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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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• What is mixed methods research?

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20 February 2023

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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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• Volume 20, Issue 3
• Mixed methods research: expanding the evidence base
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• Allison Shorten 1 ,
• Joanna Smith 2
• 1 School of Nursing , University of Alabama at Birmingham , USA
• 2 Children's Nursing, School of Healthcare , University of Leeds , UK
• Correspondence to Dr Allison Shorten, School of Nursing, University of Alabama at Birmingham, 1720 2nd Ave South, Birmingham, AL, 35294, USA; [email protected]; ashorten{at}uab.edu

https://doi.org/10.1136/eb-2017-102699

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Introduction

‘Mixed methods’ is a research approach whereby researchers collect and analyse both quantitative and qualitative data within the same study. 1 2 Growth of mixed methods research in nursing and healthcare has occurred at a time of internationally increasing complexity in healthcare delivery. Mixed methods research draws on potential strengths of both qualitative and quantitative methods, 3 allowing researchers to explore diverse perspectives and uncover relationships that exist between the intricate layers of our multifaceted research questions. As providers and policy makers strive to ensure quality and safety for patients and families, researchers can use mixed methods to explore contemporary healthcare trends and practices across increasingly diverse practice settings.

What is mixed methods research?

Mixed methods research requires a purposeful mixing of methods in data collection, data analysis and interpretation of the evidence. The key word is ‘mixed’, as an essential step in the mixed methods approach is data linkage, or integration at an appropriate stage in the research process. 4 Purposeful data integration enables researchers to seek a more panoramic view of their research landscape, viewing phenomena from different viewpoints and through diverse research lenses. For example, in a randomised controlled trial (RCT) evaluating a decision aid for women making choices about birth after caesarean, quantitative data were collected to assess knowledge change, levels of decisional conflict, birth choices and outcomes. 5 Qualitative narrative data were collected to gain insight into women’s decision-making experiences and factors that influenced their choices for mode of birth. 5

In contrast, multimethod research uses a single research paradigm, either quantitative or qualitative. Data are collected and analysed using different methods within the same paradigm. 6 7 For example, in a multimethods qualitative study investigating parent–professional shared decision-making regarding diagnosis of suspected shunt malfunction in children, data collection included audio recordings of admission consultations and interviews 1 week post consultation, with interactions analysed using conversational analysis and the framework approach for the interview data. 8

What are the strengths and challenges in using mixed methods?

Selecting the right research method starts with identifying the research question and study aims. A mixed methods design is appropriate for answering research questions that neither quantitative nor qualitative methods could answer alone. 4 9–11 Mixed methods can be used to gain a better understanding of connections or contradictions between qualitative and quantitative data; they can provide opportunities for participants to have a strong voice and share their experiences across the research process, and they can facilitate different avenues of exploration that enrich the evidence and enable questions to be answered more deeply. 11 Mixed methods can facilitate greater scholarly interaction and enrich the experiences of researchers as different perspectives illuminate the issues being studied. 11

The process of mixing methods within one study, however, can add to the complexity of conducting research. It often requires more resources (time and personnel) and additional research training, as multidisciplinary research teams need to become conversant with alternative research paradigms and different approaches to sample selection, data collection, data analysis and data synthesis or integration. 11

What are the different types of mixed methods designs?

Mixed methods research comprises different types of design categories, including explanatory, exploratory, parallel and nested (embedded) designs. 2   Table 1 summarises the characteristics of each design, the process used and models of connecting or integrating data. For each type of research, an example was created to illustrate how each study design might be applied to address similar but different nursing research aims within the same general nursing research area.

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Types of mixed methods designs*

What should be considered when evaluating mixed methods research?

When reading mixed methods research or writing a proposal using mixed methods to answer a research question, the six questions below are a useful guide 12 :

Does the research question justify the use of mixed methods?

Is the method sequence clearly described, logical in flow and well aligned with study aims?

Is data collection and analysis clearly described and well aligned with study aims?

Does one method dominate the other or are they equally important?

Did the use of one method limit or confound the other method?

When, how and by whom is data integration (mixing) achieved?

For more detail of the evaluation guide, refer to the McMaster University Mixed Methods Appraisal Tool. 12 The quality checklist for appraising published mixed methods research could also be used as a design checklist when planning mixed methods studies.

• Elliot AE , et al
• Creswell JW ,
• Plano ClarkV L
• Greene JC ,
• Caracelli VJ ,
• Ivankova NV
• Shorten A ,
• Shorten B ,
• Halcomb E ,
• Cheater F ,
• Bekker H , et al
• Tashakkori A ,
• Creswell JW
• 12. ↵ National Collaborating Centre for Methods and Tools . Appraising qualitative, quantitative, and mixed methods studies included in mixed studies reviews: the MMAT . Hamilton, ON : BMJ Publishing Group , 2015 . http://www.nccmt.ca/resources/search/232 (accessed May 2017) .

Competing interests None declared.

Provenance and peer review Commissioned; internally peer reviewed.

