qualitative quantitative or mixed methods research

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Chapter 3: Developing a Research Question

3.5 Quantitative, Qualitative, & Mixed Methods Research Approaches

Generally speaking, qualitative and quantitative approaches are the most common methods utilized by researchers. While these two approaches are often presented as a dichotomy, in reality it is much more complicated. Certainly, there are researchers who fall on the more extreme ends of these two approaches, however most recognize the advantages and usefulness of combining both methods (mixed methods). In the following sections we look at quantitative, qualitative, and mixed methodological approaches to undertaking research. Table 2.3 synthesizes the differences between quantitative and qualitative research approaches.

Quantitative Research Approaches

A quantitative approach to research is probably the most familiar approach for the typical research student studying at the introductory level. Arising from the natural sciences, e.g., chemistry and biology), the quantitative approach is framed by the belief that there is one reality or truth that simply requires discovering, known as realism. Therefore, asking the “right” questions is key. Further, this perspective favours observable causes and effects and is therefore outcome-oriented. Typically, aggregate data is used to see patterns and “truth” about the phenomenon under study. True understanding is determined by the ability to predict the phenomenon.

Qualitative Research Approaches

On the other side of research approaches is the qualitative approach. This is generally considered to be the opposite of the quantitative approach. Qualitative researchers are considered phenomenologists, or human-centred researchers. Any research must account for the humanness, i.e., that they have thoughts, feelings, and experiences that they interpret of the participants. Instead of a realist perspective suggesting one reality or truth, qualitative researchers tend to favour the constructionist perspective: knowledge is created, not discovered, and there are multiple realities based on someone’s perspective. Specifically, a researcher needs to understand why, how and to whom a phenomenon applies. These aspects are usually unobservable since they are the thoughts, feelings and experiences of the person. Most importantly, they are a function of their perception of those things rather than what the outside researcher interprets them to be. As a result, there is no such thing as a neutral or objective outsider, as in the quantitative approach. Rather, the approach is generally process-oriented. True understanding, rather than information based on prediction, is based on understanding action and on the interpretive meaning of that action.

Note: Researchers in emergency and safety professions are increasingly turning toward qualitative methods. Here is an interesting peer paper related to qualitative research in emergency care (two parts).

Qualitative Research in Emergency Care Part I: Research Principles and Common Applications by Choo, Garro, Ranney, Meisel, and Guthrie (2015)

Interview-based Qualitative Research in Emergency Care Part II: Data Collection, Analysis and Results Reporting

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• Mixed Methods Research | Definition, Guide & Examples

Mixed Methods Research | Definition, Guide & Examples

Published on August 13, 2021 by Tegan George . Revised on June 22, 2023.

Mixed methods research combines elements of quantitative research and qualitative research in order to answer your research question . Mixed methods can help you gain a more complete picture than a standalone quantitative or qualitative study, as it integrates benefits of both methods.

Mixed methods research is often used in the behavioral, health, and social sciences, especially in multidisciplinary settings and complex situational or societal research.

• To what extent does the frequency of traffic accidents ( quantitative ) reflect cyclist perceptions of road safety ( qualitative ) in Amsterdam?
• How do student perceptions of their school environment ( qualitative ) relate to differences in test scores ( quantitative ) ?
• How do interviews about job satisfaction at Company X ( qualitative ) help explain year-over-year sales performance and other KPIs ( quantitative ) ?
• How can voter and non-voter beliefs about democracy ( qualitative ) help explain election turnout patterns ( quantitative ) in Town X?
• How do average hospital salary measurements over time (quantitative) help to explain nurse testimonials about job satisfaction (qualitative) ?

Table of contents

When to use mixed methods research, mixed methods research designs, advantages of mixed methods research, disadvantages of mixed methods research, other interesting articles, frequently asked questions.

Mixed methods research may be the right choice if your research process suggests that quantitative or qualitative data alone will not sufficiently answer your research question. There are several common reasons for using mixed methods research:

• Generalizability : Qualitative research usually has a smaller sample size , and thus is not generalizable. In mixed methods research, this comparative weakness is mitigated by the comparative strength of “large N,” externally valid quantitative research.
• Contextualization: Mixing methods allows you to put findings in context and add richer detail to your conclusions. Using qualitative data to illustrate quantitative findings can help “put meat on the bones” of your analysis.
• Credibility: Using different methods to collect data on the same subject can make your results more credible. If the qualitative and quantitative data converge, this strengthens the validity of your conclusions. This process is called triangulation .

As you formulate your research question , try to directly address how qualitative and quantitative methods will be combined in your study. If your research question can be sufficiently answered via standalone quantitative or qualitative analysis, a mixed methods approach may not be the right fit.

But mixed methods might be a good choice if you want to meaningfully integrate both of these questions in one research study.

Keep in mind that mixed methods research doesn’t just mean collecting both types of data; you need to carefully consider the relationship between the two and how you’ll integrate them into coherent conclusions.

Mixed methods can be very challenging to put into practice, and comes with the same risk of research biases as standalone studies, so it’s a less common choice than standalone qualitative or qualitative research.

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There are different types of mixed methods research designs . The differences between them relate to the aim of the research, the timing of the data collection , and the importance given to each data type.

As you design your mixed methods study, also keep in mind:

• Your research approach ( inductive vs deductive )
• Your research questions
• What kind of data is already available for you to use
• What kind of data you’re able to collect yourself.

Here are a few of the most common mixed methods designs.

Convergent parallel

In a convergent parallel design, you collect quantitative and qualitative data at the same time and analyze them separately. After both analyses are complete, compare your results to draw overall conclusions.

• On the qualitative side, you analyze cyclist complaints via the city’s database and on social media to find out which areas are perceived as dangerous and why.
• On the quantitative side, you analyze accident reports in the city’s database to find out how frequently accidents occur in different areas of the city.

In an embedded design, you collect and analyze both types of data at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.

This is a good approach to take if you have limited time or resources. You can use an embedded design to strengthen or supplement your conclusions from the primary type of research design.

Explanatory sequential

In an explanatory sequential design, your quantitative data collection and analysis occurs first, followed by qualitative data collection and analysis.

You should use this design if you think your qualitative data will explain and contextualize your quantitative findings.

Exploratory sequential

In an exploratory sequential design, qualitative data collection and analysis occurs first, followed by quantitative data collection and analysis.

You can use this design to first explore initial questions and develop hypotheses . Then you can use the quantitative data to test or confirm your qualitative findings.

“Best of both worlds” analysis

Combining the two types of data means you benefit from both the detailed, contextualized insights of qualitative data and the generalizable , externally valid insights of quantitative data. The strengths of one type of data often mitigate the weaknesses of the other.

For example, solely quantitative studies often struggle to incorporate the lived experiences of your participants, so adding qualitative data deepens and enriches your quantitative results.

Solely qualitative studies are often not very generalizable, only reflecting the experiences of your participants, so adding quantitative data can validate your qualitative findings.

Method flexibility

Mixed methods are less tied to disciplines and established research paradigms. They offer more flexibility in designing your research, allowing you to combine aspects of different types of studies to distill the most informative results.

Mixed methods research can also combine theory generation and hypothesis testing within a single study, which is unusual for standalone qualitative or quantitative studies.

Mixed methods research is very labor-intensive. Collecting, analyzing, and synthesizing two types of data into one research product takes a lot of time and effort, and often involves interdisciplinary teams of researchers rather than individuals. For this reason, mixed methods research has the potential to cost much more than standalone studies.

Differing or conflicting results

If your analysis yields conflicting results, it can be very challenging to know how to interpret them in a mixed methods study. If the quantitative and qualitative results do not agree or you are concerned you may have confounding variables , it can be unclear how to proceed.

Due to the fact that quantitative and qualitative data take two vastly different forms, it can also be difficult to find ways to systematically compare the results, putting your data at risk for bias in the interpretation stage.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

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Qualitative vs Quantitative Research: Differences, Examples, and Methods

There are two broad kinds of research approaches: qualitative and quantitative research that are used to study and analyze phenomena in various fields such as natural sciences, social sciences, and humanities. Whether you have realized it or not, your research must have followed either or both research types. In this article we will discuss what qualitative vs quantitative research is, their applications, pros and cons, and when to use qualitative vs quantitative research . Before we get into the details, it is important to understand the differences between the qualitative and quantitative research.     

Table of Contents

Qualitative v s Quantitative Research  

Quantitative research deals with quantity, hence, this research type is concerned with numbers and statistics to prove or disapprove theories or hypothesis. In contrast, qualitative research is all about quality – characteristics, unquantifiable features, and meanings to seek deeper understanding of behavior and phenomenon. These two methodologies serve complementary roles in the research process, each offering unique insights and methods suited to different research questions and objectives.    

Qualitative and quantitative research approaches have their own unique characteristics, drawbacks, advantages, and uses. Where quantitative research is mostly employed to validate theories or assumptions with the goal of generalizing facts to the larger population, qualitative research is used to study concepts, thoughts, or experiences for the purpose of gaining the underlying reasons, motivations, and meanings behind human behavior .   

What Are the Differences Between Qualitative and Quantitative Research  

Qualitative and quantitative research differs in terms of the methods they employ to conduct, collect, and analyze data. For example, qualitative research usually relies on interviews, observations, and textual analysis to explore subjective experiences and diverse perspectives. While quantitative data collection methods include surveys, experiments, and statistical analysis to gather and analyze numerical data. The differences between the two research approaches across various aspects are listed in the table below.    

Data Collection Methods  

There are differences between qualitative and quantitative research when it comes to data collection as they deal with different types of data. Qualitative research is concerned with personal or descriptive accounts to understand human behavior within society. Quantitative research deals with numerical or measurable data to delineate relations among variables. Hence, the qualitative data collection methods differ significantly from quantitative data collection methods due to the nature of data being collected and the research objectives. Below is the list of data collection methods for each research approach:    

Qualitative Research Data Collection  

• Interviews  
• Focus g roups  
• Content a nalysis  
• Literature review  
• Observation  
• Ethnography  

Qualitative research data collection can involve one-on-one group interviews to capture in-depth perspectives of participants using open-ended questions. These interviews could be structured, semi-structured or unstructured depending upon the nature of the study. Focus groups can be used to explore specific topics and generate rich data through discussions among participants. Another qualitative data collection method is content analysis, which involves systematically analyzing text documents, audio, and video files or visual content to uncover patterns, themes, and meanings. This can be done through coding and categorization of raw data to draw meaningful insights. Data can be collected through observation studies where the goal is to simply observe and document behaviors, interaction, and phenomena in natural settings without interference. Lastly, ethnography allows one to immerse themselves in the culture or environment under study for a prolonged period to gain a deep understanding of the social phenomena.   

Quantitative Research Data Collection  

• Surveys/ q uestionnaires  
• Experiments
• Secondary data analysis  
• Structured o bservations  
• Case studies   
• Tests and a ssessments  

Quantitative research data collection approaches comprise of fundamental methods for generating numerical data that can be analyzed using statistical or mathematical tools. The most common quantitative data collection approach is the usage of structured surveys with close-ended questions to collect quantifiable data from a large sample of participants. These can be conducted online, over the phone, or in person.   

Performing experiments is another important data collection approach, in which variables are manipulated under controlled conditions to observe their effects on dependent variables. This often involves random assignment of participants to different conditions or groups. Such experimental settings are employed to gauge cause-and-effect relationships and understand a complex phenomenon. At times, instead of acquiring original data, researchers may deal with secondary data, which is the dataset curated by others, such as government agencies, research organizations, or academic institute. With structured observations, subjects in a natural environment can be studied by controlling the variables which aids in understanding the relationship among various variables. The secondary data is then analyzed to identify patterns and relationships among variables. Observational studies provide a means to systematically observe and record behaviors or phenomena as they occur in controlled environments. Case studies form an interesting study methodology in which a researcher studies a single entity or a small number of entities (individuals or organizations) in detail to understand complex phenomena within a specific context.   

Qualitative vs Quantitative Research Outcomes  

Qualitative research and quantitative research lead to varied research outcomes, each with its own strengths and limitations. For example, qualitative research outcomes provide deep descriptive accounts of human experiences, motivations, and perspectives that allow us to identify themes or narratives and context in which behavior, attitudes, or phenomena occurs.  Quantitative research outcomes on the other hand produce numerical data that is analyzed statistically to establish patterns and relationships objectively, to form generalizations about the larger population and make predictions. This numerical data can be presented in the form of graphs, tables, or charts. Both approaches offer valuable perspectives on complex phenomena, with qualitative research focusing on depth and interpretation, while quantitative research emphasizes numerical analysis and objectivity.  

When to Use Qualitative vs Quantitative Research Approach  

The decision to choose between qualitative and quantitative research depends on various factors, such as the research question, objectives, whether you are taking an inductive or deductive approach, available resources, practical considerations such as time and money, and the nature of the phenomenon under investigation. To simplify, quantitative research can be used if the aim of the research is to prove or test a hypothesis, while qualitative research should be used if the research question is more exploratory and an in-depth understanding of the concepts, behavior, or experiences is needed.     

Qualitative research approach  

Qualitative research approach is used under following scenarios:   

• To study complex phenomena: When the research requires understanding the depth, complexity, and context of a phenomenon.  
• Collecting participant perspectives: When the goal is to understand the why behind a certain behavior, and a need to capture subjective experiences and perceptions of participants.  
• Generating hypotheses or theories: When generating hypotheses, theories, or conceptual frameworks based on exploratory research.  

Example: If you have a research question “What obstacles do expatriate students encounter when acquiring a new language in their host country?”  

This research question can be addressed using the qualitative research approach by conducting in-depth interviews with 15-25 expatriate university students. Ask open-ended questions such as “What are the major challenges you face while attempting to learn the new language?”, “Do you find it difficult to learn the language as an adult?”, and “Do you feel practicing with a native friend or colleague helps the learning process”?  

Based on the findings of these answers, a follow-up questionnaire can be planned to clarify things. Next step will be to transcribe all interviews using transcription software and identify themes and patterns.   

