced by argument quality, while those in the low elaboration likelihood state are more motivated by peripheral cues. Elaboration likelihood is a situational characteristic and not a personal trait. For instance, a doctor may employ the central route for diagnosing and treating a medical ailment (by virtue of his or her expertise of the subject), but may rely on peripheral cues from auto mechanics to understand the problems with his car. As such, the theory has widespread implications about how to enact attitude change toward new products or ideas and even social change.

General Deterrence Theory. Two utilitarian philosophers of the eighteenth century, Cesare Beccaria and Jeremy Bentham, formulated General Deterrence Theory (GDT) as both an explanation of crime and a method for reducing it. GDT examines why certain individuals engage in deviant, anti-social, or criminal behaviors. This theory holds that people are fundamentally rational (for both conforming and deviant behaviors), and that they freely choose deviant behaviors based on a rational cost-benefit calculation. Because people naturally choose utility-maximizing behaviors, deviant choices that engender personal gain or pleasure can be controlled by increasing the costs of such behaviors in the form of punishments (countermeasures) as well as increasing the probability of apprehension. Swiftness, severity, and certainty of punishments are the key constructs in GDT.

While classical positivist research in criminology seeks generalized causes of criminal behaviors, such as poverty, lack of education, psychological conditions, and recommends strategies to rehabilitate criminals, such as by providing them job training and medical treatment, GDT focuses on the criminal decision making process and situational factors that influence that process. Hence, a criminal’s personal situation (such as his personal values, his affluence, and his need for money) and the environmental context (such as how protected is the target, how efficient is the local police, how likely are criminals to be apprehended) play key roles in this decision making process. The focus of GDT is not how to rehabilitate criminals and avert future criminal behaviors, but how to make criminal activities less attractive and therefore prevent crimes. To that end, “target hardening” such as installing deadbolts and building self-defense skills, legal deterrents such as eliminating parole for certain crimes, “three strikes law” (mandatory incarceration for three offenses, even if the offenses are minor and not worth imprisonment), and the death penalty, increasing the chances of apprehension using means such as neighborhood watch programs, special task forces on drugs or gang -related crimes, and increased police patrols, and educational programs such as highly visible notices such as “Trespassers will be prosecuted” are effective in preventing crimes. This theory has interesting implications not only for traditional crimes, but also for contemporary white-collar crimes such as insider trading, software piracy, and illegal sharing of music.

[1] Bacharach, S. B. (1989). “Organizational Theories: Some Criteria for Evaluation,” Academy of Management Review (14:4), 496-515.

[2] Steinfield, C.W. and Fulk, J. (1990). “The Theory Imperative,” in Organizations and Communications Technology , J. Fulk and C. W. Steinfield (eds.), Newbury Park, CA: Sage Publications.

[3] Markus, M. L. (1987). “Toward a ‘Critical Mass’ Theory of Interactive Media: Universal Access, Interdependence, and Diffusion,” Communication Research (14:5), 491-511.

[4] Ross, S. A. (1973). “The Economic Theory of Agency: The Principal’s Problem,” American Economic Review (63:2), 134-139.

[5] Ajzen, I. (1991). “The Theory of Planned Behavior,” Organizational Behavior and Human Decision Processes (50), 179-211.

[6] Rogers, E. (1962). Diffusion of Innovations . New York: The Free Press. Other editions 1983, 1996, 2005.

[7] Petty, R. E., and Cacioppo, J. T. (1986). Communication and Persuasion: Central and Peripheral Routes to Attitude Change . New York: Springer-Verlag.

Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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Book Description: Building Blocks of Academic Writing covers typical writing situations for developing academic writers, from prewriting and research through expressing themselves online. Developmental work in different types of paragraphs—descriptive, narrative, expository, persuasive—allows students to build capacity for longer essays. Each chapter includes review questions with a Canadian focus that instructors can assign to help students practise the skills developed in the text.

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Building Blocks of Academic Writing covers typical writing situations for developing academic writers, from prewriting and research through expressing themselves online. Developmental work in different types of paragraphs—descriptive, narrative, expository, persuasive—allows students to build capacity for longer essays. Each chapter includes review questions with a Canadian focus that instructors can assign to help students practise the skills developed in the text.

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CERN Accelerating science

The Standard Model

The Standard Model explains how the basic building blocks of matter interact, governed by four fundamental forces.

The theories and discoveries of thousands of physicists since the 1930s have resulted in a remarkable insight into the fundamental structure of matter: everything in the universe is found to be made from a few basic building blocks called fundamental particles, governed by four fundamental forces. Our best understanding of how these particles and three of the forces are related to each other is encapsulated in the Standard Model of particle physics. Developed in the early 1970s, it has successfully explained almost all experimental results and precisely predicted a wide variety of phenomena. Over time and through many experiments, the Standard Model has become established as a well-tested physics theory.

Particles of the Standard Model of particle physics (Image: Daniel Dominguez/CERN)

Matter particles

All matter around us is made of elementary particles, the building blocks of matter. These particles occur in two basic types called quarks and leptons. Each group consists of six particles, which are related in pairs, or “generations”. The lightest and most stable particles make up the first generation, whereas the heavier and less-stable particles belong to the second and third generations. All stable matter in the universe is made from particles that belong to the first generation; any heavier particles quickly decay to more stable ones. The six quarks are paired in three generations – the “up quark” and the “down quark” form the first generation, followed by the “charm quark” and “strange quark”, then the “top quark” and “bottom (or beauty) quark”. Quarks also come in three different “colours” and only mix in such ways as to form colourless objects. The six leptons are similarly arranged in three generations – the “electron” and the “electron neutrino”, the “muon” and the “muon neutrino”, and the “tau” and the “tau neutrino”. The electron, the muon and the tau all have an electric charge and a sizeable mass, whereas the neutrinos are electrically neutral and have very little mass.

Forces and carrier particles

There are four fundamental forces at work in the universe: the strong force, the weak force, the electromagnetic force, and the gravitational force. They work over different ranges and have different strengths. Gravity is the weakest but it has an infinite range. The electromagnetic force also has infinite range but it is many times stronger than gravity. The weak and strong forces are effective only over a very short range and dominate only at the level of subatomic particles. The weak force is weaker than the strong force and the electromagnetic force, but it is still much stronger than gravity. The strong force, as the name suggests, is the strongest of all four fundamental interactions.

Three of the fundamental forces result from the exchange of force-carrier particles, which belong to a broader group called “bosons”. Particles of matter transfer discrete amounts of energy by exchanging bosons with each other. Each fundamental force has its own corresponding boson – the strong force is carried by the “gluon”, the electromagnetic force is carried by the “photon”, and the “ W and Z bosons” are responsible for the weak force. Although not yet found, the “graviton” should be the corresponding force-carrying particle of gravity. The Standard Model includes the electromagnetic, strong and weak forces and all their carrier particles, and explains well how these forces act on all of the matter particles. However, the most familiar force in our everyday lives, gravity, is not part of the Standard Model, as fitting gravity comfortably into this framework has proved to be a difficult challenge. The quantum theory used to describe the micro world, and the general theory of relativity used to describe the macro world, are difficult to fit into a single framework. No one has managed to make the two mathematically compatible in the context of the Standard Model. But luckily for particle physics, when it comes to the minuscule scale of particles, the effect of gravity is so weak as to be negligible. Only when matter is in bulk, at the scale of the human body or of the planets for example, does the effect of gravity dominate. So the Standard Model still works well despite its reluctant exclusion of one of the fundamental forces.

So far so good, but...

...it is not time for physicists to call it a day just yet. Even though the Standard Model is currently the best description there is of the subatomic world, it does not explain the complete picture. The theory incorporates only three out of the four fundamental forces, omitting gravity. There are also important questions that it does not answer, such as “ What is dark matter? ”, or “ What happened to the antimatter after the big bang? ”, “Why are there three generations of quarks and leptons with such a different mass scale?” and more. Last but not least is a particle called the Higgs boson , an essential component of the Standard Model.

On 4 July 2012, the ATLAS and CMS experiments at CERN's Large Hadron Collider (LHC) announced they had each observed a new particle in the mass region around 126 GeV. This particle was consistent with the Higgs boson but it took further work to determine whether or not it was the Higgs boson predicted by the Standard Model. The Higgs boson, as proposed within the Standard Model, is the simplest manifestation of the Brout-Englert-Higgs mechanism. Other types of Higgs bosons are predicted by other theories that go beyond the Standard Model.

On 8 October 2013 the Nobel prize in physics was awarded jointly to François Englert and Peter Higgs “for the theoretical discovery of a mechanism that contributes to our understanding of the origin of mass of subatomic particles, and which recently was confirmed through the discovery of the predicted fundamental particle, by the ATLAS and CMS experiments at CERN's Large Hadron Collider”.

So although the Standard Model accurately describes the phenomena within its domain, it is still incomplete. Perhaps it is only a part of a bigger picture that includes new physics hidden deep in the subatomic world or in the dark recesses of the universe. New information from experiments at the LHC will help us to find more of these missing pieces.

Introduction: Teaching and its Building Blocks

Published: 04 December 2018
Volume 9 , pages 719–749, ( 2018 )

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Elena Pasquinelli 1 , 2 &
Sidney Strauss 3 , 4

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This editorial is intended to provide a broad overview of current approaches to teaching as a cognitive ability, as well as a background to the articles of the present special issue. The contributions are from the fields of developmental psychology, archaeology, anthropology, comparative cognition, robotics and artificial intelligence. So broad is the range of disciplines that need to be mobilized in order to characterize and understand human teaching.

The main goal of this introduction to this Special Issue is to suggest what basic building blocks of teaching may be. The route we chose to achieve this goal is to explore several approaches to teaching, and see how they provide insights into these basic building blocks.

