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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.

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?

Last updated

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

Dovetail streamlines analysis to help you uncover and share actionable insights

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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6 “Sixth Floor”: Writing Up, Presenting, and Teaching Mixed Methods

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This chapter discusses writing up (both research proposals and final mixed methods reports), presenting, and teaching mixed methods. It includes a brief description of some peer-reviewed journals and their receptivity to and publication of mixed method studies. It also provides an overview of the types of diagrams used in mixed methods research, as well as helpful hints for communicating mixed methods study results to various audiences. Since some readers are likely teaching mixed methods either as a stand-alone course or as part of qualitative or quantitative courses, teaching models for conveying mixed methods material alone and its placement within existing courses are described.

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Mixed methods research

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Introduction to Psychology

SI0030 – Social Research Methods Week 10 Luke Sloan Mixed Methods Week 10 Luke Sloan Mixed Methods.

Issues in a research team using different methods: Changing hats? Julia Brannen Thomas Coram Research Unit Institute of Education, University of London.

About this Template In the past decades, mixed methods research has been increasingly applied to the dominant research fields of social, behavioral or.

Chapter 17 Mixed Method Designs

Qualitative Data Analysis and Interpretation

Chapter 17 Mixed Methods Designs

 It’s an approach to research that examines a concept or phenomenon from the perspective of the individual who is experiencing it  The research purpose.

Quantitative and Qualitative Approaches

研究方法論課程報告 報告人：余惟茵 指導老師：任維廉教授

Quantitative and Qualitative Approaches Dr. William M. Bauer

Research Design Mixed Methods

RESEARCH DESIGN.

Chapter 19: Mixed Methods Research

+ Using Mixed Methods Research Designs for Research in Teaching and Learning Dr. Elizabeth G. Creamer, Virginia Tech Dr. Beth Mac Donald, Utah State University.

Copyright © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 18 Mixed Methods and Other Special Types of Research.

RESEARCH IN MATH EDUCATION-3

Copyright © 2011 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 8 Qualitative Inquiry.

Lecturer: Dr. Johan Presented by :- Huwaida binti Tajudin Hanafi bin Hasbullah Saiful Nizam bin Mat Saat Mohd Faizal Ridha.

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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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Chapter 8: Research designs using mixed methods

2014, Doing Research in the Real World, 3rd edn

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This chapter explores various ways of conceiving what is called mixed methods research (MMR) – mixing quantitative and qualitative traditions. After reading this chapter, you should be able to discuss the debates around what it might mean to “mix” different methods employed in the process of doing MMR; explore the philosophical underpinnings which can be said to underlie the use of different methods in MMR; reflect on whether we should regard mixed methods research as encapsulating a different paradigm from those that are seen as traditionally underpinning quantitatively-directed or qualitatively-directed research traditions; articulate how a “third” paradigmatic stance as an underpinning for MMR can be justified; differentiate between multi-methods and MMR; describe key considerations that should inform the choice of MMR as a methodology to conduct research; outline key MMR designs; and reflect on how to conduct MMR.

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Achieving Integration in Mixed Methods Designs—Principles and Practices

Michael d fetters.

Family Medicine, University of Michigan, 1018 Fuller St., Ann Arbor, MI 48104-1213

Leslie A Curry

Yale School of Public Health (Health Policy), New Haven, CT

John W Creswell

Educational Psychology, University of Nebraska-Lincoln, Lincoln, NE

Associated Data

Mixed methods research offers powerful tools for investigating complex processes and systems in health and health care. This article describes integration principles and practices at three levels in mixed methods research and provides illustrative examples. Integration at the study design level occurs through three basic mixed method designs—exploratory sequential, explanatory sequential, and convergent—and through four advanced frameworks—multistage, intervention, case study, and participatory. Integration at the methods level occurs through four approaches. In connecting, one database links to the other through sampling. With building, one database informs the data collection approach of the other. When merging, the two databases are brought together for analysis. With embedding, data collection and analysis link at multiple points. Integration at the interpretation and reporting level occurs through narrative, data transformation, and joint display. The fit of integration describes the extent the qualitative and quantitative findings cohere. Understanding these principles and practices of integration can help health services researchers leverage the strengths of mixed methods.

This article examines key integration principles and practices in mixed methods research. It begins with the role of mixed methods in health services research and the rationale for integration. Next, a series of principles describe how integration occurs at the study design level, the method level, and the interpretation and reporting level. After considering the “fit” of integrated qualitative and quantitative data, the article ends with two examples of mixed methods investigations to illustrate integration practices.

Health services research includes investigation of complex, multilevel processes, and systems that may require both quantitative and qualitative forms of data (Creswell, Fetters, and Ivankova 2004 ; Curry et al. 2013 ). The nature of the research question drives the choice of methods. Health services researchers use quantitative methodologies to address research questions about causality, generalizability, or magnitude of effects. Qualitative methodologies are applied to research questions to explore why or how a phenomenon occurs, to develop a theory, or to describe the nature of an individual's experience. Mixed methods research studies draw upon the strengths of both quantitative and qualitative approaches and provides an innovative approach for addressing contemporary issues in health services. As one indication of the growing interest in mixed methods research, the Office of Behavioral and Social Sciences at the National Institutes of Health recently developed for researchers and grant reviewers the first best practices guideline on mixed methods research from the National Institutes of Health (Creswell et al. 2011 ).

