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• Mixed Methods Research | Definition, Guide & Examples

Mixed Methods Research | Definition, Guide & Examples

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

Convergent parallel

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

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

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

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

Explanatory sequential

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

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

Exploratory sequential

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

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

“Best of both worlds” analysis

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

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

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

Method flexibility

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

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

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

Differing or conflicting results

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

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

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

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

Research bias

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

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

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

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

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

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

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

These are four of the most common mixed methods designs :

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

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Home » Mixed Methods Research – Types & Analysis

Mixed Methods Research – Types & Analysis

Table of Contents

Mixed Methods Research

Mixed methods research is an approach to research that combines both quantitative and qualitative research methods in a single study or research project. It is a methodological approach that involves collecting and analyzing both numerical (quantitative) and narrative (qualitative) data to gain a more comprehensive understanding of a research problem.

Types of Mixed Research

There are different types of mixed methods research designs that researchers can use, depending on the research question, the available data, and the resources available. Here are some common types:

Convergent Parallel Design

This design involves collecting both qualitative and quantitative data simultaneously, analyzing them separately, and then merging the findings to draw conclusions. The qualitative and quantitative data are given equal weight, and the findings are integrated during the interpretation phase.

Sequential Explanatory Design

In this design, the researcher collects and analyzes quantitative data first, and then uses qualitative data to explain or elaborate on the quantitative findings. The researcher may use the qualitative data to clarify unexpected or contradictory results from the quantitative analysis.

Sequential Exploratory Design

This design involves collecting qualitative data first, analyzing it, and then collecting and analyzing quantitative data to confirm or refute the qualitative findings. Qualitative data are used to generate hypotheses that are tested using quantitative data.

Concurrent Triangulation Design

This design involves collecting both qualitative and quantitative data concurrently and then comparing the results to find areas of agreement and disagreement. The findings are integrated during the interpretation phase to provide a more comprehensive understanding of the research question.

Concurrent Nested Design

This design involves collecting one type of data as the primary method and then using the other type of data to elaborate or clarify the primary data. For example, a researcher may use quantitative data as the primary method and qualitative data as a secondary method to provide more context and detail.

Transformative Design

This design involves using mixed methods research to not only understand the research question but also to bring about social change or transformation. The research is conducted in collaboration with stakeholders and aims to generate knowledge that can be used to improve policies, programs, and practices.

Concurrent Embedded Design

Concurrent embedded design is a type of mixed methods research design in which one type of data is embedded within another type of data. This design involves collecting both quantitative and qualitative data simultaneously, with one type of data being the primary method and the other type of data being the secondary method. The secondary method is embedded within the primary method, meaning that it is used to provide additional information or to clarify the primary data.

Data Collection Methods

Here are some common data collection methods used in mixed methods research:

Surveys are a common quantitative data collection method used in mixed methods research. Surveys involve collecting standardized responses to a set of questions from a sample of participants. Surveys can be conducted online, in person, or over the phone.

Interviews are a qualitative data collection method that involves asking open-ended questions to gather in-depth information about a participant’s experiences, perspectives, and opinions. Interviews can be conducted in person, over the phone, or online.

Focus groups

Focus groups are a qualitative data collection method that involves bringing together a small group of participants to discuss a topic or research question. The group is facilitated by a researcher, and the discussion is recorded and analyzed for themes and patterns.

Observations

Observations are a qualitative data collection method that involves systematically watching and recording behavior in a natural setting. Observations can be structured or unstructured and can be used to gather information about behavior, interactions, and context.

Document Analysis

Document analysis is a qualitative data collection method that involves analyzing existing documents, such as reports, policy documents, or media articles. Document analysis can be used to gather information about trends, policy changes, or public attitudes.

Experimentation

Experimentation is a quantitative data collection method that involves manipulating one or more variables and measuring their effects on an outcome. Experiments can be conducted in a laboratory or in a natural setting.

Data Analysis Methods

Mixed methods research involves using both quantitative and qualitative data analysis methods to analyze data collected through different methods. Here are some common data analysis methods used in mixed methods research:

Quantitative Data Analysis

Quantitative data collected through surveys or experiments can be analyzed using statistical methods. Statistical analysis can be used to identify relationships between variables, test hypotheses, and make predictions. Common statistical methods used in quantitative data analysis include regression analysis, t-tests, ANOVA, and correlation analysis.

Qualitative Data Analysis

Qualitative data collected through interviews, focus groups, or observations can be analyzed using a variety of qualitative data analysis methods. These methods include content analysis, thematic analysis, narrative analysis, and grounded theory. Qualitative data analysis involves identifying themes and patterns in the data, interpreting the meaning of the data, and drawing conclusions based on the findings.

Integration of Data

The integration of quantitative and qualitative data involves combining the results from both types of data analysis to gain a more comprehensive understanding of the research question. Integration can involve either a concurrent or sequential approach. Concurrent integration involves analyzing quantitative and qualitative data at the same time, while sequential integration involves analyzing one type of data first and then using the results to inform the analysis of the other type of data.

Triangulation

Triangulation involves using multiple sources or types of data to validate or corroborate findings. This can involve using both quantitative and qualitative data or multiple qualitative methods. Triangulation can enhance the credibility and validity of the research findings.

Mixed Methods Meta-analysis

Mixed methods meta-analysis involves the systematic review and synthesis of findings from multiple studies that use mixed methods designs. This involves combining quantitative and qualitative data from multiple studies to gain a broader understanding of a research question.

How to conduct Mixed Methods Research

Here are some general steps for conducting mixed methods research:

• Identify the research problem: The first step is to clearly define the research problem and determine if mixed methods research is appropriate for addressing it.
• Design the study: The research design should include both qualitative and quantitative data collection and analysis methods. The specific design will depend on the research question and the purpose of the study.
• Collect data : Data collection involves collecting both qualitative and quantitative data through various methods such as surveys, interviews, observations, and document analysis.
• Analyze data: Both qualitative and quantitative data need to be analyzed separately and then integrated. Analysis methods may include coding, statistical analysis, and thematic analysis.
• Interpret results: The results of the analysis should be interpreted, taking into account both the quantitative and qualitative findings. This involves integrating the results and identifying any patterns, themes, or discrepancies.
• Draw conclusions : Based on the interpretation of the results, conclusions should be drawn that address the research question and objectives.
• Report findings: Finally, the findings should be reported in a clear and concise manner, using both quantitative and qualitative data to support the conclusions.

Applications of Mixed Methods Research

Mixed methods research can be applied to a wide range of research fields and topics, including:

• Education : Mixed methods research can be used to evaluate educational programs, assess the effectiveness of teaching methods, and investigate student learning experiences.
• Health and social sciences: Mixed methods research can be used to study health interventions, understand the experiences of patients and their families, and assess the effectiveness of social programs.
• Business and management: Mixed methods research can be used to investigate customer satisfaction, assess the impact of marketing campaigns, and analyze the effectiveness of management strategies.
• Psychology : Mixed methods research can be used to explore the experiences and perspectives of individuals with mental health issues, investigate the impact of psychological interventions, and assess the effectiveness of therapy.
• Sociology : Mixed methods research can be used to study social phenomena, investigate the experiences and perspectives of marginalized groups, and assess the impact of social policies.
• Environmental studies: Mixed methods research can be used to assess the impact of environmental policies, investigate public perceptions of environmental issues, and analyze the effectiveness of conservation strategies.

Examples of Mixed Methods Research

Here are some examples of Mixed-Methods research:

• Evaluating a school-based mental health program: A researcher might use a concurrent embedded design to evaluate a school-based mental health program. The researcher might collect quantitative data through surveys and qualitative data through interviews with students and teachers. The quantitative data might be analyzed using statistical methods, while the qualitative data might be analyzed using thematic analysis. The results of the two types of data analysis could be integrated to provide a comprehensive evaluation of the program’s effectiveness.
• Understanding patient experiences of chronic illness: A researcher might use a sequential explanatory design to investigate patient experiences of chronic illness. The researcher might collect quantitative data through surveys and then use the results of the survey to inform the selection of participants for qualitative interviews. The qualitative data might be analyzed using content analysis to identify common themes in the patients’ experiences.
• Assessing the impact of a new public transportation system : A researcher might use a concurrent triangulation design to assess the impact of a new public transportation system. The researcher might collect quantitative data through surveys and qualitative data through focus groups with community members. The results of the two types of data analysis could be triangulated to provide a more comprehensive understanding of the impact of the new transportation system on the community.
• Exploring teacher perceptions of technology integration in the classroom: A researcher might use a sequential exploratory design to investigate teacher perceptions of technology integration in the classroom. The researcher might collect qualitative data through in-depth interviews with teachers and then use the results of the interviews to develop a survey. The quantitative data might be analyzed using descriptive statistics to identify trends in teacher perceptions.

When to use Mixed Methods Research

Mixed methods research is typically used when a research question cannot be fully answered by using only quantitative or qualitative methods. Here are some common situations where mixed methods research is appropriate:

• When the research question requires a more comprehensive understanding than can be achieved by using only quantitative or qualitative methods.
• When the research question requires both an exploration of individuals’ experiences, perspectives, and attitudes, as well as the measurement of objective outcomes and variables.
• When the research question requires the examination of a phenomenon in its natural setting and context, which can be achieved by collecting rich qualitative data, as well as the generalization of findings to a larger population, which can be achieved through the use of quantitative methods.
• When the research question requires the integration of different types of data or perspectives, such as combining data collected from participants with data collected from stakeholders or experts.
• When the research question requires the validation of findings obtained through one method by using another method.
• When the research question involves studying a complex phenomenon that cannot be understood by using only one method, such as studying the impact of a policy on a community’s well-being.
• When the research question involves studying a topic that has not been well-researched, and using mixed methods can help provide a more comprehensive understanding of the topic.

Purpose of Mixed Methods Research

The purpose of mixed methods research is to provide a more comprehensive understanding of a research problem than can be obtained through either quantitative or qualitative methods alone.

Mixed methods research is particularly useful when the research problem is complex and requires a deep understanding of the context and subjective experiences of participants, as well as the ability to generalize findings to a larger population. By combining both qualitative and quantitative methods, researchers can obtain a more complete picture of the research problem and its underlying mechanisms, as well as test hypotheses and identify patterns that may not be apparent with only one method.

Overall, mixed methods research aims to provide a more holistic and nuanced understanding of the research problem, allowing researchers to draw more valid and reliable conclusions, make more informed decisions, and develop more effective interventions and policies.

Advantages of Mixed Methods Research

Mixed methods research offers several advantages over using only qualitative or quantitative research methods. Here are some of the main advantages of mixed methods research:

• Comprehensive understanding: Mixed methods research provides a more comprehensive understanding of the research problem by combining both qualitative and quantitative data, which allows for a more nuanced interpretation of the data.
• Triangulation : Mixed methods research allows for triangulation, which is the use of multiple sources of data to verify findings. This improves the validity and reliability of the research.
• Addressing limitations: Mixed methods research can address the limitations of qualitative or quantitative research by compensating for the weaknesses of each method.
• Flexibility : Mixed methods research is flexible, allowing researchers to adapt the research design and methods as needed to best address the research question.
• Validity : Mixed methods research can increase the validity of the research by using multiple methods to measure the same concept.
• Generalizability : Mixed methods research can improve the generalizability of the findings by using quantitative data to test the applicability of qualitative findings to a larger population.
• Practical applications: Mixed methods research is useful for developing practical applications, such as interventions or policies, as it provides a more comprehensive understanding of the research problem.

Limitations of Mixed Methods Research

Here are some of the main limitations of mixed methods research:

• Time-consuming: Mixed methods research can be time-consuming and may require more resources than using only one research method.
• Complex data analysis: Integrating qualitative and quantitative data can be challenging and requires specialized skills for data analysis.
• Sampling bias: Mixed methods research can be subject to sampling bias, particularly if the sampling strategies for the qualitative and quantitative components are not aligned.
• Validity and reliability: Mixed methods research requires careful attention to the validity and reliability of both the qualitative and quantitative data, as well as the integration of the two data types.
• Difficulty in balancing the two methods: Mixed methods research can be difficult to balance the qualitative and quantitative methods effectively, particularly if one method dominates the other.
• Theoretical and philosophical issues: Mixed methods research raises theoretical and philosophical questions about the compatibility of qualitative and quantitative research methods and the underlying assumptions about the nature of reality and knowledge.

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• What is mixed methods research?