But before presenting these approaches, we first justify our choice to seek basic building blocks of teaching. Strauss ( 2018 ), without denying the importance of basic building blocks, claimed that a helpful way to come to better understand teaching is to attempt to determine its complexity. That complexity could serve as a map of its components that allows us to do backward engineering to get at teaching’s basic building blocks. And that further enables us to reverse course to seek these building blocks’ developmental trajectories, as they eventually allow us to describe human teaching’s complexity and its fundamental functional units.

All of that having been said, what could basic building blocks give us that is of importance? Several reasons can be offered for their significance. One is that once we have determined what they are, we can seek their origins in human infants’ cognition and then study their developmental trajectories. Second, we can determine which basic building blocks are common to teaching among humans, nonhuman animals, computers that teach and any other teaching systems. In addition, they have significance for biological evolution in that we could describe which building blocks appear in non-human animals and see which get added on as we move up the phylogenetic ladder. This gets at the phylogenesis of teaching. Yet a fourth reason for their importance is that they can help us define the elusive concept of teaching. That having been said, knowing what teaching’s building blocks are doesn’t mean we have a definition of what teaching is. We also have to know how these components work and how they produce acts of teaching. But deciding what the building blocks are is a good first step to finding a definition of teaching.

In this introduction, we look at four basic building blocks we believe are fundamental to teaching. They do not exhaust the list but they are central for it to happen. They are responsiveness , relevance , information-giving and motivation . We elaborate on them below.

In pursuing a better understanding of teaching, we look at both practical and theoretical aims. On the practical side, research in cognitive science has been valuable in inspiring better practices in education. However, although progress has been made in analyzing, say, the neurocognitive mechanisms underlying learning, much less is known about those supporting teaching (Battro 2010 , 2013 ; Battro et al. 2013 ; Clark and Lampert 1986 ; Kane and Staiger 2008 ; Konstantopoulos 2007 ; Nye et al. 2004 ; Olson and Bruner 1996 ; Pearson 1989 ; Rivkin et al. 2005 ; Rodriguez 2012 ; Strauss 2001 , 2005 , 2018 ; Strauss and Ziv 2012 ; Strauss et al. 2014 ). This gap has become untenable in the light of increasing evidence that teachers have a long-lasting impact on the socio-economic fate of their pupils (Chetty et al. 2011 ; Chetty et al. 2014 ). In that regard, an agreed-upon measure of what counts as good teaching and a good teacher is still missing (Coe et al. 2014 , Darling-Hammond 2012 , Gordon et al. 2006 , Kane et al. 2013 , Kane and Staiger 2008 , 2010 , Rothstein and Mathis 2013 , Konstantopoulos 2008 , Rothstein 2010 ).

We take this a step further by stating that in order to know who good teachers are and what good teaching is, we need to know what teaching is. This has eluded scientists and academics so far.

However, interest in teaching goes beyond practical, educational aims. Teaching is a topic of fundamental research that has potential to lead to a better understanding of human culture, cultural evolution, cognitive development, and cognition writ large. Teaching is considered to play a crucial role in the transmission of skills, behaviors, ways of doing, but also norms in the emergence and development of culture among humans (Atran and Sperber 1991 ; Boyd and Richerson 1995 , 1996 ; Castro and Toro 2014 ; Csibra and Gergely 2011a , b ; Dean et al. 2012 ; Gergely and Csibra 2006 ; Laland 2008 ; Richerson and Boyd 2005 ; Sperber 1996 ; Tomasello et al. 1993 ; Tomasello and Rakoczy 2003 ; Whiten et al. 2011 ). Experiments in cognitive archeology show that teaching improves transmission of techniques for the fabrication of clapped stones. They suggest that the emergence of teaching is a favorable factor for the appearance of complex technologies (Morgan et al. 2015 ; Tehrani and Riede 2008 ). Developmental studies on babies and children point to the existence of specific mechanisms that facilitate learning from teaching (Csibra 2007 ; Gergely and Csibra 2006 ; Csibra and Gergely 2006 , 2009 ; Csibra and Gergely 2011a , b ) and of processes that shape children’s selective choice of whom to learn from (Corriveau and Harris 2009 , Eaves and Shafto 2012 , Gweon et al. 2014 , Harris and Corriveau 2011 , Shafto et al. 2012 , Sperber et al. 2010 ). Children have also been described as natural teachers, because their motivation and capacity to transmit information and knowledge appears early in life, and follows a developmental path even in the absence of explicit instruction about “how to teach” (Akagi 2012 , Ashley and Tomasello 1998 , Bonawitz et al. 2011 , Calero et al. 2015 , Corriveau and Harris 2009 , Davis-Unger and Carlson 2008 , Eaves and Shafto 2012 , Gweon et al. 2014 , Harris & Corriveau 2011 , Knudsen and Liszkowski 2012 , Köymen et al. 2015 , Shafto et al. 2012 , Sperber et al. 2010 , Strauss 2005 , 2018 , Strauss and Ziv 2012 , Strauss et al. 2002 , Tomasello 2009 ). Moreover, growing evidence supports the existence of teaching across cultures and societies, including contemporary traditional societies of hunter-gatherers and agro-pastoralists (Hewlett et al. 2011 ; Kline 2013 , 2015 ; Kline et al. 2013 ; Lancy & Grove 2010 ; Maynard 2002 , 2004 ; Maynard and Greenfield 2005 ; Paradise & Rogoff 2009 ).

Not only are the contributions to this special issue representative of this broad range of aspects and forms of teaching, they constitute an attempt toward a productive dialogue between different approaches.

2 Obstacles

A major obstacle in the direction of the understanding of teaching is related to the absence of an agreed-upon characterization of teaching and a shared view of what counts as “teaching behavior”.

Educational and cognitive approaches tend to focus on complex forms of teaching. They usually define teaching as an intentional behavior that mobilizes demanding cognitive skills - such as reflexive metacognitive skills and a theory of mind. Animal teaching is thus seen as proto-teaching by some developmental and cognitive researchers (Csibra 2007 , Premack 2007 ). Teaching in young children is sometimes described as proto-teaching because while lacking the necessary ingredients of teaching, they are considered to be precursors of teaching (Strauss 2018 ; Strauss and Ziv 2012 ). At the other end of the spectrum, functional-behavioral definitions have been proposed in the framework of animal and cultural studies, which admit the existence of simple forms of teaching but abandon any reference to the cognitive requirements that are necessary for teaching and do so at the risk of throwing away the baby with the bath-water (Byrne and Rapaport 2011 ; Caro and Hauser 1992 ; Fogarty et al. 2011 ; Hoppitt et al. 2008 ; Thornton & McAuliffe 2012 ; Thornton & Raihani 2008 ).

In the absence of a shared characterization of teaching, the very existence of a well-defined object of study can be put into doubt: is “teaching” one thing or many? (Kline 2015 ; Skerry et al. 2013 ). Will it be possible to “close the gap” between the different disciplinary approaches and to arrive at a minimalist but still meaningful common sense of teaching?

3 Methodology

We put forward a proposal to bridge the gap between different disciplinary approaches to teaching. In this way, we aim to create a suitable background for the dialogue between the different contributions to the special issue. More ambitiously, our aim is to help pave the way for the development of new theoretical and empirical studies to teaching that do not suffer from the disciplinary gap.

We proceed by summarizing four prominent approaches to teaching and bring empirical evidence to bear regarding their claims. In this way, it will be possible to extract a small number of functional units that appear to be necessary for teaching to occur. These are the “building blocks” of teaching, which could provide a common ground to the various approaches we discuss.

The building blocks of teaching are quite simple. None of them requires a complex cognitive architecture. Moreover, they are described in terms of functional units that are compatible with human, non-human, and artificial systems’ architectures. Compared to existing approaches to teaching, the main advantage of an approach based on “functional building blocks” is that it is intended to be more inclusive. It includes minimal forms of teaching without giving away cognition. Moreover, the building blocks can be combined in different ways, thus giving rise to the different forms of teaching that are described in various literatures.

This having been said, the hypothesis of the existence of a limited number of building blocks of teaching should be tested empirically. For instance, we can use the building blocks as predictors of the presence of teaching behaviors – and even of different forms of teaching behaviors, according to different combinations of prerequisites. They could be tested in different species, at different developmental stages, in relationship with specific neurocognitive deficits, and also by implementing the building blocks approach in artificial systems and by running simulations of the appearance of teaching behaviors.

Our introduction explores some aspects of teaching’s complexity and gleans from it what we believe are teaching’s four basic building blocks: (1) selective responsiveness, (2) relevance signaling, (3) information giving and (4) motivation to influence. We glean them from four approaches to teaching to which we now turn.

4 First Approach. David Premack’s Three-Pronged Theory of Teaching

Among the first and most prominent cognitive scientists to have dealt with teaching and its cognitive underpinnings is David Premack who described teaching as a developmental ability, consisting of three main actions (observation, judgment and modification of the learner’s behavior) arising from the interweaving of three uniquely human dispositions and abilities. These are: (1) aesthetics , (2) Theory of Mind (ToM) and (3) language plus an expertise in gesture modification (Premack and Premack 2003 , 2004 ). Aesthetics represents a set of standards that define what makes a gesture, product, or conduct appropriate. It is a normative attitude, and constitutes the motivation for correcting others and norming their behavior.

“ A parent has a conception of a proper act or product and dislikes the appearance of an improper one. The evidence for such standards is twofold. First, humans “practice” e.g., swing a golf club repeatedly, flip an omelet, sing a song, write a poem, etc., trying to improve their performance of a chosen activity. Second, humans seek to improve their appearance. The mirror is where they begin their day, combing their hair, applying makeup, etc. That humans have mental representations of preferred actions or appearances is suggested not only by the demands they make on themselves but by the corrections they make of children when teaching them. Teaching, the attempt to correct others, is the social side of the attempt to correct self. ” (Premack 2007 , 13862).

