mixed methods research examples

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• 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.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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.

Prevent plagiarism. Run a free check.

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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

George, T. (2023, June 22). Mixed Methods Research | Definition, Guide & Examples. Scribbr. Retrieved March 21, 2024, from https://www.scribbr.com/methodology/mixed-methods-research/

Is this article helpful?

Tegan George

Other students also liked, writing strong research questions | criteria & examples, what is quantitative research | definition, uses & methods, what is qualitative research | methods & examples, unlimited academic ai-proofreading.

✔ Document error-free in 5minutes ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

Join thousands of product people at Insight Out Conf on April 11. Register free.

Insights hub solutions

Analyze data

Uncover deep customer insights with fast, powerful features, store insights, curate and manage insights in one searchable platform, scale research, unlock the potential of customer insights at enterprise scale.

Featured reads

Tips and tricks

Make magic with your customer data in Dovetail

Four ways Dovetail helps Product Managers master continuous product discovery

Product updates

Dovetail retro: our biggest releases from the past year

Events and videos

© Dovetail Research Pty. Ltd.

• 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

Dovetail streamlines analysis to help you uncover and share actionable insights

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

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

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

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

• When to use mixed methods research

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

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

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

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

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

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

• Mixed methods research design

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

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

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

• Types of mixed method research designs

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

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

Convergent parallel

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

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

Embedded design

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

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

Explanatory sequential design

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

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

Exploratory sequential design

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

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

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

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

• Strengths of mixed methods research

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

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

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

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

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

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

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

• Challenges of mixed methods research

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

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

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

• Advanced frameworks in mixed methods research

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

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

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

Intervention

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

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

Participatory

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

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

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

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

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

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

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

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

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

• Techniques for integrating data in mixed method studies

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

Triangulation protocol

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

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

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

Following a thread

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

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

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

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

Mixed methods matrix

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

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

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

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

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

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

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

What is an example of mixed methods research?

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

Which sampling method is best for mixed methods?

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

What is the difference between mixed methods and multiple methods?

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

Get started today

Go from raw data to valuable insights with a flexible research platform

Editor’s picks

Last updated: 21 December 2023

Last updated: 16 December 2023

Last updated: 17 February 2024

Last updated: 19 November 2023

Last updated: 5 March 2024

Last updated: 15 February 2024

Last updated: 11 March 2024

Last updated: 12 December 2023

Last updated: 6 March 2024

Last updated: 10 April 2023

Last updated: 20 December 2023

Latest articles

Related topics, log in or sign up.

Get started for free

• Privacy Policy

Buy Me a Coffee

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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Case Study – Methods, Examples and Guide

Qualitative Research – Methods, Analysis Types...

Descriptive Research Design – Types, Methods and...

Qualitative Research Methods

Exploratory Research – Types, Methods and...

One-to-One Interview – Methods and Guide

No internet connection.

All search filters on the page have been cleared., your search has been saved..

• All content
• Dictionaries
• Encyclopedias
• Expert Insights
• Foundations
• How-to Guides
• Journal Articles
• Little Blue Books
• Little Green Books
• Project Planner
• Tools Directory
• Sign in to my profile My Profile

• Sign in Signed in
• My profile My Profile

The Sage Handbook of Mixed Methods Research Design

• Edited by: Cheryl N. Poth
• Publisher: Sage Publications Ltd
• Publication year: 2023
• Online pub date: January 16, 2024
• Discipline: Sociology , Criminology and Criminal Justice , Business and Management , Communication and Media Studies , Education , Psychology , Health , Social Work , Political Science and International Relations
• Methods: Mixed methods , Research design , Quantitative data collection
• DOI: https:// doi. org/10.4135/9781529682663
• Keywords: handbooks , teams Show all Show less
• Print ISBN: 9781529723960
• Online ISBN: 9781529682663
• Buy the book icon link

Subject index

The Sage Handbook of Mixed Methods Research Design is a ground-breaking edited work that weaves together diverse perspectives and global examples of mixed-methods research to present a timely picture of this rapidly evolving field. With contributions from over 80 of the biggest names and rising stars of the field, this Handbook is an essential resource for anyone interested in the contemporary, emerging, and evolving practice of mixed methods research and scholarship. Exploring new and novel applications of existing mixed methods research design practices, the handbook provides comprehensive integration guidance while showcasing how design innovations inspire and contribute to investigating previously under-researched social issues and populations. Through its unique focus on design and the diverse contexts in which mixed methods research is being applied, this Handbook prepares researchers for the changing conditions in which they will conduct studies. Newcomers and seasoned mixed methods researchers alike will find this Handbook a go-to source for tools to think and act 'complexively' and creatively in research design. Using accessible language and illustrative examples, this Handbook is written for those with various roles and experience in mixed methods research design. The in-depth discussions led by the interdisciplinary group of 11 internationally renowned editorial section leads project our collective thinking of mixed methods research design into the future across the following six sections: Section 1: Inspiring Diversity and Innovation in Mixed Methods Design; Section 2: The Craft of Mixed Methods Research Design; Section 3: Expanding Mixed Methods Design Approaches; Section 4: Designing Innovative Integrations with Technology; Section 5: Navigating Research Cultures in Mixed Methods Design; and Section 6: Exploring Design Possibilities and Challenges for Mixed Methods Research

Front Matter

• Editorial Section Leads
• International Advisory Board
• List of Figures
• List of Tables
• List of Box
• Notes on the Editor, Section Leads, and Chapter Contributors
• Acknowledgements
• Chapter 1: Dilemmas and Opportunities for Mixed Methods Research Design: Handbook Introduction
• Evolving Tensions and Conversations in Mixed Methods Research Design Approaches: Section 1 Introduction
• Chapter 2: Revisiting Mixed Methods Research Designs Twenty Years Later
• Chapter 3: Mixed Methods Design in Historical Perspective: Implications for Researchers
• Chapter 4: Mixed Methods Designs to Further Social, Economic and Environmental Justice
• Chapter 5: Developments in Mixed Methods Designs: What Have Been the Dominant Pathways and Where Might They Take Us in the Future?
• Chapter 6: The Role of Methodological Paradigms for Dialogic Knowledge Production: Using a Conceptual Map of Discourse Development to Inform Mixed Methods Research Design
• Future Tensions and Design Conversations in the Mixed Methods Field: Section 1 Conclusions
• The Craft of Mixed Methods Research Design: Section 2 Introduction
• Chapter 7: Embracing Emergence in Mixed Methods Designs: Theoretical Foundations and Empirical Applications
• Chapter 8: The Methods-Inference Map: Visualizing the Interactions Between Methods and Inferences in Mixed Methods Research
• Chapter 9: Towards Sampling Designs that are Transparent, Rigorous, Ethical and Equitable (TREE): Using a Tree Metaphor as a Sampling Meta-Framework in Mixed Methods Research
• Chapter 10: Data Integration as a Form of Integrated Mixed Analysis in Mixed Methods Research Designs
• Chapter 11: Ethical Issues and Practices for Mixed Methods Research in an Era of Big Data
• Chapter 12: Building the Logic for an Integrated Methodology: Mixed Method Grounded Theory as an Example of Constructing a Methodology to Guide Design and Integration
• The Craft of Mixed Methods Research Design: Section 2 Conclusions
• Expanding Beyond Typology-Based Mixed Methods Designs: Section 3 Introduction
• Chapter 13: Exploring Interlocking Relationships of Race, Gender, and Class with an Intersectionality-Informed Mixed Methods Research Design Framework
• Chapter 14: Indigenous Cultural Values Instrument Development: Using Mixed Methods Research
• Chapter 15: What Can Mixed Methods Partnerships Learn from Kaupapa Māori Research Principles?
• Chapter 16: Prioritizing Cultural Responsiveness in Mixed Methods Research and Team Science with Underrepresented Communities
• Chapter 17: Using Participatory Methods in Randomised Controlled Trials of Complex Interventions
• Chapter 18: Illustrating the Mixed Methods Phenomenological Approach (MMPR)
• Chapter 19: Intersection of Mixed Methods and Case Study Research (MM+CSR): Two Design Options in Educational Research
• Chapter 20: Harnessing Mixed Methods for Research Instrument Development and Legitimation
• Chapter 21: Mixed Methods-Grounded Theory: Best Practices for Design and Implementation
• Moving Beyond Tradition: The Need for Expanded and Culturally Relevant Mixed Methods Design Typologies: Section 3 Conclusions
• Expanding Innovative Integrations with Technology: Section 4 Introduction
• Chapter 22: Using Software for Innovative Integration in Mixed Methods Research: Joint Displays, Insights and Inferences with MAXQDA
• Chapter 23: Grounded Text Mining Approach: An Integration Strategy of Grounded Theory and Textual Data Mining
• Chapter 24: A “Mixed Methods Way of Thinking” in Game-based Research Integrations
• Chapter 25: Integrating Secondary Data from Ethnically and Racially Minoritized Groups in Mixed Methods Research
• Chapter 26: Beyond the Joint Display in Mixed Methods Convergent Designs: A Case-Oriented Merged Analysis
• The Untapped Potential of Technology for Integration: Section 4 Conclusions
• From Margin to Center: The Design Implications of a Cultural Component in Mixed Methods Research: Section 5 Introduction
• Chapter 27: Culturally Responsive Mixed Methods Evaluation Design
• Chapter 28: Integrating a Four-Step Japanese Cultural Narrative Framework, Ki-Shou-Ten-Ketsu, into a Mixed Methods Study
• Chapter 29: Leveraging Mixed Methods Community-based Participatory Research (MMCBPR) in Diverse Social and Cultural Contexts to Advance Health Equity
• Chapter 30: Cultural Diversity in Intervention Designs: A Chinese Illustrative Example
• Chapter 31: Examining the Influences of Spanish Research Culture in Systematic Observation with Mixed Methods
• Future Direction for Navigating Research Cultures in Designs: Section 5 Conclusions
• Exploring Possibilities and Challenges for Mixed Methods Research for the Future: Section 6 Introduction
• Chapter 32: Visualizing the Process: Using Visuals to Teach and Learn Mixed Methods Research
• Chapter 33: Towards the Future Legitimacy of Mixed Methods Designs: Responsible Mixed Methods Research for Tackling Grand Challenges for the Betterment of Society
• Chapter 34: Realizing Methodological Potentials and Advantages of Mixed Methods Research Design for Knowledge Translation
• Chapter 35: Opportunities and Challenges for a Transdisciplinary Mixed Methods Research Future
• Chapter 36: Mapping Design Trends and Evolving Directions Using the Sage Handbook of Mixed Methods Research Design
• Where to Next in Exploring Possibilities and Challenges for Mixed Methods Research for the Future? Section 6 Conclusions
• Chapter 37: An Emerging and Exciting Future for Mixed Methods Research Design: Handbook Conclusions

Sign in to access this content

Get a 30 day free trial, more like this, sage recommends.

We found other relevant content for you on other Sage platforms.

Have you created a personal profile? Login or create a profile so that you can save clips, playlists and searches

• Sign in/register

Navigating away from this page will delete your results

Please save your results to "My Self-Assessments" in your profile before navigating away from this page.

Sign in to my profile

Sign up for a free trial and experience all Sage Learning Resources have to offer.

You must have a valid academic email address to sign up.

Get off-campus access

• View or download all content my institution has access to.

Sign up for a free trial and experience all Sage Research Methods has to offer.

• view my profile
• view my lists

• What Is Mixed Methods Research? Definition, Guide & Examples

As the world continues to evolve, we face increasingly complex problems,  from climate change to global health disparities. These issues are becoming increasingly difficult to address through conventional research methods.

Mixed methods research offers a new way to tackle these challenges, by providing us with a deeper understanding of the underlying causes and effects of complex topics.

In this article, we’ll explore how mixed-method research works, and how it helps us solve real-world problems.

The Foundation of Mixed Methods Research

Mixed methods research is an effective approach to understanding complex phenomena. It combines the strengths of quantitative and qualitative methods to provide a more comprehensive and nuanced perspective.

Here is a breakdown of the pioneers of mixed-method research and how it has evolved over the years:

A. Historical Development of Mixed Methods

Mixed methods research dates back to the early 1900s , but it didn’t become widely adopted until the late 1980s. Before that, people thought that quantitative and qualitative methods were two different concepts.

Quantitative research focuses on numbers and facts, while qualitative research focuses on people’s experiences and meanings. Combining these two using mixed-method research gives you a more accurate understanding of complex concepts.

Today, Mixed methods research is widely used across different industries, such as education, health science, social science, business, etc. This is because it gives a holistic view of research findings, making them easily reproducible and accurate.

Ensure clear and accurate clinical communication with our comprehensive Clinical Report Form Template.

B. Key Figures and Contributions in the Field

• Abbas Tashakkori

Tashakkori is one of the leading experts in mixed-methods research with his work has provided a valuable framework for understanding and conducting mixed-methods research. 

He has published several books and papers on mixed methods research, including the “Foundation of mixed methods” and “Mixed Methodology: combining quantitative and qualitative research approaches.” 

• John W. Creswell

Creswell has established himself as a leading authority on mixed methods research. He has published several books and papers on the subject, including the groundbreaking textbook “Qualitative Inquiry: Choosing Between Five Traditions”. 

Creswell’s work has contributed to the legitimization of mixed-method research as a robust and scientifically sound research approach.

• Charles Teddlie

Another big name in mixed methods research is Charles Teddlie. He’s co-authored several books and journals about mixed methods, including the textbook, “Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences.” His work has helped make mixed methods research better understood and practiced across different fields.

C. Paradigms and Philosophies Underlying Mixed Methods Research

Mixed methods research is grounded in several paradigms and philosophies, each of which offers unique insights into why mixed methods should be adopted for research processes. 

Some of the most common paradigms and philosophies underlying mixed methods research include:

• Pragmatism 

Pragmatism on the practical implications of things. This approach focuses on researching concepts to see how they help solve real-world problems.