Last updated

20 February 2023

Reviewed by

Miroslav Damyanov

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

Analyze your mixed methods research

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

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

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

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

• When to use mixed methods research

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

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

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

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

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

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

• Mixed methods research design

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

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

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

• Types of mixed method research designs

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

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

Convergent parallel

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

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

Embedded design

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

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

Explanatory sequential design

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

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

Exploratory sequential design

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

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

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

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

• Strengths of mixed methods research

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

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

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

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

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

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

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

• Challenges of mixed methods research

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

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

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

• Advanced frameworks in mixed methods research

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

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

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

Intervention

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

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

Participatory

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

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

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

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

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

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

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

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

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

• Techniques for integrating data in mixed method studies

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

Triangulation protocol

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

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

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

Following a thread

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

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

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

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

Mixed methods matrix

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

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

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

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

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

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

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

What is an example of mixed methods research?

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

Which sampling method is best for mixed methods?

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

What is the difference between mixed methods and multiple methods?

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

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• 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.

• View inline

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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Mixed Methods Research – Different Types & Examples

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Researchers often conduct various types of research in the same study to investigate the different variables in a research project.

Mixed method research is a crucial aspect of research methodology as it combines qualitative and quantitative research approaches, thereby providing a comprehensive understanding of complex phenomena through numerical data and nuanced contextual insights.

Inhaltsverzeichnis

• 1 Mixed methods research – In a Nutshell
• 2 Definition – Mixed methods research
• 3 When to use mixed methods research
• 4 Types of mixed methods research designs
• 5 Advantages of mixed methods research
• 6 Disadvantages of mixed methods research

Mixed methods research – In a Nutshell

• Mixed methods research is a hybrid of quantitative research and qualitative research methodology.
• Researchers use the mixed approach to leverage the benefits of each research method.
• Mixed methods often yield more detailed findings, although they are limited by timelines and inadequate resources.

Definition – Mixed methods research

Mixed methods research incorporates qualitative and quantitative research elements to propose a solution for a research problem . When used together, quantitative and qualitative methods provide more comprehensive findings than the use of each method alone.

Qualitative methods are used to study natural phenomena using observations, interviews, and analysis of text data. Quantitative research involves numerical analysis of quantifiable variables. Mixed methods research is often used in research cases with various variables and data sets such as social and behavioral sciences.

When to use mixed methods research

Mixed methods research is best used when your research displays variables with both qualitative and quantitative characteristics. You can use mixed methods research to formulate generalizable findings, often limited by a standalone quantitative approach.

In addition, using mixed methods research lends credibility to your research findings. By showing how you applied different research methods, your work can hold up under scrutiny since you have covered several aspects. Highlight how your research question will deploy quantitative and qualitative techniques and why it is necessary to use both through mixed methods research.

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Research example

Maybe you want to study road safety on a particular road. You can take a purely quantitative approach if your main metric is the daily average number of road accidents and in which sections they happen. For a qualitative study, you can interview drivers on their thoughts on driving in certain road sections.

A mixed methods research approach seems like the most appropriate way to answer both questions to uncover deeper insights. It can find cause and effect relationships between qualitative and quantitative variables in a detailed study.

For this research problem, a mixed methods research framework may explore whether the sections drivers deem to be more hazardous report more accidents. Note that mixed methods research doesn’t just imply qualitative and quantitative data collection. Both methods should complement each other to answer a common research problem.

Types of mixed methods research designs

There are various mixed methods research designs. The appropriate mixed methods research design choice depends on the research objective, the duration of data collection, and other factors.

We will discuss some designs of mixed methods research. They are used in different contexts to answer different kinds of research problems.

Explanatory sequential

In this type of mixed research, you first collect and analyze quantitative data. This is followed by gathering and analyzing qualitative data. This approach best applies to a research problem where researchers believe the qualitative data will explain the quantitative analysis.

You can estimate the average number of accidents and determine which areas are classified as high risk. From these conclusions, you can interview drivers in these areas and analyze their responses in a qualitative framework.

Based on your qualitative data, you can give possible explanations for why accidents happen in some sections and investigate specific causes.

Exploratory sequential

In this inverse approach, researchers examine qualitative data points and then collect and analyze quantitative data sets.

This approach can be used to formulate research problems and hypotheses. After developing a valid hypothesis, quantitative methods are used to test or validate the qualitative conclusions.

You can begin by talking to drivers or handing out questionnaires to discover hazardous road sections. This is followed by looking at the number of accidents in these sections to compare the statistics with the general drivers’ sentiments.

In a parallel approach, researchers collect both quantitative and qualitative data simultaneously. The findings are analyzed separately, then their respective conclusions are compared to give a general conclusion.

In the analysis of road safety, you can carry out both quantitative and qualitative research as follows:

Qualitative research – You can look at the driver’s comments and issues raised on online platforms such as Twitter.

Quantitative research – You can analyze traffic police reports on the frequency of accidents in various road sections.

The nested approach is also known as the embedded method. In this design, both qualitative and quantitative data are collected concurrently. However, one type of data takes precedence over the other.

Researchers usually adopt a nested approach when there are time restrictions or scarce resources. The nested design is used to support the findings of the main research design.

In the quantitative test, you can investigate if the frequency of the drivers’ concerns about a particular road section corresponds with the frequency of accidents in that section. You can include some qualitative questionnaires to support your quantitative findings.

Advantages of mixed methods research

A win-win scenario – Using both qualitative and quantitative methods takes advantage of the benefits of both research methods. A mixed approach ensures in-depth and generalizable findings.

Versatility in research – Mixed research methods offer more flexibility when formulating research problems. They let researchers break down a research problem into its constituent qualitative and quantitative elements for more comprehensive conclusions.

Expanding the scope of the study – Researchers can expand the subject matter of a research problem using a mixed framework. This often leads to more discoveries beyond the initial research problem.

Disadvantages of mixed methods research

Mismatch of conclusions – Some research designs, such as the parallel design, may yield contrasting results. This poses the problem of generalization as the findings have no similarities.

Lack of sufficient resources – Most research undertakings rely on external funding. Collecting and analyzing both qualitative and quantitative data may consume a lot of time and resources.

Skill gaps – A mixed approach requires skilled qualitative and quantitative analysts. The quantitative field currently has a shortage of skilled personnel due to the complex nature of the quantitative methods available.

What are the key aspects of mixed methods research?

Mixed methods research involves qualitative and quantitative data collection and analysis methods. There are different designs under this approach for various research problems.

When should I use a mixed approach in research?

A mixed approach delivers the best results when the research problem has qualitative and quantitative aspects. Using both methods offers more granular-level insights.

What is the difference between qualitative and quantitative research?

Qualitative is a text analysis of data collected from observation and questionnaires. Quantitative research is a numerical method of collecting and analyzing figures associated with certain research variables.

Which are the 4 mixed research designs?

The main forms of mixed research designs are embedded, parallel, explanatory sequential, and exploratory sequential. They are used in different research proposals to answer research problems.

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Handbook of Research Methods in Health Social Sciences pp 695–713 Cite as

The Use of Mixed Methods in Research

• Kate A. McBride 2 ,
• Freya MacMillan 3 ,
• Emma S. George 4 &
• Genevieve Z. Steiner 5  
• Reference work entry
• First Online: 13 January 2019

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9 Citations

Mixed methods research is becoming increasingly popular and is widely acknowledged as a means of achieving a more complex understanding of research problems. Combining both the in-depth, contextual views of qualitative research with the broader generalizations of larger population quantitative approaches, mixed methods research can be used to produce a rigorous and credible source of data. Using this methodology, the same core issue is investigated through the collection, analysis, and interpretation of both types of data within one study or a series of studies. Multiple designs are possible and can be guided by philosophical assumptions. Both qualitative and quantitative data can be collected simultaneously or sequentially (in any order) through a multiphase project. Integration of the two data sources then occurs with consideration is given to the weighting of both sources; these can either be equal or one can be prioritized over the other. Designed as a guide for novice mixed methods researchers, this chapter gives an overview of the historical and philosophical roots of mixed methods research. We also provide a practical overview of its application in health research as well as pragmatic considerations for those wishing to undertake mixed methods research.

• Mixed methods
• Concurrent triangulation
• Sequential exploratory
• Sequential explanatory
• Convergent parallel
• Embedded design
• Transformative design
• Multiphase design

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McBride, K.A., MacMillan, F., George, E.S., Steiner, G.Z. (2019). The Use of Mixed Methods in Research. In: Liamputtong, P. (eds) Handbook of Research Methods in Health Social Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-10-5251-4_97

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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.

The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Introduction

What is a mixed methods design?

Triangulation in mixed methods research, types of mixed methods research designs, using atlas.ti for mixed methods research.

• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research question
• Conceptual framework
• Conceptual vs. theoretical framework
• Data collection
• Qualitative research methods
• Focus groups
• Observational research
• Case studies
• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

What is mixed methods research?

When starting the research process, researchers sometimes think they have to decide whether qualitative research or quantitative research is more appropriate for their research design. However, the more important question is whether the methods they employ in data collection and analysis sufficiently capture the phenomenon they want to study. In some cases, answering this question requires using multiple methods of research.

Mixed methods research is a research paradigm that involves collecting qualitative data and quantitative data on the same object of inquiry. Researchers who employ mixed methods research synthesize qualitative findings with quantitative findings to achieve a better understanding.

Let's look at the established research paradigms, then mixed methods research, why it's useful, and which research methods complement each other. Then we'll examine how ATLAS.ti can help you execute a mixed methods design.

Mixed methods research is followed out of the need to understand concepts or phenomena at a deep level. A standalone quantitative study or qualitative study can provide great insight. Still, one method alone may not be able to capture all knowledge necessary to fully understand a topic or issue.

Those who conduct mixed methods research acknowledge the importance of pursuing both qualitative and quantitative research to achieve more complete results. However, this is not simply an issue of collecting more data just for its own sake. Mixed methods design is purposeful in carefully crafting research questions and employing appropriate research methods to essentially fill in the gaps of knowledge surrounding a particular research inquiry.

To determine which methods and data can address particular research needs, let's look at the capabilities of and differences between qualitative and quantitative data collection .

Qualitative and quantitative data

Researchers are often quick to make conclusions about whether qualitative research is better than quantitative research or vice versa. The reality is that quantitative and qualitative data can both look at the world in different ways that are useful at various points of a research inquiry. Qualitative and quantitative research are established research paradigms precisely because they provide relevant insights with the appropriate research design, data collection, and analysis.

One of the main goals of qualitative research is to generate a description of a social phenomenon. When something is difficult to quantify, it needs to be broken down into more constituent elements that are, by themselves, easier to perceive. In educational evaluation, for example, it is difficult to evaluate good academic writing with just a single score alone. Writing teachers employ a rubric to measure writing by a number of aspects which may include argumentation, organization, and cohesion.

Qualitative methods of research tend to collect data for an analysis that is capable of generating frameworks of constituent elements. Such a framework can then be used in subsequent research, evaluation, or decision-making processes. Researchers can collect qualitative data from observations , interviews , or records searches. Qualitative data analysis then aims to identify patterns and themes frequently appearing in the collected data.

The efficacy of experimental drugs in clinical trials, for example, is seldom easy to measure through quantitative methods alone. Qualitative research methods are often employed to determine a research participant's well-being, emotional state of mind, and other factors to help researchers decide the overall success of their clinical trials.

Quantitative research

If qualitative methods describe a concept or phenomenon, quantitative methods employ the resulting framework to measure that concept or phenomenon. Quantitative research methodology takes the theories generated from qualitative findings to collect quantitative data that can be used to measure a concept or phenomenon at scale.

Ultimately, numbers and values inform decision-making processes in many contexts. Quantitative results are useful in research areas where precision is valued or required. Still, they are also used in social and behavioral research to numerically describe phenomena that may not appear to be naturally quantifiable.

Mixing methods

Quantitative and qualitative strands of research are often pitted against each other for various reasons. Researchers might shun qualitative data collection as it is often time-consuming. In contrast, quantitative data collection is often critiqued for its reductive power (i.e., reducing ambiguous concepts into simplistic numerical values). Many scholarly disciplines, as a result, tend to prefer one research paradigm over the other (e.g., chemistry tends toward quantitative data collection, while anthropology tends toward qualitative data collection).

In the long run of any sufficiently complex research inquiry, however, it is seldom necessary to remain confined to one research approach. The main objective of scientific research is to organize knowledge through theories about the world around us. As a result, researchers employ mixed methods to combine theory generation in qualitative research with confirmatory testing in quantitative research to ultimately produce a robust theory and new knowledge.