ToM includes a theory of development, which helps teachers identify children and youngsters as needing to be taught. Language and the capacity of passive guidance are the tools that humans mostly use for teaching - the latter being involved, for instance, in placing the body of others in the desired positions (Premack 1984 , 1991 , 2010 ). There is a fourth, hidden ingredient in successful teaching, which rests on the side of the learner: the learner’s ability and motivation to imitate models. The human capacity to teach relies not only on the teacher’s motivations, theories and tools, but also on those of the learner (Premack 2007 ). The fact that all these capacities can mesh together makes teaching a domain-general competence with indeterminately many targets . This means that human teaching is flexible. At the opposite end, instances of nonhuman teaching are examples, in Premack’s view, of rigid adaptations to single goals - in that they are limited to predation, and to specific acts of predation (Premack 2007 ). Finally, in Premack’s view, the disposition to teach constitutes an example of altruistic behavior because the beneficiary is not the teacher himself, but the learner, and the willingness to serve others comes with a cost to oneself.

4.1 From Theory to Evidence: Enforcing Norms and the Motivation for Teaching

There is evidence that the normative attitude described by Premack exists at an early stage of development and follows a developmental path. Children aged 3 years understand norms (e.g. the rules of a game, Rakoczy and Schmidt 2013 ); tend to correct a puppet violating the rules of a game and may use normative language (“It is not like this” “You have to do like that”, see Rakoczy Warneken & Tomasello 2009 , Rakoczy et al. 2009 , Schmidt and Tomasello 2012 ). Children 3 to 5 years old use normative language both when enforcing social norms and when teaching others. In the transition from 3 to 5 years, the use of language shifts from specific to generic and law-like (Köymen et al. 2015 ). Children of that age can extract the norms they successfully enforce both from the adult’s pedagogical attitude (the adult explaining the rule of the game) and from the observation of non-pedagogical interactions. For instance, when children observe an adult interacting with an artifact in a confident way, and then observe a puppet interacting with the same artifact, but in a different way, they tend to correct the puppet; however, if the adult seems to ignore the “right use” of the artifact and simply explores its features, children do not feel compelled to correct the puppet whose behavior differs from that of the adult (Schmidt et al. 2011 ). It is also worth noting that children of that age distinguish between moral norms (such as norms whose violation can produce harm) and simple conventions (or norms related to game-play): moral norms are enforced for everybody with no distinction of group membership, whereas conventional ones are selectively enforced to members of their own group (Schmidt and Tomasello 2012 ).

In the light of this evidence, it seems plausible to advance that , from an early age, enforcing social norms (conventions, ways of doing, rules of the game, standards, criteria) represents a motivation for teaching. It is possible that the motivations that sustain teaching to in-group members are slightly different from those of law-enforcing to both in-group and out-group members. Teaching is a normative enterprise, not just intended as a means for the transmission of factual knowledge or skills, but as a means for reducing deviations from social norms and standards, by enforcing them in non-compliant individuals of the in-group (see also Rakoczy et al. 2009 ). Clearly, the normative dimension of teaching deserves further attention. We claim here that one basic building block of teaching involves its motivational aspects.

4.2 From Theory to Evidence: The Problem with ToM

It is often the case that cognitive approaches to teaching – such as Premack’s – invoke the capacity of attributing mental states to others and evoke the notion of Theory of Mind – a theory of others’ minds that enables, among other things, false beliefs ascriptions and the identification of lack of knowledge in others (Baron-Cohen et al. 1985 ; Premack and Woodruff 1978 ; Wimmer and Perner 1983 ). However, as explicitly stated by Premack & Premack ( 1994 ), the skilled interpretation of others’ mental states is most probably a composite function (see also Korkmaz 2011 ; Call and Tomasello 2008 ). It is thus not the most promising level of analysis for identifying the simple functional units – the building blocks – of teaching. Moreover, the attribution of mental states is not agreed-upon, as shown by the simultaneous existence of different terminologies for addressing mentalistic capacities (“ToM”, “mindreading”, “understanding others’ minds”). It is not among the objectives of this paper to summarize the disagreements found in the literature. But because a lack of agreement has a potentially negative impact on the understanding of the role of ToM in teaching, it deserves attention.

First, experts disagree on the age at which children succeed at false beliefs tests. With different tests, in fact, the time for achieving this capacity ranges from 4 years old to 2 years old and even to 13 months of age (Baron-Cohen et al. 1985 ; Wimmer and Perner 1983 ; Hutto et al. 2011 ; Southgate et al. 2007 ; Surian et al. 2007 ; Scott and Baillargeon 2009 ). But it isn’t so much the age that is of importance here. Rather, it is a methodological and theoretical problem about how we can draw equivalence between verbal tasks used when tapping toddlers’ false beliefs and the non-verbal tasks employed with infants.

Second, it has been suggested that, even in the case of false belief tasks, what is being measured, cognitively, is subject to debate. Experts disagree about the conceptual vs non-conceptual nature of such capacities. Some of them consider ToM to be a specifically conceptual capacity – a theory (Gopnik and Meltzoff 1997 ). Others, such as developmental psychologist and primatologist Daniel Povinelli, posit the existence of both conceptual and purely perceptual (tracking) systems, serving similar purposes. Perceptual systems track “ statistical regularities that exist among certain events and the behaviors, postures, and head movements (for instance) of others ” (Povinelli 2004 ). Humans could appeal to both perceptual and conceptual systems, while chimpanzees and other primates might lack the second form, thus making the distinction more evident (Povinelli and Vonk 2003 ; Penn and Povinelli 2007 ).

In a similar vein, philosopher Ian Apperly and psychologist Stephen Butterfill have also hypothesized the existence of a double system for tracking beliefs. The first system is inflexible, hard-wired, “fast and efficient”, cheap (from the point of view of cognitive computation), evolutionary and ontogenetically ancient. The second system is flexible, effortful, implies the explicit and deliberate attribution of mental states and gradually develops, helped by the maturation of capacities such as language and executive functions, and by experience. The tracking system would not disappear with the development of thoughtful mindreading, but would continue to operate – especially under conditions of stress, cognitive load, time pressure, etc. (Apperly 2012 ; Apperly and Butterfill 2009 ; Butterfill and Apperly 2013 ). It is tricky to decide whether humans are relying upon a tracking system, or a conceptual system, or both.

Finally, experts disagree on the relative role of innate processes (Baron-Cohen et al. 1985 ; Leslie 1987 ) vs. socially acquired skills (Heyes 2012 , 2014 , 2015 ; Heyes and Frith 2014 ). A “minimal theory” of mentalizing such as the one proposed by Povinelli, Apperly and Butterfill, which recognizes the game-changing role of conceptual, explicit mentalizing, but does not limit mentalizing to it, seems to represent a suitable solution for dealing with the problem of teaching, because it allows us to take into account occurrences of teaching in young children and in nonhuman animals, and simple forms of teaching, as well.

We conclude this brief discussion by acknowledging that teaching requires some capacity for taking into account others’ mental states. But this capacity may possibly be composed of more basic perceptual skills, such as the capacity (or capacities) of tracking intentions, perceptions, knowledge, behaviors and possibly beliefs – with or without the capacity of conceptualizing these mental states and more particularly beliefs.

5 Second Approach. Teaching as a Natural Ability

In his research on education and developmental psychology, Sidney Strauss – one of the two co-editors of this special issue – has proposed a characterization of human teaching as a “natural cognitive ability”, i.e. it is universal, developmentally reliable, found in our ancient hominin ancestors, has a phylogenetic history and specific neurocognitive underpinnings (Strauss 2005 , 2018 , Strauss & Ziv 2012 , Strauss et al. 2002 ).

Strauss ( 2018 ) posits that although teaching involves information giving, complex human teaching encompasses more than that. It includes stage-setting with emotion-, motivation- and mind-reading, organizing teaching sessions, scaffolding, detecting knowledge gaps and reducing them via teaching strategies such as demonstrations and verbal explanations. In addition to teaching’s mind-to-mind coupling, it also includes heart-to-heart coupling. Teaching is prosocial in its very essence, and it is also part of its complexity. Teaching is a form of helping behaviour but is not altruistic because altruism involves the voluntary giving another something of value to another person where what has been given is no longer in the possession of the giver. When teaching, the teacher gives knowledge to another, the learner. But because the teacher doesn’t lose her knowledge when passing it on to the learner, teaching is not considered to be altruistic.

This complexity of human teaching can serve as a map for teaching’s basic building blocks.

Adult human teaching aims to reduce the knowledge gap between the one who knows (the teacher) and the one who doesn’t (the learner). Teaching is thus (1) an intentional act , motivated by (2) a prosocial stance and involving (3) information giving . To achieve this, teachers build upon (4) their understanding of other people’s minds , that is, ToM, which we discussed in critical terms in relationship with Premack’s theory.

The development of ToM introduces a distinction between precursors and more advanced forms of teaching. ToM is also the hallmark of advanced human teaching as it permits adapting to the learner’s developmental level and knowledge gaps. But Strauss claims that aspects of teaching are preceded by two kinds of precursors that do not require ToM. The first kind is proto-teaching, i.e. information giving not involving the transmission of generalizable knowledge. The second kind is early teaching, e.g. solicited information-giving or unsolicited mistakes correction shown by infants. The first form of information-giving does not require ToM and is also present in nonhuman animals (Strauss and Ziv 2012 ). At the opposite end of the spectrum, contingent teaching – teaching during which the teacher responds to the learner, and vice versa – is based on a more sophisticated form of ToM: “on-line ToM”, which implies monitoring (a form of metacognition) and executive function (working memory, flexible planning, focused attention) (Strauss 2005 , Strauss et al. 2002 ).

Teaching behaviors thus follow a trajectory of evolutionary and ontogenetic development, which go from teaching without ToM to teaching with ToM and eventually teaching with on-line ToM.