Rationale for Integration

The integration of quantitative and qualitative data can dramatically enhance the value of mixed methods research (Bryman 2006 ; Creswell and Plano Clark 2011 ). Several advantages can accrue from integrating the two forms of data. The qualitative data can be used to assess the validity of quantitative findings. Quantitative data can also be used to help generate the qualitative sample or explain findings from the qualitative data. Qualitative inquiry can inform development or refinement of quantitative instruments or interventions, or generate hypotheses in the qualitative component for testing in the quantitative component (O'Cathain, Murphy, and Nicholl 2010 ). Although there are many potential gains from data integration, the extent to which mixed methods studies implement integration remains limited (Bryman 2006 ; Lewin, Glenton, and Oxman 2009 ). Nevertheless, there are specific approaches to integrate qualitative and quantitative research procedures and data (O'Cathain, Murphy, and Nicholl 2010 ; Creswell and Plano Clark 2011 ). These approaches can be implemented at the design, methods, and interpretation and reporting levels of research (see Table ​ Table1 1 ).

Levels of Integration in Mixed Methods Research

Integration at the design level—the conceptualization of a study—can be accomplished through three basic designs and four advanced mixed methods frameworks that incorporate one of the basic designs. Basic designs include (1) exploratory sequential; (2) explanatory sequential; and (3) convergent designs. In sequential designs, the intent is to have one phase of the mixed methods study build on the other, whereas in the convergent designs the intent is to merge the phases in order that the quantitative and qualitative results can be compared.

In an exploratory sequential design , the researcher first collects and analyzes qualitative data, and these findings inform subsequent quantitative data collection (Onwuegbuzie, Bustamante, and Nelson 2010 ). For example, Wallace and colleagues conducted semistructured interviews with medical students, residents, and faculty about computing devices in medical education and used the qualitative data to identify key concepts subsequently measured in an online survey (Wallace, Clark, and White 2012 ).

In an explanatory sequential design , the researcher first collects and analyzes quantitative data, then the findings inform qualitative data collection and analysis (Ivankova, Creswell, and Stick 2006 ). For example, Carr explored the impact of pain on patient outcomes following surgery by conducting initial surveys about anxiety, depression, and pain that were followed by semistructured interviews to explore further these concepts (Carr 2000 ).

In a convergent design (sometimes referred to as a concurrent design), the qualitative and quantitative data are collected and analyzed during a similar timeframe. During this timeframe, an interactive approach may be used where iteratively data collection and analysis drives changes in the data collection procedures. For example, initial quantitative findings may influence the focus and kinds of qualitative data that are being collected or vice versa. For example, in one study Crabtree and colleagues used qualitative findings and quantitative findings iteratively in multiple phases such that the data were interacting to inform the final results (Crabtree et al. 2005 ). In the more common and technically simpler variation, qualitative and quantitative data collection occurs in parallel and analysis for integration begins well after the data collection process has proceeded or has been completed. Frequently, the two forms of data are analyzed separately and then merged. For example, Saint Arnault and colleagues conducted multiple surveys using standardized and culturally adapted instruments as well as ethnographic qualitative interviews to investigate how the illness experience, cultural interpretations, and social structural factors interact to influence help-seeking among Japanese women (Saint Arnault and Fetters 2011 ).

Advanced frameworks encompass adding to one of the three basic designs a larger framework that incorporates the basic design. The larger framework may involve (1) a multistage; (2) an intervention; (3) a case study; or (4) a participatory research framework.

In a multistage mixed methods framework , researchers use multiple stages of data collection that may include various combinations of exploratory sequential, explanatory sequential, and convergent approaches (Nastasi et al. 2007 ). By definition, such investigations will have multiple stages, defined here as three or more stages when there is a sequential component, or two or more stages when there is a convergent component; these differences distinguishes the multistage framework from the basic mixed methods designs. This type of framework may be used in longitudinal studies focused on evaluating the design, implementation, and assessment of a program or intervention. Krumholz and colleagues have used this design in large-scale outcomes research studies (Krumholz, Curry, and Bradley 2011 ). For example, a study by their team examining quality of hospital care for patients after heart attacks consisted of three phases: first, a quantitative analysis of risk-standardized mortality rates for patients with heart attacks to identify high and low performing hospitals; second, a qualitative phase to understand the processes, structures, and organizational environments of a purposeful sample of low and high performers and to generate hypotheses about factors associated with performance; and third, primary data collection through surveys of a nationally representative sample of hospitals to test these hypotheses quantitatively (Curry et al. 2011 ; Bradley et al. 2012 ). Ruffin and colleagues conducted a multistage mixed methods study to develop and test in a randomized controlled trial (RCT) a website to help users choose a screening approach to colorectal cancer. In the first stage, the authors employed a convergent design using focus groups and a survey (Ruffin et al. 2009 ). In the second stage, they developed the website based on multiple qualitative approaches (Fetters et al. 2004 ). In the third stage, the authors tested the website in an RCT to assess its effectiveness (Ruffin, Fetters, and Jimbo 2007 ). The multistage framework is the most general framework among advanced designs. The additional three frameworks frequently involve multiple stages or phases but differ from multistage by having a particular focus.

In an intervention mixed methods framework , the focus is on conducting a mixed methods intervention. Qualitative data are collected primarily to support the development of the intervention, to understand contextual factors during the intervention that could affect the outcome, and/or explain results after the intervention is completed (Creswell et al. 2009 ; Lewin, Glenton, and Oxman 2009 ). For example, Plano Clark and colleagues utilized data from a pretrial qualitative study to inform the design of a trial developed to compare a low dose and high dose behavioral intervention to improve cancer pain management—the trial also included prospective qualitative data collection during the trial (Plano Clark et al. 2013 ). The methodological approach for integrating qualitative data into an intervention pretrial, during the trial, or post-trial is called embedding (see below), and some authors refer to such trials as embedded designs (Creswell et al. 2009 ; Lewin, Glenton, and Oxman 2009 ).