Mixed methods research is compatible with pragmatism because it gives researchers the freedom to use different methods so they can determine the most effective way to solve research problems.

• Triangulation

Triangulation uses multiple techniques to collect data on the same subject to improve the validity and robustness of the research results. Mixed methods research frequently uses triangulation to gather and analyze data from quantitative and qualitative sources.

• Integration

Integration is the process of combining quantitative and qualitative data to provide a more comprehensive understanding of a phenomenon. 

The purpose of mixed methods research is to bring quantitative and qualitative information together in a meaningful manner, rather than just combining them. Integration methods such as data transformation, mixed methods convergence analysis, and mixed methods modeling help you do this seamlessly.

Understanding the Components of Mixed Methods Research

The following are the key elements that make up mixed-method research:

• Quantitative Research

Quantitative research focuses on collecting and analyzing numerical data. It helps you to collect numerical data test hypotheses, identify patterns and trends, and make predictions.

It’s like taking a photograph of a crowd: you can see who’s there and how many are there, but you can’t see what they’re thinking or how they feel.

You can perform quantitative research using surveys, questionnaires, experiments, and observations. The most common methods of analyzing quantitative analysis findings are statistical analysis, regression analysis, and factor analysis.

• Qualitative Research

Qualitative research focuses on gathering and analyzing non-structural data, such as text, pictures, and audio. It looks at complex phenomena by focusing on people’s experiences and opinions.

Think of qualitative research as talking to the people in a crowd. It allows you to capture their individual experiences and points of view. 

The most common methods for collecting qualitative data collection methods include interviews, focus groups, ethnography, and document analysis. You can analyze your findings using thematic analysis, discourse analysis, and grounded theory.

The Advantages of Mixed Methods Research

A. comprehensive understanding of research questions.

Quantitative research is good at identifying patterns and trends, while qualitative research is good at providing depth and understanding. Mixed method research combines these features to gain a more complete understanding of the research topic.

For example, a mixed-method study on the impact of a new teaching approach on student learning outcomes would use quantitative methods (academic performance) to measure student improvement. It would also use qualitative data (interviews and questionnaires) to gain insight into why a teaching approach is doing well or poorly.

B. Increased Validity and Reliability

Mixed methods research often employs triangulation which uses multiple methods to collect data on the same phenomenon. This reduces the risk of bias and ensures that the research findings are accurate and reliable.

For example, a mixed-method study on the challenges people with chronic illnesses face would track symptoms and interview their caregivers to get a better idea of what they’re going through and what they’re facing.

C. Enhanced Triangulation

Mixed methods research provides several opportunities for triangulation by combining multiple techniques, sources, and viewpoints to collect and analyze data. This helps improve the accuracy and completeness of research results.

For example, in a study about student performance you can triangulate quantitative and qualitative data, data from different sources (e.g., surveys, interviews, observations), and data from different perspectives (e.g., students, teachers, parents).

D. Addressing Research Bias

Research bias is a potential problem in all types of research, but it can be particularly challenging to address in qualitative research. Mixed methods research can help to address research bias by combining quantitative and qualitative data.

For example, you can use a survey to gather data on demographic factors prone to bias, like race, gender, and income. Then, you for control bias by analyzing the data using qualitative data such as focus groups and interviews.

E. Opportunities for Exploration and Discovery

Mixed-method research allows you to collect and analyze data from various perspectives and methods. This allows you to gain new insights and understandings that would not be possible with either quantitative or qualitative research alone.

For instance, a mixed-method study on the school experience of students with disabilities could collect quantitative data on student performance such as grades, standardized test results, and school attendance. Combining this data with qualitative data from the students, their teachers, and their parents would give you a deeper understanding of the unique challenges and experiences of students with disabilities in school.

Designing a Mixed Methods Study

You need a proper design to successfully execute your mixed-method research. Here is the list of steps that will get you there:

A. Research Questions and Hypotheses

Start by clearly defining your research questions and hypotheses. This will help you to choose the appropriate research design and data collection methods.

Also, ensure the research questions are specific, measurable, and relevant to your research goals.

B. Choosing the Appropriate Research Design

There are three main types of mixed methods research designs: concurrent, sequential, and exploratory.

• Concurrent designs collect and analyze quantitative and qualitative data simultaneously. This helps you explore complex phenomena in detail and to develop new theories.
• Sequential design is all about collecting and analyzing data one after the other, not simultaneously like concurrent design. It’s usually used to test hypotheses and build on existing studies.
• Exploratory design is the process of coming up with new concepts and ideas. This is the most suitable research method if you are working on a new topic that there’s little to no understanding of.

C. Sampling Strategies

Sampling is the process of selecting a subset of a population to represent the entire population. When designing a mixed methods study, you have to sample both quantitatively and qualitatively.

In qualitative sampling, participants are selected based on their likelihood of providing high-quality and meaningful data.  However, in quantitative sampling, participants are randomly selected or stratified to ensure that the sample is representative of the population.

D. Data Collection and Instrumentation

You have to choose your data collection instruments; these are the tools that allow you to collect research data. Quantitative research typically uses surveys, questionnaires, and tests, while qualitative uses interviews, focus groups, and observation guides to collect data.

E. Data Analysis

Data analysis is the process of organizing, summarizing, and interpreting data. 

Statistical and regression analysis are the most common ways of analyzing quantitative data. Qualitative research uses different analysis methods including, thematic analysis, discourse analysis, and grounded theories.

F, Integration of Findings

Integration of findings is the final step in the mixed methods research process. This involves combining the quantitative and qualitative findings in a meaningful way to answer the research questions and hypotheses.

Here are the most common methods of integrating findings in mixed-method research:

• Triangulation matrix : it uses a table to compare and contrast the quantitative and qualitative findings.
• Convergence analysis : this is a statistical analysis method that helps you determine the relationship between quantitative and qualitative results, by looking at their similarities and differences.

Real-World Applications of Mixed Methods Research

Mixed methods research allows you to gain better insights into complex topics across different industries including:

• Education : You can use mixed methods research to study a variety of topics in education, such as the effectiveness of new teaching methods, the impact of school policies on student achievement, or determining the optimal courseload for students.
• Healthcare : Mixed methods also allow you to effectively investigate healthcare topics, such as the effectiveness of new medical treatments, the impact of public health interventions on population health, etc.
• Social sciences: Mixed methods research helps you to explore social science topics like what influences crime rate in different regions, how policies affect social well-being, etc.
• Business and marketing : You can also use mixed-method research to determine the effectiveness of new marketing campaigns, the impact of customer satisfaction on business performance, etc.

Improve your teaching and classroom environment with valuable student feedback using our Student Perception Survey Template.

B. Impact of Mixed Methods Research on Policy Development and Decision-Making

Mixed methods research helps policymakers develop more effective policies and programs by giving them a deeper understanding of different topics.

For example, the findings of the mixed methods study on the effectiveness of the public health intervention on childhood obesity could be used to inform the development of other public health interventions to reduce obesity rates.

Challenges and Limitations of Mixed Methods Research

While mixed-method research is an effective approach to solving complex problems, it’s not without its limitations. Here are common mixed-method research limitations and challenges:

A. Integration Challenges

One of the most difficult aspects of mixed methods research is the integration of qualitative and quantitative data. This is because qualitative and quantitative data are very different in content and format.

You can integrate by using data transformation to convert qualitative data into quantitative data. You could also use convergence analysis to identify patterns and trends in both the quantitative and qualitative data.

B. Resource-Intensive Nature

Mixed methods studies involve collecting and analyzing both quantitative and qualitative data. This requires significant time, money, and personnel resources.

You can overcome this challenge by carefully planning the mixed methods studies by ensuring you have all the resources you need. You could also look for funding from outside sources, like the government or private foundations.

C. Potential for Researcher Bias

All types of research are susceptible to researcher bias, but this can be a particular challenge in mixed-methods research. This is because mixed methods research often involves collecting and analyzing data from multiple perspectives.

You can use strategies such as triangulation, peer review, and member checking to pinpoint your biases and mitigate them.

D. Complexity in Data Interpretation

Mixed methods studies often produce a large amount of data from multiple sources, making it difficult to interpret. 

One of the simplest ways to mitigate this difficulty is to use data visualization techniques such as graphs, maps, charts, and more. This makes it easier for you to identify trends and patterns in the data.

Best Practices for Conducting Mixed Methods Research

Here are some best practices to ensure you have an effective mixed-method research:

A. Establishing a Clear Research Plan

Start your research by outlining your research questions, hypotheses, research design, data collection methods, data analysis methods, and integration strategies.

Also, ensure you are very specific in your research plan. This will help you to stay on track throughout the research process and to ensure that your study is rigorous and well-structured.

B. Collaborative Research Teams

Mixed method research is a very rigorous and resource-intensive research method, so having a team of researchers on board makes sure you’re collecting and analyzing data thoroughly without the same amount of stress if you were doing it alone.  Having a collaborative research team also helps reduce researcher bias and generate stronger results.

C. Ongoing Reflexivity and Transparency

Being reflexive means being aware of your own biases and limitations, while transparency means honestly reporting your research methods and findings.

One way to be more reflexive and transparent is to keep a research journal. This allows you to document your thoughts and feelings about the research process, as well as any challenges or obstacles that you encounter.

You can also seek feedback from others on your research design, data collection methods, data analysis methods, and integration strategies.

D. Reporting Mixed Methods Research Findings

Clearly and honestly document your research by providing detailed descriptions of your data collection methods, data analysis methods, and integration strategies. 

You can do this by using a mixed-methods research reporting template. This ensures you have a structure for reporting your results and avoid leaving out important information.

Dive into Experimental Research Designs: Exploring Types, Examples, and Methods

Mixed method research enables you to get a better grasp on topics that would be hard to understand using just one research method. This allows you to make accurate data-driven decisions, and it works across different fields.

However, like any other research method, mixed-method research is not without its challenges and limitations. Ensure you use the best practices in this guide to get quality data and achieve your mixed-method research goals.

Connect to Formplus, Get Started Now - It's Free!

• Mixed Methods Research
• qualitative research
• quantitative research
• research design
• Research Plan
• Sampling Strategies
• Moradeke Owa

You may also like:

How to Write An Abstract For Research Papers: Tips & Examples

In this article, we will share some tips for writing an effective abstract, plus samples you can learn from.

Desk Research: Definition, Types, Application, Pros & Cons

If you are looking for a way to conduct a research study while optimizing your resources, desk research is a great option. Desk research...

Statistical Analysis Software: A Guide For Social Researchers

Introduction Social research is a complex endeavor. It takes a lot of time, energy, and resources to gather data, analyze and present...

What is Field Research: Meaning, Examples, Pros & Cons

Introduction Field research is a method of research that deals with understanding and interpreting the social interactions of groups of...

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..

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

• Open access
• Published: 29 March 2019
• Volume 48 , pages 193–216, ( 2019 )

Cite this article

You have full access to this open access article

• Rob Timans 1 ,
• Paul Wouters 2 &
• Johan Heilbron 3  

110k Accesses

88 Citations

13 Altmetric

Explore all metrics

A Correction to this article was published on 06 May 2019

This article has been updated

Combining methods in social scientific research has recently gained momentum through a research strand called Mixed Methods Research (MMR). This approach, which explicitly aims to offer a framework for combining methods, has rapidly spread through the social and behavioural sciences, and this article offers an analysis of the approach from a field theoretical perspective. After a brief outline of the MMR program, we ask how its recent rise can be understood. We then delve deeper into some of the specific elements that constitute the MMR approach, and we engage critically with the assumptions that underlay this particular conception of using multiple methods. We conclude by offering an alternative view regarding methods and method use.

Similar content being viewed by others

Mixed Methods

Avoid common mistakes on your manuscript.

The interest in combining methods in social scientific research has a long history. Terms such as “triangulation,” “combining methods,” and “multiple methods” have been around for quite a while to designate using different methods of data analysis in empirical studies. However, this practice has gained new momentum through a research strand that has recently emerged and that explicitly aims to offer a framework for combining methods. This approach, which goes by the name of Mixed Methods Research (MMR), has rapidly become popular in the social and behavioural sciences. This can be seen, for instance, in Fig.  1 , where the number of publications mentioning “mixed methods” in the title or abstract in the Thomson Reuters Web of Science is depicted. The number increased rapidly over the past ten years, especially after 2006. Footnote 1

Fraction of the total of articles mentioning Mixed Method Research appearing in a given year, 1990–2017 (yearly values sum to 1). See footnote 1

The subject of mixed methods thus seems to have gained recognition among social scientists. The rapid rise of the number of articles mentioning the term raises various sociological questions. In this article, we address three of these questions. The first question concerns the degree to which the approach of MMR has become institutionalized within the field of the social sciences. Has MMR become a recognizable realm of knowledge production? Has its ascendance been accompanied by the production of textbooks, the founding of journals, and other indicators of institutionalization? The answer to this question provides an assessment of the current state of MMR. Once that is determined, the second question is how MMR’s rise can be understood. Where does the approach come from and how can its emergence and spread be understood? To answer this question, we use Pierre Bourdieu’s field analytical approach to science and academic institutions (Bourdieu 1975 , 1988 , 2004 , 2007 ; Bourdieu et al. 1991 ). We flesh out this approach in the next section. The third question concerns the substance of the MMR corpus seen in the light of the answers to the previous questions: how can we interpret the specific content of this approach in the context of its socio-historical genesis and institutionalization, and how can we understand its proposal for “mixing methods” in practice?