Based on Strauss’ and colleagues’ studies on professional teachers (Strauss and Shilony 1994 ), however, none of these natural forms is enough for efficient professional teaching. Whilst grounded on natural bases, sophisticated and mature teaching is a learnt ability. Professional teachers are required to develop their mastery of subject matter knowledge and their pedagogical content knowledge, eventually modifying their implicit mental models of children’s mind and learning (their folk psychology and pedagogy: Strauss 2001 , Strauss and Shilony 1994 , Strauss and Ziv 2012 ).

5.1 From Theory to Evidence: Information Giving during Ontogenetic Development

There is some evidence that giving information appears relatively early in ontogenesis, follows a developmental trajectory, and is preceded by precursors that involve the solicited transmission of information and the correction of others’ mistakes (Calero et al. 2015 ; Strauss 2005 ; Strauss et al. 2014 ). For instance, as has been described in previous paragraphs, children aged 3–5 protest when norms are not respected and try to enforce them by demonstrating actions and stating rules (Köymen et al. 2015 ). There is an ontogenetic trend in solicited peer-teaching with coordination. When engaged in a collaborative problem-solving task requiring joint and coordinated actions, children aged 3.5 years have been observed to engage significantly more in explicit teaching actions than their peers aged 2, 2.5 and 3 years (each “knowledgeable” child being paired with a naïve peer and asked to teach the necessary actions to solve a task; Ashley and Tomasello 1998 ). Children aged 3 years still coordinate poorly with the behaviors of the other child and do not engage much in verbal teaching. Younger children do not even master the task alone. Other studies have confirmed the existence of an ontogenetic path, especially in relationship with the engagement of children in verbal interaction, in collaboration with and sensitivity to the learner’s states of mind as compared to simple demonstration of actions to be taught (Astington & Pelletier 1996 , Davis-Unger and Carlson 2008 , Wood et al. 1995 ). For instance, when taught to play a board game, 3-year-old children spontaneously show other children how to play. They do so with little or no explanation and without systematically correcting others’ errors. Children age 5, on the other hand, demonstrate and explain by reminding the rules of the game, thus correcting the mistakes of the co-player. They also engage more in feedback and mistake diagnosis. For a review of this literature, see Strauss and Ziv ( 2012 ).

At least two explanations are possible for the development from simple to more sophisticated information-giving behavior: first, younger children are more motivated to win the game than to explain it; second, older children have developed the necessary skills for monitoring and reacting on-line to the errors of their co-players (they display more sophisticated forms of ToM, as suggested by Davis-Unger and Carlson 2008 ).

Spontaneous engagement in teaching younger siblings has also been observed among Zinacantec Maya populations in Chiapas, with children teaching skills such as how to prepare tortillas and how to take care of dolls in the context of everyday chores (Maynard 2002 , 2004 ). Changes have been observed in the modalities of teaching (but not in time spent at teaching), with children aged 3 to 5 years mostly sitting side by side and providing the younger sibling a task to perform, and older children giving more feedback, engaging more in explanations, commands, guidance of the learner’s body, etc.. A significant difference is observed between children aged 5 to 7 years and children aged 8 to 11 years, the latter but not the former engaging significantly more in talk with demonstration than the 3–5-year-old group (Maynard 2002 ).

Information-giving strategies thus seem to become progressively more varied and flexible starting from 3 to 5 years with children aged 7 years being capable to engage in the kind of adjustment to the learner, which is described by Strauss and Ziv as contingent teaching and as requiring on-line ToM (Strauss et al. 2002 ; Strauss and Ziv 2012 ).

5.2 From Theory to Evidence: Teaching Has Precursors in Ontogenetic Development

There is evidence that information-giving behavior is preceded by precursors that involve: a. the solicited transmission of information that is needed by another and b. the non-systematic but spontaneous correction of others’ mistakes. For instance, when exposed to an adult who is inefficiently looking for a lost object - the object being in full sight for the child but not the adult - infants aged 1-year point at the object so as to provide information, if pressed to (Liszkowski et al. 2006 , 2008 ). At about the same age, children engage spontaneously in the correction of mistakes when the experimenter suddenly pretends not to be able to put the right shape – pyramid, cube, ball – in the right hole. This attitude also follows a developmental path: infants 12 to 19 months tend to act in the place of the experimenter and to put the shape in the appropriate hole; children aged 20–23 months, stop replacing the adult and start pointing at the right solution, or utter sounds in relationship with the task. The correction of mistakes perceived in others thus progressively includes actions, deictic gestures (pointing) and proto-language as tools (Akagi 2012 ) It has been noticed that mental retardation but not autism with mild mental retardation has a negative impact of this behavior: children with autism do display active correction of mistakes once they attain 40 months of developmental age, Akagi 2012 ). Considering the correction of mistakes as a precursor of teaching is coherent with the hypothesis – suggested by Premack, and discussed above – that teaching has a normative dimension.

5.3 Challenging Evidence: Information Giving and Alliances

At least one strand of evidence hints at the possibility that information-giving is not limited to filling in a knowledge gap. In four experiments, Kim et al. have shown that, when teaching adults, children aged between 3 and 6 years do not systematically choose to teach those who are more in need (Kim et al. 2014 ). It seems, however, that children of this age do recognize the difference between adults who know more and adults who are less knowledgeable, and treat the former as better informants than the latter (for the capacity of children aged 5–7 years of identifying informants who omit information, see Gweon et al. 2014 ). Somewhat unexpectedly, though, the children who have taken part in Kim et al.’s experiments show a preference for giving information to adults who have demonstrated to be more knowledgeable than others , even when the knowledgeable adult tells the child that she already knows what the child wants to share. In the cited experiments, children never choose to inform adults who have a record of ignorance (Kim et al. 2014 ). While limited, this kind of evidence is supportive of the hypothesis that the emergence of teaching behaviors is related to different motivations: in addition to fulfilling a prosocial motive, sharing information might help establish fruitful collaborations and alliances with knowledgeable ones (or be a form of reciprocal prosociality related to the sharing of information), thus fulfilling a self-serving interest.

5.4 More Challenging Evidence: Self-Serving Motivations for Teaching (Cognitive Advantages)

A second strand of evidence hints at the possibility that teaching behavior has immediate self-serving benefits for the teacher, in this case that teaching others enhances the teacher’s capacity to learn. One study has shown that preparing oneself for teaching promotes better retention of verbal material as compared to preparing oneself as a learner; in the framework of this study, actually teaching others – as compared to verbalizing aloud alone and to working alone – does not seem to make a difference (Barg & Schul 1980 ). This result seems to be confirmed by another study in which self-explanation is shown to produce the same effects as engaging in tutoring when dealing with rote learning and better effects when dealing with deep learning (Roscoe & Chi 2008 ). It has thus been proposed that preparation for teaching mobilizes metacognition and favors a better organization of the learning material (Annis, 1983 , Benware & Deci 1984 ). For what concerns the expectancy to teach, (Renkl 1995 ) found both negative effects (anxiety) and positive effects on the time spent studying a specific problem, but no positive effects on actual learning. However, a more recent study suggests that actual teaching might have a positive impact on retention: (Fiorella & Mayer 2013 ) assessed the relative benefits of preparing to teach and of actual teaching; the specificity of their study consists in joining immediate and delayed assessment of the learning outcomes of the different conditions (learning for learning, leaning by preparing for teaching, learning by preparing for teaching and actually teaching). In immediate assessments, teaching gives better learning outcomes than preparing for learning, and actual teaching does not provide additional advantages. In delayed assessments the advantage of preparing for teaching disappears, but participants who have actually taught maintain their advantage. The authors extrapolate that preparing to teach and actually teaching promote different cognitive processing relative to memorization. However promising, existing studies on the cognitive (learning) advantages of teaching are still limited. Moreover, the cognitive mechanisms involved in the presumed advantages of teaching are not clear, the effect size appears to be small if compared to studying for oneself and the measured effects present a high degree of inter-individual variability (Cohen et al. 1982 , Rohrbeck et al. 2003 , Roscoe & Chi, 2008 , Fiorella & Mayer 2013 ).

More research is required to evaluate the cognitive benefits of teaching for the teacher, and to identify the mediating mechanisms. Likewise, more work is needed to identify the potential immediate benefits of teaching for the teacher, and to assess the hypothesis that teaching is not (or at least not only) an altruistic behavior.

6 Third Approach. Michael Tomasello’s Theory of Instructed Learning

The third and last cognitive characterization of teaching discussed here is instructed learning. Instructed learning is defined as a form of social learning in which the learner imitates a demonstrator’s actions and gestures (Tomasello 2016 , Tomasello & Carpenter 2007 , Tomasello et al. 1993 ). The characterization thus focuses on mechanisms that are relevant for social learning. It is proposed that engaging in social learning is not limited to epistemic motives (information gathering/giving) but also extends to social motivations of different kinds (Carpenter 2006 , Tomasello & Carpenter 2007 ). Over & Carpenter ( 2012 ), in particular, proposed that, due to their dependence on the group, humans have evolved a social motivation to imitate and that imitation has the function of a social glue. Children imitating adults or other children might thus be trying to make themselves more similar to the model or to the group independent of the goal of learning new behaviors. They might copy adults for communicating with them and sending them a message (of empathy, of affiliation and similarity), for strategically buying their place in the group, and possibly for complying with social pressure and with norms that are more or less explicitly enforced. Conformity can in fact promote “peace” and minimize conflict by reducing dissimilarities within the group and at the same time enhance social acceptability via the adherence to shared social norms (Over & Carpenter 2012 ). Cohesion and conformity are thus important aspects in instructed learning.