In a case study framework , both qualitative and quantitative data are collected to build a comprehensive understanding of a case, the focus of the study (Yin 1984 ; Stake 1995 ). Case study involves intensive and detailed qualitative and quantitative data collection about the case (Luck, Jackson, and Usher 2006 ). The types of qualitative and quantitative data collected are chosen based on the nature of the case, feasibility issues, and the research question(s). In one mixed methods case study, Luck and colleagues utilized qualitative data from participant observation, semistructured interviews, informal field interviews and journaling, and quantitative data about violent events collected through structured observations to understand why nurses under-report violence in the workplace and describe how they handle it (Luck, Jackson, and Usher 2008 ). Comparative case studies are an extension of this framework and can be formulated in various ways. For example, Crabtree and colleagues used a comparative case approach to examine the delivery of clinical preventive services in family medicine offices (Crabtree et al. 2005 ).

In a participatory framework , the focus is on involving the voices of the targeted population in the research to inform the direction of the research. Often researchers specifically seek to address inequity, health disparities, or a social injustice through empowering marginalized or underrepresented populations. The distinguishing feature of a participatory framework is the strong emphasis on using mixed methods data collection through combinations of basic mixed methods designs or even another advanced design, for example, an intervention framework such as an RCT. Community-based participatory research (CBPR) is a participatory framework that focuses on social, structural, and physical environmental inequities and engages community members, organizational representatives, and researchers in all aspects of the research process (Macaulay et al. 1999 ; Israel et al. 2001 , 2013 ; Minkler and Wallerstein 2008 ). In one CBPR project, Johnson and colleagues used a mixed methods CBPR approach to collaborate with the Somali community to explore how attitudes, perceptions, and cultural practices such as female genital cutting influence their use of reproductive health services—this informed the development of interventional programs to improve culturally competent care (Johnson, Ali, and Shipp 2009 ). A similar variation involving an emerging participatory approach that Mertens refers to as transformative specifically focuses on promoting social justice (Mertens 2009 , 2012 ) and has been used with Laotian refugees (Silka 2009 ).

Creswell and Plano Clark conceptualize integration to occur through linking the methods of data collection and analysis (Creswell et al. 2011 ). Linking occurs in several ways: (1) connecting; (2) building; (3) merging; and (4) embedding (Table ​ (Table2). 2 ). In a single line of inquiry, integration may occur through one or more of these approaches.

Integration through Methods

Integration through connecting occurs when one type of data links with the other through the sampling frame . For example, consider a study with a survey and qualitative interviews. The interview participants are selected from the population of participants who responded to the survey. Connecting can occur through sampling regardless of whether the design is explanatory sequential or convergent. That is, if the baseline survey data are analyzed, and then the participants sampled based on findings from the analysis, then the design is explanatory sequential. In contrast, the design is convergent if the data collection and analyses occur at the same time for the baseline survey and interviews of all or a subsample of the participants of the survey. A key defining factor in sequential or convergent is how the analysis occurs, either through building or merging, respectively.

Integration through building occurs when results from one data collection procedure informs the data collection approach of the other procedure, the latter building on the former. Items for inclusion in a survey are built upon previously collected qualitative data that generate hypotheses or identify constructs or language used by research participants. For example, in a project involving the cultural adaptation of the Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey for use in the Arabian Gulf (Hammoud et al. 2012 ), baseline qualitative interviews identified new domains of importance such as gender relations, diet, and interpreter use not found in the existing CAHPS instrument. In addition, phrases participants used during the interviews informed the wording of individual items.

Integration through merging of data occurs when researchers bring the two databases together for analysis and for comparison . Ideally, at the design phase, researchers develop a plan for collecting both forms of data in a way that will be conducive to merging the databases. For example, if quantitative data are collected with an instrument with a series of scales, qualitative data can be collected using parallel or similar questions (Castro et al. 2010 ). Merging typically occurs after the statistical analysis of the numerical data and qualitative analysis of the textual data. For example, in a multistage mixed methods study, Tomoaia-Cortisel and colleagues used multiple sources of existing quantitative and qualitative data as well as newly collected quantitative and qualitative data (Tomoaia-Cortisel et al. 2013 ). The researchers examined the relationship between quality of care according to key patient-centered medical home (PCMH) measures, and quantity of care using a productivity measure. By merging both scores of quality and quantity, with qualitative data from interviews, the authors illuminated the difficulty of achieving highly on both PCMH quality measures and productivity. The authors extended this understanding further by merging staff satisfaction scores and staff interview data to illustrate the greater work complexity but lower satisfaction for staff achieving measures for high-quality care (Tomoaia-Cortisel et al. 2013 ).

Integration through embedding occurs when data collection and analysis are being linked at multiple points and is especially important in interventional advanced designs, but it can also occur in other designs. Embedding may involve any combination of connecting, building, or merging, but the hallmark is recurrently linking qualitative data collection to quantitative data collection at multiple points. Embedding may occur in the pretrial period, when qualitative (or even a combination of qualitative and quantitative) data can be used in various ways such as clarifying outcome measures, understanding contextual factors that could lead to bias and should be controlled for, or for developing measurement tools to be utilized during the trial. During the trial, qualitative data collection can be used to understand contextual factors that could influence the trial results or provide detailed information about the nature of the experience of subjects. Post-trial qualitative data collection can be used to explain outliers, debrief subjects or researchers about events or experiences that occurred during the trial, or develop hypotheses about changes that might be necessary for widespread implementation outside of a controlled research environment. Such studies require caution to avoid threatening the validity of the trial design. In a site-level controlled trial of a quality improvement approach for implementing evidence-based employment services for patients at specialty mental health clinics, Hamilton and colleagues collected semistructured interview data before, during, and after implementation (Hamilton et al. 2013 ). In another interesting example, Jaen and colleagues used an embedded approach for evaluating practice change in a trial comparing facilitated and self-directed implementation strategies for PCMH. The authors use both embedded quantitative and qualitative evaluation procedures including medical record audit, patient and staff surveys, direct observation, interviews, and text review (Jaen et al. 2010 ).