Quantitative research approach  

Quantitative research approach is used under following scenarios:   

• Testing hypotheses or proving theories: When aiming to test hypotheses, establish relationships, or examine cause-and-effect relationships.   
• Generalizability: When needing findings that can be generalized to broader populations using large, representative samples.  
• Statistical analysis: When requiring rigorous statistical analysis to quantify relationships, patterns, or trends in data.   

Example : Considering the above example, you can conduct a survey of 200-300 expatriate university students and ask them specific questions such as: “On a scale of 1-10 how difficult is it to learn a new language?”  

Next, statistical analysis can be performed on the responses to draw conclusions like, on an average expatriate students rated the difficulty of learning a language 6.5 on the scale of 10.    

Mixed methods approach  

In many cases, researchers may opt for a mixed methods approach , combining qualitative and quantitative methods to leverage the strengths of both approaches. Researchers may use qualitative data to explore phenomena in-depth and generate hypotheses, while quantitative data can be used to test these hypotheses and generalize findings to broader populations.  

Example: Both qualitative and quantitative research methods can be used in combination to address the above research question. Through open-ended questions you can gain insights about different perspectives and experiences while quantitative research allows you to test that knowledge and prove/disprove your hypothesis.   

How to Analyze Qualitative and Quantitative Data  

When it comes to analyzing qualitative and quantitative data, the focus is on identifying patterns in the data to highlight the relationship between elements. The best research method for any given study should be chosen based on the study aim. A few methods to analyze qualitative and quantitative data are listed below.  

Analyzing qualitative data  

Qualitative data analysis is challenging as it is not expressed in numbers and consists majorly of texts, images, or videos. Hence, care must be taken while using any analytical approach. Some common approaches to analyze qualitative data include:  

• Organization: The first step is data (transcripts or notes) organization into different categories with similar concepts, themes, and patterns to find inter-relationships.  
• Coding: Data can be arranged in categories based on themes/concepts using coding.  
• Theme development: Utilize higher-level organization to group related codes into broader themes.  
• Interpretation: Explore the meaning behind different emerging themes to understand connections. Use different perspectives like culture, environment, and status to evaluate emerging themes.  
• Reporting: Present findings with quotes or excerpts to illustrate key themes.   

Analyzing quantitative data  

Quantitative data analysis is more direct compared to qualitative data as it primarily deals with numbers. Data can be evaluated using simple math or advanced statistics (descriptive or inferential). Some common approaches to analyze quantitative data include:  

• Processing raw data: Check missing values, outliers, or inconsistencies in raw data.  
• Descriptive statistics: Summarize data with means, standard deviations, or standard error using programs such as Excel, SPSS, or R language.  
• Exploratory data analysis: Usage of visuals to deduce patterns and trends.  
• Hypothesis testing: Apply statistical tests to find significance and test hypothesis (Student’s t-test or ANOVA).  
• Interpretation: Analyze results considering significance and practical implications.  
• Validation: Data validation through replication or literature review.  
• Reporting: Present findings by means of tables, figures, or graphs.   

Benefits and limitations of qualitative vs quantitative research  

There are significant differences between qualitative and quantitative research; we have listed the benefits and limitations of both methods below:  

Benefits of qualitative research  

• Rich insights: As qualitative research often produces information-rich data, it aids in gaining in-depth insights into complex phenomena, allowing researchers to explore nuances and meanings of the topic of study.  
• Flexibility: One of the most important benefits of qualitative research is flexibility in acquiring and analyzing data that allows researchers to adapt to the context and explore more unconventional aspects.  
• Contextual understanding: With descriptive and comprehensive data, understanding the context in which behaviors or phenomena occur becomes accessible.   
• Capturing different perspectives: Qualitative research allows for capturing different participant perspectives with open-ended question formats that further enrich data.   
• Hypothesis/theory generation: Qualitative research is often the first step in generating theory/hypothesis, which leads to future investigation thereby contributing to the field of research.

Limitations of qualitative research  

• Subjectivity: It is difficult to have objective interpretation with qualitative research, as research findings might be influenced by the expertise of researchers. The risk of researcher bias or interpretations affects the reliability and validity of the results.   
• Limited generalizability: Due to the presence of small, non-representative samples, the qualitative data cannot be used to make generalizations to a broader population.  
• Cost and time intensive: Qualitative data collection can be time-consuming and resource-intensive, therefore, it requires strategic planning and commitment.   
• Complex analysis: Analyzing qualitative data needs specialized skills and techniques, hence, it’s challenging for researchers without sufficient training or experience.   
• Potential misinterpretation: There is a risk of sampling bias and misinterpretation in data collection and analysis if researchers lack cultural or contextual understanding.   

Benefits of quantitative research  

• Objectivity: A key benefit of quantitative research approach, this objectivity reduces researcher bias and subjectivity, enhancing the reliability and validity of findings.   
• Generalizability: For quantitative research, the sample size must be large and representative enough to allow for generalization to broader populations.   
• Statistical analysis: Quantitative research enables rigorous statistical analysis (increasing power of the analysis), aiding hypothesis testing and finding patterns or relationship among variables.   
• Efficiency: Quantitative data collection and analysis is usually more efficient compared to the qualitative methods, especially when dealing with large datasets.   
• Clarity and Precision: The findings are usually clear and precise, making it easier to present them as graphs, tables, and figures to convey them to a larger audience.  

Limitations of quantitative research  

• Lacks depth and details: Due to its objective nature, quantitative research might lack the depth and richness of qualitative approaches, potentially overlooking important contextual factors or nuances.   
• Limited exploration: By not considering the subjective experiences of participants in depth , there’s a limited chance to study complex phenomenon in detail.   
• Potential oversimplification: Quantitative research may oversimplify complex phenomena by boiling them down to numbers, which might ignore key nuances.   
• Inflexibility: Quantitative research deals with predecided varibales and measures , which limits the ability of researchers to explore unexpected findings or adjust the research design as new findings become available .  
• Ethical consideration: Quantitative research may raise ethical concerns especially regarding privacy, informed consent, and the potential for harm, when dealing with sensitive topics or vulnerable populations.   

Frequently asked questions  

• What is the difference between qualitative and quantitative research? 

Quantitative methods use numerical data and statistical analysis for objective measurement and hypothesis testing, emphasizing generalizability. Qualitative methods gather non-numerical data to explore subjective experiences and contexts, providing rich, nuanced insights.  

• What are the types of qualitative research? 

Qualitative research methods include interviews, observations, focus groups, and case studies. They provide rich insights into participants’ perspectives and behaviors within their contexts, enabling exploration of complex phenomena.  

• What are the types of quantitative research? 

Quantitative research methods include surveys, experiments, observations, correlational studies, and longitudinal research. They gather numerical data for statistical analysis, aiming for objectivity and generalizability.  

• Can you give me examples for qualitative and quantitative research? 

Qualitative Research Example: 

Research Question: What are the experiences of parents with autistic children in accessing support services?  

Method: Conducting in-depth interviews with parents to explore their perspectives, challenges, and needs.  

Quantitative Research Example: 

Research Question: What is the correlation between sleep duration and academic performance in college students?  

Method: Distributing surveys to a large sample of college students to collect data on their sleep habits and academic performance, then analyzing the data statistically to determine any correlations.  

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• Qualitative vs Quantitative Research | Examples & Methods

Qualitative vs Quantitative Research | Examples & Methods

Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.

When collecting and analysing data, quantitative research deals with numbers and statistics, while qualitative research  deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs quantitative research, how to analyse qualitative and quantitative data, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyse data, and they allow you to answer different kinds of research questions.

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Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations: Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups: Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organisation for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis)
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: ‘on a scale from 1-5, how satisfied are your with your professors?’

You can perform statistical analysis on the data and draw conclusions such as: ‘on average students rated their professors 4.4’.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: ‘How satisfied are you with your studies?’, ‘What is the most positive aspect of your study program?’ and ‘What can be done to improve the study program?’

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analysed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analysing quantitative data

Quantitative data is based on numbers. Simple maths or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analysing qualitative data

Qualitative data is more difficult to analyse than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analysing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Streefkerk, R. (2023, May 08). Qualitative vs Quantitative Research | Examples & Methods. Scribbr. Retrieved 12 August 2024, from https://www.scribbr.co.uk/research-methods/quantitative-qualitative-research/

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Qualitative vs Quantitative Research 101

A plain-language explanation (with examples).

By: Kerryn Warren (PhD, MSc, BSc) | June 2020

So, it’s time to decide what type of research approach you’re going to use – qualitative or quantitative . And, chances are, you want to choose the one that fills you with the least amount of dread. The engineers may be keen on quantitative methods because they loathe interacting with human beings and dealing with the “soft” stuff and are far more comfortable with numbers and algorithms. On the other side, the anthropologists are probably more keen on qualitative methods because they literally have the opposite fears.

However, when justifying your research, “being afraid” is not a good basis for decision making. Your methodology needs to be informed by your research aims and objectives , not your comfort zone. Plus, it’s quite common that the approach you feared (whether qualitative or quantitative) is actually not that big a deal. Research methods can be learnt (usually a lot faster than you think) and software reduces a lot of the complexity of both quantitative and qualitative data analysis. Conversely, choosing the wrong approach and trying to fit a square peg into a round hole is going to create a lot more pain.

In this post, I’ll explain the qualitative vs quantitative choice in straightforward, plain language with loads of examples. This won’t make you an expert in either, but it should give you a good enough “big picture” understanding so that you can make the right methodological decision for your research.

Qualitative vs Quantitative: Overview  

• Qualitative analysis 101
• Quantitative analysis 101
• How to choose which one to use
• Data collection and analysis for qualitative and quantitative research
• The pros and cons of both qualitative and quantitative research
• A quick word on mixed methods

Qualitative Research 101: The Basics

The bathwater is hot.

Let us unpack that a bit. What does that sentence mean? And is it useful?

The answer is: well, it depends. If you’re wanting to know the exact temperature of the bath, then you’re out of luck. But, if you’re wanting to know how someone perceives the temperature of the bathwater, then that sentence can tell you quite a bit if you wear your qualitative hat .

Many a husband and wife have never enjoyed a bath together because of their strongly held, relationship-destroying perceptions of water temperature (or, so I’m told). And while divorce rates due to differences in water-temperature perception would belong more comfortably in “quantitative research”, analyses of the inevitable arguments and disagreements around water temperature belong snugly in the domain of “qualitative research”. This is because qualitative research helps you understand people’s perceptions and experiences  by systematically coding and analysing the data .

With qualitative research, those heated disagreements (excuse the pun) may be analysed in several ways. From interviews to focus groups to direct observation (ideally outside the bathroom, of course). You, as the researcher, could be interested in how the disagreement unfolds, or the emotive language used in the exchange. You might not even be interested in the words at all, but in the body language of someone who has been forced one too many times into (what they believe) was scalding hot water during what should have been a romantic evening. All of these “softer” aspects can be better understood with qualitative research.

In this way, qualitative research can be incredibly rich and detailed , and is often used as a basis to formulate theories and identify patterns. In other words, it’s great for exploratory research (for example, where your objective is to explore what people think or feel), as opposed to confirmatory research (for example, where your objective is to test a hypothesis). Qualitative research is used to understand human perception , world view and the way we describe our experiences. It’s about exploring and understanding a broad question, often with very few preconceived ideas as to what we may find.

But that’s not the only way to analyse bathwater, of course…

Quantitative Research 101: The Basics

The bathwater is 45 degrees Celsius.

Now, what does this mean? How can this be used?

I was once told by someone to whom I am definitely not married that he takes regular cold showers. As a person who is terrified of anything that isn’t body temperature or above, this seemed outright ludicrous. But this raises a question: what is the perfect temperature for a bath? Or at least, what is the temperature of people’s baths more broadly? (Assuming, of course, that they are bathing in water that is ideal to them). To answer this question, you need to now put on your quantitative hat .

If we were to ask 100 people to measure the temperature of their bathwater over the course of a week, we could get the average temperature for each person. Say, for instance, that Jane averages at around 46.3°C. And Billy averages around 42°C. A couple of people may like the unnatural chill of 30°C on the average weekday. And there will be a few of those striving for the 48°C that is apparently the legal limit in England (now, there’s a useless fact for you).

With a quantitative approach, this data can be analysed in heaps of ways. We could, for example, analyse these numbers to find the average temperature, or look to see how much these temperatures vary. We could see if there are significant differences in ideal water temperature between the sexes, or if there is some relationship between ideal bath water temperature and age! We could pop this information onto colourful, vibrant graphs , and use fancy words like “significant”, “correlation” and “eigenvalues”. The opportunities for nerding out are endless…

In this way, quantitative research often involves coming into your research with some level of understanding or expectation regarding the outcome, usually in the form of a hypothesis that you want to test. For example:

Hypothesis: Men prefer bathing in lower temperature water than women do.

This hypothesis can then be tested using statistical analysis. The data may suggest that the hypothesis is sound, or it may reveal that there are some nuances regarding people’s preferences. For example, men may enjoy a hotter bath on certain days.

So, as you can see, qualitative and quantitative research each have their own purpose and function. They are, quite simply, different tools for different jobs .

Need a helping hand?

Qualitative vs Quantitative Research: Which one should you use?

And here I become annoyingly vague again. The answer: it depends. As I alluded to earlier, your choice of research approach depends on what you’re trying to achieve with your research. 

If you want to understand a situation with richness and depth , and you don’t have firm expectations regarding what you might find, you’ll likely adopt a qualitative research approach. In other words, if you’re starting on a clean slate and trying to build up a theory (which might later be tested), qualitative research probably makes sense for you.

On the other hand, if you need to test an already-theorised hypothesis , or want to measure and describe something numerically, a quantitative approach will probably be best. For example, you may want to quantitatively test a theory (or even just a hypothesis) that was developed using qualitative research.

Basically, this means that your research approach should be chosen based on your broader research aims , objectives and research questions . If your research is exploratory and you’re unsure what findings may emerge, qualitative research allows you to have open-ended questions and lets people and subjects speak, in some ways, for themselves. Quantitative questions, on the other hand, will not. They’ll often be pre-categorised, or allow you to insert a numeric response. Anything that requires measurement , using a scale, machine or… a thermometer… is going to need a quantitative method.