Instructed learning also includes responsiveness, on the part of the learner, to a special class of communicative acts that the instructor performs while demonstrating a target action. Skills and motivations that are deemed relevant for instructed learning are: a. mechanisms related to the attitude of taking the perspective of someone else, which stems from the possibility of sharing attention (of bringing attention on a shared target) and of sharing intentions (of doing something together with the same intention, not just in parallel, but cooperating) and b. social motivations to cooperate and to conform; c. the capacity of imitating intentional behaviors with both fidelity and rationality – where fidelity refers to the fact that the learner copies the details of the model’s actions, i.e., the specific gestures employed by the model in order to achieve the outcome; and rationality refers to the understanding of the goals behind the action (see Gergely et al. 2002 , Lyons et al. 2007 , Meltzoff 1988 , Whiten et al. 2009 ). Evidence related to the three groups of requirements is discussed in what follows.

6.1 From Theory to Evidence: Perspective-Taking Evolves

There is evidence that capacities related to perspective-taking and cooperation follow a developmental trajectory, which includes: i. Gaze-following and ii. flexible and intentional attention-reading/attraction relative to a third party. Gaze-following is present at birth and can be considered a precursor of full-fledged attention-reading. At around 9–12 months of age infants reliably follow the gaze of an adult, but she also intentionally performs gestures that attract the attention of the adult toward an external object or event, e.g. through pointing. At the same age, infants infer an intention (e.g. a goal) in relationship with a shared framework or common ground (Call 2009 ).

Cooperation also follows a developmental trajectory. Skills and motivations for coordinating actions around a common goal start to appear around 14 months of age. However, at this stage, children are more proficient in helping others than in cooperating with them. This observation suggests that helping and cooperating differ significantly from a cognitive point of view, the latter probably requiring more than perspective-taking of others’ goals. Children aged 18 months perform cooperative actions with adults by coordinating gestures around a common goal. They have acquired the ability to form shared plans and to coordinate actions in a timely manner (Warneken & Tomasello 2007 ). Nonhuman animals, and in particular chimpanzees, which are often compared with children in studies, do not show shared intentionality, not because of an incapacity to read attention or intentions, but because they lack the intrinsic motivation to do so (Tomasello & Carpenter 2007 ). This can be a partial explanation for why some nonhuman animals do not teach (Call 2009 ).

6.2 From Theory to Evidence: The Role of Social Motivations in Social Learning and Teaching (Conformity)

There is evidence that preschool children can be quite strategic when imitating their peers: in the situation in which one child receives information at odds with that received by a group of peers, and is asked to express his or her opinion publicly, in a majority of cases he or she will conform to the opinion of the group; however, when asked to silently – not publicly – express his or her views, the tendency to conform to the group is reduced (Haun & Tomasello 2011 ). There is also evidence that – starting from 5 years of age – prosocial behavior (e.g. unsolicited helping, collaboration) attracts more “followers” than coercive behavior or coercive behavior alone (Hawley 1999 , 2002 ). Since having more “followers” is considered to be a sign of prestige (Boyd and Richerson 1995 , Henrich & Gil-White 2001 , Richerson and Boyd 2005 ; van de Waal et al 2013 ), it is possible that prosocial behaviors, such as teaching, bring teachers prestige, with relative advantages in terms of social position (for the social advantages brought about by prestige, see Cheng et al. 2013 ).

Teaching might then represent a means for the end of gaining social status without engaging in dominance-related, aggressive behaviors, which imply a risk for the dominance-seeking individual (for the evolution and psychology of prestige vs. dominance, see Henrich & Gil-White 2001 ). Fusaro & Harris ( 2008 ) show that 4-year-old children selectively choose to learn from individuals who are “popular”, having received manifest assent to their assertions from other individuals. Chudek et al. ( 2012 ) extended these data by substituting manifest assent or dissent with attention paid to the “teacher” (time of gaze); they showed that children are twice as likely to learn from an adult who has been accorded 10 s of attention from another adult than from an adult who has received no attention.

Children thus seem to respond to simple cues that identify others as being popular, and to defer to them in a way that attracts popular teachers more followers/learners. By gaining more followers, these individuals achieve greater prestige and are more likely to be copied, i.e., to be adopted as teachers. With regard to these data, some (Boyd and Richerson 1995 , Henrich & Gil-White 2001 , Richerson and Boyd 2005 ) have advanced the hypothesis that prestige mechanisms have evolved because they help choose the “best teachers” via proxies that can be easily and rapidly assessed. Because of the social advantages of prestige, it is plausible that teaching is a way of gaining prestige. That is, individuals become proactive in attracting their followers by displaying pro-social behaviors, by advertising their skills and knowledge, and by actively teaching others, rather than waiting to be copied. This hypothesis requires more evidence in order to be supported.

6.3 From Theory to Evidence: Responsiveness to Pedagogical Cues

Multiple experimental results indicate that when ostensive acts (eye-contact, addressing a person by name, etc.) are addressed to children and even infants, they react differently than when demonstrations are proposed without ostensive cues. For instance, when adults perform demonstrations in combination with ostensive communicative cues, children as young as 14 months of age respond by imitating with fidelity the specific gestures performed by the adult. When these cues are absent, children tend to copy “rationally”, that is, they copy the result of the action. Also, in the presence, but not in the absence of ostensive communication with eye-contact, infants 14 to 18 months old tend to generalize the moral reactions of an adult to the class of objects they are directed towards. For instance, rather than assuming that the adult does not like the particular item that lies in front of him, they assume that the class the item belongs to is not likable. In the absence of eye-contact the generalization is not made and the child attributes the reaction to a particularity of the demonstrator (Csibra and Gergely 2009 , Egyed et al. 2013 , Gergely et al. 2002 ).

Based on the receptivity of learners to this particular class of cues – which includes linguistic utterances and prosody (namely: motherese) in addition to eye-contact and ostensive gestures – Csibra and Gergely have developed a theory of cultural transmission of knowledge called “natural pedagogy” (Csibra and Gergely 2006 , 2009 , 2011 , Gergely and Csibra 2006 ). Natural pedagogy is hypothesized to represent an adaptation related to the growth of artifacts in hominin societies, and thus to constitute a solution to a learning problem of acquiring knowledge that is “opaque” – in the sense that it cannot be easily extracted via experience and/or imitation of social models (e.g. how to use or produce a particular tool with a specific technique) – and it is general (i.e., valid beyond the current situation and specific content) (see also Morgan et al. 2015 ).

Pedagogical protocols of the kind described as natural pedagogy might thus constitute adaptations that signal to the learner that the information transmitted has a particular relevance and should be learnt and generalized. Experimental studies by Csibra, Gergely and colleagues also support the hypothesis that there is more to teaching than demonstration and explicit, verbalized instruction. Eye-contact, gaze direction, manual, vocal, bodily attitudes and gestures directed towards external events and objects (ostensive gestures) modify the receptivity of the learner, convey signals about the relevance and generalizability of the contents of communication, and are understood by the learner without requiring linguistic exchange. This class of communicative acts thus deserves further attention and empirical studies in order to better characterize the palette of “teaching tools” that are involved in initiating pedagogical interactions.

6.4 From Evidence to Research Questions: Teachers’ Actions and Gestures

Despite the term “natural pedagogy”, Csibra’s and Gergely’s work has been aimed mostly at the learner, e.g., how do learners respond to ostensive versus non-ostensive cues? Given this, it would be useful were researchers to develop the theory of natural pedagogy in the direction of identifying the relevant actions that are put in place by teachers and that make one apt to convey a pedagogical intention. Useful insights into the cognitive processes underlying teaching and the capacity of teachers of conveying pedagogical intents to learners might come from the implication of participants for which one or the other of these capacities is selectively impaired. One could also understand teaching better from the study of non-verbal behaviors – e.g., communicative, iconic, deictic and ostensive gestures – put in place by children while teaching.

This particular line of research has been recently investigated by Calero and colleagues (Calero in preparation, Calero et al. 2015 , Strauss et al. 2014 ). They have been able to show that children aged 3 to 8 years use gestures, namely ostensive gestures and referential signals, while providing explanations to adults. They do so more than during other communicative acts. Children aged 3 to 5 years use ostensive gestures at both the onset and offset of pedagogical acts aimed at conveying information about how to play a game, whereas children aged 6 to 8 years use ostensive cues such as eye-contact only at the offset. This suggests a developmental pattern: older children make use of referential signals especially at the onset of teaching episodes, while these are almost absent in the gestural repertoire of younger children. Also, in Calero et al.’s experiments children, that included children age 7, on average do so “rationally” – by tuning the receptivity of the learner to their ostensive gestures – and “intuitively”, i.e., not as a form of imitation of adult’s behavior. Ostensive gestures emitted by children during pedagogical episodes are reduced if the adult learner does not pay attention to them; they increase after having been exposed to a teacher who minimizes the use of ostensive gestures.

From these studies children emerge not just as the receptive participants of the pedagogical protocol, but as emitters, too (Calero et al. 2015 ). These studies do more than confirm that teaching is a natural behavior, which develops from childhood. They hint at the possibility that teaching is anchored in gestural, pre-verbal cues, to which infants are tuned and that children put in place when they are on the emitter, teacher, side of pedagogical events. These cues need not be reflective or intentional. It is possible that they are part of an automatic cue-response system.

7 Fourth Approach. Non Cognitive, but Cultural and Evolutionary

“ An individual actor A can be said to teach if it modifies its behaviors only in the presence of a naïf observer, B, at some cost or at least without obtaining an immediate benefit for itself. A’s behavior thereby encourages or punishes B’s behavior, or provides B with experience or sets an example for B. As a result, B acquires knowledge or learns a skill earlier in life or more rapidly or efficiently than it might otherwise do, or that it would not learn at all .” (Caro and Hauser 1992 ).

Animal studies and comparative cultural studies indicate that minimalist forms of teaching exist, which do not require complex computations to be carried out (evidence is reported below). These studies rely on a characterization of teaching that makes no reference to cognitive requirements. The characterization, originally proposed by biologists Caro and Hauser ( 1992 ), focus on teaching behaviors and their effects on the learner (function), and in this way avoids any reference to complex and even basic cognitive requisites. Teaching is: any modification of the teacher’s behavior in the presence and only in the presence of naïve observers that produces better learning outcomes on the side of the learner, with no immediate benefit for the teacher .