We proceed as follows. In the next section, we give an account of our theoretical approach. Then, in the third, we assess the degree of institutionalization of MMR, drawing on the indicators of academic institutionalization developed by Fleck et al. ( 2016 ). In the fourth section, we address the second question by examining the position of the academic entrepreneurs behind the rise of MMR. The aim is to understand these agents’ engagement in MMR, as well as its distinctive content as being informed by their position in this field. Viewing MMR as a position-taking of academic entrepreneurs, linked to their objective position in this field, allows us to reflect sociologically on the substance of the approach. We offer this reflection in the fifth section, where we indicate some problems with MMR. To get ahead of the discussion, these problems have to do with the framing of MMR as a distinct methodology and its specific conceptualization of data and methods of data analysis. We argue that these problems hinder fruitfully combining methods in a practical understanding of social scientific research. Finally, we conclude with some tentative proposals for an alternative view on combining methods.

A field approach

Our investigation of the rise and institutionalization of MMR relies on Bourdieu’s field approach. In general, field theory provides a model for the structural dimensions of practices. In fields, agents occupy a position relative to each other based on the differences in the volume and structure of their capital holdings. Capital can be seen as a resource that agents employ to exert power in the field. The distribution of the form of capital that is specific to the field serves as a principle of hierarchization in the field, differentiating those that hold more capital from those that hold less. This principle allows us to make a distinction between, respectively, the dominant and dominated factions in a field. However, in mature fields all agents—dominant and dominated—share an understanding of what is at stake in the field and tend to accept its principle of hierarchization. They are invested in the game, have an interest in it, and share the field’s illusio .

In the present case, we can interpret the various disciplines in the social sciences as more or less autonomous spaces that revolve around the shared stake in producing legitimate scientific knowledge by the standards of the field. What constitutes legitimate knowledge in these disciplinary fields, the production of which bestows scholars with prestige and an aura of competence, is in large part determined by the dominant agents in the field, who occupy positions in which most of the consecration of scientific work takes place. Scholars operating in a field are endowed with initial and accumulated field-specific capital, and are engaged in the struggle to gain additional capital (mainly scientific and intellectual prestige) in order to advance their position in the field. The main focus of these agents will generally be the disciplinary field in which they built their careers and invested their capital. These various disciplinary spaces are in turn part of a broader field of the social sciences in which the social status and prestige of the various disciplines is at stake. The ensuing disciplinary hierarchy is an important factor to take into account when analysing the circulation of new scientific products such as MMR. Furthermore, a distinction needs to be made between the academic and the scientific field. While the academic field revolves around universities and other degree-granting institutions, the stakes in the scientific field entail the production and valuation of knowledge. Of course, in modern science these fields are closely related, but they do not coincide (Gingras and Gemme 2006 ). For instance, part of the production of legitimate knowledge takes place outside of universities.

This framework makes it possible to contextualize the emergence of MMR in a socio-historical way. It also enables an assessment of some of the characteristics of MMR as a scientific product, since Bourdieu insists on the homology between the objective positions in a field and the position-takings of the agents who occupy these positions. As a new methodological approach, MMR is the result of the position-takings of its producers. The position-takings of the entrepreneurs at the core of MMR can therefore be seen as expressions in the struggles over the authority to define the proper methodology that underlies good scientific work regarding combining methods, and the potential rewards that come with being seen, by other agents, as authoritative on these matters. Possible rewards include a strengthened autonomy of the subfield of MMR and an improved position in the social-scientific field.

The role of these entrepreneurs or ‘intellectual leaders’ who can channel intellectual energy and can take the lead in institution building has been emphasised by sociologists of science as an important aspect of the production of knowledge that is visible and recognized as distinct in the larger scientific field (e.g., Mullins 1973 ; Collins 1998 ). According to Bourdieu, their position can, to a certain degree, explain the strategy they pursue and the options they perceive to be viable in the trade-off regarding the risks and potential rewards for their work.

We do not provide a full-fledged field analysis of MMR here. Rather, we use the concept as a heuristic device to account for the phenomenon of MMR in the social context in which it emerged and diffused. But first, we take stock of the current situation of MMR by focusing on the degree of institutionalization of MMR in the scientific field.

The institutionalization of mixed methods research

When discussing institutionalization, we have to be careful about what we mean by this term. More precisely, we need to be specific about the context and distinguish between institutionalization in the academic field and institutionalization within the scientific field (see Gingras and Gemme 2006 ; Sapiro et al. 2018 ). The first process refers to the establishment of degrees, curricula, faculties, etc., or to institutions tied to the academic bureaucracy and academic politics. The latter refers to the emergence of institutions that support the autonomization of scholarship such as scholarly associations and scientific journals. Since MMR is still a relatively young phenomenon and academic institutionalization tends to lag scientific institutionalization (e.g., for the case of sociology and psychology, see Sapiro et al. 2018 , p. 26), we mainly focus here on the latter dimension.

Drawing on criteria proposed by Fleck et al. ( 2016 ) for the institutionalization of academic disciplines, MMR seems to have achieved a significant degree of institutionalization within the scientific field. MMR quickly gained popularity in the first decade of the twenty-first century (e.g., Tashakkori and Teddlie 2010c , pp. 803–804). A distinct corpus of publications has been produced that aims to educate those interested in MMR and to function as a source of reference for researchers: there are a number of textbooks (e.g., Plowright 2010 ; Creswell and Plano Clark 2011 ; Teddlie and Tashakkori 2008 ); a handbook that is now in its second edition (Tashakkori and Teddlie 2003 , 2010a ); as well as a reader (Plano Clark and Creswell 2007 ). Furthermore, a journal (the Journal of Mixed Methods Research [ JMMR] ) was established in 2007. The JMMR was founded by the editors John Creswell and Abbas Tashakkori with the primary aim of “building an international and multidisciplinary community of mixed methods researchers.” Footnote 2 Contributions to the journal must “fit the definition of mixed methods research” Footnote 3 and explicitly integrate qualitative and quantitative aspects of research, either in an empirical study or in a more theoretical-methodologically oriented piece.

In addition, general textbooks on social research methods and methodology now increasingly devote sections to the issue of combining methods (e.g., Creswell 2008 ; Nagy Hesse-Biber and Leavy 2008 ; Bryman 2012 ), and MMR has been described as a “third paradigm” (Denscombe 2008 ), a “movement” (Bryman 2009 ), a “third methodology” (Tashakkori and Teddlie 2010b ), a “distinct approach” (Greene 2008 ) and an “emerging field” (Tashakkori and Teddlie 2011 ), defined by a common name (that sets it apart from other approaches to combining methods) and shared terminology (Tashakkori and Teddlie 2010b , p. 19). As a further indication of institutionalization, a research association (the Mixed Methods International Research Association—MMIRA) was founded in 2013 and its inaugural conference was held in 2014. Prior to this, there have been a number of conferences on MMR or occasions on which MMR was presented and discussed in other contexts. An example of the first is the conference on mixed method research design held in Basel in 2005. Starting also in 2005, the British Homerton School of Health Studies has organised a series of international conferences on mixed methods. Moreover, MMR was on the list of sessions in a number of conferences on qualitative research (see, e.g., Creswell 2012 ).

Another sign of institutionalization can be found in efforts to forge a common disciplinary identity by providing a narrative about its history. This involves the identification of precursors and pioneers as well as an interpretation of the process that gave rise to a distinctive set of ideas and practices. An explicit attempt to chart the early history of MMR is provided by Johnson and Gray ( 2010 ). They frame MMR as rooted in the philosophy of science, particularly as a way of thinking about science that has transcended some of the most salient historical oppositions in philosophy. Philosophers like Aristotle and Kant are portrayed as thinkers who sought to integrate opposing stances, forwarding “proto-mixed methods ideas” that exhibited the spirit of MMR (Johnson and Gray 2010 , p. 72, p. 86). In this capacity, they (as well as other philosophers like Vico and Montesquieu) are presented as part of MMR providing a philosophical validation of the project by presenting it as a continuation of ideas that have already been voiced by great thinkers in the past.

In the second edition of their textbook, Creswell and Plano Clark ( 2011 ) provide an overview of the history of MMR by identifying five historical stages: the first one being a precursor to the MMR approach, consisting of rather atomised attempts by different authors to combine methods in their research. For Creswell and Plano Clark, one of the earliest examples is Campbell and Fiske’s ( 1959 ) combination of quantitative methods to improve the validity of psychological scales that gave rise to the triangulation approach to research. However, they regard this and other studies that combined methods around that time, as “antecedents to (…) more systematic attempts to forge mixed methods into a complete research design” (Creswell and Plano Clark 2011 , p. 21), and hence label this stage as the “formative period” (ibid., p. 25). Their second stage consists of the emergence of MMR as an identifiable research strand, accompanied by a “paradigm debate” about the possibility of combining qualitative and quantitative data. They locate its beginnings in the late 1980s when researchers in various fields began to combine qualitative and quantitative methods (ibid., pp. 20–21). This provoked a discussion about the feasibility of combining data that were viewed as coming from very different philosophical points of view. The third stage, the “procedural development period,” saw an emphasis on developing more hands-on procedures for designing a mixed methods study, while stage four is identified as consisting of “advocacy and expansion” of MMR as a separate methodology, involving conferences, the establishment of a journal and the first edition of the aforementioned handbook (Tashakkori and Teddlie 2003 ). Finally, the fifth stage is seen as a “reflective period,” in which discussions about the unique philosophical underpinnings and the scientific position of MMR emerge.

Creswell and Plano Clark thus locate the emergence of “MMR proper” at the second stage, when researchers started to use both qualitative and quantitative methods within a single research effort. As reasons for the emergence of MMR at this stage they identify the growing complexity of research problems, the perception of qualitative research as a legitimate form of inquiry (also by quantitative researchers) and the increasing need qualitative researchers felt for generalising their findings. They therefore perceive the emergence of the practice of combining methods as a bottom up process that grew out of research practices, and at some point in time converged towards a more structural approach. Footnote 4 Historical accounts such as these add a cognitive dimension to the efforts to institutionalize MMR. They lay the groundwork for MMR as a separate subfield with its own identity, topics, problems and intellectual history. The use of terms such as “third paradigm” and “third methodology” also suggests that there is a tendency to perceive and promote MMR as a distinct and coherent way to do research.

In view of the brief exploration of the indicators of institutionalisation of MMR, it seems reasonable to conclude that MMR has become a recognizable and fairly institutionalized strand of research with its own identity and profile within the social scientific field. This can be seen both from the establishment of formal institutions (like associations and journals) and more informal ones that rely more on the tacit agreement between agents about “what MMR is” (an example of this, which we address later in the article, is the search for a common definition of MMR in order to fix the meaning of the term). The establishment of these institutions supports the autonomization of MMR and its emancipation from the field in which it originated, but in which it continues to be embedded. This way, it can be viewed as a semi-autonomous subfield within the larger field of the social sciences and as the result of a differentiation internal to this field (Steinmetz 2016 , p. 109). It is a space that is clearly embedded within this higher level field; for example, members of the subfield of MMR also qualify as members of the overarching field, and the allocation of the most valuable and current form of capital is determined there as well. Nevertheless, as a distinct subfield, it also has specific principles that govern the production of knowledge and the rewards of domination.

We return to the content and form of this specific knowledge later in the article. The next section addresses the question of the socio-genesis of MMR.

Where does mixed methods research come from?

The origins of the subfield of MMR lay in the broader field of social scientific disciplines. We interpret the positions of the scholars most involved in MMR (the “pioneers” or “scientific entrepreneurs”) as occupying particular positions within the larger academic and scientific field. Who, then, are the researchers at the heart of MMR? Leech ( 2010 ) interviewed 4 scholars (out of 6) that she identified as early developers of the field: Alan Bryman (UK; sociology), John Creswell (USA; educational psychology), Jennifer Greene (USA; educational psychology) and Janice Morse (USA; nursing and anthropology). Educated in the 1970s and early 1980s, all four of them indicated that they were initially trained in “quantitative methods” and later acquired skills in “qualitative methods.” For two of them (Bryman and Creswell) the impetus to learn qualitative methods was their involvement in writing on, and teaching of, research methods; for Greene and Morse the initial motivation was more instrumental and related to their concrete research activity at the time. Creswell describes himself as “a postpositivist in the 1970s, self-education as a constructivist through teaching qualitative courses in the 1980s, and advocacy for mixed methods (…) from the 1990s to the present” (Creswell 2011 , p. 269). Of this group, only Morse had the benefit of learning about qualitative methods as part of her educational training (in nursing and anthropology; Leech 2010 , p. 267). Independently, Creswell ( 2012 ) identified (in addition to Bryman, Greene and Morse) John Hunter, Allen Brewer (USA; Northwestern and Boston College) and Nigel Fielding (University of Surrey, UK) as important early movers in MMR.

The selections that Leech and Creswell make regarding the key actors are based on their close involvement with the “MMR movement.” It is corroborated by a simple analysis of the articles that appeared in the Journal of Mixed Methods Research ( JMMR ), founded in 2007 as an outlet for MMR.

Table 1 lists all the authors that have published in the issues of the journal since its first publication in 2007 and that have either received more than 14 (4%) of the citations allocated between the group of 343 authors (the TLCS score in Table 1 ), or have written more than 2 articles for the Journal (1.2% of all the articles that have appeared from 2007 until October 2013) together with their educational background (i.e., the discipline in which they completed their PhD).

All the members of Leech’s selection, except for Morse, and the members of Creswell’s selection (except Hunter, Brewer, and Fielding) are represented in the selection based on the entries in the JMMR . Footnote 5 The same holds for two of the three additional authors identified by Creswell. Hunter and Brewer have developed a somewhat different approach to combining methods that explicitly targets data gathering techniques and largely avoids epistemological discussions. In Brewer and Hunter ( 2006 ) they discuss the MMR approach very briefly and only include two references in their bibliography to the handbook of Tashakkori and Teddlie ( 2003 ), and at the end of 2013 they had not published in the JMMR . Fielding, meanwhile, has written two articles for the JMMR (Fielding and Cisneros-Puebla 2009 ; Fielding 2012 ). In general, it seems reasonable to assume that a publication in a journal that positions itself as part of a systematic attempt to build a research tradition, and can be viewed as part of a strategic effort to advance MMR as a distinct alternative to more “traditional” academic research—particularly in methods—at least signals a degree of adherence to the effort and acceptance of the rules of the game it lays out. This would locate Fielding closer to the MMR movement than the others.