Method level integration commonly relates to the type of design used in a study. For example, connecting follows naturally in sequential designs, while merging can occur in any design. Embedding generally occurs in an interventional design. Thus, the design sets parameters for what methodological integration choices can be made.

Integration of qualitative and quantitative data at the interpretation and reporting level occurs through three approaches: (1) integrating through narrative; (2) integrating through data transformation; and (3) integrating through joint displays. A variety of strategies have been offered for publishing that incorporate these approaches (Stange, Crabtree, and Miller 2006 ; Creswell and Tashakkori 2007 ).

When integrating through narrative , researchers describe the qualitative and quantitative findings in a single or series of reports. There are three approaches to integration through narrative in research reports. The weaving approach involves writing both qualitative and quantitative findings together on a theme-by-theme or concept-by-concept basis. For example, in their work on vehicle crashes among the elderly, Classen and colleagues used a weaving approach to integrate results from a national crash dataset and perspectives of stakeholders to summarize causative factors of vehicle crashes and develop empirical guidelines for public health interventions (Classen et al. 2007 ). The contiguous approach to integration involves the presentation of findings within a single report, but the qualitative and quantitative findings are reported in different sections. For example, Carr and colleagues reported survey findings in the first half of the results section and the qualitative results about contextual factors in a subsequent part of the report (Carr 2000 ). In their study of a quality improvement approach for implementing evidence-based employment services at specialty mental health clinics, Hamilton and colleagues used this approach but differ by presenting the qualitative results first and the quantitative results second (Hamilton et al. 2013 ). The staged approach to integration often occurs in multistage mixed methods studies when the results of each step are reported in stages as the data are analyzed and published separately. For example, Wilson and colleagues used an intervention mixed methods framework involving a clinical trial of usual care, nicotine gum, and gum plus counseling on smoking cessation (Wilson et al. 1988 ). They also used interviews to find the meaning patients attributed to their stopping smoking (Willms 1991 ). The authors published the papers separately but in the second published paper, the interview paper, they only briefly mention the original clinical trial paper.

Integration through data transformation happens in two steps. First, one type of data must be converted into the other type of data (i.e., qualitative into quantitative or quantitative into qualitative). Second, the transformed data are then integrated with the data that have not been transformed. In qualitative studies, researchers sometimes code the qualitative data and then count the frequency of codes or domains identified, a process known also as content analysis (Krippendorff 2013 ). Data transformation in the mixed methods context refers to transforming the qualitative data into numeric counts and variables using content analysis so that the data can be integrated with a quantitative database. Merging in mixed methods goes beyond content analysis by comparing the transformed qualitative data with a quantitative database. Zickmund and colleagues used qualitatively elicited patient views of self transformed to a numerical variable, and mortality data to conduct hierarchical multivariable logistical modeling (Zickmund et al. 2013 ).

Researchers have used additional variations. Qualitative data can be transformed to quantitative data, then integrated with illustrative examples from the original qualitative dataset. For example, Ruffin and colleagues transformed qualitative responses from focus group data about colorectal cancer (CRC) screening preferences into quantitative variables, and then integrated these findings with representative quotations from three different constituencies (Ruffin et al. 2009 ). Quantitative data can also be transformed into a qualitative format that could be used for comparison with qualitatively accessed data. For example, Pluye and colleagues examined a series of study outcomes with variable strengths of association that were converted into qualitative levels and compared across the studies based on patterns found (Pluye et al. 2005 ).

When integrating through joint displays , researchers integrate the data by bringing the data together through a visual means to draw out new insights beyond the information gained from the separate quantitative and qualitative results. This can occur through organizing related data in a figure, table, matrix, or graph. In their quality improvement study to enhance colorectal cancer screening in practices, Shaw and colleagues collocated a series of qualitatively identified factors with CRC screening rates at baseline and 12 months later (Shaw et al. 2013 ).

When using any of these analytical and representation procedures, a potential question of coherence of the quantitative and qualitative findings may occur. The “fit” of data integration refers to coherence of the quantitative and qualitative findings. The assessment of fit of integration leads to three possible outcomes. Confirmation occurs when the findings from both types of data confirm the results of the other. As the two data sources provide similar conclusions, the results have greater credibility. Expansion occurs when the findings from the two sources of data diverge and expand insights of the phenomenon of interest by addressing different aspects of a single phenomenon or by describing complementary aspects of a central phenomenon of interest. For example, quantitative data may speak to the strength of associations while qualitative data may speak to the nature of those associations. Discordance occurs if the qualitative and quantitative findings are inconsistent, incongruous, contradict, conflict, or disagree with each other. Options for reporting the findings include looking for potential sources of bias, and examining methodological assumptions and procedures. Investigators may handle discordant results in different ways such as gathering additional data, re-analyzing existing databases to resolve differences, seeking explanations from theory, or challenging the validity of the constructs. Further analysis may occur with the existing databases or in follow-up studies. Authors deal with this conundrum by discussing reasons for the conflicting results, identifying potential explanations from theory, and laying out future research options (Pluye et al. 2005 ; Moffatt et al. 2006 ).

Below, two examples of mixed methods illustrate the integration practices. The first study used an exploratory sequential mixed methods design (Curry et al. 2011 ) and the second used a convergent mixed methods design (Meurer et al. 2012 ).