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Three techniques for integrating data in mixed methods studies

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• Alicia O’Cathain , professor 1 ,
• Elizabeth Murphy , professor 2 ,
• Jon Nicholl , professor 1
• 1 Medical Care Research Unit, School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
• 2 University of Leicester, Leicester, UK
• Correspondence to: A O’Cathain a.ocathain{at}sheffield.ac.uk
• Accepted 8 June 2010

Techniques designed to combine the results of qualitative and quantitative studies can provide researchers with more knowledge than separate analysis

Health researchers are increasingly using designs that combine qualitative and quantitative methods, and this is often called mixed methods research. 1 Integration—the interaction or conversation between the qualitative and quantitative components of a study—is an important aspect of mixed methods research, and, indeed, is essential to some definitions. 2 Recent empirical studies of mixed methods research in health show, however, a lack of integration between components, 3 4 which limits the amount of knowledge that these types of studies generate. Without integration, the knowledge yield is equivalent to that from a qualitative study and a quantitative study undertaken independently, rather than achieving a “whole greater than the sum of the parts.” 5

Barriers to integration have been identified in both health and social research. 6 7 One barrier is the absence of formal education in mixed methods research. Fortunately, literature is rapidly expanding to fill this educational gap, including descriptions of how to integrate data and findings from qualitative and quantitative methods. 8 9 In this article we outline three techniques that may help health researchers to integrate data or findings in their mixed methods studies and show how these might enhance knowledge generated from this approach.

Triangulation protocol

Researchers will often use qualitative and quantitative methods to examine different aspects of an overall research question. For example, they might use a randomised controlled trial to assess the effectiveness of a healthcare intervention and semistructured interviews with patients and health professionals to consider the way in which the intervention was used in the real world. Alternatively, they might use a survey of service users to measure satisfaction with a service and focus groups to explore views of care in more depth. Data are collected and analysed separately for each component to produce two sets of findings. Researchers will then attempt to combine these findings, sometimes calling this process triangulation. The term triangulation can be confusing because it has two meanings. 10 It can be used to describe corroboration between two sets of findings or to describe a process of studying a problem using different methods to gain a more complete picture. The latter meaning is commonly used in mixed methods research and is the meaning used here.

The process of triangulating findings from different methods takes place at the interpretation stage of a study when both data sets have been analysed separately (figure ⇓ ). Several techniques have been described for triangulating findings. They require researchers to list the findings from each component of a study on the same page and consider where findings from each method agree (convergence), offer complementary information on the same issue (complementarity), or appear to contradict each other (discrepancy or dissonance). 11 12 13 Explicitly looking for disagreements between findings from different methods is an important part of this process. Disagreement is not a sign that something is wrong with a study. Exploration of any apparent “inter-method discrepancy” may lead to a better understanding of the research question, 14 and a range of approaches have been used within health services research to explore inter-method discrepancy. 15

Point of application for three techniques for integrating data in mixed methods research

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The most detailed description of how to carry out triangulation is the triangulation protocol, 11 which although developed for multiple qualitative methods, is relevant to mixed methods studies. This technique involves producing a “convergence coding matrix” to display findings emerging from each component of a study on the same page. This is followed by consideration of where there is agreement, partial agreement, silence, or dissonance between findings from different components. This technique for triangulation is the only one to include silence—where a theme or finding arises from one data set and not another. Silence might be expected because of the strengths of different methods to examine different aspects of a phenomenon, but surprise silences might also arise that help to increase understanding or lead to further investigations.

The triangulation protocol moves researchers from thinking about the findings related to each method, to what Farmer and colleagues call meta-themes that cut across the findings from different methods. 11 They show a worked example of triangulation protocol, but we could find no other published example. However, similar principles were used in an iterative mixed methods study to understand patient and carer satisfaction with a new primary angioplasty service. 16 Researchers conducted semistructured interviews with 16 users and carers to explore their experiences and views of the new service. These were used to develop a questionnaire for a survey of 595 patients (and 418 of their carers) receiving either the new service or usual care. Finally, 17 of the patients who expressed dissatisfaction with aftercare and rehabilitation were followed up to explore this further in semistructured interviews. A shift of thinking to meta-themes led the researchers away from reporting the findings from the interviews, survey, and follow-up interviews sequentially to consider the meta-themes of speed and efficiency, convenience of care, and discharge and after care. The survey identified that a higher percentage of carers of patients using the new service rated the convenience of visiting the hospital as poor than those using usual care. The interviews supported this concern about the new service, but also identified that the weight carers gave to this concern was low in the context of their family member’s life being saved.

Morgan describes this move as the “third effort” because it occurs after analysis of the qualitative and the quantitative components. 17 It requires time and energy that must be planned into the study timetable. It is also useful to consider who will carry out the integration process. Farmer and colleagues require two researchers to work together during triangulation, which can be particularly important in mixed methods studies if different researchers take responsibility for the qualitative and quantitative components. 11

Following a thread

Moran-Ellis and colleagues describe a different technique for integrating the findings from the qualitative and quantitative components of a study, called following a thread. 18 They state that this takes place at the analysis stage of the research process (figure ⇑ ). It begins with an initial analysis of each component to identify key themes and questions requiring further exploration. Then the researchers select a question or theme from one component and follow it across the other components—they call this the thread. The authors do not specify steps in this technique but offer a visual model for working between datasets. An approach similar to this has been undertaken in health services research, although the researchers did not label it as such, probably because the technique has not been used frequently in the literature (box)

An example of following a thread 19

Adamson and colleagues explored the effect of patient views on the appropriate use of services and help seeking using a survey of people registered at a general practice and semistructured interviews. The qualitative (22 interviews) and quantitative components (survey with 911 respondents) took place concurrently.