However, research studies that combine qualitative and quantitative methods for the sake of having multiple methods of data collection and analysis are not as persuasive or impactful as true mixed methods studies where research methods are purposefully chosen to achieve a better understanding.

An example of mixed methods research

The objective of mixed methods research designs is to employ different inquiry components under one larger study. However, it might be easier to think of mixed methods research designs as having at least one qualitative study and one quantitative study, each with related but ultimately separate research questions . Examining a mixed methods research design in this way might make it easier to understand the need for pursuing multiple methods in certain cases.

• Consider the following example:

Remote work performance and job satisfaction

- RQ1: How have work outputs at XYZ Company changed since the shift to fully remote work?

- RQ2: What perceptions do remote workers at XYZ Company have about the shift to fully remote work?

In general terms, the goal of the study is to examine the efficacy of remote work in comparison to traditional, in-office work at one company. Actually determining this efficacy requires looking at the phenomenon of remote work through different methods.

As a result, one possible mixed methods study might look at the performance metrics of the company. Research question 1 (RQ1) is posed to conduct a quantitative research study that collects data on possibly quantifiable concepts related to work (e.g., amount of sales generated, number of new clients acquired). In this case, the researchers collect quantitative data to compare post-remote work performance to pre-remote work performance and determine if productivity has changed over time.

While this is a useful angle to examine remote work, it does not tell the whole story. After all, if people at Company XYZ are more or less productive than before, what are the reasons that explain this change? To address research question 2 (RQ2), researchers collect qualitative data on the level of satisfaction employees have with their jobs. Qualitative data from interviews with employees can be used to determine which aspects of their job they find satisfying or not.

With all the data collected, mixed methods researchers can combine the initial quantitative results and the initial qualitative results to form a deeper understanding of their topic of inquiry. In this case, if the quantitative data shows that worker productivity has suffered since the switch to remote work, the qualitative data might illuminate the aspects of remote work that employees don't like.

Other mixed methods research examples

While there are many different forms of mixed-methods research, the research approach is generally the same across mixed-methods research designs. A mixed methods research design is likely to require researchers to collect quantitative and qualitative data relevant to an overarching topic that necessitates examination from different methods. A couple of examples are:

Literacy development among children

RQ1: What is the rate of literacy development among children at ABC School based on scores from a standardized reading test?

RQ2: What are the instructional practices common in classrooms with high-performing students on standardized reading tests?

Market research for a new computer model

RQ1: How much time does it take to complete a series of tasks on an experimental computer model compared to a comparable computer model?

RQ2: What factors do potential customers take into consideration when buying a new computer?

Notice that qualitative and quantitative data pursue related but ultimately different aspects of the phenomena under study. As a result, the discrete inquiries in a mixed methods study will most likely employ different methods to collect data.

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Researchers do not employ mixed methods research just for the sake of having different methods in one research inquiry. The objective behind mixing methods is to generate new knowledge and strengthen understanding of that knowledge by examining it from different angles. This is a concept in research called triangulation, which refers to affirming a given location based on measures taken from different points. The equivalent notion in research is that viewing the same object of inquiry from multiple angles will provide a more reliable understanding of that object.

To further understand the utility of a mixed methods approach, imagine you and your friends are looking at a merry-go-round. You can only see one part of it at any one time, while other parts are obscured from your view. On the other hand, if your friends are positioned to see the merry-go-round from different angles, your combined observations can capture a more complete picture of the object you are studying.

Mixed methods research relies on multiple research methods, data sets, or theoretical approaches to assemble a more comprehensive picture of a concept or phenomenon. Especially in qualitative research or social science research, any set of findings can be considered more credible if they are supported with evidentiary data that comes from different perspectives.

Method triangulation

Method triangulation involves combining qualitative and quantitative methods together to study different but related aspects. In this respect, quantitative and qualitative research study the same phenomenon to lend support to each method's findings. Note that the goal of triangulated mixed methods research is not to simply use multiple methods to arrive at the same answer but to generate a better understanding of a phenomenon that one method alone cannot sufficiently capture.

In this case, method triangulation is a useful concept for a mixed methods researcher because it requires them to acknowledge the strengths and weaknesses of each particular research method. At scale, quantitative methods cannot capture concepts that are unquantifiable (e.g., beauty, convenience). In contrast, qualitative methods often do not conduct data collection at scales necessary to make generalizations about phenomena. Integrating quantitative and qualitative research components under the same mixed methods design ensures a comprehensive examination of a phenomenon that one method alone cannot accomplish.

Ethnography provides ample opportunities to pursue method triangulation. Data collection in ethnographic research often involves collecting qualitative data through observations and interviews . In contrast, data analysis can assess quantitative data by identifying patterns in behavior and perspectives and determining their frequencies.

Another example is a mixed methods study that examines patient outcomes at a hospital. Initial qualitative results might come from field notes from observations of doctors and nurses and interview data with patients. The quantitative findings might come from conducting a statistical analysis of the money and resources used for each patient observed or interviewed to determine whether the expenditure is commensurate with the patient outcomes achieved.

A standalone quantitative study might look only at the financial aspects of health care, while a qualitative study might do better at examining the social and emotional aspects. Conducting both of these studies in tandem can help researchers determine actionable insights for streamlining health care services while maintaining satisfactory standards of care.

Data triangulation

Mixed methods research usually depends on method triangulation, but it's important to identify other forms of triangulation that can strengthen the findings in any research. A study that relies on data triangulation looks at different sets of data. For example, an educational researcher might examine student outcomes at different schools or at the same school but at different times. Data triangulation is useful in affirming that the findings in one context are applicable across other contexts.

Theory triangulation

Another kind of triangulation less commonly associated with mixed methods research deals with analyzing data using different theories. A sequential research design, for example, may use the initial quantitative results from a survey study to generate a conceptual framework for the analysis of a subsequent qualitative study. At the same time, existing theories may also be employed in that analysis to compare and contrasts the kinds of insights and outcomes that each may produce.

Theory generation in mixed methods research

Many forms of research seek to generate or develop a theoretical framework to understand the object of inquiry. There are two common forms of theory generation, and both can manifest in the research questions that are posed in any study.

Research questions can either be exploratory, which try to define or gain a greater understanding of a phenomenon, or confirmatory, which try to test a theory or hypothesis regarding that phenomenon. With some exceptions, exploratory research questions call for collecting qualitative data , while confirmatory research questions require quantitative data .

In that respect, common mixed methods designs combine qualitative and quantitative components to generate a theory and either strengthen or challenge that theory, respectively. To understand what that theory generation looks like when employing mixed methods, we need to examine some of the different kinds of mixed methods research designs.

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Data collection and analysis in mixed methods research depends on the research design you adopt. Ultimately, it might be easy to think about the different research designs in terms of the timing of the discrete inquiries within a mixed methods inquiry.

Concurrent triangulation design

A study that collects quantitative and qualitative data simultaneously is a common form of mixed methods design to achieve triangulation. The goal of a concurrent triangulation design is to observe the object of inquiry from multiple methods.

For example, imagine an educational researcher who wants to examine the efficacy of an after-school reading program. The researcher can then pursue two concurrent studies, one that qualitatively observes the reading program in action between educators and students and another that quantitatively tests students' reading comprehension. Over time, the researcher can draw correlations between improvements in test scores and any observations of the students in the program.

Exploratory sequential design

Another way to look at mixed methods research is with the idea that data collection and analysis are cyclical and evolve as new knowledge is generated. Researchers might undertake an exploratory sequential design if they don't yet know the aspects of a concept or phenomenon they want to test. In short, they need to conduct a qualitative study first in order to generate a conceptual framework to apply in a subsequent quantitative study.

Exploratory sequential design is useful in market research, for example, to identify the potential needs and preferences of prospective customers. Focus group research with a group of target customers can inquire about what they are looking for when choosing from a line of products. The researcher can take the initial qualitative findings to inform the design of a subsequent survey study that can confirm the extent to which the preferences of the focus group are reflected in the larger market.

Researchers can also conduct a quantitative study to preface observations in a qualitative study. Imagine that an educational researcher is adopting mixed methods approaches when examining learning outcomes among schools within a given geographical area. They might start by examining test scores published by these schools, using the initial quantitative results to determine where students are struggling and might need intervention. The resulting qualitative study might conduct observations in struggling schools to determine potential shortcomings in teaching and learning.

Concurrent nested design

This research design involves conducting multiple inquiries at the same time for the purpose of using one inquiry to strengthen the other. In a mixed methods approach, concurrent nested design places one research paradigm within another (e.g., a quantitative study within a qualitative study).

Sequential transformative design

This is a mixed methods research design with a critical or social justice orientation, meaning that the research is ultimately conducted to challenge the understanding of existing theory or produce meaningful social change, respectively. In either case, a sequential mixed methods research design can have a transformative effect by employing one study to create the rationale for a second critical or social justice research inquiry.

As you employ multiple research methods for a single mixed methods research design, you might find that your data collection will involve large sets of data, presenting a challenge in managing all that information in an orderly manner. Whether you are conducting research through qualitative data collection, quantitative data collection, or both, ATLAS.ti can help you organize and analyze your data. A robust mixed methods approach requires systematic organization of your data collection to ensure efficient and insightful analysis.

Document groups

Data in ATLAS.ti is stored in documents, which can be classified by the data type they contain. ATLAS.ti allows you to analyze text, images, video, audio , and more, and each document's data type is marked in the Document Manager for easy organization.

However, you may also need to divide your documents by type of study or method employed. In that case, you can use Document Groups in ATLAS.ti to label your documents so your project has categories for quantitative and qualitative data, interviews and focus groups, observations and test scores. Documents can belong to multiple document groups, allowing for easy organization of documents into multiple categories.

Once you have fully coded your data , it might be a challenge to narrow down your analysis to the relevant data you're looking for. If you have to sift through large numbers of documents, the Query Tool can help you look for the most relevant quotations based on the codes you have applied to your data.

Global filters

Studies that employ mixed methods research can accumulate such vast amounts of qualitative and quantitative data that it might become cumbersome for the human eye to keep track of it all manually. Even the most organized project in ATLAS.ti can have thousands of documents or hundreds of codes, making it a challenge to find the right data.

In ATLAS.ti, you can set a global filter using any of the elements of your project. For example, if you have a document group labeled " interviews ," you can set a global filter for that document group, which will lead ATLAS.ti to only show the documents in that group.

Working with both qualitative and quantitative software

ATLAS.ti has a number of tools that provide visualizations to help illustrate quantitative findings. However, you may find that other software, such as Microsoft Excel or SPSS, can help you further analyze and visualize the quantitative research components in your study. As a result, ATLAS.ti allows you to export your analysis into a Microsoft Excel spreadsheet. The Code Co-Occurrence Analysis and Code-Document Analysis tools, for example, can export their resulting tables into Microsoft Excel, which includes tools for deeper statistical analysis or for creating other kinds of data visualizations.

ATLAS.ti projects can also be exported as syntax files that can be imported into other statistical analysis software such as SPSS and R. These files convert qualitative data into quantitative data for further statistical analyses, regressions, and quantitative visualizations. Researchers can fully realize the convergence between qualitative and quantitative research when using multiple software platforms to conduct their analysis.

From data collection to data analysis, rely on ATLAS.ti.

Start with a free trial of our software to conduct your mixed methods research.

• Student Notebook

Mixed Methods Research

• Experimental Psychology
• Quantitative
• Statistical Analysis

Traditionally, there are three branches of methodology: quantitative (numeric data), qualitative (observational or interview data), and mixed methods (using both types of data). Psychology relies heavily on quantitative-based data analyses but could benefit from incorporating the advantages of both quantitative and qualitative methodologies into one cohesive framework. Mixed Methods (MM) ideally includes the benefits of both methods (Johnson, Onwuegbuzie, & Turner, 2007): Quantitative analyses employ descriptive and inferential statistics, whereas qualitative analyses produce expressive data that provide descriptive details (often in narrative form) to examine the study’s research objectives. Whereas quantitative data may be collected via measures such as self-reports and physiological tests, qualitative data are collected via focus groups, structured or semistructured interviews, and other forms (Creswell, 2013).