Let’s look at an example.

Say you want to ask people about their bath water temperature preferences. There are many ways you can do this, using a survey or a questionnaire – here are 3 potential options:

• How do you feel about your spouse’s bath water temperature preference? (Qualitative. This open-ended question leaves a lot of space so that the respondent can rant in an adequate manner).
• What is your preferred bath water temperature? (This one’s tricky because most people don’t know or won’t have a thermometer, but this is a quantitative question with a directly numerical answer).
• Most people who have commented on your bath water temperature have said the following (choose most relevant): It’s too hot. It’s just right. It’s too cold. (Quantitative, because you can add up the number of people who responded in each way and compare them).

The answers provided can be used in a myriad of ways, but, while quantitative responses are easily summarised through counting or calculations, categorised and visualised, qualitative responses need a lot of thought and are re-packaged in a way that tries not to lose too much meaning.

Qualitative vs Quantitative Research: Data collection and analysis

The approach to collecting and analysing data differs quite a bit between qualitative and quantitative research.

A qualitative research approach often has a small sample size (i.e. a small number of people researched) since each respondent will provide you with pages and pages of information in the form of interview answers or observations. In our water perception analysis, it would be super tedious to watch the arguments of 50 couples unfold in front of us! But 6-10 would be manageable and would likely provide us with interesting insight into the great bathwater debate.

To sum it up, data collection in qualitative research involves relatively small sample sizes but rich and detailed data.

On the other side, quantitative research relies heavily on the ability to gather data from a large sample and use it to explain a far larger population (this is called “generalisability”). In our bathwater analysis, we would need data from hundreds of people for us to be able to make a universal statement (i.e. to generalise), and at least a few dozen to be able to identify a potential pattern. In terms of data collection, we’d probably use a more scalable tool such as an online survey to gather comparatively basic data.

So, compared to qualitative research, data collection for quantitative research involves large sample sizes but relatively basic data.

Both research approaches use analyses that allow you to explain, describe and compare the things that you are interested in. While qualitative research does this through an analysis of words, texts and explanations, quantitative research does this through reducing your data into numerical form or into graphs.

There are dozens of potential analyses which each uses. For example, qualitative analysis might look at the narration (the lamenting story of love lost through irreconcilable water toleration differences), or the content directly (the words of blame, heat and irritation used in an interview). Quantitative analysis  may involve simple calculations for averages , or it might involve more sophisticated analysis that assesses the relationships between two or more variables (for example, personality type and likelihood to commit a hot water-induced crime). We discuss the many analysis options other blog posts, so I won’t bore you with the details here.

Qualitative vs Quantitative Research: The pros & cons on both sides

Quantitative and qualitative research fundamentally ask different kinds of questions and often have different broader research intentions. As I said earlier, they are different tools for different jobs – so we can’t really pit them off against each other. Regardless, they still each have their pros and cons.

Let’s start with qualitative “pros”

Qualitative research allows for richer , more insightful (and sometimes unexpected) results. This is often what’s needed when we want to dive deeper into a research question . When we want to find out what and how people are thinking and feeling , qualitative is the tool for the job. It’s also important research when it comes to discovery and exploration when you don’t quite know what you are looking for. Qualitative research adds meat to our understanding of the world and is what you’ll use when trying to develop theories.

Qualitative research can be used to explain previously observed phenomena , providing insights that are outside of the bounds of quantitative research, and explaining what is being or has been previously observed. For example, interviewing someone on their cold-bath-induced rage can help flesh out some of the finer (and often lost) details of a research area. We might, for example, learn that some respondents link their bath time experience to childhood memories where hot water was an out of reach luxury. This is something that would never get picked up using a quantitative approach.

There are also a bunch of practical pros to qualitative research. A small sample size means that the researcher can be more selective about who they are approaching. Linked to this is affordability . Unless you have to fork out huge expenses to observe the hunting strategies of the Hadza in Tanzania, then qualitative research often requires less sophisticated and expensive equipment for data collection and analysis.

Qualitative research also has its “cons”:

A small sample size means that the observations made might not be more broadly applicable. This makes it difficult to repeat a study and get similar results. For instance, what if the people you initially interviewed just happened to be those who are especially passionate about bathwater. What if one of your eight interviews was with someone so enraged by a previous experience of being run a cold bath that she dedicated an entire blog post to using this obscure and ridiculous example?

But sample is only one caveat to this research. A researcher’s bias in analysing the data can have a profound effect on the interpretation of said data. In this way, the researcher themselves can limit their own research. For instance, what if they didn’t think to ask a very important or cornerstone question because of previously held prejudices against the person they are interviewing?

Adding to this, researcher inexperience is an additional limitation . Interviewing and observing are skills honed in over time. If the qualitative researcher is not aware of their own biases and limitations, both in the data collection and analysis phase, this could make their research very difficult to replicate, and the theories or frameworks they use highly problematic.

Qualitative research takes a long time to collect and analyse data from a single source. This is often one of the reasons sample sizes are pretty small. That one hour interview? You are probably going to need to listen to it a half a dozen times. And read the recorded transcript of it a half a dozen more. Then take bits and pieces of the interview and reformulate and categorize it, along with the rest of the interviews.

Now let’s turn to quantitative “pros”:

Even simple quantitative techniques can visually and descriptively support or reject assumptions or hypotheses . Want to know the percentage of women who are tired of cold water baths? Boom! Here is the percentage, and a pie chart. And the pie chart is a picture of a real pie in order to placate the hungry, angry mob of cold-water haters.

Quantitative research is respected as being objective and viable . This is useful for supporting or enforcing public opinion and national policy. And if the analytical route doesn’t work, the remainder of the pie can be thrown at politicians who try to enforce maximum bath water temperature standards. Clear, simple, and universally acknowledged. Adding to this, large sample sizes, calculations of significance and half-eaten pies, don’t only tell you WHAT is happening in your data, but the likelihood that what you are seeing is real and repeatable in future research. This is an important cornerstone of the scientific method.

Quantitative research can be pretty fast . The method of data collection is faster on average: for instance, a quantitative survey is far quicker for the subject than a qualitative interview. The method of data analysis is also faster on average. In fact, if you are really fancy, you can code and automate your analyses as your data comes in! This means that you don’t necessarily have to worry about including a long analysis period into your research time.

Lastly – sometimes, not always, quantitative research may ensure a greater level of anonymity , which is an important ethical consideration . A survey may seem less personally invasive than an interview, for instance, and this could potentially also lead to greater honesty. Of course, this isn’t always the case. Without a sufficient sample size, respondents can still worry about anonymity – for example, a survey within a small department.

But there are also quantitative “cons”:

Quantitative research can be comparatively reductive – in other words, it can lead to an oversimplification of a situation. Because quantitative analysis often focuses on the averages and the general relationships between variables, it tends to ignore the outliers. Why is that one person having an ice bath once a week? With quantitative research, you might never know…

It requires large sample sizes to be used meaningfully. In order to claim that your data and results are meaningful regarding the population you are studying, you need to have a pretty chunky dataset. You need large numbers to achieve “statistical power” and “statistically significant” results – often those large sample sizes are difficult to achieve, especially for budgetless or self-funded research such as a Masters dissertation or thesis.

Quantitative techniques require a bit of practice and understanding (often more understanding than most people who use them have). And not just to do, but also to read and interpret what others have done, and spot the potential flaws in their research design (and your own). If you come from a statistics background, this won’t be a problem – but most students don’t have this luxury.

Finally, because of the assumption of objectivity (“it must be true because its numbers”), quantitative researchers are less likely to interrogate and be explicit about their own biases in their research. Sample selection, the kinds of questions asked, and the method of analysis are all incredibly important choices, but they tend to not be given as much attention by researchers, exactly because of the assumption of objectivity.

Mixed methods: a happy medium?

Some of the richest research I’ve seen involved a mix of qualitative and quantitative research. Quantitative research allowed the researcher to paint “birds-eye view” of the issue or topic, while qualitative research enabled a richer understanding. This is the essence of mixed-methods research – it tries to achieve the best of both worlds .

In practical terms, this can take place by having open-ended questions as a part of your research survey. It can happen by having a qualitative separate section (like several interviews) to your otherwise quantitative research (an initial survey, from which, you could invite specific interviewees). Maybe it requires observations: some of which you expect to see, and can easily record, classify and quantify, and some of which are novel, and require deeper description.

A word of warning – just like with choosing a qualitative or quantitative research project, mixed methods should be chosen purposefully , where the research aims, objectives and research questions drive the method chosen. Don’t choose a mixed-methods approach just because you’re unsure of whether to use quantitative or qualitative research. Pulling off mixed methods research well is not an easy task, so approach with caution!

Recap: Qualitative vs Quantitative Research

So, just to recap what we have learned in this post about the great qual vs quant debate:

• Qualitative research is ideal for research which is exploratory in nature (e.g. formulating a theory or hypothesis), whereas quantitative research lends itself to research which is more confirmatory (e.g. hypothesis testing)
• Qualitative research uses data in the form of words, phrases, descriptions or ideas. It is time-consuming and therefore only has a small sample size .
• Quantitative research uses data in the form of numbers and can be visualised in the form of graphs. It requires large sample sizes to be meaningful.
• Your choice in methodology should have more to do with the kind of question you are asking than your fears or previously-held assumptions.
• Mixed methods can be a happy medium, but should be used purposefully.
• Bathwater temperature is a contentious and severely under-studied research topic.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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thanks much it has given me an inside on research. i still have issue coming out with my methodology from the topic below: strategies for the improvement of infastructure resilience to natural phenomena

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Aug 15, 2024 • 13 minutes read

Mixed methods research explained: Combine data like a pro

From heatmaps to interviews, here’s how to blend qualitative and quantitative data for holistic user insights.

Ella Webber

Mixed methods research is one of the most popular and powerful UX research approaches—blending numbers with narrative to garner a holistic understanding of your product or research question.

Whether you’re in UX research and design, education, healthcare, or social sciences, mixed methods research can help you find insights and make better decisions.

Read on for a breakdown of what mixed methods are, their strengths and weaknesses, when to use them, and how to analyze the data.

UX research made easy

Explore the power of combining quantitative and qualitative research to discover new insights and test final solutions.

What is mixed methods research and when should you use it?

Mixed methods research involves collecting, analyzing, and integrating both quantitative and qualitative UX research methods within a single study. It is unique to other UX research techniques in that it combines data types, encouraging product teams to use qualitative feedback to explain the story behind quantitative numbers.

• Quantitative data can come from UX surveys , product analytics , usability testing , experiments, or statistical databases and provide broad numerical insights
• Qualitative data is gathered through user interviews , focus groups, or contextual inquiries and offers a deep, contextual understanding of the subject matter

Why use a mixed methods approach?

The power of mixed methods research is simple: it allows you to combine the best parts of both types of data—quantitative research methods, like surveys, give you broad trends, while qualitative methods, such as interviews, dig deep into personal experiences.

Anthony J. Onwuegbuzie and R. Burke Johnson, in Mixed Methods Research: A Research Paradigm Whose Time Has Come , highlight how blending these methods allows researchers to leverage the strengths of both approaches. They identify mixed methods research as one of the “three core research paradigms: qualitative, quantitative, and mixed methods.”

Like any technique, however, mixed methods research has both strengths and weaknesses to consider.

When should you use mixed methods research?

Mixed UX research methods are useful when neither qualitative nor quantitative data alone can fully answer your research question . Evaluative research further helps to assess the effectiveness of your mixed method research findings and ensure they meet user needs.

For example, use mixed methods research when:

• You need to go beyond numbers (generalizability): Quantitative methods, like surveys, provide broad trends and patterns that are relevant to a wider population. For example, a survey might show that most users enjoy a new app feature, but it won’t capture why some users might dislike it.
• The why matters (contextualization): Mixed methods allow you to put numerical findings in context, adding rich detail to your conclusions. For example, if analytics show that users are spending less time on your app (quantitative), interviews can help you understand the reasons behind this behavior, such as frustration with a recent update or a lack of engaging content (qualitative).
• Credibility is important (credibility and triangulation): When both data types converge on the same conclusion, it strengthens your findings. For example, the combined evidence is more credible if survey data indicates that most users prefer a particular software interface and focus groups echo this preference.
• You need to track changes (developmental purposes): Mixed methods are invaluable when one type of data informs the other. For example, initial qualitative research with a small group of beta testers can uncover key issues and user needs, which can then be explored quantitatively with a larger user base to see how widespread these issues are.
• Understand complex issues (complementary insights): Different data types can offer complementary insights. For example, in a study on software usability, quantitative data might show a drop in task completion rates, while qualitative data reveals specific pain points and user frustrations. This combined approach can guide more effective design improvements.

What are the types of mixed methods research design?

The type of mixed methods research design you choose depends on your research goals, the timing of data collection, and each data type. Here are some key factors to consider:

• Your research approach: Are you trying to understand existing findings (explanatory) or dig deeper into a topic (exploratory)?
• Your research questions: Do your questions need big-picture answers (like how many users are happy) and detailed explanations (like why some users are unhappy)?
• Existing data availability: Is there any existing information you can use from previous studies or a research repository (like user demographics)?
• Data you can collect yourself: What kind of in-depth information do you need to gather from users (through interviews, testing, etc.)?

Whether you're a data diver or a narrative novelist, understanding these research methods can make your studies more dynamic and insightful.

📚 A UX research repository is crucial for keeping track of research findings. You need a centralized database to store and manage all your qualitative and quantitative data. This ensures that your research is organized, accessible, and reusable for future studies.

Let’s look at the most common types of mixed methods research design:

Convergent parallel

Convergent parallel design involves collecting qualitative and quantitative data simultaneously but analyzing them separately. The primary goal is to merge the two datasets to provide a complete understanding of the research problem.