Example 1. Integration in an Exploratory Sequential Mixed Methods Study—The Survival after Acute Myocardial Infarction Study (American College of Cardiology 2013 )

Despite more than a decade of efforts to improve care for patients with acute myocardial infarction (AMI), there remains substantial variation across hospitals in mortality rates for patients with AMI (Krumholz et al. 2009 ; Popescu et al. 2009 ). Yet the vast majority of this variation remains unexplained (Bradley et al. 2012 ), and little is known about how hospitals achieve reductions in risk-standardized mortality rates (RSMRs) for patients with AMI. This study sought to understand diverse and complex aspects of AMI care including hospital structures (e.g., emergency department space), processes (e.g., emergency response protocols, coordination within hospital units), and hospital internal environments (e.g., organizational culture).

Integration through design . An exploratory sequential mixed methods design using both qualitative and quantitative approaches was best suited to gain a comprehensive understanding of how these features may be related to quality of AMI care as reflected in RSMRs. The 4-year investigation aimed to first generate and then empirically test hypotheses concerning hospital-based efforts that may be associated with lower RSMRs (Figure ​ (Figure1 1 ).

Example Illustrating Integration in an Exploratory Sequential Mixed Methods Design from the Survival after Acute Myocardial Infarction Study

Integration through methods . The first phase was a qualitative study of acute care hospitals in the United States (Curry et al. 2011 ). Methodological integration occurred through connecting as the 11 hospitals in the purposeful sample ranked in either the top 5 percent or bottom 5 percent of RSMRs for each of the two most recent years of data (2005–2006, 2006–2007) from the Centers for Medicare & Medicaid Services (CMS). The qualitative data from 158 key staff interviews informed the generation of hypotheses regarding factors potentially associated with better performance (see Table ​ Table3) 3 ) (Curry et al. 2011 ). These hypotheses were used to build an online quantitative survey that was administered in a cross-sectional study of 537 acute care hospitals (91 percent response rate) (Curry et al. 2011 ; Krumholz, Curry, and Bradley 2011 ; Bradley et al. 2012 ).

Examples of How the Qualitative Data Were Used to Build Quantitative Survey Items in the Survival after Acute Myocardial Infarction Study

AMI, acute myocardial infarction; CEO, chief executive officer. Adapted with permission from Bradley, Curry et al., Annals of Internal Medicine , May 1, 2012.

Mixed methods were used to characterize the care practices and processes in higher performing organizations as well as the organizational environment where they were implemented. Figure ​ Figure1 1 illustrates points in the process of integration. In Aim 1, the qualitative component connected with the CMS database in order to identify a positive deviance sample. The investigators conducted a systematic analysis of the qualitative data using a multidisciplinary team. This provided (point 1, Figure 1 ) a rich characterization of prominent themes that distinguished higher-performing from lower-performing hospitals and generated hypotheses regarding factors influencing AMI mortality rates (Curry et al. 2011 ). In Aim 2, the investigators built a 68 item-survey from the qualitative data. Key concepts from the qualitative data (point 2, Figure 1 ) were operationalized as quantitative items for inclusion in a web-based survey in order to test the hypotheses statistically in a nationally representative sample of hospitals (Bradley et al. 2012 ). The authors analyzed the quantitative survey data and then merged the quantitative findings (point 3, Figure 1 ) and qualitative analysis (point 4, Figure 1 ) in a single paper. The merging of the qualitative and quantitative produced a comprehensive, multifaceted description of factors influencing RSMRs as well as the impact of these factors on RSMRs that was presented using a weaving narrative . For example, problem-solving and learning was a prominent theme that differentiated higher-performing from lower-performing hospitals. In higher-performing hospitals, adverse events were perceived as opportunities for learning and improvement, approaches to data feedback were nonpunitive, innovation and creativity were valued and supported, and new ideas were sought. In the multivariable analysis, having an organizational environment where clinicians are encouraged to creatively solve problems was significantly associated with lower RSMRs (0.84 percentage points). Finally, additional analyses of qualitative data examining organizational features related to high-quality discharge planning (point 5, Figure ​ Figure1) 1 ) (Cherlin et al. 2013 ), and examining collaborations with emergency medical services (point 6, Figure ​ Figure1) 1 ) (Landman et al. 2013 ) were also methodologically connected through sampling of high-performing hospitals in the CMS database.

Integration through Interpretation and Reporting . The authors used primarily a staged narrative approach for reporting their results. The process and outcomes of integration of qualitative and quantitative data were primarily described in the quantitative paper (Bradley et al. 2012 ). The qualitative data informed the development of domains and concepts for a quantitative survey. Mapping of all survey items to corresponding concepts from the qualitative findings was reported in a web appendix of the published article. In the presentation of results from the multivariate model, multiple strategies that had significant associations with RSMRs were reported, with a summary of how these strategies corresponded to five of the six domains from the qualitative component. Quantitative and qualitative findings were synthesized through narrative both in the results and discussion using weaving . Key aspects of the organizational environment included effective communication and collaboration among groups, broad staff presence, and expertise. A culture of problem solving and learning were apparent in the qualitative findings and statistically associated with higher RSMRs in the quantitative findings. Regarding fit , the quantitative findings (Bradley et al. 2012 ) primarily confirmed the qualitative findings (Curry et al. 2011 ). Thus, higher performing hospitals were not distinguished by specific practices, but instead by organizational environments that could foster higher quality care. An accompanying editorial (Davidoff 2012 ) discusses the complementary relationship between the qualitative and quantitative findings, highlighting again the respective purposes of each component. The additional qualitative analyses were published separately (Cherlin et al. 2013 ; Landman et al. 2013 ) and illustrate staged approach to reporting through narrative with ample referencing to the previous studies. This example also illustrates expansion of the previously published findings (Stange, Crabtree, and Miller 2006 ).