MM hypotheses differ in comparison with solely quantitative or qualitative research questions. Not only must the quantitative and qualitative data be integrated, but the hypotheses also must be integrated. MM practitioners promote the development of a theory-based set of three hypotheses. Hypotheses should be conducted a priori and be both logical and sequential research questions (for more information, see Onwuegbuzie & Leech, 2006). Specialists encourage researchers to construct three separate types of hypotheses for an MM research project. There can be more than three hypotheses but there must be at least one of each type. The first hypothesis should be quantitative and the second should be qualitative. The third hypothesis will be an MM hypothesis.

Integration of these data is often complex, even when there is a strong theoretical rationale for doing so. Data integration occurs when quantitative and qualitative are combined in a data set. There are multiple ways for this to occur, including triangulation, following a thread, and the mixed methods matrix (see O’Cathain, Murphy, & Nicholl, 2010, for a brief review). Yet understanding the overall reasoning for using MM and how to best combine the approaches in practice can help lessen the challenge of MM data integration (Bryman, 2006).

Types of MM Research

• There are dozens of MM designs, but for the purpose of this article, six MM designs will be presented:
• The sequential explanatory method employs two different data-collection time points; the quantitative data are collected first and the qualitative collected last.
• The sequential exploratory design is best for testing emergent theory because both types of data are interpreted during the data integration phase.
• The sequential transformative approach has no preference for sequencing of data collection and emphasizes theory.
• Concurrent triangulation is the ideal method for cross-validation studies and has only one point of data collection.
• The concurrent nested design is best used to gain perspectives on understudied phenomena.
• The concurrent transformative approach is theory driven and allows the researcher to examine phenomena on several different levels.

Strengths and Challenges of MM Research

An MM approach is helpful in that one is able to conduct in-depth research and, when using complementary MM, provide for a more meaningful interpretation of the data and phenomenon being examined (Teddlie & Tashakkori, 2003).  Another strength of MM is the dynamic between the qualitative and quantitative portions of the study. If the design is planned appropriately, each type of data can mirror the other’s findings, so the methodology can benefit many types of research. However, interpreting data using the MM framework can be complicated and time intensive given that the data and interpretations are often abstract. Additionally, conducting MM research requires training and mastery of the methodology, so there can be a learning curve for researchers who traditionally use only quantitative or qualitative methods. Sticking to the theory-based and evidence-based designs will aid in your understanding and interpretation of the data.

Qualitative Data Analysis

Qualitative coding is a multistep process that includes different types of analyses depending on the nature of your data. Codebooks are important before, during, and after qualitative coding due to the detailed nature of the qualitative data. It is also important to know your expected codes and themes in order to promote interrater reliability (Hruschka et al., 2004). Expected codes are based on the theoretical foundation of your project. I suggest including the expected codes and themes in your codebooks. As previously mentioned, research designs involving this type of data can vary greatly, but in general, the following is a framework of how to conduct a thematic data analysis: Know your data inside and out, generate codes, search for themes, and review themes with a research team (Braun & Clarke, 2006). For more detailed instructions on conducting a qualitative analysis, please refer to last month’s Student Notebook article (Heydarian, 2016).

Lessons Learned

From the start, the researcher or research team must have a clear idea of their resources and the pros and cons of each method. Researchers also must be flexible. I am interested in examining the factors that compose seeking health information online. To investigate this topic, I developed an online, two-part study. Information obtained from qualitative prompts was used to inform the development of a scale measuring health-information-seeking behavior online. The first study used MM, and the data collection occurred on Amazon Mechanical Turk, a marketplace where researchers can post their available studies. Potential participants are paid a small fee, and data collection usually is completed in less than a week. I expected to conduct magnitude coding — a type of qualitative coding that evaluates the emphasis of content — but instead I had to choose a more appropriate type of coding because the participants provided extremely brief responses.

In closing, the design of your study (quantitative, qualitative, or MM) should align with your training and your research objectives. MM has the potential to bring your research to the next level by combining the strengths of quantitative and qualitative methodologies.

Suggestions for Conducting MM Research

Be proficient in MM research by keeping up to date with the latest techniques, software, textbooks, and manuals.

Think “outside the box” and consider other data-analytic approaches that are not used in your field.

Choose the research design that best fits the hypotheses, and know the assumptions and limitations of that design.

Incorporate figures and tables into your qualitative codebook to deepen the conceptualizations for the coders and provide a few examples of already coded data in order to provide thorough instructions.

Create and use summary statements for each participant to help with the abstract portion of the analyses. Summary statements should be a few sentences that describe the participant’s statement and provide an overall gist of the available qualitative information.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3 , 77–101. doi:10.1191/1478088706qp063oa

Bryman, A. (2006). Integrating quantitative and qualitative research: How is it done? Qualitative Research, 6 , 97–113. doi:10.1177/1468794106058877

Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches . Thousand Oaks, CA: Sage Publications.

Heydarian, N. (2016). Developing theory with the grounded-theory approach and thematic analysis. Observer, 29(4) , 38–39.

Hruschka, D. J., Schwartz, D., John, D. C. S., Picone-Decaro, E., Jenkins, R. A., & Carey, J. W. (2004). Reliability in coding open-ended data: Lessons learned from HIV behavioral research. Field Methods, 16 , 307–331. doi:10.1177/1525822X04266540

Johnson, R. B., Onwuegbuzie, A. J., & Turner, L. A. (2007). Toward a definition of mixed methods research. Journal of Mixed Methods Research, 1 , 112–133. doi:10.1177/1558689806298224

O’Cathain, A., Murphy, E., & Nicholl, J. (2010). Three techniques for integrating data in mixed methods studies. BMJ, 341 , c4587. doi:10.1136/bmj.c4587

Onwuegbuzie, A. J., & Leech, N. L. (2006). Linking research questions to mixed methods data analysis procedures 1. The Qualitative Report, 11 , 474–498.

Teddlie, C., & Tashakkori, A. (2003). Major issues and controversies in the use of mixed methods in the social and behavioral sciences. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social & behavioral research (pp. 3–50). Thousand Oaks, CA: Sage Publications.

VERY RELEVANT AND COMPREHENSIVE TEXT ON MM ETHODS

The analysis of mixed methods is fairly comprehensive and educative especially for scholars and/researchers who are used to the traditional Qualitative and Quantitatve research as a stand alone methodologies. I feel like looking for a workshop sponsor so that I can share these ideas to our colleagues in African universities generally and Kenya in particular. Our postgraduate students have not yet embrased the use of mixed methods. Four of my own supervised doctoral students have successfully used th MMR.We should do much more!

I am currently pursuing my PhD and using mixed method. I am interested in this combination of research methods.

I have gained much from the source which clearly spells out the strengths of MM and its applicability in research.

Iam conducting a sequential explanatory mixed methods study in PhD Management and I have benefited a lot from combining quantitative and qualitative research approaches operating with what works best per given research probem.

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About the Author

Allyson S. Hughes is a Health Psychology doctoral student at The University of Texas at El Paso. Her research examines judgment and decision-making concerning health decisions using Internet resources. She can be reached at [email protected].

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Mixed-Methods Design in Biology Education Research: Approach and Uses

• Abdi-Rizak M. Warfa

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Educational research often requires mixing different research methodologies to strengthen findings, better contextualize or explain results, or minimize the weaknesses of a single method. This article provides practical guidelines on how to conduct such research in biology education, with a focus on mixed-methods research (MMR) that uses both quantitative and qualitative inquiries. Specifically, the paper provides an overview of mixed-methods design typologies most relevant in biology education research. It also discusses common methodological issues that may arise in mixed-methods studies and ways to address them. The paper concludes with recommendations on how to report and write about MMR.

INTRODUCTION

An increasing number of studies in biology education are reporting the use of mixed-methods research (MMR), in which quantitative and qualitative data are combined to investigate questions of interest in biology teaching and learning (e.g., Andrews et al. , 2012 ; Jensen et al. , 2012 ; Höst et al. , 2013 ; Ebert-May et al. , 2015 ; Seidel et al. , 2015 ). This increase coincides with general growth and expanded interest in mixed-methods approaches to research in various fields of study over the past 30 years ( Plano Clark, 2010 ). Consequently, several handbooks and articles have been written that describe the use of mixed methods in the social and behavioral sciences ( Tashakkori and Teddlie, 1998 , 2010 ; Creswell et al. , 2003 ; Creswell and Plano Clark, 2011 ; Greene, 2008 ; Terrell, 2011 ), focusing on both the theoretical underpinnings and procedural steps of conducting MMR. However, given the disciplinary ethos and divergent content perspectives of academic disciplines, it is important that researchers planning to use MMR become familiar with the theory and designs most commonly used within their disciplinary context. This article, therefore, focuses on the various ways in which quantitative and qualitative methods can be combined to address questions of interest in biology education and the many productive ways in which MMR can be used to support claims about biology teaching and learning.

The paper is organized into three parts. Part 1 provides introductory remarks that situate MMR within the larger context of research paradigms in science education. Part 2 provides a general description of mixed-methods approaches commonly found in biology education research (BER). Part 3 provides general guidelines on how to select an appropriate MMR design and attend to methodological issues that may arise when using MMR.

PART 1: UNDERSTANDING MIXED METHODS

Mixed methods emerged as a credible research design on the heels of a larger debate on research paradigms in education and the social sciences in the mid-1980s ( Johnson and Onwuegbuzie, 2004 ; Tashakkori and Teddlie, 2010 ; Treagust et al. , 2014 ). Biology researchers, however, have long used mixed-methods approaches to address issues of interest in biological sciences. It is, for example, common to determine the effect of a gene mutation by quantitative analysis and then characterize the context of that effect through qualitative analysis. It is also common to define behaviors of an animal and then count and analyze their frequency in different circumstances. In educational settings, the new approach provided a “third methodological” pathway that permitted combining quantitative and qualitative modes of social inquiry ( Johnson et al. , 2007 ; Tashakkori and Teddlie, 2010 ). In the words of Jennifer Greene (2008) ,

A mixed-methods way of thinking is an orientation toward social inquiry that actively invites us to participate in dialogue about multiple ways of seeing and hearing, multiple ways of making sense of the social world, and multiple standpoints on what is important and to be valued and cherished. (p. 20)

Green's description captures the essence of mixed methods—a pragmatic choice to address research problems through multiple methods with the goal of increasing the breadth, depth, and consistency of research findings. Integration of research findings from quantitative and qualitative inquiries in the same study or across studies maximizes the affordances of each approach and can provide better understanding of biology teaching and learning than either approach alone. While quantitative methods can reveal empirical evidence showing causal or correlative relationships or the effects of interventional studies, qualitative methods provide contextual information that colors the experiences of individual learners. The goal of mixed methods is not, however, to replace either the quantitative or the qualitative approaches. Certain problems—for example, addressing gains in standardized test scores—are better addressed through quantitative methods (e.g., Knight and Wood, 2005 ), and some—for example, understanding the meaning students assign to reaction arrows—merit qualitative research (e.g., Wright et al ., 2014 ). Rather, the goal of mixed methods is to build on the strengths of both methods and minimize their weaknesses when the research merits using more than one method ( Creswell et al. , 2003 ; Johnson and Onwuegbuzie, 2004 ). Recent studies from the biology education literature will help illustrate the types of research that benefit from a mixed-methods approach.

In a recent study that used both quantitative and qualitative methods, Seidel et al. (2015) investigated non–content related conversational language, such as procedural talk, used by course instructors in a large reform-based introductory biology classroom cotaught by two instructors. Such language, which the authors termed “Instructor Talk,” is the language used to facilitate overall learning in the classroom, for example, language used to give directions on homework assignments or justifying use of active-learning strategies. Instructor Talk is distinct from language used to describe specific course concepts. To understand the prevalence of such language in biology classrooms, the authors asked, “What types of Instructor Talk exist in a selected introductory college biology course?” This question was exploratory in nature and merited qualitative inquiry that focused on identifying the types of Instructor Talk the two instructors used. The authors’ subsequent question, “To what extent do two instructors differ in the types and quantity of Instructor Talk they appear to use?,” aimed to enhance the findings from the qualitative phase and provided ways to further study and generalize this construct in a variety of class types ( Seidel et al. , 2015 ). The authors were able to address their initial research question through analysis of classroom transcripts containing more than 600 instructor quotes, identifying five emergent categories that were present in the analyzed sessions. They followed this exploratory qualitative phase of the study with statistical analyses that compared how often the instructors used identified categories and the average instances of Instructor Talk per class session. Without first characterizing and identifying patterns of Instructor Talk through the exploratory initial qualitative data, the authors could not have addressed the second question. Neither qualitative nor quantitative method was sufficient to address both research questions, but combining them strengthened the overall findings of the study.