For example, let’s say you want to study user satisfaction with a new mobile app. Here’s how you might use the convergent parallel design:

• Qualitative results: Conduct in-depth user interviews with 30 participants to gather detailed insights into their experiences and perceptions of the app. Plus, analyze 200 user reviews from app stores. You might use prompts like, "What features do you find most valuable?" and "Please describe any difficulties you've experienced while using the app."
• Quantitative study: Use analytics data to measure user engagement metrics like session duration and feature usage, then distribute UX surveys to gather quantitative satisfaction scores.

Concurrent embedded design

Embedded design is a mixed methods research approach where qualitative and quantitative data are collected simultaneously, but one type of data is supplementary to the other.

The secondary data provides additional context and can help explain or clarify the primary findings. This approach is particularly beneficial when time or resources are limited, as it allows for a more comprehensive analysis without doubling the workload.

Explanatory sequential design

Explanatory sequential design is a popular mixed methods research approach introduced by John W. Creswell and Vicki L. Plano Clark. This research design involves collecting and analyzing quantitative data first, followed by qualitative data collection and analysis.

According to Creswell, this approach is particularly useful when researchers need to explain relationships found in quantitative data.

The process typically involves two phases:

• Quantitative phase: This involves collecting numerical data through methods like surveys or experiments. The goal here is to identify patterns, trends, or relationships.
• Qualitative phase: Qualitative phase: After analyzing the quantitative data, researchers collect qualitative data with qualitative approaches, like interviews or focus groups, to provide deeper insights. This phase helps explain the ‘why’ or ‘how’ behind the quantitative findings.

Creswell emphasizes that one of the strengths of this design is its simple structure, making it easy for researchers to manage and for audiences to understand the research process and findings.

Exploratory sequential design

Exploratory sequential design begins with qualitative data collection and analysis, followed by quantitative data collection. This immersive approach helps generate rich, detailed data that lays a strong foundation for the subsequent quantitative analysis.

For example, let’s say a researcher wants to understand why people don't meditate regularly. They could start with generative research techniques , like conducting workshops where participants discuss their daily routines and barriers to meditation. These qualitative insights reveal underlying themes and patterns, like time constraints and lack of motivation.

Next, the researcher analyzes these qualitative data to identify key factors impacting wellbeing habits. Based on these insights, they develop a survey to quantitatively measure how widespread these barriers are among a larger population.

So, that’s how you collect data. But how do you analyze it? Unsurprisingly, there are multiple analysis and interpretation methods commonly used in mixed methods research. Let’s look at some.

How to analyze mixed methods research data: 3 Ways to combine qualitative and quantitative data

Combining different types of research data can add credibility to your research findings. Let’s look at how to conduct mixed methods research:

Triangulation protocol

Following a thread, mixed methods matrix.

The triangulation protocol in mixed methods research is a systematic way to use multiple data sources, techniques, or perspectives to get a clear understanding of a research problem. The goal is to capitalize on the strengths of both types of data while minimizing their individual weaknesses.

Let's say you want to conduct a study aiming to evaluate the effectiveness of a new educational program on student performance, and you arrive at the following datasets:

• Quantitative finding: 80% of students improved their math scores after the program
• Qualitative finding: Students reported that interactive activities helped them understand math concepts better

When you merge these findings, the research concludes that the interactive activities (identified qualitatively) are likely a significant factor contributing to the improved scores (quantitatively).

The following a thread method allows researchers to trace a specific theme or concept across both qualitative and quantitative data sets.

Here’s how it works:

• Identify key themes: Begin by identifying key themes or variables that are central to your research questions. These themes will serve as the ‘threads’ you’ll follow through your data.
• Extracting data: Extract relevant data segments related to each theme from qualitative (e.g. interviews, focus groups) and quantitative (e.g. surveys, statistical data) sources. This involves coding qualitative data and identifying relevant quantitative measures.
• Mapping data: Create a map or matrix that links data segments from different sources according to the identified themes. This matrix helps visualize how different data points converge or diverge on the same theme.
• Comparative analysis: Compare the data segments within each theme to identify patterns, consistencies, and discrepancies. Look for how qualitative narratives support or contradict quantitative findings.
• Synthesis and interpretation: Synthesize the findings to develop an understanding of each theme. Interpret the data by integrating the qualitative insights with the quantitative results, explaining how they complement or contrast with each other.

A mixed methods matrix is a visual tool used to integrate and compare qualitative and quantitative data in mixed methods research. It helps researchers organize data according to key themes or variables, facilitating a comprehensive analysis and interpretation.

The matrix consists of several rows and columns:

• Rows represent key themes or research questions
• Columns represent different data sources or methods (e.g. interviews, surveys, observations)

By populating each cell with relevant data segments, researchers can easily identify areas of convergence, divergence, and complementarity. Let’s say you want to answer this research question: How does a new health intervention impact patient satisfaction and health outcomes?

You would populate the matrix as follows:

How to conduct mixed methods research: A mixed method research example

Let’s say you own a project management app and want to understand user satisfaction and identify areas for improvement. Here are eight steps to apply mixed methods research—using the convergent parallel technique—to discover user pain points and create a better user experience.

Step 1: Define your research objectives

In UX research , asking the right questions is crucial for identifying user needs and pain points effectively. But in order to write the right user research questions , you need to define a clear objective. What are you looking to understand?

Defining a clear UX research objective helps guide all other research decisions and acts as a lighthouse that guides your research project.

In our example , our research objective could be ‘to explore user experience and identify areas for improvement within our project management app’.

Step 2: Design your study and recruit participants

Ensure your study is designed to allow integration of both quantitative and qualitative data. There are various mixed method research designs to choose from—the right one for you depends on your research objectives and preferences.

At this stage, you should also establish a clear strategy for data integration and decide how you’ll combine the qualitative and quantitative data during the UX reporting and analysis phase. This might involve merging data sets for comparative analysis , or embedding one data set within the other to provide additional context.

The integration plan should reflect your research goals and ensure that the combined data offers a clear understanding. For our study, we’ll design a convergent parallel mixed methods study and triangulate our data during the analysis phase. This enables us to find our what and our why.

This is also when you need to recruit research participants for your study. Consider what you’re studying and identify your target test audience. You then need to create a call-out for your research study—either on socials, via email, or with In-Product Prompts .

Alternatively, you can find and filter research participants using Maze Panel , then manage your participant relationships using Maze Reach .

Step 3: Collect quantitative data

Next up, you want to start gathering your quantitative data. A good way to do this is with a survey to collect numerical data that can be statistically analyzed. For example, a user satisfaction survey that includes rating scales (1–10) for various aspects of the software.

For our research into app user satisfaction, we asked:

• Please rate your overall satisfaction with the app (1–10)
• How often do you use the app per week?
• How easy is the app to use on a scale of 1 to 10?
• How likely are you to recommend the app to a friend or colleague (1–10)?

❓ Need a quick and easy way to create and manage surveys? Maze Feedback Surveys simplify your feedback collection process so you can focus on making the changes your customers want to see. You can quickly create surveys tailored to your needs with Maze's survey templates .

Step 4: Collect qualitative data

Once you’ve got your quantitative data, it’s time to collect your qualitative data. Consider conducting user interviews or focus groups to obtain detailed, descriptive data that provides context and deep understanding.

For our study, we selected 20 users from the survey who gave varied ratings and conducted 30-minute interviews, asking:

• What do you like most about the app?
• What features do you find difficult to use?
• Can you describe a recent experience using the app?
• What improvements would you suggest?

💬 User interviews are resource-intensive and time-consuming. Speed them up with Maze’s end-to-end user interview solution: Interview Studies .

Step 5: Quantitative data analysis

Now you’ve got all your data—it’s time to dig in. For your quantitative data, this involves using statistical methodology to identify trends and patterns.

When we looked at our example data, we calculated:

• CSAT score: 75%
• Frequency of use: 70% use the app daily
• Ease of use average score: 6.8/10
• Net Promoter Score (NPS): 65

Step 6: Qualitative data analysis

Analyzing qualitative data involves coding and categorizing qualitative responses to uncover themes and patterns. Identify recurring themes in user feedback, such as ease of use, functionality, and improvement areas. If you’re using Maze Interview Studies to analyze your findings, you can automatically extract key themes and summaries to speed this process up.

When reviewing qualitative data, we found a number of interesting nuggets in our qualitative data:

• Users express dissatisfaction with the app’s usability, specifically the navigation between different functionalities
• Users wish they could access their billing details via the app, instead of solely via the web
• User find the core functionality—the project management features—to be highly valuable to their day-to-day, but also report finding the interface to be clunky and unintuitive

Step 7: Integrate data and interpret findings

Following your analysis, combine the findings from both data sets and draw conclusions. Look for correlations and insights that span both types of data.

Example integration:

• High satisfaction scores (75%) but lower ease of use (6.8/10) prove a strong product market fit but call for a more intuitive experience
• Further qualitative research agreed with this conclusion and identified specific areas for improvement, such as adding additional functionalities and improving the interface

Step 8: Report findings to stakeholders for buy-in

Present the integrated results to highlight how qualitative insights support or explain quantitative trends.

The format of your report will depend on your audience:

• Internal stakeholders (project managers, designers): Consider a concise report with clear visuals like charts, graphs, and user quotes to highlight key findings and actionable recommendations
• External stakeholders (clients, investors): Create a formal report with a clear introduction, methodology section, and comprehensive results presentation, summarizing key findings and highlighting the impact on user satisfaction and app usage

Always strive to go beyond what the data says and explain why it matters.

For example, once we’d conducted our research and drawn conclusions, we compiled this into a report that shared:

• Research methods: We used mixed methods research (surveys and interviews) to explore existing user pain points and satisfaction levels.
• Overall findings: User satisfaction is moderately high (7.5/10), indicating a generally positive reception. However, the ease of use score (6.8/10) and qualitative feedback highlight significant usability issues for new users.
• Actionable next steps based on findings: Simplify the user interface to improve the experience for new users, potentially increasing overall satisfaction and ease of use scores.

Conducting mixed methods research with Maze

Mixed methods research is one of the most effective ways to boost your UX insights, and gather a more rounded understanding of your users’ problems and perspectives. Combining research methods and types of data can uncover insights you may otherwise miss. And while there are ideal times to conduct qualitative, quantitative, or mixed methods research, ultimately it really is as simple as more research = more insights .

If you’re looking for the ideal research companion to help conduct mixed methods research, consider Maze. Maze is the user research platform that empowers all teams with the research methods they need to get game-changing insights. Whether it’s a mixed methods study or a one-off test—Maze helps you gather accurate insights, faster, for more informed decision-making.

Frequently asked questions about mixed methods research

What is the purpose of mixed methods research?

The purpose of mixed methods research is to combine quantitative and qualitative data to provide a more complete understanding of a research problem. This approach helps validate findings, explore complex issues from multiple perspectives, and produce more reliable and actionable results.

What’s the difference between qualitative and quantitative research?

• Qualitative research explores non-numerical data to understand concepts, opinions, or experiences. It uses methods like interviews, focus groups, and observations to gather in-depth insights.
• Quantitative research focuses on numerical data to quantify variables and uncover patterns. It uses methods like surveys, experiments, and statistical analysis to measure and analyze data.

What is the difference between mixed methods and multiple methods?

Mixed methods research integrates qualitative (e.g. interviews) and quantitative (e.g. surveys) data within a single study. Multiple methods research uses various research approaches, but they can be either qualitative or quantitative. For example, it might use surveys and experiments (quantitative) or interviews and focus groups (qualitative) in different parts of a study without combining the data.

Quantitative, Qualitative, and Mixed-Methods Research: Home

Quantitative, qualitative, and mixed-methods research.

Depending on the philosophy of the researcher, the nature of the data, and how it is collected, behavioral science can be classified into qualitative, quantitative, or mixed methods research. Below are descriptions of each method. 

Quantitative Research

Collects numerical data, such as frequencies or scores to focus on cause-and-effect relationships among variables

Variables and research methodologies are defined in advance by theories and hypotheses derived from other theories. These remain unchanged throughout the research process. 

The researcher tries to achieve objectivity by distancing himself or herself from the research, not allowing himself or herself to be emotionally involved.

The researcher mostly studies research in artificial or less than its natural setting, and manipulates behavior as opposed to studying the behavior in its natural context.

The researcher tries to maintain internal validity and focuses on average behavior or thoughts of people in a population

Qualitative Research

Where researchers collect non-numerical information, such as descriptions of behavioral phenomena, how people experience or interpret events, and/or answers to participants' open-ended responses.

The researcher's variables andmethods used come from the researcher's experiences and can be modified as the research progresses.

The researcher is involved and his or her experiences are valuable as well as the participants' experiences. 

The researcher studies behavior as it naturally happens in the natural context.

The researcher tries to maximize ecological validity.

The researcher focuses on similarities and differences in experiences and how people interpret them. 

Mixed-Methods Research

Involves both quantitative and qualitative components. 

The researcher specifies in advance the types of information necessary to accomplish the study's goals.

The researcher needs to carefully consider the order in which the data types will be collected and the selection criteria for participants in the various parts of the study (e.g., which people will participate in the qualitative assessment if a sub-selection of participants will be involved). 

Involves development (where the researcher uses one method to inform data collection or analysis with another method) initiation (where unexpected results change protocol in the other method), corroboration (where consistency is evaluated and compared between methods), and elaboration (where one method is used to expand on the results of the other method).

Whitley, B. E. & Kite, M. E. (2013).  Principles of research in behavioral science  (3rd ed.). Routledge. 

• Last Updated: Sep 2, 2020 12:29 PM
• URL: https://library.divinemercy.edu/research-types

• What is mixed methods research?

Last updated

20 February 2023

Reviewed by

Miroslav Damyanov

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By blending both quantitative and qualitative data, mixed methods research allows for a more thorough exploration of a research question. It can answer complex research queries that cannot be solved with either qualitative or quantitative research .

Analyze your mixed methods research

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Mixed methods research combines the elements of two types of research: quantitative and qualitative.

Quantitative data is collected through the use of surveys and experiments, for example, containing numerical measures such as ages, scores, and percentages. 