The majority of the researchers listed in Table 1 have a background in psychology or social psychology (35%), and sociology (25%). Most of them work in the United States or are UK citizens, and the positions they occupied at the beginning of 2013 indicates that most of these are in applied research: educational research and educational psychology account for 50% of all the disciplinary occupations of the group that were still employed in academia. This is consistent with the view that MMR originated in applied disciplines and thematic studies like education and nursing, rather than “pure disciplines” like psychology and sociology (Tashakkori and Teddlie ( 2010b ), p. 32). Although most of the 20 individuals mentioned in Table 1 have taught methods courses in academic curricula (for 15 of them, we could determine that they were involved in the teaching of qualitative, quantitative, or mixed methods), there are few individuals with a background in statistics or a neighbouring discipline: only Amy Dellinger did her PhD in “research methodology.” In addition, as far as we could determine, only three individuals held a position in a methodological department at some time: Dellinger, Tony Onwuegbuzie, and Nancy Leech.

The pre-eminence of applied fields in MMR is supported when we turn our attention to the circulation of MMR. To assess this we proceeded as follows. We selected 10 categories in the Web of Science that form a rough representation of the space of social science disciplines, taking care to include the most important so-called “studies.” These thematically orientated, interdisciplinary research areas have progressively expanded since they emerged at the end of the 1960s as a critique of the traditional disciplines (Heilbron et al. 2017 ). For each category, we selected the 10 journals with the highest 5-year impact factor in their category in the period 2007–2015. The lists were compiled bi-annually over this period, resulting in 5 top ten lists for the following Web of Science categories: Economics, Psychology, Sociology, Anthropology, Political Science, Nursing, Education & Educational Research, Business, Cultural Studies, and Family Studies. After removing multiple occurring journals, we obtained a list of 164 journals.

We searched the titles and abstracts of the articles appearing in these journals over the period 1992–2016 for occurrences of the terms “mixed method” or “multiple methods” and variants thereof. We chose this particular period and combination of search terms to see if a shift from a more general use of the term “multiple methods” to “mixed methods” occurred following the institutionalization of MMR. In total, we found 797 articles (out of a total of 241,521 articles that appeared in these journals during that time), published in 95 different journals. Table 2 lists the 20 journals that contain at least 1% (8 articles) of the total amount of articles.

As is clear from Table 2 , the largest number of articles in the sample were published in journals in the field of nursing: 332 articles (42%) appeared in journals that can be assigned to this category. The next largest category is Education & Educational Research, to which 224 (28 percentage) of the articles can be allocated. By contrast, classical social science disciples are barely represented. In Table 2 only the journal Field Methods (Anthropology) and the Journal of Child Psychology and Psychiatry (Psychology) are related to classical disciplines. In Table 3 , the articles in the sample are categorized according to the disciplinary category of the journal in which they appeared. Overall, the traditional disciplines are clearly underrepresented: for the Economics category, for example, only the Journal of Economic Geography contains three articles that make a reference to mixed methods.

Focusing on the core MMR group, the top ten authors of the group together collect 458 citations from the 797 articles in the sample, locating them at the center of the citation network. Creswell is the most cited author (210 citations) and his work too receives most citations from journals in nursing and education studies.

The question whether a terminological shift has occurred from “multiple methods” to “mixed methods” must be answered affirmative for this sample. Prior to 2001 most articles (23 out of 31) refer to “multiple methods” or “multi-method” in their title or abstract, while the term “mixed methods” gains traction after 2001. This shift occurs first in journals in nursing studies, with journals in education studies following somewhat later. The same fields are also the first to cite the first textbooks and handbooks of MMR.

Taken together, these results corroborate the notion that MMR circulates mainly in nursing and education studies. How can this be understood from a field theoretical perspective? MMR can be seen as an innovation in the social scientific field, introducing a new methodology for combining existing methods in research. In general, innovation is a relatively risky strategy. Coming up with a truly rule-breaking innovation often involves a small probability of great success and a large probability of failure. However, it is important to add some nuance to this general observation. First, the risk an innovator faces depends on her position in the field. Agents occupying positions at the top of their field’s hierarchy are rich in specific capital and can more easily afford to undertake risky projects. In the scientific field, these are the agents richest in scientific capital. They have the knowledge, authority, and reputation (derived from recognition by their peers; Bourdieu 2004 , p. 34) that tends to decrease the risk they face and increase the chances of success. Moreover, the positions richest in scientific capital will, by definition, be the most consecrated ones. This consecration involves scientific rather than academic capital (cf. Wacquant 2013 , p. 20) and within disciplines these consecrated positions often are related to orthodox position-takings. This presents a paradox: although they have the capital to take more risks, they have also invested heavily in the orthodoxy of the field and will thus be reluctant to upset the status quo and risk destroying the value of their investment. This results in a tendency to take a more conservative stance, aimed at preserving the status quo in the field and defending their position. Footnote 6

For agents in dominated positions this logic is reversed. Possessing less scientific capital, they hold less consecrated positions and their chances of introducing successful innovations are much lower. This leaves them too with two possible strategies. One is to revert to a strategy of adaptation, accepting the established hierarchy in the field and embarking on a slow advancement to gain the necessary capital to make their mark from within the established order. However, Bourdieu notes that sometimes agents with a relatively marginal position in the field will engage in a “flight forward” and pursue higher risk strategies. Strategies promoting a heterodox approach challenge the orthodoxy and the principles of hierarchization of the field, and, if successful (which will be the case only with a small probability), can rake in significant profits by laying claim to a new orthodoxy (Bourdieu 1975 , p. 104; Bourdieu 1993 , pp. 116–117).

Thus, the coupling of innovative strategies to specific field positions based on the amount of scientific capital alone is not straightforward. It is therefore helpful to introduce a second differentiation in the field that, following Bourdieu ( 1975 , p. 103), is based on the differences between the expected profits from these strategies. Here a distinction can be made between an autonomous and a heteronomous pole of the field, i.e., between the purest, most “disinterested” positions and the most “temporal” positions that are more pervious to the heteronomous logic of social hierarchies outside the scientific field. Of course, this difference is a matter of degree, as even the works produced at the most heteronomous positions still have to adhere to the standards of the scientific field to be seen as legitimate. But within each discipline this dimension captures the difference between agents predominantly engaged in fundamental, scholarly work—“production solely for the producers”—and agents more involved in applied lines of research. The main component of the expected profit from innovation in the first case is scientific, whereas in the second case the balance tends to shift towards more temporal profits. This two-fold structuring of the field allows for a more nuanced conception of innovation than the dichotomy “conservative” versus “radical.” Holders of large amounts of scientific capital at the autonomous pole of the field are the producers and conservators of orthodoxy, producing and diffusing what can be called “orthodox innovations” through their control of relatively powerful networks of consecration and circulation. Innovations can be radical or revolutionary in a rational sense, but they tend to originate from questions raised by the orthodoxy of the field. Likewise, the strategy to innovate in this sense can be very risky in that success is in no way guaranteed, but the risk is mitigated by the assurance of peers that these are legitimate questions, tackled in a way that is consistent with orthodoxy and that does not threaten control of the consecration and circulation networks.

These producers are seen as intellectual leaders by most agents in the field, especially by those aspiring to become part of the specific networks of production and circulation they maintain. The exception are the agents located at the autonomous end of the field who possess less scientific capital and outright reject this orthodoxy produced by the field’s elite. Being strictly focused on the most autonomous principles of legitimacy, they are unable to accommodate and have no choice but to reject the orthodoxy. Their only hope is to engage in heterodox innovations that may one day become the new orthodoxy.

The issue is less antagonistic at the heteronomous side of the field, at least as far as the irreconcilable position-takings at the autonomous pole are concerned. The main battle here is also for scientific capital, but is complemented by the legitimacy it brings to gain access to those who are in power outside of the scientific field. At the dominant side, those with more scientific capital tend to have access to the field of power, agents who hold the most economic and cultural capital, for example by holding positions in policy advisory committees or company boards. The dominated groups at this side of the field will cater more to practitioners or professionals outside of the field of science.

Overall, there will be fewer innovations on this side. Moreover, innovative strategies will be less concerned with the intricacies of the pure discussions that prevail at the autonomous pole and be of a more practical nature, but pursued from different degrees of legitimacy according to the differences in scientific capital. This affects the form these more practical, process-orientated innovations take. At the dominant side of this pole, agents tend to accept the outcome of the struggles at the autonomous pole: they will accept the orthodoxy because mastery of this provides them with scientific capital and the legitimacy they need to gain access to those in power. In contrast, agents at the dominated side will be more interested in doing “what works,” neutralizing the points of conflict at the autonomous pole and deriving less value from strictly following the orthodoxy. This way, a four-fold classification of innovative strategies in the scientific field emerges (see Fig.  2 ) that helps to understand the context in which MMR was developed.

Scientific field and scientific innovation

In summary, the small group of researchers who have been identified as the core of MMR consist predominantly of users of methods, who were educated and have worked exclusively at US and British universities. The specific approach to combining methods that is proposed by MMR has been successful from an institutional point of view, achieving visibility through the foundation of a journal and association and a considerable output of core MMR scholars in terms of books, conference proceedings, and journal articles. Its origins and circulation in vocational studies rather than classical academic disciplines can be understood from the position these studies occupy in the scientific field and the kinds of position-taking and innovations these positions give rise to. This context allows a reflexive understanding of the content of MMR and the issues that are dominant in the approach. We turn to this in the next section.

Mixed methods research: Position-taking

The position of the subfield of MMR in the scientific field is related to the position-takings of agents that form the core of this subfield (Bourdieu 1993 , p. 35). The space of position takings, in turn, provides the framework to study the most salient issues that are debated within the subfield. Since we can consider MMR to be an emerging subfield, where positions and position takings are not as clearly defined as in more mature and settled fields, it comes as no surprise that there is a lively discussion of fundamental matters. Out of the various topics that are actively discussed, we have distilled three themes that are important for the way the subfield of MMR conveys its autonomy as a field and as a distinct approach to research. Footnote 7 In our view, these also represent the main problems with the way MMR approaches the issue of combining methods.

Methodology making and standardization

The first topic is that the approach is moving towards defining a unified MMR methodology. There are differences in opinion as to how this is best achieved, but there is widespread agreement that some kind of common methodological and conceptual foundation of MMR is needed. To this end, some propose a broad methodology that can serve as distinct marker of MMR research. For instance, in their introduction to the handbook, Tashakkori and Teddlie ( 2010b ) propose a definition of the methodology of mixed methods research as “the broad inquiry logic that guides the selection of specific methods and that is informed by conceptual positions common to mixed methods practitioners” (Tashakkori and Teddlie 2010b , p. 5). When they (later on in the text) provide two methodological principles that differentiate MMR from other communities of scholars, they state that they regard it as a “crucial mission” for the MMR community to generate distinct methodological principles (Tashakkori and Teddlie 2010b , pp. 16–17). They envision an MMR methodology that can function as a “guide” for selecting specific methods. Others are more in favour of finding a philosophical foundation that underlies MMR. For instance, Morgan ( 2007 ) and Hesse-Biber ( 2010 ) consider pragmatism as a philosophy that distinguishes MMR from qualitative (constructivism) and quantitative (positivist) research and that can provide a rationale for the paradigmatic pluralism typical of MMR.

Furthermore, there is wide agreement that some unified definition of MMR would be beneficial, but it is precisely here that there is a large variation in interpretations regarding the essentials of MMR. This can be seen in the plethora of definitions that have been proposed. Johnson et al. ( 2007 ) identified 19 alternative definitions of MMR at the time, out of which they condensed their own:

[MMR] is the type of research in which a researcher or team of researchers combines elements of qualitative and quantitative research approaches (e.g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purpose of breath and depth of understanding and corroboration. Footnote 8

Four years later, the issue is not settled yet. Creswell and Plano Clark ( 2011 ) list a number of authors who have proposed a different definition of MMR, and conclude that there is a common trend in the content of these definitions over time. They take the view that earlier texts on mixing methods stressed a “disentanglement of methods and philosophy,” while later texts locate the practice of mixing methods in “all phases of the research process” (Creswell and Plano Clark 2011 , p. 2). It would seem, then, that according to these authors the definitions of MMR have become more abstract, further away from the practicality of “merely” combining methods. Specifically, researchers now seem to speak of mixing higher order concepts: some speak of mixing methodologies, others refer to mixing “research approaches,” or combining “types of research,” or engage in “multiple ways of seeing the social world” (Creswell and Plano Clark 2011 ).

This shift is in line with the direction in which MMR has developed and that emphasises practical ‘manuals’ and schemas for conducting research. A relatively large portion of the MMR literature is devoted to classifications of mixed methods designs. These classifications provide the basis for typologies that, in turn, provide guidelines to conduct MMR in a concrete research project. Tashakkori and Teddlie ( 2003 ) view these typologies as important elements of the organizational structure and legitimacy of the field. In addition, Leech and Onwuegbuzie ( 2009 ) see typologies as helpful guides for researchers and of pedagogical value (Leech and Onwuegbuzie 2009 , p. 272). Proposals for typologies can be found in textbooks, articles, and contributions to the handbook(s). For example, Creswell et al. ( 2003 , pp. 169-170) reviewed a number of studies and identified 8 different ways to classify MMR studies. This list was updated and extended by Creswell and Plano Clark ( 2011 , pp. 56-59) to 15 typologies. Leech and Onwuegbuzie ( 2009 ) identified 35 different research designs in the contributions to Teddlie and Tashakkori (2003) alone, and proposed their own three-dimensional typology that resulted in 8 different types of mixed methods studies. As another example of the ubiquity of these typologies, Nastasi et al. ( 2010 ) classified a large number of existing typologies in MMR into 7”meta-typologies” that each emphasize different aspects of the research process as important markers for MMR. According to the authors, these typologies have the same function in MMR as the more familiar names of “qualitative” or “quantitative” methods (e.g., “content analysis” or “structural equation modelling”) have: to signal readers of research what is going on, what procedures have been followed, how to interpret results, etc. (see also Creswell et al. 2003 , pp. 162–163). The criteria underlying these typologies mainly have to do with the degree of mixing (e.g., are methods mixed throughout the research project or not?), the timing (e.g., sequential or concurrent mixing of methods) and the emphasis (e.g., is one approach dominant, or do they have equal status?).