In another BER study, Andrews et al. (2012) used a mixed-methods study to investigate undergraduate biology students’ misconceptions about genetic drift. Using qualitative data analysis, the authors identified 16 misconceptions students held about genetic drift that fit into one of five broad categories (e.g., novice genetics, genetic drift comprehension). Subsequent use of quantitative methods examined the frequency of misconceptions present before and after introductory instruction on genetic drift. The quantitative data supplemented the results of the qualitative analysis and shed light on changes in student misconceptions as a result of instruction. In this study, although data collection was separated in time and space, the quantitative and qualitative analyses were integrated, and the different data sets were used to generate the categories of misconceptions about genetic drift and to corroborate the findings. Again, we see the utility of both methods within the same study.

The Andrews et al . (2012) and Seidel et al . (2015) studies illustrate the types of research problems that merit a mixed-methods study: research problems in which a single method, qualitative or quantitative, is insufficient to fully understand the problem ( Creswell et al ., 2003 ). Another role of MMR research is to use qualitative work to follow up/elaborate on quantitative findings or to validate findings in multiple ways. More broadly, teaching and learning occur in social environments with specific cultural contexts, personal value systems, and classroom dynamics that color how students learn and teachers teach. In such environments, understanding the educational processes in which teachers and students engage becomes crucial to understanding how students learn. MMR is particularly appropriate for BER, because it contextualizes quantitative differences observed in BER studies, capturing the contextual, sociocultural norms and the experiential factors that characterize undergraduate biology classrooms.

PART 2: GENERAL TYPOLOGIES OF MIXED-METHODS DESIGNS

For those questions that merit a mixed-methods approach, this section of the paper describes different typologies of mixed-methods designs available for biology educators. General guidelines on the use of MMR and the methodological issues to consider are described in Part 3.

Based on a review of the literature (see Creswell et al. , 2003 ), there are three general approaches to mixed methods—sequential, concurrent, or data transformation—that are most applicable to BER studies. These basic designs can get more complicated and advanced as merited by the phenomenon studied. More advanced variants of MMR, for example, multiphase designs or the transformative designs appropriate for social justice, are not addressed in this paper ( Creswell et al. , 2003 ; Terrell, 2011 ). The following paragraphs discuss each of the basic designs with respect to data-collection sequencing, method priority, and data-integration steps. As discussed below, these decisions often influence which MMR design to choose.

Sequential Designs

The sequential design approach implies linear data collection and analysis in which the collection of one set of data (e.g., qualitative) is followed by the collection and analysis of the other (e.g., quantitative). There are two general approaches within this design (see Figure 1, A and B ) based on the implementation sequence of the data and their intended usage ( Creswell et al. , 2003 ): 1) sequential explanatory and 2) sequential exploratory. Each subdesign, along with illustrative examples in biology education, is further described in the following sections.

FIGURE 1. Basic typologies of MMR. There are three basic designs of mixed methods that differ in how data collection and analysis is sequenced: (A) sequential explanatory design, in which the quantitative method precedes the qualitative method; (B) sequential exploratory design, in which the qualitative method precedes the quantitative method; and (C) concurrent triangulation design, in which qualitative and quantitative data are collected concurrently.

Sequential Explanatory Design

The sequential explanatory approach is characterized by two distinct phases: an initial phase of quantitative data collection and analysis followed by a second qualitative data-collection and analysis phase (see Figure 1A ). Findings from both phases are integrated during the data-interpretation stage. The general aim of this approach is to further explain the phenomenon under study qualitatively or to explore the findings of the quantitative study in more depth ( Tashakkori and Teddlie, 2010 ). Given the sequential nature of data collection and analysis, a fundamental research question in a study using this design asks, “In what ways do the qualitative findings explain the quantitative results?” ( Creswell et al. , 2003 ). Often, the initial quantitative phase has greater priority over the second, qualitative phase. At the interpretation stage, the results of the qualitative data often provide a better understanding of the research problem than simply using the quantitative study alone. As such, the findings in the quantitative study frequently guide the formation of research questions addressed in the qualitative phase ( Creswell et al. , 2003 ), for example, by helping formulate appropriate follow-up questions to ask during individual or focus group interviews. The following examples from the extant literature illustrate how this design has been used in the BER field.

In an interventional study with an overtly described two-phase sequential explanatory design, Buchwitz et al. (2012) assessed the effectiveness of the University of Washington’s Biology Fellows Program, a premajors’ course that introduced incoming biology majors to the rigor expected of bioscience majors and assisted them in their development as science learners. The program emphasized the development of process skills (i.e., data analysis, experimental design, and scientific communication) and provided supplementary instruction for those enrolled in introductory biology courses. To assess the effectiveness of the program, the authors initially used nonhierarchical linear regression analysis with six explanatory variables inclusive of college entry data (high school grade point average and Scholastic Aptitude Test scores), university-related factors (e.g., economically disadvantaged and first-generation college student status), program-related data (e.g., project participation), and subsequent performance in introductory biology courses. Analysis showed that participation in the Biology Fellows Program was associated with higher grades in two subsequent gateway biology courses across multiple quarters and instructors. To better understand how participating in the Biology Fellows Program may be facilitating change, the authors asked two external reviewers to conduct a focus group study with program participants. Their goal was to gather information from participants retrospectively (2 to 4 years after their participation in the program) about their learning experiences in and beyond the program and how those experiences reflected program goals. Students’ responses in the focus group study were used to generate themes and help explain the quantitative results. The manner in which the quantitative and qualitative data were collected and analyzed was described in detail. The authors justified the use of this design by stating, “A mixed-methods approach with complementary quantitative and qualitative assessments provides a means to address [their research] question and to capture more fully the richness of individuals’ learning” (p. 274).

In a similar study, Fulop and Tanner (2012) administered written assessments to 339 high school students in an urban school district and subsequently interviewed 15 of the students. The goal of this two-phased sequential study was to examine high school students’ conceptions about the biological basis of learning. To address their research problem, they used two questions to guide their study: 1) “After [their] mandatory biology education, how do high school students conceptualize learning?,” and 2) “To what extent do high school students have a biological framework for conceptualizing learning?” The authors used statistical analysis (post hoc quantitative analysis and quantification of open-ended items) to score the written assessment and used thematic analysis to interpret the qualitative data. Although the particular design of the sequential explanatory model is not mentioned in the article, the authors make it clear that they used a mixed-methods approach and particularly mention how the individual interviews with a subset of students drawn from the larger study population were used to further explore how individual students think about learning and the brain. In drawing their conclusions about students’ conceptualization of the biological basis of learning, the authors integrated analysis of the quantitative and qualitative data. For example, on the basis of the written assessment, the authors concluded that 75% of the study participants demonstrated a nonbiological framework for learning but also determined that 67% displayed misconceptions about the biological basis of learning during the interviews.

Sequential Exploratory Design

The sequential exploratory approach is similarly characterized by two distinct phases: an initial qualitative phase followed by a second phase of quantitative data collection and analysis (see Figure 1B ). Similar to the sequential explanatory approach, findings from both phases in this design are integrated during the data-interpretation stage. Unlike the sequential explanatory approach, the general aim of this approach is to further explore the phenomenon under study quantitatively or to perform quantitative studies to generalize qualitative findings to different samples ( Tashakkori and Teddlie, 2010 ). Given the sequential nature of data collection and analysis, a fundamental research question in a study using this design often asks, “In what ways do the quantitative findings generalize the qualitative results?” ( Creswell et al. , 2003 ).

As a research method, the sequential exploratory approach is often the most appropriate design when developing new instruments or when a researcher intends to generalize findings from a qualitative study to different groups ( Tashakkori and Teddlie, 1998 , 2010 ; Creswell et al. , 2003 ). Consider, for example, the case of a biology education researcher interested in examining student misconceptions in evolution. Using the sequential exploratory approach, the researcher would collect qualitative data from interviews to identify commonly held student misconceptions in evolutionary concepts. The researcher can then use the qualitative data to develop an instrument on evolution misconceptions that allows the collection of quantitative data from a large number of participants in various settings and institutions (after instrument validation and psychometric analysis). In this case, the initial qualitative data would inform the design of the instrument used to collect the quantitative data, often using identified student misconceptions as distractors. An example of studies that followed the instrument development process outlined here can be found in Hanauer and Dolan (2013) and Hanauer et al. (2012) .

Pugh et al . (2014) used the sequential exploratory design in a study that investigated high school biology students’ conceptual understanding of the concept of natural selection and their ability to generatively use the newly learned concepts across knowledge domains in biology. To assess students’ transfer ability and conceptual understanding, the authors first collected qualitative data by administering open-response items to 138 students and were able to identify, on the basis of thematic analysis, particular patterns of surface and deep-level transfer. Subsequently, the authors collected quantitative data that showed a small but significant relationship between deep-level, but not surface-level, transfer and conceptual understanding. The principal methodology of the study was qualitative in nature and in turn informed the quantitative component of the study. The combination of the two methods shed light on the relationship between concept understanding and the patterns of knowledge transfer.

Strengths and Weaknesses of the Sequential Designs

In both of the sequential models described above (exploratory and explanatory), the data collection and analysis proceeded in two distinct phases. As illustrated by the examples from the BER literature, the main strengths of the sequential designs include the ability to 1) contextualize and generalize qualitative findings to larger samples (in the case of sequential exploratory); 2) enable one to gain a deeper understanding of findings revealed by quantitative studies (in the case of sequential explanatory); and 3) collect and analyze the different methods separately. Additionally, the two-phase approach makes sequential designs easy to implement, describe, and report.

One weakness of sequential designs is the length of time required to complete both data-collection phases, especially given that the second phase is often in response to the results of the first phase. That is, by collecting the data at two different time points, one essentially doubles the length of time required to complete a single-method study. Moreover, because data collection is sequential, it may be difficult to decide when to proceed to the next phase. It may also be difficult to integrate or connect the findings of the two phases. For those projects with shorter time lengths, concurrent designs in which both data sets are collected in a single phase may be more appropriate. The next section of the paper provides details of concurrent designs of MMR.

Concurrent Designs

In the concurrent design, both qualitative and quantitative data are collected in a single phase. Because the general aim of this approach is to better understand or obtain more developed understanding of the phenomenon under study, the data can be collected from the same participants or similar target populations. The goal is to obtain different but complementary data that validate the overall results. There are two basic approaches within concurrent design: 1) concurrent triangulation ( Figure 1C ) and 2) concurrent nested ( Figure 2A ). These are described below.

FIGURE 2. Complex typologies of MMR. Two complex forms of MMR are (A) concurrent nested design, in which either the qualitative or the quantitative method is nested within a primary quantitative or qualitative approach (in this case, the main difference hinges on data prioritization); and (B) transformation design, in which one data form is transformed to the other (e.g., qualitative to quantitative).

Concurrent Triangulation Design

The concurrent triangulation design is the most common approach used in BER studies. The main objective is to corroborate or cross-validate findings by using both quantitative and qualitative studies. Data collection and analysis is done separately but merged afterward (see Figure 1C ). In interpreting the overall merged results ( Figure 1C ), one looks for data convergence, divergence, contradictions, or any relationship the separate data analyses reveal. This can be done using several strategies, for example, through side-by-side comparison that discusses how the findings of one data set confirm or refute findings of the other data set. As the following examples from the BER literature illustrate, one method (qualitative or quantitative) can have priority over the other in the concurrent triangulation approach.

In a recent study that used the concurrent triangulation approach, Jensen et al. (2012) explored the effectiveness of a first-year class project in supporting student progress toward selected student learning and development outcomes. The students were required to complete a group video project focused on nutrition and healthy eating as a capstone class assignment. Using a structured rubric to track frequency data, the authors collected and analyzed quantitative measures of student behavior. They similarly collected qualitative data through observations and interviews with representative individual students and a focus group. In justifying why they used this design, the authors stated, “the data-collection techniques used in this study provide a degree of triangulation aimed at establishing validity of the conclusions drawn from the evaluation” (p. 72). The study was primarily qualitative in nature, with the objective of understanding a particular student experience validated by the quantitative measures. In this case, the authors found convergent results that strengthened the overall study—student behaviors as measured by both the quantitative and qualitative results were consistent with targeted learning and development outcomes.