Qualitative data involves non-numerical measures like beliefs, motivations, attitudes, and experiences, often derived through interviews and focus group research to gain a deeper understanding of a research question or phenomenon.

Mixed methods research is often used in the behavioral, health, and social sciences, as it allows for the collection of numerical and non-numerical data.

• When to use mixed methods research

Mixed methods research is a great choice when quantitative or qualitative data alone will not sufficiently answer a research question. By collecting and analyzing both quantitative and qualitative data in the same study, you can draw more meaningful conclusions. 

There are several reasons why mixed methods research can be beneficial, including generalizability, contextualization, and credibility. 

For example, let's say you are conducting a survey about consumer preferences for a certain product. You could collect only quantitative data, such as how many people prefer each product and their demographics. Or you could supplement your quantitative data with qualitative data, such as interviews and focus groups , to get a better sense of why people prefer one product over another.

It is important to note that mixed methods research does not only mean collecting both types of data. Rather, it also requires carefully considering the relationship between the two and method flexibility.

You may find differing or even conflicting results by combining quantitative and qualitative data . It is up to the researcher to then carefully analyze the results and consider them in the context of the research question to draw meaningful conclusions.

When designing a mixed methods study, it is important to consider your research approach, research questions, and available data. Think about how you can use different techniques to integrate the data to provide an answer to your research question.

• Mixed methods research design

A mixed methods research design  is   an approach to collecting and analyzing both qualitative and quantitative data in a single study.

Mixed methods designs allow for method flexibility and can provide differing and even conflicting results. Examples of mixed methods research designs include convergent parallel, explanatory sequential, and exploratory sequential.

By integrating data from both quantitative and qualitative sources, researchers can gain valuable insights into their research topic . For example, a study looking into the impact of technology on learning could use surveys to measure quantitative data on students' use of technology in the classroom. At the same time, interviews or focus groups can provide qualitative data on students' experiences and opinions.

• Types of mixed method research designs

Researchers often struggle to put mixed methods research into practice, as it is challenging and can lead to research bias. Although mixed methods research can reveal differences or conflicting results between studies, it can also offer method flexibility.

Designing a mixed methods study can be broken down into four types: convergent parallel, embedded, explanatory sequential, and exploratory sequential.

Convergent parallel

The convergent parallel design is when data collection and analysis of both quantitative and qualitative data occur simultaneously and are analyzed separately. This design aims to create mutually exclusive sets of data that inform each other. 

For example, you might interview people who live in a certain neighborhood while also conducting a survey of the same people to determine their satisfaction with the area.

Embedded design

The embedded design is when the quantitative and qualitative data are collected simultaneously, but the qualitative data is embedded within the quantitative data. This design is best used when you want to focus on the quantitative data but still need to understand how the qualitative data further explains it.

For instance, you may survey students about their opinions of an online learning platform and conduct individual interviews to gain further insight into their responses.

Explanatory sequential design

In an explanatory sequential design, quantitative data is collected first, followed by qualitative data. This design is used when you want to further explain a set of quantitative data with additional qualitative information.

An example of this would be if you surveyed employees at a company about their satisfaction with their job and then conducted interviews to gain more information about why they responded the way they did.

Exploratory sequential design

The exploratory sequential design collects qualitative data first, followed by quantitative data. This type of mixed methods research is used when the goal is to explore a topic before collecting any quantitative data.

An example of this could be studying how parents interact with their children by conducting interviews and then using a survey to further explore and measure these interactions.

Integrating data in mixed methods studies can be challenging, but it can be done successfully with careful planning.

No matter which type of design you choose, understanding and applying these principles can help you draw meaningful conclusions from your research.

• Strengths of mixed methods research

Mixed methods research designs combine the strengths of qualitative and quantitative data, deepening and enriching qualitative results with quantitative data and validating quantitative findings with qualitative data. This method offers more flexibility in designing research, combining theory generation and hypothesis testing, and being less tied to disciplines and established research paradigms.

Take the example of a study examining the impact of exercise on mental health. Mixed methods research would allow for a comprehensive look at the issue from different angles. 

Researchers could begin by collecting quantitative data through surveys to get an overall view of the participants' levels of physical activity and mental health. Qualitative interviews would follow this to explore the underlying dynamics of participants' experiences of exercise, physical activity, and mental health in greater detail.

Through a mixed methods approach, researchers could more easily compare and contrast their results to better understand the phenomenon as a whole.  

Additionally, mixed methods research is useful when there are conflicting or differing results in different studies. By combining both quantitative and qualitative data, mixed methods research can offer insights into why those differences exist.

For example, if a quantitative survey yields one result while a qualitative interview yields another, mixed methods research can help identify what factors influence these differences by integrating data from both sources.

Overall, mixed methods research designs offer a range of advantages for studying complex phenomena. They can provide insight into different elements of a phenomenon in ways that are not possible with either qualitative or quantitative data alone. Additionally, they allow researchers to integrate data from multiple sources to gain a deeper understanding of the phenomenon in question.  

• Challenges of mixed methods research

Mixed methods research is labor-intensive and often requires interdisciplinary teams of researchers to collaborate. It also has the potential to cost more than conducting a stand alone qualitative or quantitative study . 

Interpreting the results of mixed methods research can be tricky, as it can involve conflicting or differing results. Researchers must find ways to systematically compare the results from different sources and methods to avoid bias.

For example, imagine a situation where a team of researchers has employed an explanatory sequential design for their mixed methods study. After collecting data from both the quantitative and qualitative stages, the team finds that the two sets of data provide differing results. This could be challenging for the team, as they must now decide how to effectively integrate the two types of data in order to reach meaningful conclusions. The team would need to identify method flexibility and be strategic when integrating data in order to draw meaningful conclusions from the conflicting results.

• Advanced frameworks in mixed methods research

Mixed methods research offers powerful tools for investigating complex processes and systems, such as in health and healthcare.

Besides the three basic mixed method designs—exploratory sequential, explanatory sequential, and convergent parallel—you can use one of the four advanced frameworks to extend mixed methods research designs. These include multistage, intervention, case study , and participatory. 

This framework mixes qualitative and quantitative data collection methods in stages to gather a more nuanced view of the research question. An example of this is a study that first has an online survey to collect initial data and is followed by in-depth interviews to gain further insights.

Intervention

This design involves collecting quantitative data and then taking action, usually in the form of an intervention or intervention program. An example of this could be a research team who collects data from a group of participants, evaluates it, and then implements an intervention program based on their findings .

This utilizes both qualitative and quantitative research methods to analyze a single case. The researcher will examine the specific case in detail to understand the factors influencing it. An example of this could be a study of a specific business organization to understand the organizational dynamics and culture within the organization.

Participatory

This type of research focuses on the involvement of participants in the research process. It involves the active participation of participants in formulating and developing research questions, data collection, and analysis.

An example of this could be a study that involves forming focus groups with participants who actively develop the research questions and then provide feedback during the data collection and analysis stages.

The flexibility of mixed methods research designs means that researchers can choose any combination of the four frameworks outlined above and other methodologies , such as convergent parallel, explanatory sequential, and exploratory sequential, to suit their particular needs.

Through this method's flexibility, researchers can gain multiple perspectives and uncover differing or even conflicting results when integrating data.

When it comes to integration at the methods level, there are four approaches.

Connecting involves collecting both qualitative and quantitative data during different phases of the research.

Building involves the collection of both quantitative and qualitative data within a single phase.

Merging involves the concurrent collection of both qualitative and quantitative data.

Embedding involves including qualitative data within a quantitative study or vice versa.

• Techniques for integrating data in mixed method studies

Integrating data is an important step in mixed methods research designs. It allows researchers to gain further understanding from their research and gives credibility to the integration process. There are three main techniques for integrating data in mixed methods studies: triangulation protocol, following a thread, and the mixed methods matrix.

Triangulation protocol

This integration method combines different methods with differing or conflicting results to generate one unified answer.

For example, if a researcher wanted to know what type of music teenagers enjoy listening to, they might employ a survey of 1,000 teenagers as well as five focus group interviews to investigate this. The results might differ; the survey may find that rap is the most popular genre, whereas the focus groups may suggest rock music is more widely listened to. 

The researcher can then use the triangulation protocol to come up with a unified answer—such as that both rap and rock music are popular genres for teenage listeners. 

Following a thread

This is another method of integration where the researcher follows the same theme or idea from one method of data collection to the next. 

A research design that follows a thread starts by collecting quantitative data on a specific issue, followed by collecting qualitative data to explain the results. This allows whoever is conducting the research to detect any conflicting information and further look into the conflicting information to understand what is really going on.

For example, a researcher who used this research method might collect quantitative data about how satisfied employees are with their jobs at a certain company, followed by qualitative interviews to investigate why job satisfaction levels are low. They could then use the results to explore any conflicting or differing results, allowing them to gain a deeper understanding of job satisfaction at the company. 

By following a thread, the researcher can explore various research topics related to the original issue and gain a more comprehensive view of the issue.

Mixed methods matrix

This technique is a visual representation of the different types of mixed methods research designs and the order in which they should be implemented. It enables researchers to quickly assess their research design and adjust it as needed. 

The matrix consists of four boxes with four different types of mixed methods research designs: convergent parallel, explanatory sequential, exploratory sequential, and method flexibility. 

For example, imagine a researcher who wanted to understand why people don't exercise regularly. To answer this question, they could use a convergent parallel design, collecting both quantitative (e.g., survey responses) and qualitative (e.g., interviews) data simultaneously.

If the researcher found conflicting results, they could switch to an explanatory sequential design and collect quantitative data first, then follow up with qualitative data if needed. This way, the researcher can make adjustments based on their findings and integrate their data more effectively.

Mixed methods research is a powerful tool for understanding complex research topics. Using qualitative and quantitative data in one study allows researchers to understand their subject more deeply. 

Mixed methods research designs such as convergent parallel, explanatory sequential, and exploratory sequential provide method flexibility, enabling researchers to collect both types of data while avoiding the limitations of either approach alone.

However, it's important to remember that mixed methods research can produce differing or even conflicting results, so it's important to be aware of the potential pitfalls and take steps to ensure that data is being correctly integrated. If used effectively, mixed methods research can offer valuable insight into topics that would otherwise remain largely unexplored.

What is an example of mixed methods research?

An example of mixed methods research is a study that combines quantitative and qualitative data. This type of research uses surveys, interviews, and observations to collect data from multiple sources.

Which sampling method is best for mixed methods?

It depends on the research objectives, but a few methods are often used in mixed methods research designs. These include snowball sampling, convenience sampling, and purposive sampling. Each method has its own advantages and disadvantages.

What is the difference between mixed methods and multiple methods?

Mixed methods research combines quantitative and qualitative data in a single study. Multiple methods involve collecting data from different sources, such as surveys and interviews, but not necessarily combining them into one analysis. Mixed methods offer greater flexibility but can lead to differing or conflicting results when integrating data.

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Research Design: Qualitative, Quantitative, and Mixed Methods Approaches

You are here, student resources, welcome to the companion website.

Welcome to the SAGE edge site for Research Design, Fifth Edition .

The SAGE edge site for Research Design by John W. Creswell and J. David Creswell offers a robust online environment you can access anytime, anywhere, and features an array of free tools and resources to keep you on the cutting edge of your learning experience.

This best-selling text pioneered the comparison of qualitative, quantitative, and mixed methods research design. For all three approaches, John W. Creswell and new co-author J. David Creswell include a preliminary consideration of philosophical assumptions, key elements of the research process, a review of the literature, an assessment of the use of theory in research applications, and reflections about the importance of writing and ethics in scholarly inquiry.

The  Fifth   Edition  includes more coverage of: epistemological and ontological positioning in relation to the research question and chosen methodology; case study, PAR, visual and online methods in qualitative research; qualitative and quantitative data analysis software; and in quantitative methods more on power analysis to determine sample size, and more coverage of experimental and survey designs; and updated with the latest thinking and research in mixed methods.

Acknowledgments

We gratefully acknowledge John W. Creswell and J. David Creswell for writing an excellent text. Special thanks are also due to Tim Guetterman of the University of Michigan, Shannon Storch of the University of Creighton, and Tiffany J. Davis of the University of Houston for developing the ancillaries on this site.

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Research design: qualitative, quantitative, and mixed methods approaches / sixth edition

• Published: 15 November 2023
• Volume 58 , pages 1011–1013, ( 2024 )

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• James P. Takona   ORCID: orcid.org/0009-0001-4591-8136 1  

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This review examines John W. Creswell and David Creswell’s sixth edition, which covers the most popular research methods, offering readers a comprehensive understanding and practical guidance in qualitative, quantitative, and mixed methods. The review includes observations on existing drawbacks, gaps, and ideas on potential areas for improvement in the book. The book is an excellent entry point for understanding the three broad research designs. It stands out for incorporating various methods and empowering researchers to effectively align them with specific research questions, objectives, and philosophical underpinnings. However, it could be further refined by incorporating newer research approaches and expanding practical aspects such as data collection, sampling strategies, and data analysis techniques. With these improvements, the sixth edition could further solidify its position as a comprehensive and accessible guide adeptly catering to researchers, educators, and students. Despite the book’s many strengths, there are opportunities for refinement in future editions, incorporating newer approaches to research designs and expanding practical aspects such as data collection, sampling strategies, and data analysis techniques. This review highlights that, with these suggested improvements, future editions could not only maintain but also enhance the text’s comprehensive and accessible nature, further solidifying its status as a vital resource for researchers, educators, and student.

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Chapter 15. Mixed Methods

Introduction.

Where deep ethnography (chapter 14) is a tradition that relies on naturalistic techniques of data collection, foregrounding the specificity of a particular culture and site, there are other times when researchers are looking for approaches that allow them to make use of some of the analytical techniques developed by statisticians and quantitative researchers to generalize the data they are collecting. Rather than push into a deeper understanding of a culture through thick interpretive descriptions, these researchers would rather abstract from a sufficiently large body of cases (or persons) to hazard predictions about a connection, relationship, or phenomenon. You may already have some experience learning basic statistical techniques for analyzing large data sets. In this chapter, we describe how some research harnesses those techniques to supplement or augment qualitative research, mixing methods for the purpose of building stronger claims and arguments. There are many ways this can be done, but perhaps the most common mixed methods research design involves the use of survey data (analyzed statistically via descriptive cross-tabs or fairly simple regression analyses of large number probability samples) plus semistructured interviews. This chapter will take a closer look at mixed methods approaches, explain why you might want to consider them (or not), and provide some guidance for successful mixed methods research designs.