The researchers describe what they call an iterative or cyclical approach to analysis. Firstly, the preliminary findings from the interviews generated a hypothesis for testing in the survey data. A key theme from the interviews concerned the self rationing of services as a responsible way of using scarce health care. This theme was then explored in the survey data by testing the hypothesis that people’s views of the appropriate use of services would explain their help seeking behaviour. However, there was no support for this hypothesis in the quantitative analysis because the half of survey respondents who felt that health services were used inappropriately were as likely to report help seeking for a series of symptoms presented in standardised vignettes as were respondents who thought that services were not used inappropriately. The researchers then followed the thread back to the interview data to help interpret this finding.

After further analysis of the interview data the researchers understood that people considered the help seeking of other people to be inappropriate, rather than their own. They also noted that feeling anxious about symptoms was considered to be a good justification for seeking care. The researchers followed this thread back into the survey data and tested whether anxiety levels about the symptoms in the standardised vignettes predicted help seeking behaviour. This second hypothesis was supported by the survey data. Following a thread led the researchers to conclude that patients who seek health care for seemingly minor problems have exceeded their thresholds for the trade-off between not using services inappropriately and any anxiety caused by their symptoms.

Mixed methods matrix

A unique aspect of some mixed methods studies is the availability of both qualitative and quantitative data on the same cases. Data from the qualitative and quantitative components can be integrated at the analysis stage of a mixed methods study (figure ⇑ ). For example, in-depth interviews might be carried out with a sample of survey respondents, creating a subset of cases for which there is both a completed questionnaire and a transcript. Cases may be individuals, groups, organisations, or geographical areas. 9 All the data collected on a single case can be studied together, focusing attention on cases, rather than variables or themes, within a study. The data can be examined in detail for each case—for example, comparing people’s responses to a questionnaire with their interview transcript. Alternatively, data on each case can be summarised and displayed in a matrix 8 9 20 along the lines of Miles and Huberman’s meta-matrix. 21 Within a mixed methods matrix, the rows represent the cases for which there is both qualitative and quantitative data, and the columns display different data collected on each case. This allows researchers to pay attention to surprises and paradoxes between types of data on a single case and then look for patterns across all cases 20 in a qualitative cross case analysis. 21

We used a mixed methods matrix to study the relation between types of team working and the extent of integration in mixed methods studies in health services research (table ⇓ ). 22 Quantitative data were extracted from the proposals, reports, and peer reviewed publications of 75 mixed methods studies, and these were analysed to describe the proportion of studies with integrated outputs such as mixed methods journal articles. Two key variables in the quantitative component were whether the study was assessed as attempting to integrate qualitative or quantitative data or findings and the type of publications produced. We conducted qualitative interviews with 20 researchers who had worked on some of these studies to explore how mixed methods research was practised, including how the team worked together.

Example of a mixed methods matrix for a study exploring the relationship between types of teams and integration between qualitative and quantitative components of studies* 22

• View inline

The shared cases between the qualitative and quantitative components were 21 mixed methods studies (because one interviewee had worked on two studies in the quantitative component). A matrix was formed with each of the 21 studies as a row. The first column of the matrix contained the study identification, the second column indicated whether integration had occurred in that project, and the third column the score for integration of publications emerging from the study. The rows were then ordered to show the most integrated cases first. This ordering of rows helped us to see patterns across rows.

The next columns were themes from the qualitative interview with a researcher from that project. For example, the first theme was about the expertise in qualitative research within the team and whether the interviewee reported this as adequate for the study. The matrix was then used in the context of the qualitative analysis to explore the issues that affected integration. In particular, it helped to identify negative cases (when someone in the analysis doesn’t fit with the conclusions the analysis is coming to) within the qualitative analysis to facilitate understanding. Interviewees reported the need for experienced qualitative researchers on mixed methods studies to ensure that the qualitative component was published, yet two cases showed that this was neither necessary nor sufficient. This pushed us to explore other factors in a research team that helped generate outputs, and integrated outputs, from a mixed methods study.

Themes from a qualitative study can be summarised to the point where they are coded into quantitative data. In the matrix (table ⇑ ), the interviewee’s perception of the adequacy of qualitative expertise on the team could have been coded as adequate=1 or not=2. This is called “quantitising” of qualitative data 23 ; coded data can then be analysed with data from the quantitative component. This technique has been used to great effect in healthcare research to identify the discrepancy between health improvement assessed using quantitative measures and with in-depth interviews in a randomised controlled trial. 24

We have presented three techniques for integration in mixed methods research in the hope that they will inspire researchers to explore what can be learnt from bringing together data from the qualitative and quantitative components of their studies. Using these techniques may give the process of integration credibility rather than leaving researchers feeling that they have “made things up.” It may also encourage researchers to describe their approaches to integration, allowing them to be transparent and helping them to develop, critique, and improve on these techniques. Most importantly, we believe it may help researchers to generate further understanding from their research.