In a similar manner, Höst et al. (2013) used a concurrent triangulation design to investigate the impact of using two external representations of virus self-assembly, an interactive tangible three-dimensional model or a static two-dimensional image, on student learning about the process of viral self-assembly. All the students in a biochemistry course at a Swedish university engaged in a small-group exercise that included the same series of tasks. They were randomly assigned to groups, some of which used the three-dimensional tangible model and some of which used the static images. Students completed a test before and after the group exercise. Rese­archers used an analysis of variance to test for an association between two factors (external representation: tangible model versus image; and testing time: pre versus post) and the score a student earned on the test. The authors found that test scores differed between the pre- and posttests but not between the two types of external representations. The researchers used qualitative analyses of open-response questions to further assess how the group exercise influenced students’ conceptual understanding of self-assembly. The findings from the qualitative analysis corroborated the findings from the quantitative analysis.

In the preceding examples of the concurrent triangulation design, the authors collected quantitative and qualitative data concurrently, using closed-ended and open-ended items. In the Jensen et al. (2012) study, the qualitative data had priority over the quantitative data. In the Höst et al. (2013) study, the quantitative data appear to have had priority over the qualitative data. In concurrent triangulation studies, either method can have priority over the other or both can be on equal footing. In both studies, the authors justified their use of the specific concurrent method as a way to triangulate their findings. The next section contrasts this triangulation approach with the concurrent nested design.

Concurrent Nested Design

In the concurrent nested design, a strong supplemental study is collected during the data-collection and analysis phase of the primary study (see Figure 2A ). In this type of study, it does not matter whether the primary study is quantitative and the nested study qualitative or vice versa. The major aim of this design, which is often used in the health sciences, is to use the nested analysis to address different research questions than those addressed by the primary method or to use the nested method to seek information about different levels of the research problem. The general idea is that a need arises to address different types of questions within the research project that require different methods ( Creswell et al. , 2003 ). Most published mixed-methods studies that use this approach tend to be experimental designs that require qualitative aspects to examine how an intervention is working or to follow up specific aspects of the experiments ( Creswell et al. , 2003 ). An illustrative example in the context of biology education would be for a researcher to implement a specific interventional study in his or her classroom and to evaluate the effectiveness of the intervention by using quasi-experimental pre/posttest measures. However, in between the two measurements, the researcher interviews or collects open-ended written responses from the students to examine students’ experiences with the intervention.

A study by Tomanek and Montplaisir (2004) used this approach. In this study, the authors examined the study habits of students enrolled in a large introductory biology class. The authors collected two types of data: 1) pre/posttest assessment data from lecture sessions that covered cell division and Mendelian genetics, and 2) preinstruction and postinstruction interviews with a purposely selected sample of 13 students. The pre/posttest items constituted the quantitative data, while the pre/postinstruction interviews constituted the qualitative data. Because the main goal of the study was to understand students’ study habits both during lecture (e.g., how they used the information presented in lectures) and outside the classroom (e.g., how they studied, what resources they used), the study was primarily qualitative. The quantitative data were concurrently collected but addressed a different question: Did the study tasks and habits in which the students engaged help them academically? The quantitative data were thus nested within the larger qualitative study, illustrating the general scheme of the concurrent nested design. This research illustrates a distinguishing feature of nested and triangulated designs: in the nested design the two methods of analysis often address different research questions, whereas in triangulated design the two methods address the same question.

Strengths and Weaknesses of the Concurrent Designs

In both of the concurrent designs described above (triangulation and nested), the data collection occurs during a single phase of the research and the analysis occurs separately. Given the shorter period of time and the separate nature of the analysis, the concurrent designs tend to be the most efficient of the mixed-methods typology. There are two main drawbacks of the concurrent designs. For one, the concurrent nature of data collection precludes follow-up on any interesting or confusing issues that may arise as analysis unfolds. Second, data integration may become an impediment if the results are contradictory and/or diverge. The difficulty in this case becomes how to resolve divergent results short of declaring the study a failure. Additionally, it may be difficult to compare and contrast qualitative and quantitative data without transforming them to a common scale—for example, by transforming the qualitative data to dichotomous variables that can be subjected to statistical analysis, thus enabling comparison with the other quantitative data. However, as described later, such transformation may sacrifice the depth and the contextual data associated with the qualitative research.

Data-Transformation Designs

The data-transformation design implies changing one data set (e.g., qualitative) to another (e.g., quantitative) through either quantitating or qualitizing . Quantitating refers to the act of transforming qualitative data (codes) into quantitative data (variables), whereas qualitizing is the act of transforming numerical data (variables) into codes (or themes) that can be analyzed qualitatively ( Tashakkori and Teddlie, 2010 ). For example, Witcher et al. (2001) examined preservice teachers’ perceptions of the characteristics of effective teachers. To address their research questions, the authors collected quantitative data but then transformed those data into six general themes (e.g., student-centeredness, enthusiasm) that were prevalent in the participants’ responses. While qualitizing quantitative data, as done by Witcher et al. (2001) , is theoretically possible, in practice, quantitating of qualitative data is far more common, a practice reinforced by the rhetorical appeal of numbers and their association with rigor ( Sandelowski et al. , 2009 ). Figure 2B shows a general scheme of qualitative data that are transformed into quantitative form. As can be seen in the figure, data collection can happen sequentially or concurrently (as described earlier) for the sequential and concurrent designs. The underlying rationale for choosing this design is also similar to that described previously for sequential and concurrent designs. The only difference in this case is that one data set (quantitative or qualitative) is transformed. Comparison and merging of the two data sets occurs at the data-interpretation stage.

The quantitative survey data were entered into a chosen database (e.g., Excel) and organized under two broad categories of positive and negative attitudes.

The qualitative interview data were also analyzed and coded as positive and negative responses. This data set was then quantified into dichotomous variables 0 or 1 based on the absence or presence of negative and positive responses.

The two data sets were merged, and the combined data were analyzed for association using statistical analyses.

The overall data interpretation examined the prevalence of positive versus negative attitudes in the student population.

In this hypothetical case, the qualitative interview data are transformed into dichotomous variables corresponding to negative and positive attitudinal aspects of CUREs, categories predetermined before data collection. The overall data analysis occurred after the qualitative and the quantitative data were integrated. The goal of the interview data was to capture any contextual variables that were not explained by the survey data (not to triangulate the findings). That is, the interview data had been used to interpret the survey data and fill any holes that the survey did not capture, as it might not have identified a priori all the things that students might have feelings about with respect to CURE labs. The combined data thus provided a more complete picture than was possible only with the survey (i.e., quantitative) data.

The study by Ebert-May et al. (2015) is also an example of this type of research. In their study, Ebert-May and colleagues examined the extent to which biology postdoctoral fellows (postdocs) believed in and implemented evidence-based pedagogies after completion of a 2-year professional development program. The authors used the Approaches to Teaching Inventory ( Trigwell and Prosser, 2004 ) and local surveys to characterize the postdocs’ beliefs about teaching and knowledge and experiences with active-learning pedagogies. To capture and analyze the postdocs’ teaching practices, the participants submitted videos for at least two complete class sessions for each full course that they taught while participating in the professional development program. To analyze teaching practices captured by the videos, the authors chose a validated measurement of teacher practices in the classroom, Reformed Teaching Observation Protocol ( Sawada et al. , 2002 ), to measure the degree to which classroom instruction used active-learning pedagogies. The authors did not develop thematic analysis of the postdocs’ teaching practices but rather transformed the qualitative video data into numerical units that were analyzed with statistical tools. Thus, despite the collection of both quantitative data (i.e., surveys) and qualitative data (i.e., videos), only quantitative data-analysis strategies were ultimately used to examine their research question.

Strengths and Weaknesses of the Transformation Designs

The transformation designs can enable researchers to convert qualitative data into a quantitative format to meet specific goals of quantitatively oriented research, such as evaluating the effect size of an intervention or generalizing. This advantage, however, also reveals a major weakness of this design: transformation of qualitative into quantitative data causes some richness and depth of the qualitative data to be lost. Some researchers (e.g., Bazeley, 2004 ) contend that transforming qualitative data into dichotomous variables makes them one-dimensional and strips them of the flexibility associated with thematic analysis; that is, the quantitated data are no longer mutable to analysis of emergent themes characteristic of qualitative research. For this reason, the transformation designs may be most effective when the focus is quantification of a phenomenon rather than an in-depth, comprehensive understanding of the phenomenon.

PART 3: PRACTICAL GUIDELINES AND ISSUES TO CONSIDER

Having determined that a research question merits a mixed-methods approach, it is necessary to select an appropriate MMR design. As we have seen, the major influence on which design to choose is driven by data-collection sequencing, method priority, and the planned data-integration steps. Figure 3 provides summary guidelines on how to select a specific design among the different MMR topologies discussed in the previous section. In this section, we discuss major methodological issues that may arise during the study design.

FIGURE 3. MMR design decision tree. This if–use dichotomous key is designed to help researchers select appropriate MMR design based on the intent of their research. Refer to part 2 for the main differences between the different MMR designs.

Methodological Issues to Consider

In addition to issues surrounding the selection of an appropriate MMR design, several methodological issues such as those listed in Table 1 may arise. These issues (sampling, participant burden, data analysis, and transparency) are crucial in all research methods but more so when mixing quantitative and qualitative methods. For example, if a researcher collects both qualitative and quantitative data from the same participants, what burden does that place on the participants? On the other hand, if the researcher collects data from different participants, what complications does that present for data analysis? One has to be cognizant of both the burden and the complexity that arises from data collection when conducting a mixed-methods study.

Sampling is one issue that may present complications, as it is likely to vary between quantitative and qualitative work due to practical concerns like money, time, and effort, but also because the purpose of these different methods varies. The purpose of qualitative work is generally not to infer to a broader population, so a large random sample is not necessary, as may be the case in a quantitative study. The trick for a researcher designing a mixed-methods study is to consider questions that are specific to quantitative analyses (i.e., power) and those that are specific to qualitative analysis (i.e., variations in views and perspectives, representativeness of the qualitative sample). Thus, one has to consider what sample size is appropriate to make useful inferences from both the quantitative and qualitative data.

Certain sampling issues are specific to mixed-methods approaches, For example, one must consider whether the interview sample must be a subset of the quantitative sample or from a different population. If it is a subset, how will that subset be targeted—for example, should it be representative of extreme performances or large enough to capture the most common groups? There are advantages and disadvantages to both approaches. If one chooses to represent extreme performances, that allows using a smaller sample but necessitates a sequential or at least a nested design. If, on the other hand, one chooses to interview a sample large enough to capture the most common groups, trade-offs come in terms of investment of time and resources versus the ability to make generalizable inferences as a result of the large sample size. These issues require thought and attention as one designs a mixed-methods study.

Data integration is another methodological issue that may arise. For example, in sequential designs, data analysis occurs separately, and findings are integrated at the interpretation stage. In contrast, when using concurrent triangulation designs, data analysis occurs simultaneously. Theoretically, data analysis can occur at any point in the research process. So, when is the best time for analyzing the data and how should they be integrated? Some authors (e.g., Yoshikawa et al. , 2008 ) argue that it is not the best methodological choice to separate analysis of quantitative and qualitative data but instead it is preferable to integrate the results throughout the analysis phase of the research project. Yoshikawa et al. (2008) argue that such an integration approach (i.e., integrating the results throughout the analysis phase) can result in rich integration across methods and analyses. Such integration, however, will require expertise and a certain level of competence in both qualitative and quantitative methods, as qualitative and quantitative methods require different skill sets. These issues can be resolved through collaboration and utilization of the skill sets of different team members who can take the lead in specific aspects of the research while communicating with the research team on the results.

Finally, given the varied nature and purposes of the different methods used in MMR, it is important to report in detail how analyses are conducted. For sequential designs, the need is to discuss matters such as how results from the first phase informed data collection and analysis in the second phase. For concurrent designs, the need is to discuss what strategies are used to resolve conflicts that may arise from contradictory results (e.g., gathering more data to address the conflict). One must also describe how it was ensured that the depth and flexibility associated with qualitative data were not lost in the analysis, especially if a transformation design was used. If at all possible, it helps to publish data-collection tools such as interview protocols, quantitative instruments, and visual representations of data-integration plans. That is, methodological transparency becomes an important consideration in mixed-methods studies.