What Is It? Triangulation, Multiple Methods, and Mixed Methods

First, a bit of nomenclature. Mixed methods can be understood as a path toward triangulation . Triangulation is a way of strengthening the validity of a study by employing multiple forms of data, multiple investigators, multiple theoretical perspectives, or multiple research methods. Let’s say that Anikit wants to know more about how first-year college students acclimate to college. He could talk to some college students (conduct interviews) and also observe their behavior (fieldwork). He is strengthening the validity of his study by including multiple forms of data. If both the interview and the observations indicate heavy reliance on peer networks, a reported finding about the importance of peers would be more credible than had he only interviewed students or only observed them. If he discovers that students say one thing but do another (which is pretty common, after all), then this, too, becomes an interesting finding (e.g., Why do they forget to talk about their peers when peers have so much observable influence?). In this case, we say that Anikit is employing multiple forms of data, or even that he relies on multiple methods. But he is not, strictly speaking, mixing data. Mixed methods refer specifically to the use of both quantitative and qualitative research methods. If Anikit were to supplement his interviews and/or observations with a random sample of one thousand college students, he would then be employing a mixed methods approach. Although he might not get the rich details of how friends matter in the survey, the large sample size allows statistical analyses of relationships among variables, perhaps showing which groups of students are more likely to benefit from strong peer networks. So to summarize, both multiple methods and mixed methods are forms of research triangulation, [1] but mixed methods include mixing both qualitative and quantitative research elements.

Mixed methods techniques, then, are pretty unique. Where many qualitative researchers have little interest in statistical generalizability, and many quantitative researchers undervalue the importance of rich descriptions of singular cases, the mixed methods researcher has an open mind about both approaches simultaneously. And they use the power of both approaches to build stronger results: [2]

Quantitative (mainly deductive) methods are ideal for measuring pervasiveness of “known” phenomena and central patterns of association, including inferences of causality. Qualitative (mainly inductive) methods allow for identification of previously unknown processes, explanations of why and how phenomena occur, and the range of their effects (Pasick et al. 2009). Mixed methods research, then, is more than simply collecting qualitative data from interviews, or collecting multiple forms of qualitative evidence (e.g., observations and interviews) or multiple types of quantitative evidence (e.g., surveys and diagnostic tests). It involves the intentional collection of both quantitative and qualitative data and the combination of the strengths of each to answer research questions . ( Creswell et al. 2011:5 ; emphases added)

Why Use Mixed Methods?

As with all methodological choices, the answer depends on your underlying research questions and goals. Some research questions are better answered by the strengths of the mixed methods approach. Small ( 2011 ) discusses the use of mixed methods as a confirmation or complement of one set of findings from one method by another. Creswell and Clark ( 2017:8ff .) note the following situations as being particularly aided by combining qualitative and quantitative data collection and analysis: (1) when you need to obtain both more complete (need for qualitative) and more corroborated (need for quantitative) information; (2) when you need to explain (need for qualitative) initial results (quantitative); (3) when you need to do an exploratory study (need for qualitative) before you can really create and administer a survey or other instrument (quantitative); (4) when you need to describe and compare different types of cases to get a more holistic understanding of what is going on; (5) when you need (or very much want!) to include participants in the study, adding in qualitative elements as you build a quantitative design; (6) when you need all the tools at your disposal to develop, implement, and evaluate a program.

Please note what is not included in this list: because you can . Mixed methods research is not always preferable, even if in general it makes your study “stronger.” Strength is not the only criterion for quality or value. I have met many students in my career who assume that the mixed methods approach is optimal because it includes both qualitative and quantitative research. That is the wrong way of looking at things. Mixed methods are optimal when and only when they fit the necessities of your research question (e.g., How can I corroborate this interesting finding from my interviews so that proper solutions can be fashioned?) or underlying goal (e.g., How can I make sure to include the people in this program as participants of the study?).

If you are just starting out and learning your way through designing your first study, mixed methods are not default requirements. As you will see in the next section on design, mixed methods studies often happen sequentially rather than consecutively, so I recommend you start with the study that has the most meaning to you, the one that is the most compelling. Later on, if you want to add (mix) another approach for the sake of strength or validity or “corroboration” (if you are adding quantitative) or “explanation” (if you are adding qualitative), you can always do that then, after the completion of your first study.

Segue: Historical Interlude

For those interested in a little history, one could make the case that mixed methods research in the social sciences actually predates the development of either quantitative or qualitative research methods. The very first social scientists (what we call “social science” in the West, which is itself a historical construct, as many other peoples have been exploring meaning and interpretation of the social world for centuries if not millennia) often employed a mélange of methods to address their research questions. For example, the first sociologists in the US operating out of the “Chicago School” of the early twentieth century surveyed neighborhoods, interviewing people, observing demographic subcultures, and making tallies of everything from the numbers of persons in households to what languages were being spoken. They learned many of these techniques from early statisticians and demographers in Europe—people like Charles Booth ( 1902 ), who surveyed neighborhoods in London, and Frédéric Le Play, who spent decades examining the material conditions of the working classes across Europe, famously including family “budgets” along with interviews and observations (see C. B. Silver 1982). The renowned American sociologist W. E. B. Du Bois, who was the first Black man to earn a PhD from Harvard University, also conducted one of the very first mixed methods studies in the US, The Philadelphia Negro ( 1899 ). This work mapped every Black residence, church, and business in Philadelphia’s Seventh Ward and included observations and details on family structure and occupation (similar to Booth’s earlier work on London). Continuing through the 1930s and 1940s, “community studies” were conducted by teams of researchers who basically tallied everything they could find about the particular town or city they chose to work in and performed countless interviews, months and years of fieldwork, and detailed mappings of community relationships and power relations. One of the most famous of these studies includes the “Middletown” studies conducted by Robert and Helen Lynd ( 1929 , 1937 ).

As statistical analysis progressed after World War II alongside the development of the technology that allowed for ever faster computations, quantitative research emerged as a separate field. There was a lot to learn about how to conduct statistical analyses, and there were more refinements in the creation of large survey instruments. Qualitative research—the observations and interviews at the heart of naturalistic inquiry—became a separate field for different kinds of researchers. One might even say qualitative research languished at the expense of new developments of quantitative analytical techniques until the 1970s, when feminist critiques of positivist social science emerged, casting doubt on the superiority of quantitative research methods. The rise of interdisciplinarity in recent decades combined with a lessening of the former harsh critique of quantitative research methods and the “paradigm wars” ( Small 2011 ) has allowed for an efflorescence of mixed methods research, which is where we are today.

Mixed-Methods Research Designs

Returning from our historical interlude to the list of possible uses of mixed methods, we now confront the question of research design. If we are using more than one method, how exactly do we do this, and when ? The how and the when will depend largely on why we are using mixed methods. For example, if we want to corroborate findings emerging from interviews, then we obviously begin with interviews and follow with, perhaps, a large survey. On the other hand, if we are seeking to explain findings generated from a survey, we begin with that survey and add interviews or observations or focus groups after its completion. And if we are seeking to include participants in the research design itself, we may want to work concurrently, interviewing and holding focus groups as surveys are administered. So it all depends on why we have chosen to use mixed methods.

We can think of our choices here in terms of three possibilities. The first, called sequential explanatory , begins with quantitative data (collection) and then follows with qualitative data (collection). After both are collected, interpretations are made. The second, called sequential exploratory , begins the other way around, with qualitative followed by quantitative. After both are collected, interpretations are made. The third, called concurrent triangulation , conceives of both quantitative and qualitative elements happening concurrently. In practice, one may still happen before the other, but one does not follow the other. The data then converge, and from that convergence, interpretations are made.

In sequential explanatory design (figure 15.1), we are asking ourselves, “In what ways do the qualitative findings explain the quantitative results?” ( Creswell et al. 2017 ). This design thus gives some priority to the quantitative data. The qualitative data, collected after the quantitative data, is used to provide a better understanding of the research problem and then the quantitative data alone.

Often, this means providing some context or explaining meanings and motivations behind the correlations found in the quantitative data. For example, in my research on college students ( Hurst 2019 ), I found a statistical correlation between upper-middle-class female students and study abroad. In other words, and stating this rather baldly, class*gender could be used to predict who studied abroad. But I couldn’t fully explain why, given the survey data I had collected. [3] To answer these (and other) questions that the survey results raised, I began interviewing students and holding focus groups. And it was through these qualitative forms of data collection that I found a partial answer: upper-middle-class female students had been taught to see study abroad as a final “finishing” component of their education in a way that other students simply had not. They often had mothers who had done the same. And they clearly saw connections here to the kinds of well-traveled cosmopolitan adults they wanted to become.

In sequential exploratory design (figure 15.2), we are asking ourselves, “In what ways do the quantitative findings generalize (or confirm) the qualitative results?” ( Creswell et al. 2018 ). This design thus gives some priority to the qualitative data. The quantitative data, collected after the qualitative data, is used to confirm the findings.

This approach is ideal for developing new instruments or when a researcher intends to generalize findings from a qualitative study to different groups or populations. The American Sociological Association (ASA) Task Force on First-Generation and Working-Class Persons wanted to understand how class background may have played a role in the success of sociology graduate students and faculty. Because this was a relatively new research question, the task force began by conducting several focus groups, asking general questions about how class might have affected careers in sociology. Based on several recurring findings (e.g., high debt burdens, mentorship, feelings of fit), the task force developed a survey instrument that it then administered to more than one thousand sociologists, thus generalizing the preliminary findings and providing corroboration of some of the key variables at play.

In concurrent triangulation design (figure 15.3), neither the quantitative nor the qualitative component takes precedence. Although in practice one might precede the other in time, neither is the tail that wags the dog, so to speak. They are both the dog. The general of this design is to better understand or deepen one’s understanding of the phenomenon under study. The goal is to obtain different but complementary data that strengthen (validate) the overall results.

These designs might be either nested or nonnested . In a nested design , a subsample of an original randomized sample is used for further interviews or observation. A common nested design form is where in-depth interviews are conducted with a subsample of those who filled out a survey. Nonnested designs occur when it is impractical or impossible to recruit the same individuals that took place in the survey. The research I conducted for my book Amplified Advantage ( Hurst 2019 ) is an example of this. I supplemented a large national survey of college students and recent college graduates with interviews and focus groups of similar college students and graduates who were not participants in the study (or who may have been randomly selected as participants but without my knowledge or linking their data). Nonnested designs are much more flexible than nested designs, but they eliminate the possibility of linking data across methods.

As with all research design, it is important to think about how best to address your particular research question. There are strengths and weaknesses of each design. Sequential design allows for the collection and analysis of different methods separately, which can make the process more manageable. Sequential designs are relatively easy to implement, design, and report. Sequential exploratory designs allow you to contextualize and generalize qualitative findings to larger samples, while sequential explanatory designs enable you to gain a deeper understanding of findings revealed by quantitative data analysis. All sequential design takes a lot of time, however. You are essentially doubling your research. This is why I do not recommend these approaches to undergraduate students or graduate students in master’s programs. In contrast, concurrent designs, whose dual methods may be conducted simultaneously, may be conducted more quickly. However, as a practical matter, you will probably end up focusing first on one data collection method and then the other, so the time saved might be minimal. [4] Concurrent design can also preclude following up on interesting findings that emerge from one side of the study, and the abbreviated form may prevent clarification of confusing issues that arise during analysis. If the results are contradictory or diverge, it may also be difficult to integrate the data. You might end up with more questions to pursue for further study and not much conclusive to say at the end of all your work.

Finally, there is what I will call here the recursive design model (figure 15.4), in which you combine both explanatory and exploratory sequential design.

This design is currently being used by the ASA task force mentioned above. The first stage of data collection involved several focus groups. From these focus groups, we constructed a survey that we administered to ASA members. The focus group survey could be viewed as an example of exploratory sequential design. As the surveys were being analyzed, we added a nested set of interviews with persons who had taken the survey and who indicated a willingness to participate in this later stage of data collection. These interviews then help explain some of the findings from the survey. The entire process takes several years, however, and involves multiple researchers!

Advanced: Crossover Design

Small’s ( 2011 ) review of the state of mixed methods research argues that mixed methods are being increasingly adopted in social science research. In addition to sequential and concurrent research designs, where quantitative and qualitative data work to either confirm or complement each other, he sets forth examples of innovative designs that go further toward truly blending the special techniques and strengths of both quantitative and qualitative methods. [5] Written in 2011, I have seen scant evidence so far that these blended techniques are becoming well established, but they are promising. As new software programs for data analysis emerge, along with increased computing power, there will be greater opportunities for crossover work. Perhaps you can take up the charge and attempt one of these more innovative approaches yourself.

Here is Small’s ( 2011:73ff .) list of innovative crossover research design:

• Network analyses of narrative textual data . Here, researchers use techniques of network analysis (typically quantitative) and apply them to narratives (qualitative), coding stories as separate “nodes” and then looking for connections between those nodes, as is done in network analysis.
• Sequence analyses of narrative textual data . Here, techniques of event structure analysis and optimal matching (designed for analysis of quantitative data) are applied to narratives (qualitative data). The narratives are reconceived as a series of events, and then causal pathways between these events are mapped. This allows for identification of crucial turning points as well as “nonsignificant” events that just happened.
• Quantitative analyses of semantic (meaning) elements of narrative textual data . The basic distinction between quantitative (data in the form of numbers) and qualitative (date in the form of words) gets blurred here, as words themselves and their meanings and contexts are coded numerically. I usually strongly advise beginning students to do this, as what often happens is that they begin to think quantitatively about the data, flattening it considerably. However, if done with full attention to meaning and context, the power of computing/analytical software may strengthen the coding process.
• Narrative analyses of large-n survey data. In contrast to the first three designs listed above, where quantitative techniques were applied to qualitative data, we now come to a situation where the reverse takes place. Here we have a large data set, either coded numerically or “raw” with various choice options for each question posed. Rather than read the data set as a series of factors (variables) whose relationship one explores through statistical analyses, the researcher creates a narrative from the survey responses, contextualizing the answers rather than abstracting them. [6]
• Regression-based analyses of small-n or narrative textual data. This is by far the most common crossover method and the reverse of the fourth example. Many qualitative software analysis programs now include basic quantitative analytical functions. The researcher can code interview transcripts and fieldnotes in such a way that allows for basic cross-tabulations, simple frequency statistics, or even basic regression analyses. Transcripts and fieldnotes can generate “variables” for such analyses.