Within this minimalist approach to teaching, it is not necessary for the teacher to possess a special form of sensitivity to the learner’s mental states in order to modify his or her own behavior (a Theory of Mind). Nonetheless, the definition implies that teaching takes place in the presence of learners that are “naïve” and not experts, which – Caro and Hauser concede – requires some form of sensitivity to conspecifics’ behaviors. The solution proposed by Caro and Hauser is that the teacher’s sensitivity is on a continuum ranging from time-locked adaptations (a stereotyped time-course for teaching, unlocked by specific behavioral patterns on the side of the learner) to highly-sensitive mechanisms capable of tracing minimal changes in the learner’s mental states, and to react by choosing the most appropriate course of action rather than stereotyped responses. The difference between low-sensitive and high-sensitive mechanisms is not represented by their efficiency, but by the ecological and social circumstances that favor one or the other, and by their costs in terms of processing. For instance, in a stable environment a stereotyped teaching behavior can be both efficient and low-cost, and so can be favored by evolutionary processes (Caro and Hauser 1992 ). Chazan (2018, this volume) makes a similar argument for teaching among hominins. The “sensitivity to the learner’s state” that emerges from this view may be analogous to the minimalist form of mentalizing discussed above in relation to ToM, and used as a system for recognizing individuals-susceptible-to-be-taught.

The second mental capacity the behavioral-functionalist account of teaching does without, is the intention to teach (Caro and Hauser 1992 ; Hoppitt et al. 2008 ). However, even in this case, the circumstantial modification of the teacher’s behavior can be considered as some form of intentionality, in that the teacher – in specific circumstances – modifies its own behavior and acts in such a way as to pass on some information or knowledge or norms. There is then a functional similarity with the intention to teach, but this form of minimalistic intentionality does not require metacognition. It is implemented in patterns of behavior that express themselves in specific circumstances and only in those circumstances.

This approach has been productive, in that it has favored the burgeoning of a strand of evolutionary research on animal teaching (Hoppitt et al. 2008 ; Byrne and Rapaport 2011 ; Fogarty et al. 2011 ).

7.1 From Theory to Evidence: Animal Teaching

There is limited but solid evidence, gathered in natural and controlled experimental conditions, that some form of teaching exists even in taxa that are not considered to display complex mentalistic capacities, namely: meerkats (Thornton 2008 ; Thornton and Clutton-Brock 2011 ; Thornton and McAuliffe 2006 ), ants ( Franks and Richardson 2006 ; Leadbeater et al. 2006 ; Richardson et al. 2007 ) and pied babblers (Raihani and Ridley 2008 ).

Experimental research on meerkats has shown that a relatively inflexible, hard-wired, pre-adapted mechanism can explain the capacity of these animals to tune their behavior to the behavioral state of learners. Adult meerkats provide young meerkats prey – scorpions – in various ways that have the consequence of sharpening pups’ predatory skills. Adults provide unarmed or armed preys, depending on the age of the learner: younger learners are assigned unarmed preys, easier and less dangerous to deal with; older ones have to deal with the risk of being stung.

Experimental manipulations have allowed researchers to identify the mechanisms at play in this form of elementary “behavior-reading”. Adult meerkats have been exposed to voice records of young meerkats while in the presence of pups of different ages. Adult meerkats do not appear to react to the physical aspect of the pups, but rather to the pitch of the voice that is played for them. If the pitch corresponds to a younger animal, the potential learner is assigned an unarmed prey; if the pitch corresponds to an older animal, the learner is assigned an armed prey, independent of its real age. The pitch thus represents a proxy of the developmental stage of the young animal. Meerkats do not need elaborate complex computations in order to interpret the behavioral state of the learner and to choose the most appropriate teaching behavior.

Tandem running behavior in ants ( Temnothorax albipennis ) also fits the behavioral-functional definition of teaching provided by Caro and Hauser. In the process of colonizing a new nest, tandem running leaders recruit a nest-mate and guide their companion to the new site. The companion taps the leader’s abdomen with its antennae and the leader adapts to the companion’s velocity. During the process, the teacher evaluates the necessity of pursuing the tandem runs and adapts to the learner, which in turn gives feedback to the leader.

This behavior is costly in terms of time spent, slowing down the leader, even when compared to other forms of transfer from an old to a new nest (carrying). However, such teaching behaviors might have the advantage of permitting transfer, in that the pupil ant learns the route and can subsequently become a tutor (carried ants do not learn the route) (Franks and Richardson 2006 ). The case of tandem running ants provides an example of how apparently complex forms of teaching, such as teaching through evaluative feedback and contingent responses to the actions of the learner (a form described as advanced teaching by Strauss 2005 ) can be carried out through simple mechanisms. This having been said, though, we do not want to claim that human teaching, with language, gestures, etc. is equivalent to that of tandem-running ants.

While this example does not explain the variety of situations in which humans teach by adapting to the learner, it suggests that simple mechanisms can suffice in the case of specific behaviors in specific circumstances. Contingent teaching is thus not specific to humans, even if the mechanisms used by our species for contingent teaching might be in large part human-specific – e.g. relying on language and other symbolic systems. The same consideration applies to the case of associative teaching in pied babblers.

Pied babblers emit purr calls during food delivery in a way that produces associative learning in their offspring. The manipulation of purr calls during and after the learning phase shows that only the pairing of purr calls with actual food delivery produces learning and that once the association is learnt, recorded purr calls with no delivery activate the paired response in the offspring (Raihani and Ridley 2008 ).

Once more, these examples suggest that it is possible to teach without mobilizing the symbolic, linguistic, explanatory, conceptual tools that are available to the human mind. It is however difficult, in our case, to establish which is the part of similar low-cost pre-adapted mechanisms in human teaching. A parsimonious stance suggests that whenever simpler mechanisms are sufficient for granting an efficient behavior, there is a good reason to look for less complex calculations.

7.2 Some Methodological Issues about Teaching in Humans and Non-human Animals

Evolutionary approaches to teaching generally espouse the view that the relevant resemblance between teaching in humans and in non-human animals is in the effects, with no proper equivalence in the (neurocognitive) mechanisms at stake (Caro and Hauser 1992 ; Fogarty et al. 2011 ; Hoppitt et al. 2008 ). From this assumption, they draw the conclusion that the study of non-human teaching cannot bring any contribution to the identification of the neurocognitive underpinnings of human teaching. “ Any functional similarities should not obscure the fact that mechanistically, cases of animal teaching are entirely different from human teaching, and are not reliant on homologous characters ” (Fogarty et al. 2011 , p. 2).

It is the conclusion and not the premise that is challenged here. It is certainly desirable to avoid the pitfalls of anthropomorphism, namely attributing complex mental states to nonhuman animals, analogous to those identified in the human cognitive architecture, on the ground that the respective (teaching) behaviors share functional similarities. And, similarly, convergent patterns of evolution should not be confounded with common descent and common origin of similar mechanisms. Mechanisms identified for one species might be irrelevant for another despite the similarity of the respective behaviors because “ evolutionary convergence may be more important than common descent in accounting for similar cognitive outcomes in different animal groups ” (Bolhuis and Wynne 2009 ) (see also Bolhuis et al. 2011 , Hemelrijk and Bolhuis 2011 ).

Nonetheless, the evolutionary approach might be too quick at dismissing the possibility that animal studies and their functionalist guiding principle can be productive for advancing the understanding of human teaching. Quite the opposite, by abstracting from the constraints that each type of cognitive architecture imposes, and by avoiding any reference to complex cognitive mechanisms, they are especially suited for identifying functional units that are necessary for teaching but are not unique to the most complex forms of teaching, e.g., comparative animal studies provide several examples that complex solutions can arise from simple mechanisms (Emery and Clayton 2004 ; Gunturkun 2012 ; Mueller and Gerardo 2002 ).

In addition, limiting oneself to human studies is susceptible to hiding functional units that can be carried out by less complex neurocognitive underpinnings or at a lesser (cognitive) cost. Even in the case of humans, some forms and some aspects or components of teaching might in fact be carried out by “a less complex mental calculator”, that is, in the form of pre-adapted, cue-response mechanisms or of low-cost processes that do not require conceptualization – such as the tracking of behavioral cues described above in the framework of the discussion about mentalizing capacities.

This claim is consistent with a more general approach to the comparative study of cognition. Psychologist and zoologist Sara Shettleworth, in particular, has insisted on the necessity of reversing the trend in comparative cognitive studies, where there is a search for human-like capacities in nonhuman animals, and of searching for simple and unconscious mechanisms, triggered by behavioral cues in humans, similar to those that have been identified in the case of nonhuman animals. It is her opinion that the de-anthropomorphization of explanations for complex cognitive functions might benefit the understanding of human behavior no less than that of other animals: “ dissecting broad abilities into elements, some of which are phylogenetically widespread, others confined to species with specific ecologies or evolutionary histories, and some perhaps unique to humans.” (Shettleworth 2010 ). Her view is similar to the one put forward by de Waal and Ferrari ( 2010 ) regarding what they call a bottom-up approach to cognition, in that it restates the continuity between species and the necessity of adopting a Darwinist point of view on the study of cognitive skills and motivations. This view motivates the “building blocks approach” proposed here to foster the understanding of teaching and to bridge gaps between disciplinary approaches.