Recommendations for Writing about MMR

communicate the intent of the study (e.g., “this mixed-methods study examined …”);

specify which design was used (e.g., “we used sequential exploratory design to…”);

describe how both data forms were collected (e.g., “through structured interviews , we addressed the question of … participants were also surveyed …”);

provide the rationale for why both quantitative and qualitative data sets were collected (e.g., “the qualitative study addressed [i.e., the research question]; the quantitative study addressed [i.e., the research question]…”); and

describe how validity and reliability (or “trustworthiness”) were established in the chosen design.

Ivankova et al . (2006) provide a sample MMR study featuring most of the elements outlined above and discuss additional guidelines on how to communicate about MMR studies. Table 2 provides further references on general approaches to MMR and writing about mixed methods, including the use of diagrams. Mixed methodologists particularly recommend the use of visual representations to depict the procedural steps involved in a mixed-methods study, such as the one shown in Figure 4 for a hypothetical two-phased sequential explanatory study. As can be seen in Figure 4 , the visual diagram depicts the progression of research activities from data collection and analysis in the initial quantitative phase to qualitative data collection and analysis in the second phase to questions that may help integrate and interpret the findings. Most studies in the MMR literature use similar visual depictions that portray the complexity and the sequence of research activities in MMR studies ( Ivankova et al. , 2006 ; Creswell and Plano Clark, 2011 ).

FIGURE 4. Sample diagram for a hypothetical BER study. Researchers using mixed-methods approaches are encouraged to visually depict the procedural steps involved in their study, as shown in this figure.

In the realm of biology education, the social nature of educational inquiry often merits the use of multiple perspectives, as was the case with the BER studies cited in this paper. Given the various approaches of mixed methods discussed here and elsewhere ( Creswell et al. , 2003 ; Tashakkori and Teddlie, 2010 ), it is important for mixed-methods researchers to describe the decisions that went into their MMR design selections and guided their research projects. Three factors were discussed in this paper that can guide that selection: data-collection sequencing, method priority, and data integration. Understanding how these factors effect which MMR design to select, being clear about data-analysis procedures, and attending to methodological issues that arise will only strengthen MMR studies in our field and undoubtedly enrich biology teaching and learning through the use of multiple perspectives.

ACKNOWLEDGMENTS

I thank Drs. Anita Schuchardt and James Nyachwaya for close reading of this article and the editor and two anonymous reviewers for their constructive feedback on an earlier version of this article.

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Submitted: 13 January 2016 Revised: 12 June 2016 Accepted: 14 June 2016

© 2016 L. A.-R. M. Warfa. CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

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Using mixed methods in health research

Shema tariq.

1 School of Health Sciences, City University London, EC1A 7QN, London, UK

Jenny Woodman

2 MRC Centre of Epidemiology for Child Health, UCL Institute of Child Health, WC1N 1EH, London, UK

Mixed methods research is the use of quantitative and qualitative methods in a single study or series of studies. It is an emergent methodology which is increasingly used by health researchers, especially within health services research. There is a growing literature on the theory, design and critical appraisal of mixed methods research. However, there are few papers that summarize this methodological approach for health practitioners who wish to conduct or critically engage with mixed methods studies. The objective of this paper is to provide an accessible introduction to mixed methods for clinicians and researchers unfamiliar with this approach. We present a synthesis of key methodological literature on mixed methods research, with examples from our own work and that of others, to illustrate the practical applications of this approach within health research. We summarize definitions of mixed methods research, the value of this approach, key aspects of study design and analysis, and discuss the potential challenges of combining quantitative and qualitative methods and data. One of the key challenges within mixed methods research is the successful integration of quantitative and qualitative data during analysis and interpretation. However, the integration of different types of data can generate insights into a research question, resulting in enriched understanding of complex health research problems.

Introduction

Mixed methods research is the use of quantitative and qualitative methods in one study. Research is often dichotomized as quantitative or qualitative. Quantitative research, such as clinical trials or observational studies, generates numerical data. On the other hand qualitative approaches tend to generate non-numerical data, using methods such as semi-structured interviews, focus group discussions and participant observation. Historically, quantitative methods have dominated health research. However, qualitative methods have been increasingly accepted by the health research community in the past two decades, with a rise in publication of qualitative studies. 1 As the value of qualitative approaches has been recognized, there has been a growing interest in combining qualitative and quantitative methods. A recent review of health services research within England has shown an increase in the proportion of studies classified as mixed methods from 17% in the mid-1990s to 30% in the early 2000s. 2 In this paper, we present a synthesis of key literature on mixed methods research, with examples from our own work and that of others to illustrate the practical applications of this approach. This paper is aimed at health researchers and practitioners who are new to the field of mixed methods research and may only have experience of either quantitative or qualitative approaches and methodologies. We wish to provide these readers with an accessible introduction to the increasingly popular methodology of mixed methods research. We hope this will help readers to consider whether their research questions might best be answered by a mixed methods study design, and to engage critically with health research that uses this approach.

The authors each independently carried out a narrative literature review and met to discuss findings. Literature was identified via searches of PubMed, Google and Google Scholar, and hand-searches of the Journal of Mixed Methods Research, with relevant publications selected after discussion. An important consideration was that papers either had a methodological focus or contained a detailed description of their mixed methods design. For PubMed and Google searches, similar terms were used. For example, the PubMed strategy consisted of title and abstract searches for: ((mixed methods) OR ((mixed OR (qualitative AND quantitative)) AND methods)). We also drew upon recommendations from mixed methods conferences and seminars, and reference lists from key publications.

What is mixed methods research?

The most widely accepted definition of mixed methods research is research that ‘focuses on collecting, analysing, and mixing both quantitative and qualitative data in a single study or a series of studies’. 3 Central to the definition is the use of both quantitative and qualitative methods in one study (or a series of connected studies). Separate quantitative and qualitative studies addressing the same research question independently would not be considered ‘mixed methods’ as there would be no integration of approaches at the design, analysis or presentation stage. A recent innovation in mixed methods research is the mixed methods systematic review, which sets out to systematically appraise both quantitative and qualitative literature on a subject area and then synthesize the findings.

Why are mixed methods approaches used?

The underlying assumption of mixed methods research is that it can address some research questions more comprehensively than by using either quantitative or qualitative methods alone. 3 Questions that profit most from a mixed methods design tend to be broad and complex, with multiple facets that may each be best explored by quantitative or qualitative methods. See Boxes 1 and ​ and2 2 for examples from our own work.

Examples of authors’ mixed methods research – JW.

Examples of authors’ mixed methods research – ST.

Usually, quantitative research is associated with a positivist stance and a belief that reality that can be measured and observed objectively. Most commonly, it sets out to test an a priori hypothesis and is therefore conventionally described as ‘deductive’. Strengths of quantitative research include its procedures to minimize confounding and its potential to generate generalizable findings if based on samples that are both large enough and representative. It remains the dominant paradigm in health research. However, this deductive approach is less suited to generating hypotheses about how or why things are happening, or explaining complex social or cultural phenomena.

Qualitative research most often comes from an interpretive framework and is usually informed by the belief that there are multiple realities shaped by personal viewpoints, context and meaning. In-depth qualitative research aims to provide a rich description of views, beliefs and meaning. It also tends to acknowledge the role of researcher and context in shaping and producing the data. Qualitative approaches are described as ‘inductive’ as questions are often open-ended with the analysis allowing hypotheses to emerge from data. High-quality qualitative research can generate robust theory that is applicable to contexts outside of the study area in question, helping to guide practitioners and policy-makers. 8 However, for research that aims to directly impact on policy and practice, the findings of qualitative research can be limited by the small sample sizes that are necessary for in-depth exploratory work and the consequent lack of generalizabilty.

Mixed methods research therefore has the potential to harness the strengths and counterbalance the weaknesses of both approaches and can be especially powerful when addressing complex, multifaceted issues such as health services interventions 9 and living with chronic illness. 10

There are many reasons why researchers choose to combine quantitative and qualitative methods in a study. 11 , 12 We list some common reasons below, using a hypothetical research question about adolescents’ adherence to anticonvulsant medication to illustrate real world applications.

• Complementarity: Using data obtained by one method to illustrate results from another. An example of this would be a survey of adolescents with epilepsy demonstrating poor levels of adherence. Semi-structured interviews with a sub-group of those surveyed may allow us to explore barriers to adherence.
• Development: Using results from one method to develop or inform the use of the other method. A focus group conducted with a group of adolescents with epilepsy may identify mobile phone technology as a potentially important tool in adherence support. We could then develop a mobile phone ‘app’ that reminds patients to take their medication and conduct an intervention study to assess its impact on adherence levels.
• Initiation: Using results from different methods specifically to look for areas of incongruence in order to generate new insights. An illustration of this would be a study exploring the discrepancy between reported adherence in clinic consultations and actual medication adherence. A review of case notes may find adherence levels of over 90% in a clinic population; however, semi-structured interviews with peer researchers may reveal lower levels of adherence and barriers to open discussion with clinicians.
• Expansion: Setting out to examine different aspects of a research question, where each aspect warrants different methods. We may wish to conduct a study that explores adherence more broadly. A large-scale survey of adolescents with epilepsy would provide information on adherence levels and associations whilst interviews and focus groups may allow us to engage with individual experiences of chronic illness and medication in adolescence.
• Triangulation: Using data obtained by both methods to corroborate findings. For example, we could conduct a clinical study measuring drug levels in individuals and documenting self-reported adherence. Qualitative methods such as video diaries may confirm adherence levels.

To this list we would also add political commitment. That is to say, researchers may recognize, and wish to deploy, the strengths of quantitative research in producing generalizable results but may also be committed to representing the voice of participants in their work.

Whatever the reasons for mixing methods, it is important that authors present these explicitly as it allows us to assess if a mixed methods study design is appropriate for answering the research question. 3 , 13

How is mixed methods research conducted?

When embarking on a mixed methods research project it is important to consider:

• the methods that will be used;
• the priority of the methods;
• the sequence in which the methods are to be used.

A wide variety of methods exists by which to collect both quantitative and qualitative data. Both the research question and the data required will be the main determinants of the methods used. To a lesser extent, the choice of methods may be influenced by feasibility, the research team’s skills and experience and time constraints.

Priority of methods relates to the emphasis placed on each method in the study. For instance, the study may be predominantly a quantitative study with a small qualitative component, or vice versa. Alternatively, both quantitative and qualitative methods and data may have equal weighting. The emphasis given to each component of the study will be driven mainly by the research question, the skills of the research team and feasibility.

Finally, researchers must decide when each method is to be used in the study. For instance a team may choose to start with a quantitative phase followed by a qualitative phase, or vice versa. Some studies use both quantitative and qualitative methods concurrently. Again the choice of when to use each method is largely dependent on the research question.

The priority and sequence of mixing methods have been elaborated in a typology of mixed methods research models. See Table 1 for typology and specific examples.

Examples of studies using mixed methods.

How is data analysed in a mixed methods project?

The most important, and perhaps most difficult, aspect of mixed methods research is integrating the qualitative and quantitative data. One approach is to analyse the two data types separately and to then undertake a second stage of analysis where the data and findings from both studies are compared, contrasted and combined. 19 The quantitative and qualitative data are kept analytically distinct and are analysed using techniques usually associated with that type of data; for example, statistical techniques could be used to analyse survey data whilst thematic analysis may be used to analyse interview data. In this approach, the integrity of each data is preserved whilst also capitalizing on the potential for enhanced understanding from combining the two data and sets of findings.

Another approach to mixed methods data analysis is the integrative strategy. 20 Rather than keeping the datasets separate, one type of data may be transformed into another type. That is to say that qualitative data may be turned into quantitative data (‘quantitizing’) or quantitative data may be converted into qualitative data (‘qualitizing’). 21 The former is probably the most common method of this type of integrated analysis. Quantitative transformation is achieved by the numerical coding of qualitative data to create variables that may relate to themes or constructs, allowing statements such as ‘six of 10 participants spoke of the financial barriers to accessing health care’. These data can then be combined with the quantitative dataset and analysed together. Transforming quantitative data into qualitative data is less common. An example of this is the development of narrative psychological ‘types’ from numerical data obtained by questionnaires. 22

Potential challenges in conducting mixed methods research

Despite its considerable strengths as an approach, mixed methods research can present researchers with challenges. 23 , 24

Firstly, combining methodologies has sometimes been seen as problematic because of the view that quantitative and qualitative belong to separate and incompatible paradigms. In this context, paradigms are the set of practices and beliefs held by an academic community at a given point in time. 25 Researchers subscribing to this view argue that it is neither possible nor desirable to combine quantitative and qualitative methods in a study as they represent essentially different and conflicting ways of viewing the world and how we collect information about it. 8 Other researchers take a more pragmatic view, believing that concerns about the incommensurability of worldviews can be set aside if the combination of quantitative and qualitative methods addresses the research question effectively. This pragmatic view informs much applied mixed methods research in health services or policy. 8

Secondly, combining two methods in one study can be time consuming and requires experience and skills in both quantitative and qualitative methods. This can mean, in reality, that a mixed methods project requires a team rather than a lone researcher in order to conduct the study rigorously and within the specified time frame. However, it is important that a team comprising members from different disciplines work well together, rather than becoming compartmentalized. 26 We believe that a project leader with experience in both quantitative and qualitative methods can act as an important bridge in a mixed methods team.