We have presented integration as unproblematic, but it is not. It may be easier for single researchers to use these techniques than a large research team. Large teams will need to pay attention to team dynamics, considering who will take responsibility for integration and who will be taking part in the process. In addition, we have taken a technical stance here rather than paying attention to different philosophical beliefs that may shape approaches to integration. We consider that these techniques would work in the context of a pragmatic or subtle realist stance adopted by some mixed methods researchers. 25 Finally, it is important to remember that these techniques are aids to integration and are helpful only when applied with expertise.

Summary points

Health researchers are increasingly using designs which combine qualitative and quantitative methods

However, there is often lack of integration between methods

Three techniques are described that can help researchers to integrate data from different components of a study: triangulation protocol, following a thread, and the mixed methods matrix

Use of these methods will allow researchers to learn more from the information they have collected

Cite this as: BMJ 2010;341:c4587

Funding: Medical Research Council grant reference G106/1116

Competing interests: All authors have completed the unified competing interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare financial support for the submitted work from the Medical Research Council; no financial relationships with commercial entities that might have an interest in the submitted work; no spouses, partners, or children with relationships with commercial entities that might have an interest in the submitted work; and no non-financial interests that may be relevant to the submitted work.

Contributors: AOC wrote the paper. JN and EM contributed to drafts and all authors agreed the final version. AOC is guarantor.

Provenance and peer review: Not commissioned; externally peer reviewed.

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Mixed-Methods Designs

• First Online: 28 November 2020

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• Martino Maggetti 4  

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This chapter focuses on mixed-method designs, an increasingly popular approach to designing research in the social sciences that is used to combine the respective advantages of qualitative and quantitative analytical procedures and to strengthen the empirical analysis. After the introduction, two general principles of mixed designs are discussed, the principle of triangulation and the principle of integration. The former involves the concomitant application of different methods in order to cross-validate their findings. The latter entails the sequential combination of different methods to produce a unified causal inference, whereby one method is used to establish the final inference, and the other one is applied to prepare, test, qualify or refine the analysis generating this inference. Afterwards, the chapter proceeds by presenting three varieties of mixed-method studies: statistics-oriented, case-oriented and QCA-based mixed-methods designs. The last section before concluding discusses several advantages and limitations of mixed-method research.

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Mixed methods research: what it is and what it could be

Mixed Methods

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Maggetti, M. (2020). Mixed-Methods Designs. In: Wagemann, C., Goerres, A., Siewert, M.B. (eds) Handbuch Methoden der Politikwissenschaft. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-16936-7_12

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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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• Mixed Methods Research | Definition, Guide, & Examples

Mixed Methods Research | Definition, Guide, & Examples

Published on 4 April 2022 by Tegan George . Revised on 25 October 2022.

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, benefits of mixed methods research, disadvantages of mixed methods research, frequently asked questions about mixed methods research.

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:

• Generalisability : Qualitative research usually has a smaller sample size , and thus is not generalisable . In mixed methods research, this comparative weakness is mitigated by the comparative strength of ‘large N’, externally valid quantitative research.
• Contextualisation: 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.

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, 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 analyse them separately. After both analyses are complete, compare your results to draw overall conclusions.

• On the qualitative side, you analyse 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 analyse 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 analyse 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 contextualise 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, contextualised insights of qualitative data and the generalisable, 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 generalisable, 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 labour-intensive. Collecting, analysing, and synthesising 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.

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 .

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.

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 analysed 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 analysed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualise your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analysed 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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Mixed methods research.

According to the National Institutes of Health , mixed methods strategically integrates or combines rigorous quantitative and qualitative research methods to draw on the strengths of each. Mixed method approaches allow researchers to use a diversity of methods, combining inductive and deductive thinking, and offsetting limitations of exclusively quantitative and qualitative research through a complementary approach that maximizes strengths of each data type and facilitates a more comprehensive understanding of health issues and potential resolutions.¹ Mixed methods may be employed to produce a robust description and interpretation of the data, make quantitative results more understandable, or understand broader applicability of small-sample qualitative findings.

Integration

This refers to the ways in which qualitative and quantitative research activities are brought together to achieve greater insight. Mixed methods is not simply having quantitative and qualitative data available or analyzing and presenting data findings separately. The integration process can occur during data collection, analysis, or in the presentation of results.

¹ NIH Office of Behavioral and Social Sciences Research: Best Practices for Mixed Methods Research in the Health Sciences

Basic Mixed Methods Research Designs 

View image description . Figure adapted from Creswell, J. W. (2014). A concise introduction to mixed methods research. SAGE Publications.

Five Key Questions for Getting Started

• What do you want to know?
• What will be the detailed quantitative, qualitative, and mixed methods research questions that you hope to address?
• What quantitative and qualitative data will you collect and analyze?
• Which rigorous methods will you use to collect data and/or engage stakeholders?
• How will you integrate the data in a way that allows you to address the first question?