Despite the promise of blending methods in this way, the possibility of doing damage to one’s study by discounting the particular values of either quantitative or qualitative approaches is a real one. Unlike mixed methods, where the two approaches work separately (even when designed to concur in time), crossover research blends or muddies the two. Small ( 2011 ) warns, “At a minimum, the application of techniques should not be fundamentally contrary to the epistemological principles from which they are derived or to the technical problems for which they were intended” ( 76 ). When employing any of these designs or blending approaches, it is very important to explain clearly and fully what one’s aims are and how the analysis has proceeded, as this allows others to evaluate the appropriateness of the design for the questions posed.

Further Readings

Cech, Erin. 2021. The Trouble with Passion: How Searching for Fulfillment at Work Fosters Inequality . Berkeley, CA: University of California Press.* Cech combines surveys with interviews to explore how people think about and talk about job searches and careers.

Cooper, Kristy S. 2014. “Eliciting Engagement in the High School Classroom: A Mixed-Methods Examination of Teaching Practices.” American Educational Research Journal 51(2):363–402. An example of using multilevel regression analyses with both interviews and observations to ascertain how best to engage students.

Creswell, John W., and J. David Creswell. 2018. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . Thousand Oaks, CA: SAGE. Essential textbook for mixed-methods research.

Edin, Kathryn, and Maureen A. Pirog. 2014. “Special Symposium on Qualitative and Mixed-Methods for Policy Analysis.” Journal of Policy Analysis and Management 33(2):345–349. A good overview of the strengths of mixed-methods research, which, the authors argue, make it particularly well suited for public policy analysis.

Hurst, Allison L. 2019. Amplified Advantage: Going to a “Good” College in an Era of Inequality . Lanham, MD: Rowman & Littlefield: Lexington Books..* Employs a national survey of recent graduates of small liberal arts colleges combined with interviews, focus groups, and archival data to explore how class background affects college outcomes.

Johnson, R. Burke, and Anthony J. Onwuegbuzie. 2004. “Mixed Methods Research: A Research Paradigm Whose Time Has Come.” Educational Researcher 33(7):14–26. Takes a pragmatic approach and provides a framework for designing and conducting mixed-methods research.

Klinenberg, Eric. 2015. Heat Wave: A Social Autopsy of Disaster in Chicago . Chicago: University of Chicago Press.* A great read and could not be more timely. Klinenberg uses a combination of fieldwork, interviews, and archival research to investigate why some neighborhoods experience greater mortality than others.

Lynd, Robert, and Helen Lynd. 1929. Middletown: A Study in American Culture . New York: Harcourt, Brace.* This early mixed-methods study of a “typical” American city was a pioneering work in sociology. The husband-and-wife team seemingly explores every aspect of life in the city, mapping social networks, surveying attitudes and beliefs, talking to people about their expectations and lives, and observing people going about their everyday business. Although none of the techniques are very sophisticated, this remains a classic example of pragmatic research.

Lynd, Robert, and Helen Lynd. 1937. Middletown in Transition . New York: Harcourt, Brace. The follow-up to the Lynds’ original study of a small American city. More theoretical and critical than the first volume.

Markle, Gail. 2017. “Factors Influencing Achievement in Undergraduate Social Science Research Methods Courses: A Mixed Methods Analysis.” Teaching Sociology 45(2):105–115.* Examines the factors that influence student achievement using an initial survey with follow-up interviews.

Matthews, Wendy K. 2017. “‘Stand by Me’: A Mixed Methods Study of a Collegiate Marching Band Members’ Intragroup Beliefs throughout a Performance Season.” Journal of Research in Music Education 65(2):179–202.* The primary method here is focus groups, but the author also employed multivariate analysis of variance (MANOVA) to shore up the qualitative findings.

Monrad, Merete. 2013. “On a Scale of One to Five, Who Are You? Mixed Methods in Identity Research.” Acta Sociologica 56(4):347–360. A call to employ mixed methods in identity research.

Silver, Catherine Bodard. 1982. Frédéric Le Play on Family, Work and Social Change . Chicago: University of Chicago Press. For anyone interested in the historic roots of mixed-methods research, the work of Frédéric Le Play is essential. This biography is a good place to start.

Small, Mario Luis. 2011. “How to Conduct a Mixed Methods Study: Recent Trends in a Rapidly Growing Literature.” Annual Review of Sociology 37:57–86. A massive review of recent mixed-methods research, distinguishing between mixed-data-collection studies, which combine two or more kinds of data, and mixed-data-analysis studies, which combine two or more analytical strategies. Essential reading for graduate students wanting to use mixed methods.

• To extend this notion of triangulation a little further: if Anikit enlisted the help of Kanchan to interpret the observations and interview transcripts, he would be strengthening the validity of the study through multiple investigators, another form of triangulation having nothing at all to do with what methods are employed. He could also bring in multiple theoretical frameworks—say, Critical Race Theory and Bourdieusian field analysis—as a form of theoretical triangulation. ↵
• If stronger is your aim, that is. For many qualitative researchers, verisimilitude, or the truthfulness of a presentation, is a more desirable aim than strength in the sense of validity. ↵
• Actually, I could do a fair amount of testing on other variables’ relationships to this finding: students who had gone far away to college (more than five hundred miles) were significantly more likely to study abroad, for example, as were students who majored in arts and humanities courses. But I still missed any way of getting at personal motivations or how individuals explained these motivations. That is the part a survey is just never going to fully get at, no matter how well or numerous the questions asked. ↵
• The big exception here is when you are relying on data that has already been collected and is ready for analysis, as in the case of large survey data sets like the General Social Survey. In that case, it is not too time consuming to design a mixed methods study that uses (nonnested) interviews to supplement your analyses of survey data. ↵
• I refer to these as blended methods rather than mixed methods because the epistemological positions and science claims, usually rather distinct from quantitative (more positivistic) and qualitative (more naturalistic), blur considerably. ↵
• I admit that trained first as a qualitative researcher, this has always been my impulse when confronting a large survey data set. ↵

A research design that employs both quantitative and qualitative methods, as in the case of a survey supplemented by interviews.

The process of strengthening a study by employing multiple methods (most often, used in combining various qualitative methods of data collection and analysis).  This is sometimes referred to as data triangulation or methodological triangulation (in contrast to investigator triangulation or theory triangulation).  Contrast mixed methods .

A mixed-methods design that conceives of both quantitative and qualitative elements happening concurrently.  In practice, one may still happen before the other, but one does not follow the other.  The data then converge and from that convergence interpretations are made.  Compare sequential exploratory design and sequential explanatory design .

A mixed-methods design that begins with quantitative data collection followed by qualitative data collection, which helps “explain” the initial quantitative findings.  Compare sequential exploratory design and concurrent triangulation .

A mixed-methods design that begins with qualitative data collection followed by quantitative data collection.  In this case, the qualitative data suggests factors and variables to include in the quantitative design.  Compare sequential explanatory design and concurrent triangulation .

A form of mixed-methods design in which a subsample of an original randomized sample is used for further interviews or observation.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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How to … do mixed‐methods research

Anu kajamaa.

1 Faculty of Educational Sciences, University of Helsinki, Helsinki Finland

Karen Mattick

2 Centre for Research in Professional Learning, University of Exeter, Exeter UK

Anne de la Croix

3 LEARN! Academy, Vrije Universiteit Amsterdam, Amsterdam the Netherlands

4 Research in Education, Amsterdam UMC, VUmc School of Medical Sciences, Amsterdam the Netherlands

As a clinician, you will often combine patients’ narratives with test results in order to obtain a coherent picture and then decide on a way forward. As an educator, you are also likely to combine different information from your learners to arrive at the best feedback, judgement or supervision plan. This is what researchers do when undertaking mixed‐methods research: qualitative and quantitative data are typically brought together to provide different insights than could be achieved with a single type of data and analysis. Mixed‐methods research has much to offer the clinical teacher but may involve more complex study designs than other types of research. Therefore, this article aims to highlight the different designs of mixed‐methods research, and the opportunities and challenges that it provides, in order to support researchers who may be undertaking their first mixed‐methods research study.

What is mixed‐methods research?

Mixed‐methods research, or multi‐strategy designs, 1 can be defined as ‘the collection, analysis and integration of both qualitative and quantitative data in a single study’: 2 semi‐structured interviews and workplace measures (e.g. attendance data) might be undertaken concurrently to gain a multifaceted perspective on a particular phenomenon; a survey with closed‐answer questions might be followed by semi‐structured interviews in order to gain an in‐depth understanding of its key findings; or interviews can be used to develop a survey. The term multiple methods is sometimes applied when more than one qualitative (or quantitative) method is used within a single study. It can also be used to describe the combination of more than one qualitative or quantitative method within the same research study. 1 Definitions for ‘mixed‐methods research’ are not static or universally agreed, however, as the field is relatively young and is constantly developing. 3 Some have argued that the term ‘mixed methods’ is misleading, as it suggests that the element of mixing sits at the level of the choice of methods only, whereas the integration of qualitative and quantitative research components should, in fact, be considered at the level of the whole research process. When used as an overarching lens to guide the formulation of aims for the research, the research gap or questions, the research design, and the data collection, data analysis and reporting of the results, however, mixed‐methods research can expand and strengthen the conclusions and contributions of a study. 4

… mixed‐methods research can expand and strengthen the conclusions and contributions of a study

What designs of mixed‐methods research are there?

Before designing the study it is important to describe the phenomenon under study, and the aim or the research question to be used for the study, in order to explain why mixed‐methods research is needed. This enables the researcher to link the philosophical underpinning of the study to its research design. 5 The role of the qualitative data can be to supplement a predominantly quantitative design: for example, a qualitative process evaluation alongside a clinical trial. Similarly, the role of the quantitative data can be to supplement a predominantly qualitative design: for example, the use of a quantitative measure to provide further insights to enrich the qualitative findings within an exploratory case study. Or, if you rely on pragmatism, 6 , 7 which is a commonly applied paradigm in mixed‐methods research, you are likely to orient the study design towards solving practical problems in the ‘real world’. 8

There are many types of mixed‐methods research design. These include: (i) sequential exploratory mixed‐methods design, which can be characterised by initial qualitative data collection and analysis, followed by a phase of quantitative data collection and analysis, leading to the integration or linking of data from the two separate corpuses of data to further explore, develop and test the qualitative analysis; (ii) sequential explanatory mixed‐methods design, which usually implies collecting, analysing and connecting quantitative and then qualitative data in two consecutive phases, resulting in integrating the findings within one study, in order to explain quantitative results using qualitative findings; (iii) convergent mixed‐methods study design with quantitative and qualitative data that aim to identify converging evidence that corroborates the validity of the conclusions drawn from different methods and data sources; and (iv) nested mixed‐methods study, in which qualitative and quantitative components sit alongside one another, but with one component clearly dominant and the other nested or embedded within it, to improve the quality of the conclusions. In Table  1 , we provide four exemplar studies that have been selected to illustrate the mixed‐methods designs described here. 9 , 10 , 11 , 12

Examples of mixed‐methods research studies

Whatever mixed‐methods research design is used, achieving the highest quality lies in describing what was done (why, how and when) in detail, and being reflexive. 13 In other words, it is important to be sensitive to the limitations of the study and to report this openly and honestly, referring to methodological references such as those cited by this article. Table  2 provides a tool that we hope will help novice researchers justify their need for more than one type of method to address their study aim and to navigate the key choices available to them. The table is based on several existing frameworks and designs of mixed‐methods research, 1 , 4 which introduce key terminology and concepts to guide the research process.

Questions to be asked in the design of mixed‐methods research studies

What are the opportunities provided by mixed‐methods research?

Mixed‐methods research is well placed to investigate complex phenomena and situations, and can provide researchers with more nuanced understanding of certain phenomena than the use of single methods. 14 Mixed‐methods research can provide a powerful tool for investigating complex processes and systems in health and social care that draw upon the strengths of both quantitative and qualitative approaches. 4 , 8 Therefore, mixed‐methods research enables the researcher to answer different kinds of research questions than the questions that could be answered by qualitative or quantitative methodologies alone.

Mixed‐methods research may be useful when researchers want to consider an activity from different levels of a system (e.g. individual, ward, department, hospital and health care system), when they want to compare results from different sources or when they want to illustrate trends or consider processes alongside outcomes. 8 It can also be useful to undertake a quantitative study to locate or define an appropriate sample for qualitative research, and to explore contradictions in existing workplace measures or reports. 5 The qualitative and quantitative methods must both be sound for the mixed‐methods study to be of high quality, as one method cannot compensate for the other. To ensure rigorous research conduct quality appraisal tools are available for mixed‐methods studies, such as the Mixed Methods Appraisal Tool. 15

Lastly, mixed‐methods research findings may ‘speak’ to diverse audiences, with those who are persuaded by statistics being persuaded by the quantitative element of the study, and with those who are persuaded by user experiences and stories being more persuaded by the qualitative element.

… mixed‐methods research findings may ‘speak’ to diverse audiences …

What are the challenges with mixed‐methods research?