Thirdly, achieving true integration of the different types of data can be difficult. We have suggested various analytic strategies above but this can be hard to achieve as it requires innovative thinking to move between different types of data and make meaningful links between them. It is therefore important to reflect on the results of a study and ask if your understanding has been enriched by the combination of different types of data. If this is not the case then integration may not have occurred sufficiently. 23

Finally, many researchers cite the difficulty in presenting the results of mixed methods study as a barrier to conducting this type of research. 23 Researchers may decide to present their quantitative and qualitative data separately for different audiences. This strategy may involve a decision to publish additional work focusing on the interpretations and conclusions which come from comparing and contrasting findings from the different data types. See Box 1 for an example of this type of publication strategy. Many journals in the medical sciences have a distinct methodological base and relatively restrictive word limits which may preclude the publication of complex, mixed methods studies. However, as the number of mixed methods studies increases in the health research literature we would expect researchers to feel more confident in the presentation of this type of work.

Many of the areas we explore in health are complex and multifaceted. Mixed methods research (combining quantitative and qualitative methods in one study) is an innovative and increasingly popular way of addressing these complexities. Although mixed methods research presents some challenges, in much the same way as every methodology does, this approach provides the research team with a wider range of tools at their disposal in order to answer a question. We believe that the production and integration of different types of data and the combination of skill sets in a team can generate insights into a research question, resulting in enriched understanding.

DECLARATIONS

Competing interests.

None declared

This work was funded by the Medical Research Council (MRC) [grant number: G0701648 to ST], and the MRC with the Economic and Social Research Council (ESRC) [grant number: G0800112 to JW]

Ethical approval

No ethical approval was required for this work

Contributorship

This work was conceived by both ST and JW who each carried out an independent literature review and collaborated on the structure and content of this report. ST wrote the manuscript with revisions and editing done by JW

Acknowledgements

We thank Professors Jonathan Elford and Ruth Gilbert for their comments on draft manuscripts

This article was submitted by the authors and peer reviewed by Geoffrey Harding

Exploring Types of Research Methods: A Comprehensive Guide

Grasping the concept of research method is essential for anyone engaged in research or assessing the outcomes of studies. Whether you're an academic student, a dedicated researcher, or just inquisitive about the world, a thorough understanding of the diverse research methods will assist you in sifting through the extensive array of information at your disposal.

Our detailed guide will walk you through the types of research design, including qualitative and quantitative approaches, as well as descriptive, correlational, experimental, and mixed methods research. We will also touch upon the different types of research methodology, ensuring a comprehensive understanding of the various types of methods in research.

This article will also highlight the pivotal factors to consider when crafting a study and the inherent strengths and limitations of different type of research methods.Whether you're embarking on your own research project or looking to enhance your critical thinking skills Armed with the research methods definition, this guide will equip you with the essential knowledge to make well-informed decisions and formulate significant conclusions in the field of research.

Qualitative vs. Quantitative Research Methods

Qualitative and quantitative research methods represent two fundamentally different approaches to data collection and analysis. Qualitative observation delves into non-numerical data, while quantitative observation involves the scrutiny of data that is numerical and quantifiable.

Qualitative Research:

• Involves gathering and interpreting non-numerical data, such as text, video, photographs, or audio recordings
• Uses sources like interviews, focus groups, documents, personal accounts, cultural records, and observation
• Unstructured or semi-structured format
• Open-ended questions
• Comprehensive perspective on individuals' experiences
• Comparison of participants' feedback and input
• Focus on answering the "why" behind a phenomenon, correlation, or behavior
• Ethnography, for instance, seeks to gain insights into phenomena, groups, or experiences that cannot be objectively measured or quantified, offering a deep dive into the cultural fabric of a community.
• This method is used to understand how an individual subjectively perceives and imparts meaning to their social reality, often revealing underlying bias that can influence the interpretation of social phenomena.
• Data analysis techniques include content analysis, grounded theory, thematic analysis, or discourse analysis

Quantitative Research:

• Focuses on numerical or measurable data
• Uses sources such as experiments, questionnaires, surveys, and database reports
• Multiple-choice format
• Countable answers (e.g., "yes" or "no")
• Numerical analysis
• Statistical picture of a trend or connection
• To define research methods, one must focus on answering the 'what' or 'how' in relation to a particular phenomenon, correlation, or behavior. This foundational approach is crucial in the realm of empirical inquiry.
• Provides precise causal explanations that can be measured and communicated mathematically
• The objectives of scientific inquiry often include hypothesis testing to examine causal relationships between variables, making accurate predictions, and generalizing findings to broader populations.
• Aims to establish general laws of behavior and phenomenon across different settings/contexts
• Used to test a theory and ultimately support or reject it
• Empirical research in psychology utilizes examples of quantitative data such as standardized psychological assessments, neuroimaging data, and clinical outcome measures to inform its findings.
• Data analysis techniques include descriptive and inferential statistics

When selecting research methodology types, it's important to consider various factors such as the study's primary goal, the nature of the research questions and conceptual framework, the variables involved, the context of the study, ethical issues, and whether the focus is on individuals or groups, or on comparing groups and understanding their relationships.

Research design methods play a pivotal role in determining the appropriateness of qualitative methods for studies involving individuals or groups, while quantitative methods are often chosen for studies aimed at comparing groups or deciphering the relationship between variables.

Descriptive Research

Descriptive research is a methodological approach that aims to accurately and systematically depict a population, situation, or phenomenon. It adeptly addresses 'what', 'where', 'when', and 'how' questions, although it steers clear of exploring 'why'. Employing a descriptive research design means observing and documenting variables without exerting control or manipulation, which is particularly beneficial when exploring new topics or problems to identify characteristics, frequencies, trends, and categories.

Descriptive research methods include:

• Surveys: Survey research is a powerful tool that enables researchers to collect extensive data sets, which can then be meticulously analyzed to uncover frequencies, averages, and emerging patterns.
• Observations: Utilizing observation allows researchers to collect data on behaviors and phenomena, ensuring the gathered information is not tainted by the honesty or accuracy of respondents.
• Case studies: Case study research delves into detailed data to pinpoint the unique characteristics of a narrowly defined subject, providing in-depth insights.

Descriptive research can be conducted in different ways:

• Cross-sectional : Observing a population at a single point in time.
• Longitudinal : Following a population over a period of time.
• Surveys or interviews : When the researcher interacts with the participant.
• Observational studies or data collection using existing records : When the researcher does not interact with the participant.

Advantages of descriptive research include:

• Varied data collection methods
• A natural environment for respondents
• Quick and cheap data collection
• A holistic understanding of the research topic

Limitations of  descriptive research studies:

• They cannot establish cause and effect relationships.
• The reliability and validity of survey responses can be compromised if respondents are not truthful or tend to provide socially desirable answers.
• The choice and wording of questions on a questionnaire may influence the descriptive findings.

Correlational Research

Correlational research, a non-experimental method, delves into the dynamics between two variables, focusing on the strength and direction of their relationship without manipulating any factors, which is pivotal in understanding associations rather than causality.

Researchers may choose correlational research in the following situations

• When manipulating the independent variable is impractical, impossible, or unethical
• When exploring non-causal relationships between variables
• When testing new measurement tools

In correlational research, the correlation coefficient is measured, which can range from -1 to +1, indicating the relationship's direction and strength. A comprehensive meta-analysis can further elucidate these types of correlations.

• Positive correlation: Both variables change in the same direction
• Negative correlation: Variables change in opposite directions
• Zero correlation: No relationship exists between the variables

Data collection methods for correlational research include

• Naturalistic observation
• Archival research or secondary data

Analytical research methods, such as correlation or regression analyses, are employed to analyze correlational data, with the former yielding a coefficient that clarifies the relationship's intensity and direction, and the latter forecasting the impact of variable changes.

Experimental Research

Experimental research, a methodical scientific approach, manipulates variables to observe their effects and is indispensable for establishing cause-and-effect relationships and making informed decisions in the face of inadequate data.

• Pre-experimental research design : Includes One-shot Case Study Research Design, One-group Pretest-posttest Research Design, and Static-group Comparison.
• True experimental research design Statistical analysis, a cornerstone in testing hypotheses, is pivotal in research for its accuracy in proving or disproving a hypothesis. It's uniquely capable of establishing a cause-effect relationship within a group, making it an indispensable tool for researchers.
• Quasi-experimental design : Similar to an experimental design but assigns participants to groups non-randomly.

Experimental research is essential for various fields, such as:

• Developing new drugs and medical treatments
• Understanding human behavior in psychology
• Improving educational outcomes
• Identifying opportunities for businesses and organizations

To conduct experimental research effectively, researchers must consider three key factors:

• A Control Group and an Experimental Group
• A variable that can be manipulated by the researcher
• Random distribution of participants

Experimental research, whether conducted in laboratory settings with high control variables and internal validity or in field settings that boast both internal and external validity, presents a spectrum of advantages and challenges. Researchers must navigate potential threats to internal validity, including history, maturation, testing, instrumentation, mortality, and regression threats.

Mixed Methods Research

Mixed methods research, an approach that synergizes the rigor of quantitative and qualitative research methods, capitalizes on the strengths of each to provide a comprehensive analysis. This integration, which can occur during data collection, analysis, or presentation of results, is a hallmark of mixed methods research designs.

• Convergent design
• Explanatory sequential design
• Exploratory sequential design
• Embedded design

The practice of triangulation in mixed methods research enhances the integration of quantitative and qualitative data, offering multiple perspectives and a more comprehensive understanding. It also allows for a deeper explanation of statistical results, as exemplified by the EQUALITY study's exploratory sequential design for patient-centered data collection.

In mixed methods research, the intricate research design and methodology combine qualitative and quantitative data collection and analysis. This purposeful mixing of methods and data integration at strategic stages of the research process can reveal relationships between complex layers of research questions, although it demands significant resources and specialized training.

• Explanatory
• Exploratory
• Nested (embedded) designs

Mixed methods research, characterized by its diverse research design and methods, integrates quantitative and qualitative approaches within a single study. Grounded in positivism and interpretivism, it provides a multifaceted understanding of research topics, despite the challenges of mastering both methodologies and collaborating with multidisciplinary teams.

In sum, a thorough grasp of the various research methodologies is crucial for conducting robust research and critically assessing others' findings. From qualitative to quantitative, descriptive, correlational, experimental, and mixed methods research, each approach offers distinct strengths and limitations, guiding researchers to the most suitable methods for effective data collection and analysis.

As we navigate the vast landscape of information available, understanding what are research methods empowers us to make informed decisions, draw meaningful conclusions, and contribute to the advancement of knowledge across various fields. Embracing the diversity of research methods, whether you're a student, researcher, or simply curious, will enhance your critical thinking skills and enable you to uncover valuable insights that shape our understanding of the world.

What are the seven most commonly used research methods? The seven most commonly used research methods are:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (a combination of some of the above)

What does comprehensive research methodology entail?

Comprehensive research methodology involves conducting a thorough and exhaustive investigation on a specific topic, subject, or issue. This approach is characterized by the meticulous collection, analysis, and evaluation of a wide array of information, data, and sources, with the objective of achieving a deep and comprehensive understanding of the subject matter.

What are the three primary methods to investigate a specific research question?

To investigate a specific research question, you can use:

• Quantitative methods for measuring something or testing a hypothesis.
• Qualitative methods for exploring ideas, thoughts, and meanings.
• Secondary data analysis for examining a large volume of readily-available data.

What does exploration mean in the context of research methodology?

Exploration in research methodology signifies a research approach that aims to delve into questions that have not been extensively explored before. Exploratory research, often qualitative and primary in nature, is focused on uncovering new insights and understanding. Nonetheless, it can also adopt a quantitative stance, particularly when it involves analyzing a large sample size, to further the scope of exploratory research.

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