Moreover, human teaching itself is not limited to complex forms of teaching. Rather, evidence (described below) indicates that human teaching comes in a variety of forms, some of which are minimalist and do not require teachers to engage in complex computations and some of which are complex requiring mind-reading, language, etc.. In this view, the functionalist approach proposed in the framework of animal and comparative studies may be no less suitable to study the varieties of human teaching than cognitive approaches. The latter could limit us to the consideration of only complex forms of teaching with ToM and explicit intentionality. The functionalist approach has potential to make a relevant contribution to the understanding of forms of human teaching as well as that of nonhuman ones. Along with that, though, using only the functionalist approach to teaching has the potential to eliminate what is unique to human teaching (Strauss and Ziv 2011 ). In our view, neither approach should have hegemony. Each helps us gain an understanding of teaching in ways different from the other.

7.3 From Theory to Evidence: Minimalist Forms of Teaching among Adult Humans

Several forms of teaching behavior have been described in the framework of different human societies, ranging from tolerance to observation, to scaffolding and simplification, to stimulus enhancement, to teaching with feedback and, finally, to explanations and demonstrations. Footnote 1 Some of these forms are minimalist, requiring only a certain degree of acceptance of the learner’s attention, with no specific modification of the teacher’s behavior and with no need for mentalization. For example, in Fijian agro-pastoral societies, women tolerate children who observe them while cooking; men and women simplify daily tasks so that children can participate; they point and use motherese in ways that enhances the salience of the stimulus to be attended to; adults and children scold other children when they violate social taboos; parents teach through verbal explanations and gestures or gestural demonstrations alone, i.e. how to weave a basket (Kline 2015 ).

Anthropologist Barry Hewlett (Boyette & Hewlett 2018 this volume) presents evidence that hunter-gatherers/foragers of the Congo Basin (Aka and Bofi) teach their children, mostly vertically (parent to child) before 5 years of age, and obliquely (adult to child) and horizontally (child to child or adult to adult) between 5 and 12 years of age (Hewlett et al. 2011 ). Aka life is based on egalitarianism, autonomy, and sharing. On the autonomy side, children and even infants are generally free to play with machetes and other cutting devices. However, if they do not share, others (including other children) react by gesturing at them or teasing. Also, “ Young children often hear stories about how people who do not share properly face sanctions (e.g. illness, death, death of a child).” (Hewlett et al. 2011 ), In addition, children declare that they learned how to share food and what to consider edible food from their parents, mainly same-sex parents.

These examples can be seen as complex forms of teaching, which include correction of behavior by peers and elders, and direct, explicit, verbal teaching about the values and norms that pervade the Aka way of life. At the same time, Aka show other, less cognitively demanding forms of teaching. First, they tolerate observation – even when observation is intrusive, e.g. sitting on one’s lap while one is doing a chore (“ Forager children’s high motivation to learn occurs early and often. Infants climb into their parents’ laps to watch them cook, play an instrument or make a net. Children want to learn more than what parents and others want to give, but forager parents seldom refuse the intrusions of a child, because of their egalitarian and autonomy ethos”, Hewlett et al. 2011 ). Second, they provide children with “toys” (axes, spears, digging tools, baskets) that have the features of adult tools, but are child-sized and are accompanied by a pedagogical stance, which consists of encouraging, commenting on the use of such pedagogical artifacts and in guiding the gestures of the child (“ The infants chop, dig, etc., and the parents watch, laugh, make sounds and sometimes physically take the infants’ hands to show them how to use the implement.” ). Another example of pedagogy in the form of scaffolding (demonstration and correction) of a naïve individual consists in an adult and young (12-year-old) Aka women teaching B. L. Hewlett how to weave a basket. Finally, there is evidence of horizontal instruction among children while at play, and namely while imitating productive activities. Note that (Wiessner 1982 ) and (Konner 2010 ) confirm the importance of teaching social norms of sharing among other populations of hunter-gatherers, namely the! Kung Of South Africa:

“Wiessner described how parents removed beads from infants’ necklaces and had them give the beads to appropriate kin relations so they could learn about sharing networks. Konner also indicated that !Kung learn to share early: ‘!Kung value sharing very highly, and from the time their infants are six months of age mothers and other adults frequently say ‘Na’ meaning ‘Give’ when a bit of food is in the infant’s hand and on the way to its mouth. The criterion is that they should inhibit the very strong impulse to eat and reliably turn the morsel over to the adult making the demand’. (Hewlett et al. 2011 ).

Not only do various pedagogical techniques exist among farming and hunter-gathering/foraging societies, which require different amounts of cognitive computation, but teaching in these societies is not limited to tool-making and food provisioning skills, also investing in values and norms that are relevant for that particular society.

8 Discussion

8.1 characterizing teaching in terms of elementary, functional units (basic building blocks).

The extent of teaching behaviors is vast, ranging from very minimalist to the very complex and demanding. It is unlikely that these behaviors are all served by a unique set of neurocognitive mechanisms and processes, especially a set made of demanding, complex neurocognitive underpinnings. Given our analysis of the four approaches and of the evidence gathered to date, we believe that it might be the case that teaching behaviors are underpinned by a finite group of functional units or building blocks . These functional units are common to minimalist and less minimalist forms of (human) teaching, and are also shared by documented forms of nonhuman animal teaching. They are neutral to the specific neurocognitive architecture that implements them.

From our review, we detected four basic building blocks, and it is to them that we now turn.

Selective-Responsiveness Block . Each form of teaching requires some form of sensitivity to the learner and of selective responsiveness to him/her/it. Educational-cognitive approaches to teaching tend to invoke high-order capacities (ToM) to fulfill the task, but it has become evident from the discussion of ToM - as well as from examples of animal teaching and of minimalist forms of teaching in humans – that simple tracking systems and pre-wired adaptations may do the job – at least when simple forms of teaching are involved. Sensitivity to the learner might be required even in the case of tolerance to observation in order to decide who will be tolerated as an observant learner and who will be treated as a disturbance.

Relevance-Signaling Block . Each form of teaching requires some mechanisms for attracting – eventually focusing – the attention of the learner upon an object, action, location, feedback, demonstration or explanation. Some mechanisms for synchronizing with the learner around a shared object or task are described in the case of human instructed learning, and more generally in the case of cooperative tasks. Ostensive gestures and other “tools” employed in natural pedagogy interaction have been described as communicating the fact that we are communicating something and, in so doing, we are intentionally preparing the L for the content of the message. When doing this, we are communicating ostensively having the effect of making the information relevant to the learner (and eliciting specific behaviors on the side of very young learners). However, in more minimalist forms of teaching, the time-lockedness of the interaction Footnote 2 and the sensitivity of the learner to the presence of a teacher can be sufficient for obtaining this effect (e.g., in the case of tolerance to observation). In the case of stimulus enhancement, for instance, the very presence of a social model (teacher) represents a cue that enhances certain physical stimuli in the learner’s environment, and drives the learner’s attention to them. The Relevance-Signaling building block can thus be implemented differently according to the teacher’s (and learner’s) neurocognitive architecture, but it does not require particular forms of metacognitive activity on the side of the teacher. Moreover, even natural pedagogy tools consist of reflexive, prewired mechanisms that human teachers employ without even knowing that they will make a difference for the learner. Such tools can thus be considered as simple functional units that do not require higher-order cognitive architectures.

Information-Giving Block. Some form of information-giving is involved in all the categories of teaching. However, these forms vary considerably from one kind of teaching to another. Cognitive approaches to teaching cite both language (or other symbolic tools) and gestures (iconic gestures, gestures for demonstrating, gestures for repositioning the body of the learner, giving feedback …). Animal studies provide examples of signals and cues, such as birdsongs. But even other forms of action – the simplification and structuring of a task (acting in the place of the learner, as in carrying ants toward a new nest or in disarming a scorpion), the provisioning of an opportunity to learn – can be considered as forms of information-giving. Sometimes a simple cue-response system is sufficient to indicate that the information given is appropriate for the learner, other times the transmission of information implies a finer matching of the task to the learner’s state of knowledge, thus implying some form of metacognition relative to the task in addition to the capacity of tracking the knowledge state of the learner. But these are not necessary to define teaching, and may only intervene in some of its complex forms. The variety of possible implementations is huge and this diversity can easily justify the impression that teaching hardly looks like “one thing”. However, the form through which the information-giving functional unit is implemented (an adaptation or a specific neurocognitive function) is not essential to its definition.

Motivation-To-Influence Block . Teaching is an activity that requires some form of explicit or implicit motivation, such as: the motivation to encourage certain behaviors (e.g. predatory behaviors) and to discourage others (e.g., not eating a poisonous leaf, violating a taboo) to influence others and modify their actions according to norms, standards, accepted rules (e.g. in games) and to accept (tolerate) becoming a model for imitation/observation. All these, and more, are forms of influence that promote some conformity to the teacher. In one way or another, the teacher is then motivated to influence the learner and to modify the learner’s behavior according to the teachers’ own knowledge, beliefs, ways of doing, norms etc. However, the motivation to influence need not be represented metacognitively. The teacher and the learner can act as if they had this motivation. The motivation can simply be the function the behavior responds to, the selective force behind the existence of the behavior. The compliance of the learner to the teacher can be to the learner’s advantage. This aspect is captured by the behavioral-functional definition of teaching, according to which, in virtue of teaching interactions, the learner’s skills are enhanced.

Typical examples of this are represented by teaching and learning opaque knowledge and by teaching and learning dangerous skills (such as predation of poisonous scorpions). However, this does not seem to be the only motivation for teaching, not even for learning (imitating) from models, e.g., conformity to teachers can be promoted because it lowers conflict, to the advantage of both the learner and the teacher. Or because it promotes collaboration, as when teaching someone else how to play a game (together) and how to solve a problem (in a cooperative task). Or even because it creates common ground, e.g. for establishing alliances, for communication and sharing, for bonding, etc. One of the effects of teaching is the reduction of distance and the creation of a shared perspective between the teacher and the learner. After the process of teaching-learning has taken place, the mental states of the learner and of the teacher are more similar - the learner having “adopted”, by learning them, those of the teacher.