We find this strong drive to develop methodologies, definitions, and typologies of MMR as guides to valid mixed methods research problematic. What it amounts to in practice is a methodology that lays out the basic guidelines for doing MMR in a “proper way.” This entails the danger of straight-jacketing reflection about the use of methods, decoupling it from theoretical and empirical considerations, thus favouring the unreflexive use of a standard methodology. Researchers are asked to make a choice for a particular MMR design and adhere to the guidelines for a “proper” MMR study. Such methodological prescription diametrically opposes the initial critique of the mechanical and unreflexive use of methods. The insight offered by Bourdieu’s notion of reflexivity is, on the contrary, that the actual research practice is fundamentally open in terms of being guided by a logic of practice that cannot be captured by a preconceived and all-encompassing logic independent of that practice. Reflexivity in this view cannot be achieved by hiding behind the construct of a standardized methodology—of whatever signature—it can only be achieved by objectifying the process of objectification that goes on within the context of the field in which the researcher is embedded. This reflexivity, then, requires an analysis of the position of the researcher as a critical component of the research process, both as the embodiment of past choices that have consequences for the strategic position in the scientific field, and as predispositions regarding the choice for the subject and content of a research project. By adding the insight of STS researchers that the point of deconstructing science and technology is not so much to offer a new best way of doing science or technology, but to provide insights into the critical moments in research (for a take on such a debate, see, for example, Edge 1995 , pp. 16–20), this calls for a sociology of science that takes methods much more seriously as objects of study. Such a programme should be based on studying the process of codification and standardization of methods in their historical context of production, circulation, and use. It would provide a basis for a sociological understanding of methods that can illuminate the critical moments in research alluded to above, enabling a systematic reflection on the process of objectification. This, in turn, allows a more sophisticated validation of using—and combining—methods than relying on prescribed methodologies.

The role of epistemology

The second theme discussed in a large number of contributions is the role epistemology plays in MMR. In a sense, epistemology provides the lifeblood for MMR in that methods in MMR are mainly seen in epistemological terms. This interpretation of methods is at the core of the knowledge claim of MMR practitioners, i.e., that the mixing of methods means mixing broad, different ways of knowing, which leads to better knowledge of the research object. It is also part of the identity that MMR consciously assumes, and that serves to set it apart from previous, more practical attempts to combine methods. This can be seen in the historical overview that Creswell and Plano Clark ( 2011 ) presented and that was discussed above. This reading, in which combining methods has evolved from the rather unproblematic level (one could alternatively say “naïve” or “unaware”) of instrumental use of various tools and techniques into an act that requires deeper thinking on a methodological and epistemological level, provides the legitimacy of MMR.

At the core of the MMR approach we thus find that methods are seen as unproblematic representations of different epistemologies. But this leads to a paradox, since the epistemological frameworks need to be held flexible enough to allow researchers to integrate elements of each of them (in the shape of methods) into one MMR design. As a consequence, the issue becomes the following: methods need to be disengaged from too strict an interpretation of the epistemological context in which they were developed in order for them to be “mixable,”’, but, at the same time, they must keep the epistemology attributed to them firmly intact.

In the MMR discourse two epistemological positions are identified that matter most: a positivist approach that gives rise to quantitative methods and a constructivist approach that is home to qualitative methods. For MMR to be a feasible endeavour, the differences between both forms of research must be defined as reconcilable. This position necessitates an engagement with those who hold that the quantitative/qualitative dichotomy is unbridgeable. Within MMR an interesting way of doing so has emerged. In the first issue of the Journal of Mixed Methods Research, Morgan ( 2007 ) frames the debate about research methodology in the social sciences in terms of Kuhnian paradigms, and he argues that the pioneers of the emancipation of qualitative research methods used a particular interpretation of the paradigm-concept to state their case against the then dominant paradigm in the social sciences. According to Morgan, they interpreted a paradigm mainly in metaphysical terms, stressing the connections among the trinity of ontology, epistemology, and methodology as used in the philosophy of knowledge (Morgan 2007 , p. 57). This allowed these scholars to depict the line between research traditions in stark, contrasting terms, using Kuhn’s idea of “incommensurability” in the sense of its “early Kuhn” interpretation. This strategy fixed the contrast between the proposed alternative approach (a “constructivist paradigm”), and the traditional approach (constructed as “the positivist paradigm”) to research as a whole, and offered the alternative approach as a valid option rooted in the philosophy of knowledge. Morgan focuses especially on the work of Egon Guba and Yvonne Lincoln who developed what they initially termed a “naturalistic paradigm” as an alternative to their perception of positivism in the social sciences (e.g., Guba and Lincoln 1985 ). Footnote 9 MMR requires a more flexible or “a-paradigmatic stance” towards research, which would entail that “in real-world practice, methods can be separated from the epistemology out of which they emerged” (Patton 2002 , quoted in Tashakkori and Teddlie 2010b , p. 14).

This proposal of an ‘interpretative flexibility’ (Bijker 1987 , 1997 ) regarding paradigms is an interesting proposition. But it immediately raises the question: why stop there? Why not take a deeper look into the epistemological technology of methods themselves, to let the muted components speak up in order to look for alternative “mixing interfaces” that could potentially provide equally valid benefits in terms of the understanding of a research object? The answer, of course, was already seen above. It is that the MMR approach requires situating methods epistemologically in order to keep them intact as unproblematic mediators of specific epistemologies and, thus, make the methodological prescriptions work. There are several problems with this. First, seeing methods solely through an epistemological lens is problematic, but it would be less consequential if it were applied to multiple elements of methods separately. This would at least allow a look under the hood of a method, and new ways of mixing methods could be opened up that go beyond the crude “qualitative” versus “quantitative” dichotomy. Second, there is also the issue of the ontological dimension of methods that is disregarded in an exclusively epistemological framing of methods (e.g., Law 2004 ). Taking this ontological dimension seriously has at least two important facets. First, it draws attention to the ontological assumptions that are woven into methods in their respective fields of production and that are imported into fields of users. Second, it entails the ontological consequences of practising methods: using, applying, and referring to methods and the realities this produces. This latter facet brings the world-making and boundary-drawing capacities of methods to the fore. Both facets are ignored in MMR. We say more about the first facet in the next section. With regard to the second facet, a crucial element concerns the data that are generated, collected, and analysed in a research project. But rather than problematizing the link between the performativity of methods and the data that are enacted within the frame of a method, here too MMR relies on a dichotomy: that between quantitative and qualitative data. Methods are primarily viewed as ways of gathering data or as analytic techniques dealing with a specific kind of data. Methods and data are conceptualised intertwiningly: methods too are seen as either quantitative or qualitative (often written as QUANT and QUAL in the literature), and perform the role of linking epistemology and data. In the final analysis, the MMR approach is based on the epistemological legitimization of the dichotomy between qualitative and quantitative data in order to define and combine methods: data obtain epistemological currency through the supposed in-severable link to certain methods, and methods are reduced to the role of acting as neutral mediators between them.

In this way, methods are effectively reduced to, on the one hand, placeholders for epistemological paradigms and, on the other hand, mediators between one kind of data and the appropriate epistemology. To put it bluntly, the name “mixed methods research” is actually a misnomer, because what is mixed are paradigms or “approaches,” not methods. Thus, the act of mixing methods à la MMR has the paradoxical effect of encouraging a crude black box approach to methods. This is a third problematic characteristic of MMR, because it hinders a detailed study of methods that can lead to a much richer perspective on mixing methods.

Black boxed methods and how to open them

The third problem that we identified with the MMR approach, then, is that with the impetus to standardize the MMR methodology by fixing methods epistemologically, complemented by a dichotomous view of data, they are, in the words of philosopher Bruno Latour, “blackboxed.” This is a peculiar result of the prescription for mixing methods as proposed by MMR that thus not only denies practice and the ontological dimensions of methods and data, but also casts methods in the role of unyielding black boxes. Footnote 10 With this in mind, it will come as no surprise that most foundational contributions to the MMR literature do not explicitly define what a method is, nor that they do not provide an elaborative historical account of individual methods. The particular framing of methods in MMR results in a blind spot for the historical and social context of the production and circulation of methods as intellectual products. Instead it chooses to reify the boundaries that are drawn between “qualitative” and “quantitative” methods and reproduce them in the methodology it proposes. Footnote 11 This is an example of “circulation without context” (Bourdieu 2002 , p. 4): classifications that are constructed in the field of use or reception without taking the constellation within the field of production seriously.

Of course, this does not mean that the reality of the differences between quantitative and qualitative research must be denied. These labels are sticky and symbolically laden. They have come, in many ways, to represent “two cultures” (Goertz and Mahony 2012 ) of research, institutionalised in academia, and the effects of nominally “belonging” to (or being assigned to) one particular category have very real consequences in terms of, for instance, access to research grants and specific journals. However, if the goal of an approach such as MMR is to open up new pathways in social science research, (and why should that not be the case?) it is hard to see how that is accomplished by defining the act of combining methods solely in terms of reified differences between research using qualitative and quantitative data. In our view, methods are far richer and more interesting constructs than that, and a practice of combining methods in research should reflect that. Footnote 12

Addressing these problems entices a reflection on methods and using (multiple) methods that is missing in the MMR perspective. A fruitful way to open up the black boxes and take into account the epistemological and ontological facets of methods is to make them, and their use, the object of sociological-historical investigation. Methods are constituted through particular practices. In Bourdieusian terms, they are objectifications of the subjectively understood practices of scientists “in other fields.” Rather than basing a practice of combining methods on an uncritical acceptance of the historically grown classification of types of social research (and using these as the building stones of a methodology of mixing methods), we propose the development of a multifaceted approach that is based on a study of the different socio-historical contexts and practices in which methods developed and circulated.

A sociological understanding of methods based on these premises provides the tools to break with the dichotomously designed interface for combining methods in MMR. Instead, focusing on the historical and social contexts of production and use can reveal the traces that these contexts leave, both in the internal structure of methods, how they are perceived, how they are put into practice, and how this practice informs the ontological effects of methods. Seeing methods as complex technologies, with a history that entails the struggles among the different agents involved in their production, and use opens the way to identify multiple interfaces for combining them: the one-sided boxes become polyhedra. The critical study of methods as “objects of objectification” also entices analyses of the way in which methods intervene between subject (researcher) and object and the way in which different methods are employed in practice to draw this boundary differently. The reflexive position generated by such a systematic juxtaposition of methods is a fruitful basis to come to a richer perspective on combining methods.

We critically reviewed the emerging practice of combining methods under the label of MMR. MMR challenges the mono-method approaches that are still dominant in the social sciences, and this is both refreshing and important. Combining methods should indeed be taken much more seriously in the social sciences.

However, the direction that the practice of combining methods is taking under the MMR approach seems problematic to us. We identified three main concerns. First, MMR scholars seem to be committed to designing a standardized methodological framework for combining methods. This is unfortunate, since it amounts to enforcing an unnecessary codification of aspects of research practices that should not be formally standardized. Second, MMR constructs methods as unproblematic representations of an epistemology. Although methods must be separable from their native epistemology for MMR to work, at the same time they have to be nested within a qualitative or a quantitative research approach, which are characterized by the data they use. By this logic, combining quantitative methods with other quantitative methods, or qualitative methods with other qualitative methods, cannot offer the same benefits: they originate from the same way of viewing and knowing the world, so it would have the same effect as blending two gradations of the same colour paint. The importance attached to the epistemological grounding of methods and data in MMR also disregards the ontological aspects of methods. In this article, we are arguing that this one-sided perspective is problematic. Seeing combining methods as equivalent to combining epistemologies that are somehow pure and internally homogeneous because they can be placed in a qualitative or quantitative framework essentially amounts to reifying these categories.

It also leads to the third problem: the black boxing of methods as neutral mediators between these epistemologies and data. This not only constitutes a problem for trying to understand methods as intellectual products, but also for regarding the practice of combining methods, because it ignores the social-historical context of the development of individual methods and hinders a sociologically grounded notion of combining methods.

We proceed from a different perspective on methods. In our view, methods are complex constructions. They are world-making technologies that encapsulate different assumptions on causality, rely on different conceptual relations and categorizations, allow for different degrees of emergence, and employ different theories of the data that they internalise as objects of analysis. Even more importantly, their current form as intellectual products cannot be separated from the historical context of their production, circulation, and use.

A fully developed exposition of such an approach will have to await further work. Footnote 13 So far, the sociological study of methods has not (yet) developed into a consistent research programme, but important elements can be derived from existing contributions such as MacKenzie ( 1981 ), Chapoulie ( 1984 ), Platt ( 1996 ), Freeman ( 2004 ), and Desrosières ( 2008a , b ). The work on the “social life of methods” (e.g., Savage 2013 ) also contains important leads for the development of a systematic sociological approach to method production and circulation. Based on the discussion in this article and the contributions listed above, some tantalizing questions can be formulated. How are methods and their elements objectified? How are epistemology and ontology defined in different fields and how do those definitions feed into methods? How do they circulate and how are they translated and used in different contexts? What are the main controversies in fields of users and how are these related to the field of production? What are the homologies between these fields?