Rationale for Using Mixed Methods

• Obtain different, multiple perspectives: validation
• Build comprehensive understanding
• Explain statistical results in more depth
• Have better contextualized measures
• Track the process of program or intervention
• Study patient-centered outcomes and stakeholder engagement

Sample Mixed Methods Research Study

The EQUALITY study used an exploratory sequential design to identify the optimal patient-centered approach to collect sexual orientation data in the emergency department.

Qualitative Data Collection and Analysis : Semi-structured interviews with patients of different sexual orientation, age, race/ethnicity, as well as healthcare professionals of different roles, age, and race/ethnicity.

Builds Into : Themes identified in the interviews were used to develop questions for the national survey.

Quantitative Data Collection and Analysis : Representative national survey of patients and healthcare professionals on the topic of reporting gender identity and sexual orientation in healthcare.

Other Resources:

  Introduction to Mixed Methods Research : Harvard Catalyst’s eight-week online course offers an opportunity for investigators who want to understand and apply a mixed methods approach to their research.

Best Practices for Mixed Methods Research in the Health Sciences [PDF] : This guide provides a detailed overview of mixed methods designs, best practices, and application to various types of grants and projects.

Mixed Methods Research Training Program for the Health Sciences (MMRTP ): Selected scholars for this summer training program, hosted by Johns Hopkins’ Bloomberg School of Public Health, have access to webinars, resources, a retreat to discuss their research project with expert faculty, and are matched with mixed methods consultants for ongoing support.

Michigan Mixed Methods : University of Michigan Mixed Methods program offers a variety of resources, including short web videos and recommended reading.

To use a mixed methods approach, you may want to first brush up on your qualitative skills. Below are a few helpful resources specific to qualitative research:

• Qualitative Research Guidelines Project : A comprehensive guide for designing, writing, reviewing and reporting qualitative research.
• Fundamentals of Qualitative Research Methods – What is Qualitative Research : A six-module web video series covering essential topics in qualitative research, including what is qualitative research and how to use the most common methods, in-depth interviews, and focus groups.

View PDF of the above information.

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Mixed methods and survey research in family medicine and community health

John w creswell.

1 Family Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA

Mariko Hirose

2 Graduate Department of Psychological Sciences, Kwansei Gakuin University, Ashiya, Japan

Many family medicine and community health researchers use surveys as an original research methodology. Our purpose is to illustrate how survey research provides an important form of quantitative research that can be effectively combined with qualitative data to form a mixed methods study. We first provide an overview of the key principles in survey research and in mixed methods research. We review the various ways that survey can be used in mixed methods studies, citing options such as beginning a study with a survey, using a survey as the second form of data collection, or combining a survey and a form of qualitative data in a single data collection procedure. Finally, we illustrate in a specific example six steps in conducting a mixed methods study using survey research. In a mixed methods study using a survey, primary care researchers should consider six steps. Step 1. Articulate the rationale for mixed methods study. Step 2. Detail quantitative and qualitative databases. Step 3. Identify a mixed methods design. Step 4. Analyse and report the results of the quantitative and qualitative databases. Step 5. Present and show integration. Step 6. Explicate the value of using mixed methods. The ability to combine and integrate survey research into a mixed methods study provides a more rigorous approach to research than conducting only a survey or conducting just a qualitative interview. While requiring skills beyond traditional survey approaches, surveys in primary care offers an opportunity for a high level of sophistication in research methodology.

Introduction

Many primary care researchers consider the implementation of a survey as means for original data collection. While there are many resources guiding the conduct of a survey, these resources typically have not been written with primary care researcher in mind. Due to many of the complex questions that arise, primary care researchers are often interested in assessing a phenomenon both quantitatively and qualitatively.

Both survey methods and mixed methods research are distinct methodology approaches in the health and social sciences. However, they can be combined in a single mixed methods study with appropriate planning and thought about their combined use. The purpose of this article is to present an applied discussion about how to develop surveys (or questionnaires) and use them in a mixed methods study. To this end, we will first discuss the basic principles involved in mixed methods research, highlight the essential characteristics of survey research and end with a discussion about the steps for using surveys in a mixed methods study. We will further explain the steps using a published mixed methods study in the health sciences.

Basic principles of mixed methods research

Mixed methods research as we know it today began between 1985 and 1990. At that time, several individuals founded this new methodology. They came from various fields and countries, such as evaluation, management, sociology, medicine and education. 1 They were writing from Great Britain and the USA, and thus mixed methods can be seen as primarily an Anglo-American invention. They were not in touch with each other, and perhaps the most well-known writers were Greene from the USA and Bryman from England. Their impetus for developing mixed methods was a perceived need that both quantitative and qualitative research had value, and there was no need to keep the two forms of social and health research separate. This is despite differences in philosophical stances between the quantitative and qualitative researchers within social science sociology, anthropology, education, and evaluation perspectives and those with epidemiological orientations.