Example 2. Integration in a Convergent Mixed Methods Study—The Adaptive Designs Accelerating Promising Trials into Treatments (ADAPT-IT) Study

The RCT is considered by many trialists to be the gold standard of evidence. Adaptive clinical trials (ACTs) have been developed as innovative trials with potential benefits over traditional trials. However, controversy remains regarding assumptions made in ACTs and the validity of results (Berry 2011 ). Adaptive designs comprise a spectrum of potential trial design changes (Meurer et al. 2012 ). A simple adaptation involves early trial termination rules based on statistical boundaries (Pocock 1977 ), while a complex adaptation in a dose-finding trial could identify promising treatments for specific subpopulations and tailor enrollment to maximize information gained (Yee et al. 2012 ). The overarching objective of ADAPT-IT is “To illustrate and explore how best to use adaptive clinical trial designs to improve the evaluation of drugs and medical devices and to use mixed methods to characterize and understand the beliefs, opinions, and concerns of key stakeholders during and after the development process”(Meurer et al. 2012 ).

Integration through design . One study from the mixed methods evaluation aim of the investigation seeks to describe and compare the beliefs and perspectives of key stakeholders in the clinical trial enterprise about potential ethical advantages and disadvantages of ACT approaches. A mixed methods convergent design was utilized to collect quantitative data through a 22-item ACTs beliefs survey using questions with a 100-point visual analog scale, and qualitative data from unstructured open-response questions on the survey and mini focus group interviews. The scales on the survey instrument assessed beliefs about ethical advantages and disadvantages of adaptive designs from the patient, research, and societal perspectives. The qualitative questions on the survey and in the interview guides elicited why participants feel there are advantages or disadvantages to using adaptive designs. A mixed methods approach was implemented to elucidate participants’ beliefs, to identify the reasoning behind the beliefs expressed, and to integrate the data together to provide the broadest possible understanding. Fifty-three individuals participated from the four stakeholder groups: academic clinicians ( n = 22); academic biostatisticians ( n = 5); consultant biostatisticians ( n = 6); and other stakeholders, including FDA and NIH personnel and patient advocates ( n = 20).

Integration through methods . The quantitative and qualitative data were collected concurrently, and the approach to integration involved merging . With the content of the scales on the survey in mind, the mixed methods team developed the open-ended responses on the survey and interview questions for mini focus groups to parallel visual analog scale (VAS) questions about ethical advantages and disadvantages. By making this choice intentionally during the design, integration through merging would naturally follow. The research team conducted separate analyses of the quantitative and qualitative data in parallel . For the quantitative analytics, the team calculated descriptive statistics, mean scores, and standard deviations across the four stakeholder groups. Box plots of the data by group were developed to allow intra- and intergroup comparisons. For the qualitative analytics, the investigators immersed themselves in the qualitative database, developed a coding scheme, and conducted thematic searches using the codes. Since the items on the VASs and the questions on the qualitative interview guides were developed in tandem, the codes in the coding scheme were similarly developed based on the items on the scales and the interview questions. As additional themes emerged, codes to capture these were added. The methodological procedures facilitated thematic searches of the text database about perceived ethical advantages and disadvantages that could be matched and merged with the scaled data on beliefs about ethical advantages and disadvantages.

Integration through Interpretation and Reporting Procedures . Having organized the quantitative and the qualitative data in a format based on thematic relevance to allow merging , higher order integration interpretation was needed. Two approaches were used. First the results from the quantitative and qualitative data were integrated using a joint display. As illustrated in Figure ​ Figure2, 2 , the left provides the participants’ quantitative ratings of their beliefs about the ethical advantages as derived from the visual analog scales, with the lowest anchor of 0 signifying definitely not agreeing with the statement and the highest anchor of 100 signifying definite agreement with the statement. The right side provides illustrative qualitative data from the free-text responses on the survey and the mini focus groups. Color matching (see online version) of the box plots and text responses was devised to help the team match visually the quantitative and qualitative responses from the constituent groups. Multiple steps in developing the joint display contributed to an interpretation of the data.

Example of Joint Display Illustrating Integration at the Interpretation and Reporting Level from the ADAPT-IT Project—Potential Ethical Advantages for Patients When Using Adaptive Clinical Trial Designs

In the final report, the quantitative data integration uses a narrative approach that describes the quantitative and qualitative results thematically. The specific type of narrative integration is weaving because the results are connected to each other thematically, and the qualitative and quantitative data weave back and forth around similar themes or concepts. The narrative provides intragroup comparisons of the results from the scales about beliefs that are supported by text from the qualitative database. Each of the six sections of the results contain quantitative scores with intergroup comparisons among the four groups studied, that is, academic researchers, academic biostatisticians, consultant biostatisticians, and “other” stakeholders and quotations from each group.

Regarding the fit of the quantitative and qualitative data, the integration resulted in an expansion of understanding. The qualitative comments provided information about the spectrum of opinions about ethical advantages and disadvantages, but the scales in particular were illustrative showing there was polarization of opinion about these issues among two of the constituencies.

This article provides an update on mixed methods designs and principles and practices for achieving integration at the design, methods, and interpretation and reporting levels. Mixed methodology offers a new framework for thinking about health services research with substantial potential to generate unique insights into multifaceted phenomena related to health care quality, access, and delivery. When research questions would benefit from a mixed methods approach, researchers need to make careful choices for integration procedures. Due attention to integration at the design, method, and interpretation and reporting levels can enhance the quality of mixed methods health services research and generate rigorous evidence that matters to patients.