Setting out to answer these questions opens up the possibility of exploring other interesting combinations of methods that emerge from the combination of different practices, situated in different historical and epistemological contexts, and with their unique set of interpretations regarding their constituent elements. One of these must surely be the data-theoretical elements that different methods incorporate. The problematization of data has become all the more pressing now that the debate about the consequences of “big data” for social scientific practices has become prominent (Savage and Burrows 2007 ; Levallois et al. 2013 ; Burrows and Savage 2014 ). Whereas MMR emphasizes the dichotomy between qualitative and quantitative data, a historical analysis of the production and use of methods can explore the more subtle, different interpretations and enactments of the “same” data. These differences inform method construction, controversies surrounding methods and, hence, opportunities for combining methods. These could then be constructed based on alternative conceptualisations of data. Again, while in some contexts it might be enlightening to rely on the distinction between data as qualitative or quantitative, and to combine methods based on this categorization, it is an exciting possibility that in other research contexts other conceptualisations of data might be of more value to enhance a specific (contextual) form of knowledge.

Change history

06 may 2019.

Unfortunately, figure 2 was incorrectly published.

The search term used was “mixed method*” in the “topic” search field of SSCI, A&HCI, and CPCI-SSH as contained in the Web of Science. A Google NGram search (not shown) confirmed this pattern. The results of a search for “mixed methods” and “mixed methods research” showed a very steep increase after 1994: in the first case, the normalized share in the total corpus increased by 855% from 1994 till 2008. Also, Creswell ( 2012 ) reports an almost hundred-fold increase in the number of theses and dissertations with mixed methods’ in the citation and abstract (from 26 in 1990–1994 to 2524 in 2005–2009).

Retrieved from https://uk.sagepub.com/en-gb/eur/journal-of-mixed-methods-research/journal201775#aims-and-scope on 1/17/2019.

In terms of antecedents of mixed methods research, it is interesting to note that Bourdieu, whose sociology of science we draw on, was, from his earliest studies in Algeria onwards, a strong advocate of combining research methods. He made it into a central characteristic of his approach to social science in Bourdieu et al. ( 1991 [1968]). His approach, as we see below, was very different from the one now proposed under the banner of MMR. Significantly, there is no mention of Bourdieu’s take on combining methods in any of the sources we studied.

Morse’s example in particular warns us that restricting the analysis to the authors that have published in the JMMR runs the risk of missing some important contributors to the spread of MMR through the social sciences. On her website, Morse lists 11 publications (journal articles, book chapters, and books) that explicitly make reference to mixed methods (and a substantial number of other publications are about methodological aspects of research), so the fact that she has not (yet) published in the JMMR cannot, by itself, be taken as an indication of a lesser involvement with the practice of combining methods. See the website of Janice Morse at https://faculty.utah.edu/u0556920-Janice_Morse_RN,_PhD,_FAAN/hm/index.hml accessed 1/17/2019.

Bourdieu ( 1999 , p. 26) mentions that one has to be a scientific capitalist to be able to start a scientific revolution. But here he refers explicitly to the autonomy of the scientific field, making it virtually impossible for amateurs to stand up against the historically accumulated capital in the field and incite a revolution.

The themes summarize the key issues through which MMR as a group comes “into difference” (Bourdieu 1993 , p. 32). Of course, as in any (sub)field, the agents identified above often differ in their opinions on some of these key issues or disagree on the answer to the question if there should be a high degree of convergence of opinions at all. For instance, Bryman ( 2009 ) worried that MMR could become “a ghetto.” For him, the institutional landmarks of having a journal, conferences, and a handbook increase the risk of “not considering the whole range of possibilities.” He added: “I don’t regard it as a field, I kind of think of it as a way of thinking about how you go about research.” (Bryman, cited in Leech 2010 , p. 261). It is interesting to note that Bryman, like fellow sociologists Morgan and Denscombe, had published only one paper in the JMMR by the end of 2016 (Bryman passed away in June of 2017). Although these papers are among the most cited papers in the journal (see Table 1 ), this low number is consistent with the more eclectic approach that Bryman proposed.

Johnson, Onwuegbuzie, and Turner ( 2007 , p. 123).

Guba and Lincoln ( 1985 ) discuss the features of their version of a positivistic approach mainly in ontological and epistemological terms, but they are also careful to distinguish the opposition between naturalistic and positivist approaches from the difference between what they call the quantitative and the qualitative paradigms. Since they go on to state that, in principle, quantitative methods can be used within a naturalistic approach (although in practice, qualitative methods would be preferred by researchers embracing this paradigm), they seem to locate methods on a somewhat “lower,” i.e., less incommensurable level. However, in their later work (both together as well as with others or individually) and that of others in their wake, there seems to have been a shift towards a stricter interpretation of the qualitative/quantitative divide in metaphysical terms, enabling Teddlie and Tashakkori (2010b) to label this group “purists” (Tashakkori and Teddlie 2010b , p. 13).

See, for instance, Onwuegbuzie et al.’s ( 2011 ) classification of 58 qualitative data analysis techniques and 18 quantitative data analysis techniques.

This can also be seen in Morgan’s ( 2018 ) response to Sandelowski’s ( 2014 ) critique of the binary distinctions in MMR between qualitative and quantitative research approaches and methods. Morgan denounces the essentialist approach to categorizing qualitative and quantitative research in favor of a categorization based on “family resemblances,” in which he draws on Wittgenstein. However, this denies the fact that the essentialist way of categorizing is very common in the MMR corpus, particularly in textbooks and manuals (e.g., Plano Clark and Ivankova 2016 ). Moreover, and more importantly, he still does not extend this non-essentialist model of categorization to the level of methods, referring, for instance, to the different strengths of qualitative and quantitative methods in mixed methods studies (Morgan 2018 , p. 276).

While it goes beyond the scope of this article to delve into the history of the qualitative-quantitative divide in the social sciences, some broad observations can be made here. The history of method use in the social sciences can briefly be summarized as first, a rather fluid use of what can retrospectively be called different methods in large scale research projects—such as the Yankee City study of Lloyd Warner and his associates (see Platt 1996 , p. 102), the study on union democracy of Lipset et al. ( 1956 ), and the Marienthal study by Lazarsfeld and his associates (Jahoda et al. 1933 ); see Brewer and Hunter ( 2006 , p. xvi)—followed by an increasing emphasis on quantitative data and the objectification and standardization of methods. The rise of research using qualitative data can be understood as a reaction against this use and interpretation of method in the social sciences. However, out of the ensuing clash a new, still dominant classification of methods emerged, one that relies on the framing of methods as either “qualitative” or “quantitative.” Moreover, these labels have become synonymous with epistemological positions that are reproduced in MMR.

A proposal to come to such an approach can be found in Timans ( 2015 ).

Bijker, W. (1987). The social construction of bakelite: Toward a theory of invention. In W. Bijker, T. Hughes, T. Pinch, & D. Douglas (Eds.), The social construction of technological systems: New directions in the sociology and history of technology . Cambridge, MA: MIT press.

Google Scholar  

Bijker, W. (1997). Of bicycles, bakelites, and bulbs: Toward a theory of sociotechnical change . Cambridge, MA: MIT Press.

Bourdieu, P. (1975). La spécifité du champ scientifique et les conditions sociales du progrès de la raison. Sociologie et Sociétés, 7 (1), 91–118.

Article   Google Scholar  

Bourdieu, P. (1988). Homo academicus . Stanford, CA: Stanford University Press.

Bourdieu, P. (1993). The field of cultural production . Cambridge, UK: Polity Press.

Bourdieu, P. (1999). Les historiens et la sociologie de Pierre Bourdieu. Le Bulletin de la Société d'Histoire Moderne et Contemporaine/SHMC, 1999 (3&4), 4–27.

Bourdieu, P. (2002). Les conditions sociales de la circulation internationale des idées. Actes de la Recherche en Sciences Sociales, 145 (5), 3–8.

Bourdieu, P. (2004). Science of science and reflexivity . Cambridge, UK: Polity.

Bourdieu, P. (2007). Sketch for a self-analysis . Cambridge, UK: Polity.

Bourdieu, P., Chamboredon, J., & Passeron, J. (1991). The craft of sociology: Epistemological preliminaries . Berlin, Germany: De Gruyter.

Book   Google Scholar  

Brewer, J., & Hunter, A. (2006). Multimethod research: A synthesis of styles . London, UK: Sage.

Bryman, A. (2009). Sage Methodspace: Alan Bryman on research methods. Retrieved from http://www.youtube.com/watch?v=bHzM9RlO6j0 . Accessed 3/7/2019.

Bryman, A. (2012). Social research methods . Oxford, UK: Oxford University Press.

Burrows, R., & Savage, M. (2014). After the crisis? Big data and the methodological challenges of empirical sociology. Big Data & Society, 1 (1), 1–6.

Campbell, D., & Fiske, D. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56 (2), 81–105.

Chapoulie, J. (1984). Everett C. Hughes et le développement du travail de terrain en sociologie. Revue Française de Sociologie, 25 (4), 582–608.

Collins, R. (1998). The sociology of philosophies: A global theory of intellectual change . Cambridge, MA: Harvard University Press.

Creswell, J. (2008). Research design: Qualitative, quantitative, and mixed methods approaches . Thousand Oaks, CA: Sage.

Creswell, J. (2011). Controversies in mixed methods research. In N. Denzin & Y. Lincoln (Eds.), The Sage handbook of qualitative research . Thousand Oaks, CA: Sage.

Creswell, J. (2012). Qualitative inquiry and research design: Choosing among five approaches . Thousand Oaks, CA: Sage.

Creswell, J., & Plano Clark, V. (2011). Designing and conducting mixed methods research (2nd ed.). Thousand Oaks, CA: Sage.

Creswell, J., Plano Clark, V., Gutmann, M., & Hanson, W. (2003). Advanced mixed methods research designs. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research . Thousand Oaks, CA: Sage.

Denscombe, M. (2008). Communities of practice a research paradigm for the mixed methods approach. Journal of Mixed Methods Research, 2 (3), 270–283.

Desrosières, A. (2008a). Pour une sociologie historique de la quantification - L’Argument statistique I . Paris, France: Presses des Mines.

Desrosières, A. (2008b). Gouverner par les nombres - L’Argument statistique II . Paris, France: Presses des Mines.

Edge, D. (1995). Reinventing the wheel. In D. Edge, S. Jasanof, G. Markle, J. Petersen, & T. Pinch (Eds.), Handbook of science and technology studies . Thousand Oaks, CA: Sage.

Fielding, N. (2012). Triangulation and mixed methods designs data integration with new research technologies. Journal of Mixed Methods Research, 6 (2), 124–136.

Fielding, N., & Cisneros-Puebla, C. (2009). CAQDAS-GIS convergence: Toward a new integrated mixed method research practice? Journal of Mixed Methods Research, 3 (4), 349–370.

Fleck, C., Heilbron, J., Karady, V., & Sapiro, G. (2016). Handbook of indicators of institutionalization of academic disciplines in SSH. Serendipities, Journal for the Sociology and History of the Social Sciences, 1 (1) Retrieved from http://serendipities.uni-graz.at/index.php/serendipities/issue/view/1 . Accessed 10/10/2018.

Freeman, L. (2004). The development of social network analysis: A study in the sociology of science . Vancouver, Canada: Empirical Press.

Gingras, Y., & Gemme, B. (2006). L’Emprise du champ scientifique sur le champ universitaire et ses effets. Actes de la Recherche en Sciences Sociales, 164 , 51–60.

Goertz, G., & Mahony, J. (2012). A tale of two cultures: Qualitative and quantitative research in the social sciences . Princeton: Princeton University Press.

Greene, J. (2008). Is mixed methods social inquiry a distinctive methodology? Journal of Mixed Methods Research, 2 (1), 7–22.

Guba, E., & Lincoln, Y. (1985). Naturalistic inquiry . Thousand Oaks, CA: Sage.

Heilbron, J., Bedecarré, M., & Timans, R. (2017). European journals in the social sciences and humanities. Serendipities, Journal for the Sociology and History of the Social Sciences, 2 (1), 33–49 Retrieved from http://serendipities.uni-graz.at/index.php/serendipities/issue/view/5 . Accessed 10/10/2018.

Hesse-Biber, S. (2010). Mixed methods research: Merging theory with practice . New York, NY: Guilford Press.

Jahoda, M., Lazarsfeld, P., & Zeisel, H. (1933). Die Arbeitslosen von Marienthal. Psychologische Monographen, 5 .

Johnson, R., & Gray, R. (2010). A history of philosophical and theoretical issues for mixed methods research. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (2nd ed.). Thousand Oaks, CA: Sage.

Johnson, R., Onwuegbuzie, A., & Turner, L. (2007). Toward a definition of mixed methods research. Journal of Mixed Methods Research, 1 (2), 112–133.

Law, J. (2004). After method: Mess in social science research . London, UK: Routledge.

Leech, N. (2010). Interviews with the early developers of mixed methods research. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (2nd ed.). Thousand Oaks, CA: Sage.

Leech, N., & Onwuegbuzie, T. (2009). A typology of mixed methods research designs. Quality & Quantity, 43 (2), 265–275.

Levallois, C., Steinmetz, S., & Wouters, P. (2013). Sloppy data floods or precise social science methodologies? In P. Wouters, A. Beaulieu, A. Scharnhorst, & S. Wyatt (Eds.), Virtual knowledge . Cambridge, MA: MIT Press.

Lipset, S., Trow, M., & Coleman, J. (1956). Union democracy: The internal politics of the international typographical union . Glencoe, UK: Free Press.

MacKenzie, D. (1981). Statistics in Britain: 1865–1930: The social construction of scientific knowledge . Edinburgh, UK: Edinburgh University Press.

Morgan, D. (2007). Paradigms lost and pragmatism regained: Methodological implications of combining qualitative and quantitative methods. Journal of Mixed Methods Research, 1 (1), 48–76.