Designs and philosophies

Over time, probably during the mid-1990s, the idea began to form that mixed methods was collecting and analysing both quantitative and qualitative data, and also that additional insight might be gained from combining or integrating the two databases and linking them in a creative way. Thus, qualitative research explores phenomenon while quantitative explains the results of tests of hypotheses or research questions. Combined, mixed methods provides the insight of both exploration and explanation. By 2003, in the Handbook of Mixed Methods in Social and Behavioral Research , 2 authors advanced specific designs or procedures for conducting mixed methods research. This was not an unusual development in research methodology in that specific designs were well known in types of quantitative research, such as experimental research, 3 and in forms of qualitative research such as grounded theory. 4 At the same time, individuals were developing an increased understanding of the philosophical underpinnings of mixed methods research that have unfolded in approaches such as pragmatism, critical realism, dialectic pluralism and yin/yang Eastern philosophy. 5 These philosophical assumptions advanced the key idea that researchers bring to their mixed methods study core assumptions or beliefs that shape the types of procedures used in their studies. These twin developments—the designs or procedures and the philosophies—have led us to today a complete methodology called ‘mixed methods research’. Thus, to characterise mixed methods is to consider how it spans the process of research that includes a philosophy, an orientation to research problems and questions, specific approaches to collecting and analysing quantitative and qualitative data, and implications from the research that provides additional insight beyond what might be gained from simply collecting and analysing quantitative data or qualitative data.

Integration and basic characteristics

Further, mixing or ‘integration’ of the two forms of data has become a central feature of mixed methods research. Integration has developed as the buzzword for the innovative feature of mixed methods research, and it provides insight beyond what is learnt from the quantitative and qualitative databases separately. For example, the ability to compare the results of both databases provides a more complete understanding than either database alone. This procedure is called a ‘convergent mixed methods design’. It provides the opportunity to check one database against the other (how do people respond when they rate questions on a questionnaire vs when they are asked in person in an interview?). 1 Insight can emerge from collecting survey data initially and then following up with interviews to help explain the survey results in more detail. This type of design is call an ‘explanatory sequential design’. Insight can also emerge from exploring first with interviews to understand the culture and specific perspectives of individuals, and then designing questionnaires or experimental interventions that respond to these cultural features (called an ‘exploratory sequential design’). A new feature to emerge in mixed methods is to think beyond these insights that come through integrating these ‘core designs’ into more complex procedures, such as experimental trials, social network analyses, evaluation procedures or into community-based health practices. These designs are called ‘complex mixed methods designs’. 1 In short, what has evolved in recent years is an understanding of the key characteristics of a well-designed mixed methods study as shown in box 1 .

Key characteristics of a well-designed mixed methods study

• Researcher collects and analyses both quantitative and qualitative data.
• Researcher engages in rigorous procedures with both databases, such as systematic sampling, adequate sample size, use of good instruments and protocols, multistep analysis and standards of qualitative such as validity, replicability, generalisability and accuracy of the findings.
• Researcher combines (or mixes) the two databases in a systematic procedure called ‘integration’, where the overall intent is to bring the databases together, and to conduct analysis that accomplishes this aim and provides new insights.
• Researcher conducts the integration within a type of mixed methods design, such as converging or comparing the responses, explaining the findings of one phase, or exploring first before measuring or assessing perspectives to build in a contextual, cultural feature of the study.
• Researcher frames the study within larger philosophical assumptions, beliefs or orientations that the researcher brings to a study and incorporates into the study design a theory or conceptual model that informs what the researcher hopes to learn from the study.

Understanding the etiology of mixed methods research, how integration operates and the key characteristics of a good mixed methods project are essential understandings that lead to a contribution of mixed methods in family medicine. For researchers in the health sciences, the ‘Best Practices in Mixed Methods in the Health Sciences’ from the National Institute of Health 6 is a useful guideline for understanding an applying mixed methods research.

Basic principles of survey research

Survey research is a quantitative approach to social and health science research. Survey research designs are a set of research procedures in which investigators administer a survey to a sample or to the entire population of people to describe the attitudes, opinions, beliefs, perceptions, behaviours or characteristics of the population. In this procedure, survey researchers collect quantitative, numbered data using questionnaires (eg, mailed questionnaires) or interviews (eg, one-on-one interviews) and statistically analyse the data to describe trends about responses to questions and to test research questions or hypotheses. Researchers also interpret the meaning of the data by relating results of the statistical test back to past research studies. Surveys typically involve quantitative items, but researchers might include qualitative open-ended questions as well.

Survey research evolved as a research methodology during the 20th century. During World War II, for example, US surveys examined issues central to the war effort, such as the morale of soldiers, production capacity for weapons and the effectiveness of strategies. Through these studies, survey researchers refined and developed their techniques of large-scale assessments, enabling the emergence of large social research organisations in American universities after the war. For example, investigators established social research centres at the University of California, Berkeley (Survey Research Center), at the University of Chicago (National Opinion Research Center) and at the University of Michigan (Institute for Social Research). In addition, opinion polling organisations, such as Gallup, Roper and the RAND Corporation, furthered the understanding of large-scale data collection. The founding of polling and survey organisations, combined with the use of computers, the availability of data archives and storage, and funding from the federal government, helped to establish the popularity of surveys in research by the mid-20th century. In recent years, both federal and state governments have funded national and state surveys. Recently, individuals have increasingly used the Internet to collect survey data. Researchers can now generate an online survey to easily administer to anyone with Internet access. 7