Acknowledgments

Joint Acknowledgment/Disclosure Statement : At the invitation of Helen I. Meissner, Office of Behavioral and Social Sciences Research, an earlier version of this article was presented for the NIH-OBSSR Workshop, “Using Mixed Methods to Optimize Dissemination and Implementation of Health Interventions,” Natcher Conference Center, NIH Campus, Bethesda, MD, May 3, 2012. Beth Ragle assisted with entry of references and formatting. Dr. Fetters acknowledges the other members of the ADAPT-IT project's Mixed Methods team, Laurie J. Legocki, William J. Meurer, and Shirley Frederiksen, for their contributions to the development of Figure 2 .

Disclosures : None.

Disclaimers : None.

Supporting Information

Additional supporting information may be found in the online version of this article:

Appendix SA1: Author Matrix.

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Mixed Methods Research Workshop (Presentation Slides)

Please cite this source as Muskat, M. (2020). Mixed Methods Research Workshop. University of Canberra [PowerPoint slides].

53 Pages Posted: 16 Jun 2020

Matt Muskat

Affiliation not provided to ssrn.

Date Written: May 13, 2020

An introductory workshop into Mixed Methods Research presenting advantages and disadvantages, the need for justification, usage and acceptance, exploratory and explanatory design, 2-step and 3-step design, and the potential pitfalls for research students with regard to additional time and resources needed. Part 2 of the slide show contains a case study of the use of mixed methods in a doctoral thesis. This workshop for postgraduate students at University of Canberra was delivered annually from 2010 to 2014, and again in Semester 1, 2020 for UC units 9504 Research Project and 9505 Research Methods. This slideshow has been updated as of June 2017 and May 2020.

Keywords: mixed methods, mixed methods research, demographic change, scenario analysis, expert interviews, cross-impact analysis

Suggested Citation: Suggested Citation

Matthias Muskat (Contact Author)

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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 .

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 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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DESIGNING YOUR MIXED METHODS RESEARCH PROJECT: AN INTERACTIVE WORKSHOP

April 25 to april 27, 2024 stanford university, palo alto, california, usa.

This 3-day intensive workshop was created for individuals who wish to design or analyze a mixed methods research project using qualitative and quantitative methods. Participants will complete the workshop with a personalized design and implementation plan for their projects.

Registration for spring 2025 will open in october 2024., 2024 workshop agenda.

Workshop Highlights:

In this 3-day interactive workshop, the Stanford- Surgery Policy Improvement Research & Education (S-SPIRE) Center has assembled an interdisciplinary team of mixed methods experts.

We will help you design and analyze a mixed methods research project or create a proposal, using qualitative and quantitative methods. Participants will complete the workshop with a personalized design and implementation plan for their projects.

Who Should Attend:

Researchers, faculty, staff, and students motivated to design a mixed methods research project using both qualitative and quantitative methods.

What to Expect:

• Work on your mixed methods proposal, research study, or manuscript

•  Refine your project with guidance from leading methodologists

•  Improve your project with rigorous methodology using a participatory format

•  Build your Mixed Methods Research presentation skills

Requirements for Enrollment:

Participants who will benefit the most from this workshop will be actively designing, conducting, revising, or analyzing a mixed methods project.

See Frequently Asked Questions list  

Testimonials from mixed methods research workshop participants, university of global health equity, rwanda, africa.

"The 3-day Stanford S-SPIRE Mixed Methods workshop has revolutionized my approach to mixed methods research, based on the close expert mentorship, interactive group sessions, and the hands-on nature of the course."

~ Barnabas Alayande, MBBS, PgDth, MBA, FMCS ~

University of Washington, United States of America

“I got so much more done during this 2.5 days at this workshop than I would have on my own—it was thought provoking, enjoyable, efficient, and super productive!"

~ Erin Blakeney, PhD, RN ~  

Toronto Metropolitan University, Ontario, Canada

"The content of the workshop was incredibly useful, and I was impressed by the level of expertise demonstrated by the faculty. They conveyed complex concepts in a way that was easy to understand and apply. Overall, I feel much more confident in my ability to design and conduct mixed methods research studies after attending this workshop.”

~ Charlotte Lee, PhD, RN ~

ACCOMMODATIONS The Stanford Guest House

2575 Sand Hill Road Menlo Park, CA 94025 Phone: 650-926-2800  Fax: 650-926-5388

The Stanford Guest House has provided a discounted rate for registrants attending the S-SPIRE Spring workshop.

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Mixed Methods Research

Nov 02, 2014

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Mixed Methods Research. Professor Dr. Khalid Mahmood University of the Punjab Lahore – PAKISTAN. Acknowledgement. This presentation has been prepared with the help of many books and presentations on the topic.

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Presentation Transcript

Mixed Methods Research Professor Dr. Khalid Mahmood University of the Punjab Lahore – PAKISTAN

Acknowledgement • This presentation has been prepared with the help of many books and presentations on the topic. • The presenter pays his sincere gratitude to all authors, professors and experts for their efforts and contributions. • Particular thanks to Professor John W. Creswell of University of Nebraska-Lincoln for his un-matched contribution on the topic.

Agenda • Three types of research designs • Qualitative vs. quantitative research • Pragmatism – Philosophy behind MMR • Reasons for “mixing” • How methods can be mixed • Planning mixed methods procedures • Notations to describe MM designs • 6 mixed methods designs • Further readings

Three types of research designs • Qualitative research – exploring and understanding the meaning individuals or groups ascribe to a social or human problem. • Quantitative research – testing objective theories by examining the relationship among variables. • Mixed methods research – an approach to inquiry that combines or associates both qualitative and quantitative forms.