Morgan, D. (2018). Living with blurry boundaries: The values of distinguishing between qualitative and quantitative research. Journal of Mixed Methods Research, 12 (3), 268–276.

Mullins, N. (1973). Theories and theory groups in contemporary American sociology . New York, NY: Harper & Row.

Nagy Hesse-Biber, S., & Leavy, P. (Eds.). (2008). Handbook of emergent methods . New York, NY and London, UK: Guilford Press.

Nastasi, B., Hitchcock, J., & Brown, L. (2010). An inclusive framework for conceptualizing mixed method design typologies. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (2nd ed.). Thousand Oaks, CA: Sage.

Onwuegbuzie, A., Leech, N., & Collins, K. (2011). Toward a new era for conducting mixed analyses: The role of quantitative dominant and qualitative dominant crossover mixed analyses. In M. Williams & P. Vogt (Eds.), Handbook of innovation in social research methods . London, UK: Sage.

Patton, M. (2002). Qualitative research and evaluation methods (3rd ed.). Thousand Oaks, CA: Sage.

Plano Clark, V., & Creswell, J. (Eds.). (2007). The mixed methods reader . Thousand Oaks, CA: Sage.

Plano Clark, V., & Ivankova, N. (2016). Mixed methods research: A guide to the field . Thousand Oaks, CA: Sage.

Platt, J. (1996). A history of sociological research methods in America: 1920–1960 . Cambridge, UK: Cambridge University Press.

Plowright, D. (2010). Using mixed methods – Frameworks for an integrated methodology . Thousand Oaks, CA: Sage.

Sandelowski, M. (2014). Unmixing mixed methods research. Research in Nursing & Health, 37 (1), 3–8.

Sapiro, G., Brun, E., & Fordant, C. (2018). The rise of the social sciences and humanities in France: Iinstitutionalization, professionalization and autonomization. In C. Fleck, M. Duller, & V. Karady (Eds.), Shaping human science disciplines: Institutional developments in Europe and beyond . Basingstoke, UK: Palgrave.

Savage, M. (2013). The ‘social life of methods’: A critical introduction. Theory, Culture and Society, 30 (4), 3–21.

Savage, M., & Burrows, R. (2007). The coming crisis of empirical sociology. Sociology, 41 (5), 885–899.

Steinmetz, G. (2016). Social fields, subfields and social spaces at the scale of empires: Explaining the colonial state and colonial sociology. The Sociological Review, 64(2_suppl). 98-123.

Tashakkori, A., & Teddlie, C. (Eds.). (2003). Handbook of mixed methods in social and behavioral research . Thousand Oaks, CA: Sage.

Tashakkori, A., & Teddlie, C. (Eds.). (2010a). Handbook of mixed methods in social and behavioral research (2nd ed.). Thousand Oaks, CA: Sage.

Tashakkori, A., & Teddlie, C. (2010b). Overview of contemporary issues in mixed methods. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (2nd ed.). Thousand Oaks, CA: Sage.

Chapter   Google Scholar  

Tashakkori, A., & Teddlie, C. (2010c). Epilogue. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (2nd ed.). Thousand Oaks, CA: Sage.

Tashakkori, A., & Teddlie, C. (2011). Mixed methods research: Contemporary issues in an emerging field. In N. Denzin & Y. Lincoln (Eds.), The SAGE handbook of qualitative research (4th ed.). Thousand Oaks, CA: Sage.

Teddlie, C., & Tashakkori, A. (2008). Foundations of mixed methods research – Integrating quantitative and qualitative approaches in the social and behavioral sciences . Thousand Oaks, CA: Sage.

Timans, R. (2015). Studying the Dutch business elite: Relational concepts and methods . Doctoral dissertation, Erasmus University Rotterdam, the Netherlands.

Wacquant, L. (2013). Bourdieu 1993: A case study in scientific consecration. Sociology, 47 (1), 15–29.

Download references

Acknowledgments

This research is part of the Interco-SSH project, funded by the European Union under the 7th Research Framework Programme (grant agreement no. 319974). Johan Heilbron would like to thank Louise and John Steffens, members of the Friends Founders’ Circle, who assisted his stay at the Princeton Institute for Advanced Study in 2017-18 during which he completed his part of the present article.

Author information

Authors and affiliations.

Erasmus Centre for Economic Sociology (ECES), Erasmus University Rotterdam, Rotterdam, Netherlands

Centre for Science and Technology Studies (CWTS), Leiden University, Leiden, Netherlands

Paul Wouters

Erasmus Centre for Economic Sociology (ECES), Rotterdam and Centre européen de sociologie et de science politique de la Sorbonne (CESSP-CNRS-EHESS), Paris, France

Johan Heilbron

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Rob Timans .

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Timans, R., Wouters, P. & Heilbron, J. Mixed methods research: what it is and what it could be. Theor Soc 48 , 193–216 (2019). https://doi.org/10.1007/s11186-019-09345-5

Download citation

Published : 29 March 2019

Issue Date : 01 April 2019

DOI : https://doi.org/10.1007/s11186-019-09345-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Data, field analysis
• Mixed methods research
• Multiple methods
• Reflexivity
• Sociology of science
• Find a journal
• Publish with us
• Track your 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.

APS regularly opens certain online articles for discussion on our website. Effective February 2021, you must be a logged-in APS member to post comments. By posting a comment, you agree to our Community Guidelines and the display of your profile information, including your name and affiliation. Any opinions, findings, conclusions, or recommendations present in article comments are those of the writers and do not necessarily reflect the views of APS or the article’s author. For more information, please see our Community Guidelines .

Please login with your APS account to comment.

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].

Research Briefs

Recent highlights from APS journals articles on learning about working memory, psychological measurement, patterns of implicit and explicit attitudes, and much more.

Careers Up Close: Jolynn Pek on Quantifying Uncertainty

Pek analyzes sources of uncertainty in statistical results and develops new approaches to practicing replicable psychological science.

Speaking Evidence to Policy

The Commission on Evidence-Based Policymaking has issued a report that points to significant new opportunities in research and leadership for psychological scientists with expertise in areas including judgment and decision making, analysis and research design, data privacy and ethics issues, and more.

Privacy Overview

The Plagiarism Checker Online For Your Academic Work

Start Plagiarism Check

Editing & Proofreading for Your Research Paper

Get it proofread now

Online Printing & Binding with Free Express Delivery

Configure binding now

• Academic essay overview
• The writing process
• Structuring academic essays
• Types of academic essays
• Academic writing overview
• Sentence structure
• Academic writing process
• Improving your academic writing
• Titles and headings
• APA style overview
• APA citation & referencing
• APA structure & sections
• Citation & referencing
• Structure and sections
• APA examples overview
• Commonly used citations
• Other examples
• British English vs. American English
• Chicago style overview
• Chicago citation & referencing
• Chicago structure & sections
• Chicago style examples
• Citing sources overview
• Citation format
• Citation examples
• College essay overview
• Application
• How to write a college essay
• Types of college essays
• Commonly confused words
• Definitions
• Dissertation overview
• Dissertation structure & sections
• Dissertation writing process
• Graduate school overview
• Application & admission
• Study abroad
• Master degree
• Harvard referencing overview
• Language rules overview
• Grammatical rules & structures
• Parts of speech
• Punctuation
• Methodology overview
• Analyzing data
• Experiments
• Observations
• Inductive vs. Deductive
• Qualitative vs. Quantitative
• Types of validity
• Types of reliability
• Sampling methods
• Theories & Concepts
• Types of research studies
• Types of variables
• MLA style overview
• MLA examples
• MLA citation & referencing
• MLA structure & sections
• Plagiarism overview
• Plagiarism checker
• Types of plagiarism
• Printing production overview
• Research bias overview
• Types of research bias
• Example sections
• Types of research papers
• Research process overview
• Problem statement
• Research proposal
• Research topic
• Statistics overview
• Levels of measurment
• Frequency distribution
• Measures of central tendency
• Measures of variability
• Hypothesis testing
• Parameters & test statistics
• Types of distributions
• Correlation
• Effect size
• Hypothesis testing assumptions
• Types of ANOVAs
• Types of chi-square
• Statistical data
• Statistical models
• Spelling mistakes
• Tips overview
• Academic writing tips
• Dissertation tips
• Sources tips
• Working with sources overview
• Evaluating sources
• Finding sources
• Including sources
• Types of sources

Your Step to Success

Plagiarism Check within 10min

Printing & Binding with 3D Live Preview

Mixed Methods Research – Different Types & Examples

How do you like this article cancel reply.

Save my name, email, and website in this browser for the next time I comment.

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.

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.

We use cookies on our website. Some of them are essential, while others help us to improve this website and your experience.

• External Media

Individual Privacy Preferences

Cookie Details Privacy Policy Imprint

Here you will find an overview of all cookies used. You can give your consent to whole categories or display further information and select certain cookies.

Accept all Save

Essential cookies enable basic functions and are necessary for the proper function of the website.

Show Cookie Information Hide Cookie Information

Statistics cookies collect information anonymously. This information helps us to understand how our visitors use our website.

Content from video platforms and social media platforms is blocked by default. If External Media cookies are accepted, access to those contents no longer requires manual consent.

Privacy Policy Imprint

Log in using your username and password

• Search More Search for this keyword Advanced search
• Latest content
• Current issue
• Write for Us
• BMJ Journals More You are viewing from: Google Indexer

You are here

• Volume 20, Issue 3
• Mixed methods research: expanding the evidence base
• Article Text
• Article info
• Citation Tools
• Rapid Responses
• Article metrics

• 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

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

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.

Read the full text or download the PDF:

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Mixed Methods Research | Definition, Guide, & Examples

Mixed Methods Research | Definition, Guide, & Examples

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

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

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

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

Table of contents

When to use mixed methods research, mixed methods research designs, benefits of mixed methods research, disadvantages of mixed methods research, frequently asked questions about mixed methods research.

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

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

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

Keep in mind that mixed methods research doesn’t just mean collecting both types of data; you need to carefully consider the relationship between the two and how you’ll integrate them into coherent conclusions. Mixed methods can be very challenging to put into practice, so it’s a less common choice than standalone qualitative or qualitative research.

Prevent plagiarism, run a free check.

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

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

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

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

Convergent parallel

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

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

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

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

Explanatory sequential

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

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

Exploratory sequential

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

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

‘Best of both worlds’ analysis

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

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

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

Method flexibility

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

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

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

Differing or conflicting results

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

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

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

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

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

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

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

These are four of the most common mixed methods designs :

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

George, T. (2022, October 25). Mixed Methods Research | Definition, Guide, & Examples. Scribbr. Retrieved 20 March 2024, from https://www.scribbr.co.uk/research-methods/mixed-methods/

Is this article helpful?

Tegan George

• Research article
• Open access
• Published: 24 September 2018

A mixed methods case study exploring the impact of membership of a multi-activity, multicentre community group on social wellbeing of older adults

• Gabrielle Lindsay-Smith   ORCID: orcid.org/0000-0003-3864-1412 1 ,
• Grant O’Sullivan 1 ,
• Rochelle Eime 1 , 2 ,
• Jack Harvey 1 , 2 &
• Jannique G. Z. van Uffelen 1 , 3  

BMC Geriatrics volume  18 , Article number:  226 ( 2018 ) Cite this article

69k Accesses

28 Citations

12 Altmetric

Metrics details

Social wellbeing factors such as loneliness and social support have a major impact on the health of older adults and can contribute to physical and mental wellbeing. However, with increasing age, social contacts and social support typically decrease and levels of loneliness increase. Group social engagement appears to have additional benefits for the health of older adults compared to socialising individually with friends and family, but further research is required to confirm whether group activities can be beneficial for the social wellbeing of older adults.

This one-year longitudinal mixed methods study investigated the effect of joining a community group, offering a range of social and physical activities, on social wellbeing of adults with a mean age of 70. The study combined a quantitative survey assessing loneliness and social support ( n  = 28; three time-points, analysed using linear mixed models) and a qualitative focus group study ( n  = 11, analysed using thematic analysis) of members from Life Activities Clubs Victoria, Australia.

There was a significant reduction in loneliness ( p  = 0.023) and a trend toward an increase in social support ( p  = 0.056) in the first year after joining. The focus group confirmed these observations and suggested that social support may take longer than 1 year to develop. Focus groups also identified that group membership provided important opportunities for developing new and diverse social connections through shared interest and experience. These connections were key in improving the social wellbeing of members, especially in their sense of feeling supported or connected and less lonely. Participants agreed that increasing connections was especially beneficial following significant life events such as retirement, moving to a new house or partners becoming unwell.

Conclusions

Becoming a member of a community group offering social and physical activities may improve social wellbeing in older adults, especially following significant life events such as retirement or moving-house, where social network changes. These results indicate that ageing policy and strategies would benefit from encouraging long-term participation in social groups to assist in adapting to changes that occur in later life and optimise healthy ageing.

Peer Review reports

Ageing population and the need to age well

Between 2015 and 2050 it is predicted that globally the number of adults over the age of 60 will more than double [ 1 ]. Increasing age is associated with a greater risk of chronic illnesses such as cardio vascular disease and cancer [ 2 ] and reduced functional capacity [ 3 , 4 ]. Consequently, an ageing population will continue to place considerable pressure on the health care systems.

However, it is also important to consider the individuals themselves and self-perceived good health is very important for the individual wellbeing and life-satisfaction of older adults [ 5 ]. The terms “successful ageing” [ 6 ] and “healthy ageing” [ 5 ] have been used to define a broader concept of ageing well, which not only includes factors relating to medically defined health but also wellbeing. Unfortunately, there is no agreed definition for what exactly constitutes healthy or successful ageing, with studies using a range of definitions. A review of 28 quantitative studies found that successful ageing was defined differently in each, with the majority only considering measures of disability or physical functioning. Social and wellbeing factors were included in only a few of the studies [ 7 ].