Types of surveys

Surveys can be conducted over time, longitudinal surveys, or administered at one point in time, cross-sectional surveys. In mixed methods research, cross-sectional surveys are frequently used. The key characteristics of good survey work include several features. The participants who fill out a survey are individuals in a specific population (eg, individuals with chronic heart failure). Survey researchers then select a sample from this population to identify individuals to complete the survey. The most rigorous form of survey sampling is probability sampling, where each individual in the population has an equal chance of being selected. The idea, then, is that the researcher draws conclusions from this sample to make inferences about the entire population. Thus, it is not necessary to sample the entire population. However, the size of the sample receiving the survey is important, and it is useful to select as large a sample as possible. Sample size tables in survey texts help identify the appropriate number. 8

Different forms of surveys exist, and they can be broadly grouped into questionnaires and interviews. A questionnaire is a form used in a survey design in which participants in a study complete a mailed instrument and return it to the researcher. The participant chooses answers to questions and supplies basic personal or demographic information. An interview survey, however, is a form on which the researcher records answers supplied by the participant in the study.

A survey researcher does have the choice of different types of questionnaires and interviews: a questionnaire mailed to the participant to be filled out, a questionnaire administered online through the Internet or through email, an interview used in a one-on-one interview or with a small focus group, or a telephone interview. 5 Of these types, mailed questionnaires are the most popular form used in mixed methods research. Also, increasingly popular is the use an Internet survey with a software program such as Qualtrics 9 or Survey Monkey. 10 In the health sciences, focus groups are frequently used, and in these the researcher identifies an instrument to record data, convenes a small group of individuals (ie, typically six) and holds a discussion about questions on the instrument. 11

Use of a survey and common problems

In terms of a survey instrument to use, researchers can develop their own survey instrument (requiring skills in scale development and design), use an existing instrument or modify (with the authors’ permission) an existing instrument. If the researcher chooses to design their own instrument, the instrument will involve different types of attitudinal or behavioural questions, the instrument needs to have psychometrically rigorous question construction and the instrument should to be pilot-tested with a few individuals before its general administration. In a pilot test, the researcher administers the instrument to a small group of participants, asks them to comment on any weaknesses in the instrument, and then modifies or changes the instrument. Further, conducting cognitive interviews of the items to ascertain that the survey participant interprets the meaning of questions as intended is important. The types of questions the instrument will include closed-ended questions with items that require a check for the most appropriate answer, such as strongly agree, agree, undecided, disagree and strongly disagree. Also, the instrument may include open-ended questions which will result in collecting qualitative data from questions that permit the participant to give short answers. Common problems in constructing these questions include using vague words, asking multiple questions in a single question, writing wordy or lengthy questions with many parts, using negative or jargon language, having response categories overlap or be unbalanced, and having a mismatch between the question and the responses.

Analysing a survey

It is useful to consider the major components of a good mailed survey instrument and how it is analysed. 5 It should include a cover letter to the participants asking for their input, have an introduction to the survey stating the reasons for the survey, include questions of a length that could be reasonably answered by the participants and have closing instructions thanking the participant for their help in providing data. The questions or items on a survey instrument need considerable thought. Basically, four to five items can be grouped into a variable that measure attitudes and behaviours. Then these variables can further be grouped into scales. An instrument thus would contain several scales. Knowing this, the data analysis steps on data collected on an instrument can now be identified as illustrated in table 1 . 5

Steps during the analysis of survey data

Various ways surveys can be used in mixed methods research

Up to this point, we have discussed the basic ideas involved in both mixed methods research and survey research. Both of these can be viewed as distinct methodological approaches for a study in the health sciences and they need to be rigorously conducted. The options for researchers to bring surveys into mixed methods research can be seen in table 2 .

Options for using survey in a mixed methods research investigation

Example of using survey research in a mixed methods study

We will use an explanatory sequential design in mixed methods because this is a design that honours a strong quantitative initial data collection, is easy to administer and is one often used by beginning mixed methods researchers. Our specific example would be the study by Sonnenberg et al 12 on an assessment of resident physicians’ communication and collaboration competencies by an interprofessional (IP) team (clinicians, pharmacists, laboratory technicians, therapist and others). 12 This is a study in competency-based medical education where there is increasing emphasis on assessing resident performance in abilities to communicate and collaborative effectively in the workplace. This assessment can take place by non-physician members on IP teams, and such teams who have valuable expertise have not been adequately engaged in resident skill assessment.

Thus, the authors in the Sonnenberg et al 12 study used an explanatory sequential mixed methods design to examine the ability of the IP clinicians to provide feedback to pediatric residents during their rotation. 12 Using survey research in the first phase , the researchers compared IP supervisors and physician supervisors in terms of communication and if collaborative training objectives were met. These objectives were based on the widely used CanMEDS Roles Framework developed by the Royal College of Physicians and Surgeons of Canada. In this Framework, seven essential skills for medical practice were identified (eg, communicator, collaborator, leader, health advocate, scholar, professional and expert). Then, in the second phase , the researchers conducted follow-up qualitative focus groups to probe a contextual understanding of the factors that influenced the process of assessment. Thus, the purpose of this mixed methods study was to examine IP clinicians’ perceptions of their ability to provide formative feedback compared with physician supervisors on the rotation and to qualitatively explore potential barriers to the feedback process.