Qualitative vs. quantitative research

Pragmatism – Philosophy behind MMR • Arises out of actions, situations, and consequences rather than antecedent conditions. • There is a concern with applications—what works—and solutions to problems. • Instead of focusing on methods, researchers emphasize the research problem and use all approaches available to understand the problem.

Reasons for “mixing” The insufficient argument – either quantitative or qualitative may be insufficient by itself Multiple angles argument – quantitative and qualitative approaches provide different “pictures” The more-evidence-the-better argument – combined quantitative and qualitative provides more evidence Community of practice argument – mixed methods may be the preferred approach within a scholarly community Eager-to-learn argument – it is the latest methodology “Its intuitive” argument – it mirrors “real life”

How methods can be mixed

Planning mixed methods procedures

Notations to describe MM designs

Mixed methods designs • Sequential Explanatory Design • Sequential Exploratory Design • Sequential Transformative Design • Concurrent Triangulation Design • Concurrent Embedded Design • Concurrent Transformative Design

qualData & Results QUANData & Results Interpretation Following up Sequential explanatory design

Sequential explanatory design: Characteristics • Viewing the study as a two-phase project • Collecting quantitative data first followed by collecting qualitative data second • Typically, a greater emphasis is placed on the quantitative data in the study • Example: You first conduct a survey and then follow up with a few individuals who answered positively to the questions through interviews

Sequential explanatory design: When do you use it? • When you want to explain the quantitative results in more depth with qualitative data (e.g., statistical differences among groups, individuals who scored at extreme levels) • When you want to identify appropriate participants to study in more depth qualitatively

Sequential explanatory design: Sample script The purpose of this two-phase, explanatory mixed methods study will be to obtain statistical, quantitative results from a sample and then follow-up with a few individuals to probe or explore those results in more depth. In the first phase, quantitative research questions or hypotheses will address the relationship or comparison of __________ (independent) and ________ (dependent) variables with ___________ (participants) at ___________(the research site). In the second phase, qualitative interviews or observations will be used to problem significant _______(quantitative results) by exploring aspects of the ________ (central phenomenon) with _______ (a few participants) at ____________ (research site).

quanData & Results QUALData & Results Interpretation Building to Sequential exploratory design

Sequential exploratory design: Characteristics • Viewing the study as a two-phase project • Qualitative data collection precedes quantitative data collection • Typically, greater emphasis is placed on the qualitative data in the study • Example: You collect qualitative diary entries, analyze the data for themes, and then develop an instrument based on the themes to measure attitudes on a quantitative survey administered to a large sample.

Sequential exploratory design: When do you use it? • To develop an instrument when one is not available (first explore, then develop instrument) • To develop a classification or typology for testing • To identify the most important variables to study quantitatively when these variable are not known

Sequential exploratory design: Sample script The purpose of this two-phase, exploratory mixed methods study will be to explore participant views with the intent of using this information to develop and test an instrument with a sample from a population. The first phase will be a qualitative exploration of a _______(central phenomenon) by collecting ___________(data) from ____________ (participants) at _______ (research site). Themes from this qualitative data will then be developed into an instrument (or survey) so that the __________ (theory and research questions/hypotheses) can be tested that ________ (relate, compare) ____________ (independent variable) with __________ (dependent variable) for _________(sample of a population) at _________ (research site).

Sequential transformative design QUAL quanSocial science theory, qualitative theory, advocacy worldview QUAN qualSocial science theory, qualitative theory, advocacy worldview

Sequential transformative design: Characteristics • Has two distinct data collection phases • A theoretical perspective is used to guide the study • Purpose is to use methods that will best serve the theoretical perspective of the researcher

Concurrent triangulation design + QUAN Data and Results QUAL Data and Results Interpretation

Concurrent triangulation design: Characteristics • Collecting both quantitative and qualitative data • Collecting these data at the same time in the research procedure • Analyzing the quantitative and qualitative data separately • Comparing or combining the results of the quantitative and qualitative analysis • Example: collect survey data (quantitative) and collect individual interviews (qualitative) and then compare the results

Concurrent triangulation design: When is it used? • When you want to combine the advantages of quantitative (trends, large numbers, generalization) with qualitative (detail, small numbers, in-depth) • When you want to validate your quantitative findings with qualitative data • When you want to expand your quantitative findings with some open-ended qualitative data (e.g., survey with closed- and open-ended data)

QUANPre-test Data & Results QUANPost-test Data & Results Interpretation qualProcess Intervention Concurrent embedded design QUAN QUAL qual quan

Concurrent embedded design: Characteristics • One data collection phase during which both quantitative and qualitative data are collected (one is determined to be the primary method). • The primary method guides the project and the secondary provides a supporting role in the procedures. • The secondary method is “embedded” or “nested” within the predominant method and addresses a different question.

Sample script for a concurrent design (Triangulation or nested) The purpose of this concurrent mixed methods study is to better understand a research problem by converging both quantitative (numeric) and qualitative (text or image) data. In this approach, ___________ (quantitative instruments) will be used to measure the relationship between the ________ (independent variables) and __________ (dependent variables). At the same time in the study, the __________ (central phenomenon) will be explored using _____________ (qualitative interviews, documents, observations, visual materials) with _________ (participants) at ____________ (the research site).

Concurrent transformative design QUAN + QUAL Social science theory, qualitative theory, advocacy worldview QUAL Social science theory, qualitative theory, advocacy worldview quan

Concurrent transformative design: Characteristics • Guided by a theoretical perspective. • Concurrent collection of both quantitative and qualitative data. • The design may have one method embedded in the other so that diverse participants are given a choice in the change process of an organization.

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