When mixing qualitative and quantitative methods, it is critically important to justify why, how and when qualitative and quantitative methods are being combined. 1 It can be more challenging for mixed‐methods research studies than single‐methods research studies to demonstrate clear alignment, from the research aim to the conclusion. When reporting mixed‐methods research in journals, it is often quite challenging to provide full transparency in describing the level of integration of the data, the various individual methods and the findings within a study. 14

… it is critically important to justify why, how and when qualitative and quantitative methods are being combined

Mixed methods may involve multiple team members and/or advisors, who may well have different world views and ways of working, given their different methodological preferences. If different people are responsible for different parts of the study, collaborative efforts will be needed from the full team to contribute to the study design and to the research process, to ensure that true integration is achieved. 8 In other words, diversity in viewpoints is likely to benefit mixed‐methods research but requires an investment of time. For these reasons, mixed‐methods research is typically quite resource intensive. We recommend that novice researchers collaborate with more experienced colleagues and protect time to read key references and access other resources that can help them. Moreover, interdisciplinary research is now being recommended and is becoming more common, but these different ways of working need to be considered early on in the design of a research project.

Optimising the integration of the study design, data collection, data interpretation and reporting is another challenge for mixed‐methods research. 16 , 17 For example, Bryman reported a lack of integration of the qualitative and quantitative components within mixed‐methods research articles, highlighting the need for researchers to focus on this aspect. 16 Without appropriate research skills for managing the (often) multiple points of integration, the overall research study can seem unfocused or disjointed. Fetters and colleagues describe the challenges and potential solutions for integration in more detail than we are able to include here. 4 It is also important for researchers using mixed methods to take into consideration ethical issues associated with qualitative and quantitative research procedures, such as the protection of the anonymity of participants.

Conclusions

In this introduction to mixed‐methods research, we have provided a brief glimpse into different types of mixed‐methods research and their opportunities and challenges. We believe the potential of mixed‐methods research is great and particularly well suited to the kinds of complexity inherent in health care and education environments, where both qualitative and quantitative measures are needed to improve practice. To get started with a mixed‐methods research study, we advocate working through the questions posed in Table  2 , namely: What is the overarching aim of the proposed study?; Why are mixed methods needed to address it?; and How and when will the data be integrated? We believe that exploring these questions will help to ensure the high quality of future mixed‐methods research.

Funding: None.

Conflict of interest: None.

Acknowledgements: We thank Esther Helmich, Aileen Barrett, Deirdre Bennett, Jenny Johnston and Terese Stenfors for providing feedback on the draft manuscript. They, together with the authors, form a research network named the European Centre of Excellence in Qualitative Study and Inquiry in Training and Education (EXQUISITE), led by Esther Helmich at the University of Groningen.

Ethical approval: No ethical approval was sought, as the work is based on our secondary data and our own experience. No primary data are presented.

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In This Article Expand or collapse the "in this article" section Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies

Introduction.

• Sampling Strategies
• Sample Size
• Qualitative Design Considerations
• Discipline Specific and Special Considerations
• Sampling Strategies Unique to Mixed Methods Designs

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Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies by Timothy C. Guetterman LAST REVIEWED: 26 February 2020 LAST MODIFIED: 26 February 2020 DOI: 10.1093/obo/9780199756810-0241

Sampling is a critical, often overlooked aspect of the research process. The importance of sampling extends to the ability to draw accurate inferences, and it is an integral part of qualitative guidelines across research methods. Sampling considerations are important in quantitative and qualitative research when considering a target population and when drawing a sample that will either allow us to generalize (i.e., quantitatively) or go into sufficient depth (i.e., qualitatively). While quantitative research is generally concerned with probability-based approaches, qualitative research typically uses nonprobability purposeful sampling approaches. Scholars generally focus on two major sampling topics: sampling strategies and sample sizes. Or simply, researchers should think about who to include and how many; both of these concerns are key. Mixed methods studies have both qualitative and quantitative sampling considerations. However, mixed methods studies also have unique considerations based on the relationship of quantitative and qualitative research within the study.

Sampling in Qualitative Research

Sampling in qualitative research may be divided into two major areas: overall sampling strategies and issues around sample size. Sampling strategies refers to the process of sampling and how to design a sampling. Qualitative sampling typically follows a nonprobability-based approach, such as purposive or purposeful sampling where participants or other units of analysis are selected intentionally for their ability to provide information to address research questions. Sample size refers to how many participants or other units are needed to address research questions. The methodological literature about sampling tends to fall into these two broad categories, though some articles, chapters, and books cover both concepts. Others have connected sampling to the type of qualitative design that is employed. Additionally, researchers might consider discipline specific sampling issues as much research does tend to operate within disciplinary views and constraints. Scholars in many disciplines have examined sampling around specific topics, research problems, or disciplines and provide guidance to making sampling decisions, such as appropriate strategies and sample size.

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Research Methodologies

August 14, 2024

Qualitative vs. Quantitative Market Research: Why Not Both?

Discover the benefits of qualitative and quantitative methods. Learn how to leverage both approaches for insights into consumer behavior and industry trends.

by Ashley Shedlock

Content Coordinator at Greenbook

Market research is vital for understanding target markets, consumer behavior, and industry trends. Qualitative and quantitative methods offer distinct advantages. Qualitative research explores concepts, opinions, and motivations, uncovering rich insights into consumer preferences through techniques like interviews and focus groups.

Quantitative market research involves gathering numerical data through surveys and questionnaires for statistical analysis to identify trends in a target market. It offers measurable results from a large sample size for generalization and prediction. On the other hand, qualitative research explores subjective experiences and motivations, aiming to provide deeper insights into consumer behavior through non-statistical analysis.

Selecting the appropriate research approach is vital for successful studies. Qualitative dives into intricate matters, while quantitative gauges broader market trends. The choice between qualitative and quantitative methods depends on research objectives and results. Both offer valuable insights for strategic decisions in competitive markets. Knowing the distinctions between the methods aids in picking the optimal approach for meaningful data and a competitive advantage.

What is Qualitative Market Research?

Qualitative research methodologies, such as in-depth interviews and focus groups, play a crucial role in providing a deeper understanding of consumer behavior. These methodologies delve into the intricacies of individual attitudes and motivations, allowing researchers to uncover rich insights that quantitative data alone cannot reveal.

One of the key advantages of qualitative research lies in its ability to explore the underlying reasons behind consumer behaviors. By allowing researchers to engage directly with participants, qualitative methods offer a more holistic view of consumer attitudes, preferences, and decision-making processes. This approach can uncover nuanced insights that quantitative surveys may overlook.

However, it is important to acknowledge the limitations of qualitative research, particularly in its subjective nature. The reliance on human interpretation in qualitative analysis introduces the potential for biases to influence outcomes. Researchers must be vigilant in managing subjectivity and potential biases throughout the research process to ensure the credibility and reliability of their findings.

In real-world applications, qualitative research proves invaluable in various contexts, from product development to marketing strategies. By gaining a deep understanding of consumer perspectives and behaviors, businesses can tailor their offerings to meet customer needs more effectively. Qualitative research also plays a crucial role in shaping communication strategies and enhancing customer engagement by tapping into the emotional and psychological drivers behind consumer decisions.

While qualitative research may come with its challenges in terms of subjectivity and potential biases, its ability to provide rich, nuanced insights into consumer behavior makes it a valuable tool for any market researcher looking to gain a deeper understanding of their target audience.

Types of Qualitative Research 

When it comes to qualitative research, there are various types that provide unique insights into consumer behavior and preferences. 

Ethnographic Research: Involves immersing researchers in the environment or context of the subjects to observe their behaviors in real-life settings. This approach goes beyond simply asking questions, allowing researchers to uncover deeper insights into how consumers interact with products or services on a day-to-day basis.

In-depth Interviews: Enable researchers to explore participants' thoughts and motivations thoroughly by asking open-ended questions. Focus groups, another qualitative method, involve a small group discussing a topic, allowing researchers to observe group dynamics and shared perspectives.

Observational Research: the direct observation and recording of participant behavior in natural settings. This approach is particularly valuable for capturing nonverbal cues and subconscious behaviors that might not surface during standard interview formats. By focusing on body language, gestures, and interactions, researchers can gain profound insights into consumer preferences and the intricacies of decision-making processes.

Advantages of Qualitative Research

Qualitative research offers valuable insights into the emotional and psychological aspects of consumer decision-making, uncovering the reasons behind consumer behaviors.

Qualitative research offers flexibility and adaptability, allowing researchers to adjust approaches based on emerging insights. By directly engaging with participants, it uncovers hidden motivations and attitudes that may not be easily quantified, fostering a human connection for authentic data collection.

Qualitative data delves into intricate consumer emotions, brand perceptions, and societal influences on purchasing choices. It forms hypotheses tested later via quantitative research , enhancing insights into consumer behavior. Utilizing the qualitative aspect provides a profound grasp of intricacies determining consumer preferences and market trends.

What is Quantitative Market Research?

Quantitative methods play a vital role in market research by using surveys and questionnaires to gather structured data systematically. This approach provides measurable insights into consumer behavior, trends, and preferences, offering a comprehensive view of the market. A key benefit is the statistical analysis capability, allowing researchers to identify correlations and predictive patterns within large datasets. This analytical depth enables data-driven decision-making and strategic planning based on empirical evidence.

However, like any methodological approach, quantitative research does have its limitations. One notable constraint is the potential difficulty in capturing nuanced or complex phenomena that may be better understood through qualitative means. While quantitative research excels in quantifying data and providing statistical significance, it may sometimes overlook the underlying reasons or motivations driving consumer behavior. This limitation underscores the complementary role that qualitative research plays in offering deeper insights into the 'whys' behind the 'whats' uncovered through quantitative analysis.

Quantitative research is widely used in different industries for tasks like market segmentation , customer profiling, and product testing. It helps businesses evaluate market share, consumer preferences, and marketing campaign effectiveness with credibility and objectivity. Stakeholders can rely on the empirical evidence from quantitative research to make informed decisions for business growth and innovation.

Types of Quantitative Research

Quantitative research involves the collection and analysis of numerical data to understand patterns, correlations, and trends in a target audience or market. This method utilizes structured questionnaires, surveys, and experiments to gather information that can be statistically analyzed for meaningful insights. 

One common type of quantitative research is surveys, which provide a snapshot of the opinions, behaviors, and preferences of a large group of people. Another approach is experiments, where variables are manipulated to study cause-and-effect relationships within a controlled environment. These quantitative methods offer the advantage of producing numerical data that can be easily quantified and compared across different groups or time periods.

Advantages of Quantitative Research

Quantitative research offers a plethora of advantages that make it an indispensable tool in the realm of market analysis. 

Quantitative research excels in offering numerical data for statistical analysis, ensuring objective insights into market trends and consumer behavior through structured surveys and experiments.

Quantitative data is ideal for large sample sizes, providing generalizable insights applicable to broader populations. The scalability enhances reliability, revealing trends not obvious in smaller studies. In fast-paced markets, the quick data collection, analysis, and interpretation of quantitative research are advantageous for swift decision-making.

Quantitative research is crucial for marketers to measure variables and determine causal relationships, aiding in understanding consumer behavior and strategic decision-making. Techniques like regression analysis or correlation studies unveil hidden patterns, offering valuable market insights and ensuring businesses remain competitive.

Quantitative research not only provides analytical rigor and scalability but also allows for easy comparability and benchmarking. It quantifies data in numerical terms, facilitating comparisons of variables, tracking changes over time, and benchmarking against industry standards or competitors. This comparative nature empowers businesses to identify best practices, evaluate market positioning, and optimize strategies effectively.

Comparing Qualitative and Quantitative Approaches

Combining qualitative and quantitative research methods provides a comprehensive understanding of market dynamics. Triangulating data from both sources offers a well-rounded perspective. Qualitative methods like interviews reveal consumer motivations, while surveys give broader trends. Selecting between qualitative and quantitative research hinges on research objectives and the nature of questions. Qualitative research delves into complex phenomena, while quantitative uncovers patterns at scale. The choice depends on study goals and required information for decision-making.

Common misconceptions about qualitative research often revolve around its perceived subjectivity and lack of generalizability. However, when conducted rigorously, qualitative research can provide valuable insights into the underlying reasons behind consumer behaviors and preferences. On the other hand, quantitative research is sometimes criticized for being too rigid and detached from the human experience. In reality, quantitative methods can yield actionable data that reveal trends and correlations with a high degree of reliability.

Market research methodologies are evolving to prioritize a balance between qualitative depth and quantitative breadth. Combining qualitative richness with quantitative rigor provides a holistic view of consumer behavior, uncovering patterns and underlying motivations effectively. Emerging trends in market research incorporate advanced analytics such as predictive modeling and machine learning for faster, more precise decision-making. The future of market research lies in integrating qualitative and quantitative approaches, emphasizing innovation to navigate complexities and derive valuable strategic insights.

How to Choose the Right Method? Quantitative vs Qualitative Research 

When choosing between quantitative and qualitative market research, align the method with your research goals. Quantitative research gathers numerical data for statistical analysis, while qualitative research explores underlying reasons and opinions through interviews or observation.

The decision depends on research objectives. Quantitative research quantifies data for testing hypotheses and generalizing results, ideal for customer satisfaction or A/B testing . Conversely, qualitative research offers insights into behavior and preferences through open-ended questions and exploratory techniques like content analysis or ethnographic studies.

Consider timing and resources: quantitative research needs larger samples for statistical significance, while qualitative research is flexible with smaller samples. For quick feedback, opt for quantitative surveys or experimental studies.

Balancing detailed insights with ample data is crucial in research. A mixed-method approach often provides the most complete understanding. The key is to align your research method with your goals, resources, and desired insights to make informed decisions that enhance your marketing strategies and business outcomes.

Ashley Shedlock

10 articles

The views, opinions, data, and methodologies expressed above are those of the contributor(s) and do not necessarily reflect or represent the official policies, positions, or beliefs of Greenbook.

Comments are moderated to ensure respect towards the author and to prevent spam or self-promotion. Your comment may be edited, rejected, or approved based on these criteria. By commenting, you accept these terms and take responsibility for your contributions.

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