What were the steps they engaged in to conduct this study? These steps could be applied to many mixed methods projects using survey research. See table 3 for information from the Sonnenberg et al example. 12

Steps in a mixed methods survey investigation as illustrated by the Sonnenberg study

Step 1. Rationale for mixed methods . Determine if mixed methods research is the best methodology to use to answer your research questions. Provide a reason for the use of mixed methods in the project.

Step 2. Quantitative and qualitative databases . Identify the types of quantitative and qualitative data to be collected and analysed. Consider these two types of data as distinct databases. In a mixed method research study, it is important to collect and analyse both quantitative and qualitative data. Use rigorous survey procedures and rigorous qualitative procedures.

Step 3. Mixed methods design . Determine what mixed methods design you will use. Draw a diagram of the design. Mixed methods researchers like to have a visual picture of their procedures. The diagram needs to be simple and straightforward without complicated components.

Step 4. Analyse and report the results of the quantitative and qualitative databases . Present the results of the study showing the quantitative statistical results and the qualitative thematic results. Keep these discussions of analysis separate under distinct headings. They should also mirror the steps in the design, and show a clear linkage between the quantitative and qualitative components in the study.

Step 5. Present and show integration . Pay attention to the point of integration of the two databases and make it specific in the diagram of the design. Use an arrow in the diagram to show the point of integration of the two databases. Discuss the ways integration appears in the study. There are two major ways of representing integration. One is to simply discuss the quantitative and qualitative results side by side in a discussion. The other way is becoming popular in mixed methods research: a joint display. A joint display is typically a table in which the quantitative and qualitative results appear side by side. In this way, comparisons can be made between the quantitative and qualitative results. In an explanatory sequential design, the quantitative results are first reported in a first column, and the qualitative results that help explain the quantitative results appear in a second column. Further, a third separate column shows the impact of the integration in the study. A template is useful for considering the type of joint display that can emerge using an explanatory sequential design as an example.

Step 6. Explicate the value of using mixed methods . In a discussion section at the end of the study, include comments about the value of using mixed methods as a methodology. In this way, readers of the study will see the utility of mixed methods in research. This means drawing the implications of using both quantitative and qualitative data in a mixed methods study.

Additional resources to consider

In addition to Creswell and Creswell, when conducting mixed methods survey studies, it is important to study survey research and consult books on it by leading writers such as Babbie, Fowler and Dillman. 5 8 13 14 In mixed methods, consider introductory mixed methods texts by Plano Clark and Ivankova, Creswell and Creswell, Plano Clark, and Creswell, and Guetterman as important resources. 1 15–17 Look for practical guides to both survey and mixed methods research.

The illustration of using a survey in mixed methods based on an explanatory sequential design is simply one possibility of using surveys. However, it provides an opportunity to see how a survey fits into mixed methods. In order to conduct mixed methods research, investigators need to know the basics of both mixed methods research and survey research, and recognise the various ways the two methodologies can be combined. Further, this combination is a rigorous design that takes time for both the quantitative and qualitative components. Some researchers may find it more economical and time-saving, to employ only quantitative or qualitative research. Knowing how to effectively combine the two methods requires knowledge across multiple research methods. Still, surveys combined with mixed methods research leverage two useful approaches.

Correction notice: This article has been corrected. Reference details have been updated.

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests: None declared.

Patient consent for publication: Not required.

Provenance and peer review: Not commissioned; internally peer reviewed.

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Unit 6: Qual vs Quant.

29 Mixed Methods in Communication Research

Mixed methods in communication research.

Many communication researchers use a combination of both quantitative and qualitative methods to gain a more comprehensive understanding of their research questions. For example, a study might use surveys to gather broad quantitative data and follow up with interviews to explore the findings in more depth. In interpersonal communication research, a study might use surveys to gather broad quantitative data on communication satisfaction and follow up with interviews to explore the findings in more depth.

By using both methods, communication researchers can obtain a richer, more nuanced understanding of how communication works and its impact on society.

Example Applications

• Relationship Dynamics : Quantitative surveys might assess the impact of communication behaviors on relationship satisfaction, while qualitative interviews explore the nuances of these behaviors in everyday interactions.
• Health Communication : Quantitative surveys could assess the effectiveness of a public health campaign, while qualitative interviews provide insights into how different audiences perceive and respond to the campaign messages.
• Media Effects : Quantitative methods might measure the impact of violent media on aggression, while qualitative methods explore how individuals interpret and make sense of violent content.
• Conflict Resolution : Quantitative methods might measure the effectiveness of different conflict resolution strategies, while qualitative methods explore how individuals perceive and experience these strategies.
• Non-Verbal Communication : Quantitative studies could analyze the impact of non-verbal cues on communication effectiveness, while qualitative research provides insights into how these cues are interpreted in different contexts.

Communication Research in Real Life Copyright © 2023 by Kate Magsamen-Conrad. All Rights Reserved.

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