To summarize, despite their diversity, teaching behaviors may require only a small number of functional units to be in place: they may have a common core. First, teaching implies certain forms of responsiveness to learners (and to teachers). Second, during teaching interactions some information is made relevant and, third, given. Fourth, teaching responds to a motivation to influence and create compliance to the teacher. The common core of teaching is not exclusive to (adult) humans. The specific mechanisms that implement these functional units (building blocks) can vary considerably. The fact that each building block can be implemented in different ways – depending on the cognitive architecture and on the circumstances of teaching – might explain why observed forms of teaching vary widely and look very different in human adults, in children, and in meerkats.

This leaves the door open for greater latitude in the search for the mechanisms that put teaching into play in different species, under different circumstances, in relationship with different neurocognitive architectures. Various domains of research (animal studies, behavioral ecology, cultural studies, developmental, cognitive, evolutionary psychology, artificial systems research) can use this characterization and the building blocks approach as a framework for specifying the mechanisms at stake, case by case.

Many other questions remain open: about the self-serving advantages of teaching, its evolutionary path in different species, the possibility of reproducing teaching artifacts from the study of natural teachers, that of “empowering” natural teachers via a better understanding of teaching behaviors and more. Nothing less than a large, multi-disciplinary effort is required in order to advance our knowledge about teaching, how we teach and why.

Another open issue is that these four basic building blocks are not unique to teaching. All four appear in behaviors other than teaching. For example, the Motivation to Influence Block can be seen in, say, alpha males of baboon tribes asserting their dominance so as to influence the other males’ behaviors towards females in the tribe. What this could mean for the thesis we have been proposing on these pages is that all four may be co-opted when teaching occurs. What makes teaching unique is a topic that needs discussion.

9 The Special Issue’s Content

The contributions included in the present special issue offer a multi-disciplinary overview of teaching as a cognitive ability (or as a sum of cognitive abilities with specific properties and functions). They can be divided into two main groups.

The first group of contributions deals with the evolution of teaching as a strategy related to cultural evolution.

Christine Caldwell, Elizabeth Renner, Mark Atkinson investigate the roles of teaching in terms of the transmission of knowledge and know-how that is particularly difficult to acquire individually or via social learning (imitation, emulation). They provide a comprehensive review of positions taken regarding the places of teaching in cumulative culture. Teaching thus might have played a role in the accumulation of culture, especially in human beings.

There is disagreement among social anthropologists as to whether or not there is teaching among hunter and gatherers (HGs). This can be framed in a larger question about teaching being universal among humans. Within this controversy, Adam H. Boyette and Barry Hewlett claim that teaching occurs in HG bands. Along with that, though, they add nuance to what teaching looks like there and cast it within the cultural emphases on autonomy and egalitarianism found among HG societies. The authors posit that HG societies offer multiple and varied examples of teaching, discuss how teaching has both co-evolved with culture and contributes to the construction of our particular cultural niche.

Michael Chazan contributes to the understanding of the evolution of teaching among hominins with a review of the literature in cognitive archaeology. He deals mostly with the “life cycle” of stone tool production (making the tool, using it, maintaining it and, eventually, discarding it) at different points in the Paleolithic era. Chazan takes into account constraints and characteristics of hominin evolution such as the variability of the dynamics of the environment, the adaptive advantage of social learning and the progressive development of technology and opaque knowledge. For example, he argues that at times of dynamic paleoclimate changes, there was more of a need for trial and error learning as opposed to periods when there was relative paleoclimate stability where there was more of a need for transmission of cultural knowledge. He believes that it is plausible that all these characteristics have influenced the specificities of human teaching, including its flexibility as compared to teaching among other animals.

Along these lines, Emily R. R. Burdett, Lewis G. Dean, and Samuel Ronfard suggest that teaching in humans is more flexible, diverse and complex than in other species. One of the balancing acts they present is the tension between the production of innovations that lead to changes in the culture and the preservation of these very cultural innovations. The former is achieved through low-fidelity transmission mechanisms, whereas the latter happens via high-fidelity transmission (including teaching). The balancing act between innovation and preservation is maintained by what they term a teaching tool-kit found among humans. Because humans are thought to have the ability to teach using both high- and low-fidelity transfer mechanisms, they can fit the kind of mechanisms they choose to the demand characteristics of the tasks at hand.

The second group of contributions deals with teaching in children as an instinct that progressively exploits different cognitive abilities.

The existence of teaching before cognitive maturity reinforces the image of a cognitive ability that is grounded into more basic functions that are present from early childhood and are possibly based on natural predispositions. Cecilia I. Calero, Andrea P. Goldin and Mariano Sigman have studied teaching in pre-school children and shown that when teaching others, they use the same tools that have been previously identified in adults who assume a pedagogical position. Rather than study the roles of ostensive cues among the receivers of teaching, learners, they tested whether or not children who are the emitters, i.e., teachers, produce ostensive cues. Their findings show that youngsters do teach using ostensive cues. In addition, they suggest that human teaching does not necessarily involve ToM. The authors also reverse the traditional relations between teaching and metacognition. They suggest that instead of teaching requiring metacognition, teaching might be the catalyst for metacognition’s development.

Teaching behaviors and capacities develop. This is further evidence that human teaching is grounded on more elementary skills and tools that develop with age, some of which do not require reflexive metacognitive capacities, but are eventually enhanced and modified by their development. In their respective contributions, Kathleen Corriveau, Samuel Ronfard & Yixin K. Cul, and Samuel Ronfard & Paul Harris show that children can teach selectively, that is, they adapt their teaching to the target learner, and namely to what the learner knows or ignores. Corriveau points out that flexible teaching of this kind depends on the development of the ability to represent others’ mental states, in real time, and on executive functions, as well. Ronfard and Harris propose that children are more susceptible to teach norms, generic knowledge and knowledge that is difficult to acquire. This differential susceptibility indicates that teaching might respond to motivations that go beyond the fact of “filling-in a knowledge gap”, and that teaching has a normative value as well as the function of transmitting knowledge that the learner might not acquire on its own and that the teachers “wants” to impinge in the learner.

The final contribution opens up new perspectives on the study of teaching and its building blocks. Anne-Lisa Vollmer and Lars Schillingmann reverse the usual teaching situations where robots teach humans. They use robots as learners, and to modify the learner properties so as to induce different teaching on the part of human teachers. In this way it is possible to study the teacher’s attitudes in response to learners in controlled settings.

The different contributions presented are compatible with the theory proposed in this editorial, that teaching is a natural cognitive ability grounded on building blocks that are present from childhood and have an evolutionary history, that are all combined together in an adaptive complex function that both responds and fosters cultural evolution. Since the basic building blocks develop with age, teaching abilities and behaviors develop, too. The specificities of human teaching are thus explained when both human cultural evolution is taken into account and human cognitive architecture is considered.

(Caro and Hauser 1992 ) have proposed to organize teaching behaviors in three major categories: a. opportunity provisioning (giving young animals the opportunity to learn), b. coaching, and c. the invitation and encouragement to imitate a demonstrator. Opportunity provisioning can be enhanced through various forms of facilitation, e.g., reducing the complexity of the task, or helping the learner to deal with it. Coaching consists of encouragement and punishment techniques and happens in response to the learner’s acts, rather than in preparation to them (as it is the case for opportunity provisioning). Invitation to imitate is accompanied by various forms of demonstrations and is considered as typically human. This basic classification as served as a basis for two others. (Hoppitt et al. 2008 ) have proposed 5 categories of teaching, each corresponding to a specific, observed form of social learning: 1. teaching through local enhancement - e.g. by attracting the learner to a particular spot, as it happens in the case described above of tandem running ants; 2. teaching by exposing the learner to the association between two stimuli - e.g. babblers conditioning the association between a particular call and food; 3. teaching through demonstration - e.g. humans demonstrating gestures and actions; 4. teaching by structuring the task for the learner so as to make it easier to learn - e.g. the case of meerkats simplifying the task for the younger learners; 5. teaching by encouraging and discouraging behaviors. The theory behind this kind of classification is that teaching has evolved to increase the likelihood of social learning, and that each form of teaching represents a superstimulus for facilitating that particular form of learning. Based on her ethnographic work in the Fiji Islands, and on an extensive review of the literature, anthropologist Michelle-Ann Kline has proposed a slightly different taxonomy of teaching types, which we have adopted here, ranging from very minimalistic to very demanding forms of teaching (in terms of the teacher’s effort). In Kline’s taxonomy, different forms of teaching represent the selected answer to different forms of learning problems, in given ecological conditions. The taxonomy proposed by Kline includes: a. teaching by social tolerance (in which the teacher simply accepts the presence and observation of another individual, beyond what she would do in “normal circumstances”, but without altering her behavior relative to the task); b. opportunity provisioning, c. stimulus and local enhancement (the teacher creates opportunities for practicing, eventually simplifies the task for the learner; or the teacher “guides” learners towards relevant objects, puts them in situations where they have opportunities for learning specific skills or acquiring specific knowledge; more generally: the teacher manipulates the learner’s attention); d. teaching by evaluative feedback (when the teacher rewards or punishes certain actions or their outcomes); e. directed active teaching (not just in the more developed forms of explicit formal teaching but also in communicative natural interactions, such as natural pedagogy) (Kline 2015 ).

Current researches in brain-to-brain coupling are susceptible of shedding new light on the neural processes that underpin the creation of this kind of synchronicity, how it affects learning and its role in creating a shared experience between individuals (Hasson et al. 2012 ).

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Acknowledgement

Elena Pasquinelli acknowledges the program FrontCog and grant ANR-10-IDEX-0001-02 for research carried out at the Department of Cognitive Studies of ENS.

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Pasquinelli, E., Strauss, S. Introduction: Teaching and its Building Blocks. Rev.Phil.Psych. 9 , 719–749 (2018). https://doi.org/10.1007/s13164-018-0422-3

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