In contrast, qualitative studies of older adults’ opinions on successful ageing have found that while good physical and mental health and maintaining physical activity levels are agreed to assist successful ageing, being independent or doing something of value, acceptance of ageing, life satisfaction, social connectedness or keeping socially active were of greater importance [ 8 , 9 , 10 ].

In light of these findings, the definition that is most inclusive is “healthy ageing” defined by the World Health Organisation as “the process of developing and maintaining the functional ability (defined as a combination of intrinsic capacity and physical and social environmental characteristics), that enables well-being in older age” (p28) [ 5 ].This definition, and those provided in the research of older adults’ perceptions of successful ageing, highlight social engagement and social support as important factors contributing to successful ageing, in addition to being important social determinants of health [ 11 , 12 ].

Social determinants of health, including loneliness and social support, are important predictors of physical, cognitive and mental health and wellbeing in adults [ 12 ] and older adults [ 13 , 14 , 15 ]. Loneliness is defined as a perception of an inadequacy in the quality or quantity of one’s social relationships [ 16 ]. Social support, has various definitions but generally it relates to social relationships that are reciprocal, accessible and reliable and provide any or a combination of supportive resources (e.g. emotional, information, practical) and can be measured as perceived or received support [ 17 ]. These types of social determinants differ from those related to inequality (health gap social determinants) and are sometimes referred to as ‘social cure’ social determinants [ 11 ]. They will be referred to as ‘social wellbeing’ outcome measures in this study.

Unfortunately, with advancing age, there is often diminishing social support, leading to social isolation and loneliness [ 18 , 19 ]. Large nationally representative studies of adults and older adults reported that social activity predicted maintenance or improvement of life satisfaction as well as physical activity levels [ 20 ], however older adults spent less time in social activity than middle age adults.

Social wellbeing and health

A number of longitudinal studies have found that social isolation for older adults is a significant predictor of mortality and institutionalisation [ 21 , 22 , 23 ]. A meta-analysis by Holt-Lunstadt [ 12 ] reported that social determinants of health, including social integration and social support (including loneliness and lack of perceived social support) to be equal to, or a greater risk to mortality as common behavioural risk factors such as smoking, physical inactivity and obesity. Loneliness is independently associated with poor physical and mental health in the general population, and especially in older adults [ 13 , 14 , 15 ]. Adequate perceived social support has also been consistently associated with improved mental and physical health in both general and older adults [ 20 , 24 , 25 , 26 , 27 , 28 , 29 ]. The mechanism suggested for this association is that social support buffers the negative impacts of stressful situations and life events [ 30 ]. The above research demonstrates the benefit of social engagement for older adults; in turn this highlights the importance of strategies that reduce loneliness and improve social support and social connectedness for older adults.

Socialising in groups seems to be especially important for the health and wellbeing of older adults who may be adjusting to significant life events [ 26 , 31 , 32 , 33 ]. This is sometimes referred to as social engagement or social companionship [ 26 , 30 , 31 ]. It seems that the mechanism enabling such health benefits with group participation is through strengthening of social identification, which in turn increases social support [ 31 , 34 , 35 ]. Furthermore, involvement in community groups can be a sustainable strategy to reduce loneliness and increase social support in older adults, as they are generally low cost and run by volunteers [ 36 , 37 , 38 , 39 ].

Despite the demonstrated importance of social factors for successful ageing and the established risk associated with reduced social engagement as people age, few in-depth studies have longitudinally investigated the impact of community groups on social wellbeing. For example, a non-significant increase in social support and reduction in depression was found in a year-long randomised controlled trial conducted in senior centres in Norway with lonely older adults in poor physical and mental health [ 37 ]. Some qualitative studies have reported that community groups and senior centres can contribute to fun and socialisation for older adults, however social wellbeing was not the primary focus of the studies [ 38 , 40 , 41 ]. Given that social wellbeing is a broad and important area for the health and quality of life in older adults, an in-depth study is warranted to understand how it can be maximised in older adults. This mixed methods case study of an existing community aims to: i) examine whether loneliness and social support of new members of Life Activities Clubs (LACs) changes in the year after joining and ii) conduct an in-depth exploration of how social wellbeing changes in new and longer-term members of LACs.

A mixed methods study was chosen as the design for this research to enable an in-depth exploration of how loneliness and social support may change as a result of joining a community group. A case study was conducted using a concurrent mixed-methods design, with a qualitative component giving context to the quantitative results. Where the survey focused on the impact of group membership on social support and loneliness, the focus groups were an open discussion of the benefits in the lived context of LAC membership. The synthesis of the two sections of the study was undertaken at the time of interpretation of the results [ 42 ].

The two parts of our study were as follows:

a longitudinal survey (three time points over 1 year: baseline, 6 and 12 months). This part of the study formed the quantitative results;

a focus group study of members of the same organisation (qualitative).

Ethics approval to conduct this study was obtained from the Victoria University Human Research Ethics Committee (HRE14–071 [survey] and HRE15–291 [focus groups]) All participants provided informed consent to partake in the study prior to undertaking the first survey or focus group.

Setting and participants

Life activities clubs victoria.

Life Activities Clubs Victoria (LACVI) is a large not-for-profit group with 23 independently run Life Activities Clubs (LACs) based in both rural and metropolitan Victoria. It has approximately 4000 members. The organisation was established to assist in providing physical, social and recreational activities as well as education and motivational support to older adults managing significant change in their lives, especially retirement.

Eighteen out of 23 LAC clubs agreed to take part in the survey study. During the sampling period from May 2014 to December 2016, new members from the participating clubs were given information about the study and invited to take part. Invitations took place in the form of flyers distributed with new membership material.

Inclusion/ exclusion criteria

Community-dwelling older adults who self-reported that they could walk at least 100 m and who were new members to LACVI and able to complete a survey in English were eligible to participate. New members were defined as people who had never been members of LACVI or who had not been members in the last 2 years.

To ensure that the cohort of participants were of a similar functional level, people with significant health problems limiting them from being able to walk 100 m were excluded from participating in the study.

Once informed consent was received, the participants were invited to complete a self-report survey in either paper or online format (depending on preference). This first survey comprised the baseline data and the same survey was completed 6 months and 12 months after this initial time point. Participants were sent reminders if they had not completed each survey more than 2 weeks after each was delivered and then again 1 week later.

Focus groups

Two focus groups (FGs) were conducted with new and longer-term members of LACs. The first FG ( n  = 6) consisted of members who undertook physical activity in their LAC (e.g. walking groups, tennis, cycling). The second FG ( n  = 5) consisted of members who took part in activities with a non-physical activity (PA) focus (e.g. book groups, social groups, craft or cultural groups). LACs offer both social and physical activities and it was important to the study to capture both types of groups, but they were kept separate to assist participants in feeling a sense of commonality with other members and improving group dynamic and participation in the discussions [ 43 ]. Of the people who participated in the longitudinal survey study, seven also participated in the FGs.

The FG interviews were facilitated by one researcher (GLS) and notes around non-verbal communication, moments of divergence and convergence amongst group members, and other notable items were taken by a second researcher (GOS). Both researchers wrote additional notes after the focus groups and these were used in the analysis of themes. Focus groups were recorded and later transcribed verbatim by a professional transcriptionist, including identification of each participant speaking. One researcher (GLS) reviewed each transcription to check for any errors and made any required modifications before importing the transcriptions into NVivo for analysis. The transcriber identified each focus group participant so themes for individuals or other age or gender specific trends could be identified.

Dependent variables

• Social support

Social support was assessed using the Duke–UNC Functional Social support questionnaire [ 44 ]. This scale specifically measures participant perceived functional social support in two areas; i) confidant support (5 questions; e.g. chances to talk to others) and ii) affective support (3 questions; e.g. people who care about them). Participants rated each component of support on a 5-item likert scale between ‘much less than I would like’ (1 point) to ‘as much as I would like’ (5 points). The total score used for analysis was the mean of the eight scores (low social support = 1, maximum social support = 5). Construct validity, concurrent validity and discriminant validity are acceptable for confidant and affective support items in the survey in the general population [ 44 ].

Loneliness was measured using the de Jong Gierveld and UCLA-3 item loneliness scales developed for use in many populations including older adults [ 45 ]. The 11-item de Jong Gierveld loneliness scale (DJG loneliness) [ 46 ] is a multi-dimensional measure of loneliness and contains five positively worded and six negatively worded items. The items fall into four subscales; feelings of severe loneliness, feelings connected with specific problem situations, missing companionship, feelings of belongingness. The total score is the sum of the items scores (i.e. 11–55): 11 is low loneliness and 55 is severe loneliness. Self-administered versions of this scale have good internal consistency (> = 0.8) and inter-item homogeneity and person scalability that is as good or better than when conducted as face-to face interviews. The validity and reliability for the scale is adequate [ 47 ]. The UCLA 3-item loneliness scale consists of three questions about how often participants feel they lack companionship, feel left out and feel isolated. The responses are given on a three-point scale ranging from hardly ever (1) to often (3). The final score is the sum of these three items with the range being from lowest loneliness (3) to highest loneliness (9). Reliability of the scale is good, (alpha = 0.72) as are discriminant validity and internal consistency [ 48 ]. The scale is commonly used to measure loneliness with older adults ([ 49 ] – review), [ 50 , 51 ].

Sociodemographic variables

The following sociodemographic characteristics were collected in both the survey and the focus groups: age, sex, highest level of education, main life occupation [ 52 ], current employment, ability to manage on income available, present marital status, country of birth, area of residence [ 53 ]. They are categorised as indicated in Table  2 .

Health variables

The following health variables were collected: Self-rated general health (from SF-12) [ 54 ] and Functional health (ability to walk 100 m- formed part of the inclusion criteria) [ 55 ]. See Table 2 for details about the categories of these variables.

The effects of becoming a member on quantitative outcome variables (i.e. Social support, DJG loneliness and UCLA loneliness) were analysed using linear mixed models (LMM). LMM enabled testing for the presence of intra-subject random effects, or equivalently, correlation of subjects’ measures over time (baseline, 6-months and 12 months). Three correlation structures were examined: independence (no correlation), compound symmetry (constant correlation of each subjects’ measures over the three time points) and autoregressive (correlation diminishing with increase in spacing in time). The best fitting correlation structure was compound symmetry; this is equivalent to a random intercept component for each subject. The LMM incorporated longitudinal trends over time, with adjustment for age as a potential confounder. Statistical analyses were conducted using SPSS for windows (v24).

UCLA loneliness and social support residuals were not normally distributed and these scales were Log10 transformed for statistical analysis.

Analyses were all adjusted for age, group attendance (calculated as average attendance at 6 and 12 months) and employment status at baseline (Full-time, Part-time, not working).

Focus group transcripts were analysed using thematic analysis [ 56 , 57 ], a flexible qualitative methodology that can be used with a variety of epistemologies, approaches and analysis methods [ 56 ]. The transcribed data were analysed using a combination of theoretical and inductive thematic analysis [ 56 ]. It was theorised that membership in a LAC would assist with social factors relating to healthy ageing [ 5 ], possibly through a social identity pathway [ 58 ], although we wanted to explore this. Semantic themes were drawn from these codes in order to conduct a pragmatic evaluation of the LACVI programs [ 56 ]. Analytic rigour in the qualitative analysis was ensured through source and analyst triangulation. Transcriptions were compared to notes taken during the focus groups by the researchers (GOS and GLS). In addition, Initial coding and themes (by GLS) were checked by a second researcher (GOS) and any disagreements regarding coding and themes were discussed prior to finalisation of codes and themes [ 57 ].

Sociodemographic and health characteristics of the 28 participants who completed the survey study are reported in Table  1 . The mean age of the participants was 66.9 and 75% were female. These demographics are representative of the entire LACVI membership. Education levels varied, with 21% being university educated, and the remainder completing high school or technical certificates. Two thirds of participants were not married. Some sociodemographic characteristics changed slightly at 6 and 12 months, mainly employment (18% in paid employment at baseline and 11% at 12-months) and ability to manage on income (36% reporting trouble managing on their income at baseline and 46% at 12 months). Almost 90% of the participants described themselves as being in good-excellent health.

Types of activities

There were a variety of types of activities that participants took part in: physical activities such as walking groups ( n  = 7), table tennis ( n  = 5), dancing class ( n  = 2), exercise class ( n  = 1), bowls ( n  = 2), golf ( n  = 3), cycling groups ( n  = 1) and non-physical leisure activities such as art and literature groups ( n  = 5), craft groups ( n  = 5), entertainment groups ( n  = 12), food/dine out groups ( n  = 18) and other sedentary leisure activities (e.g. mah jong, cards),( n  = 4). A number of people took part in more than one activity.

Frequency of attendance at LACVI and changes in social wellbeing

At six and 12 months, participants indicated how many times in the last month they attended different types of activities at their LAC. Most participants maintained the same frequency of participation over both time points. Only four people participated more frequently at 12 than at 6 months and nine reduced participation levels. The latter group included predominantly those who reduced from more than two times per week at 6 months to 2×/week at 6 months to one to two times per week ( n  = 5) or less than one time per week ( n  = 2) at 12 months. Average weekly club attendance at six and 12 months was included as a covariate in the statistical model.

Outcome measures

Overall, participants reported moderate social support and loneliness levels at baseline (See Table 2 ). Loneliness, as measured by both scales, reduced significantly over time. There was a significant effect of time on the DJG loneliness scores (F (2, 52) = 3.83, p  = 0.028), with Post-Hoc analysis indicating a reduction in DJG loneliness between baseline and 12 months ( p  = 0.008). UCLA loneliness scores (transformed variable) also changed significantly over time (F (2, 52) = 4.08, p  = 0.023). Post hoc tests indicated a reduction in UCLA loneliness between baseline and 6 months ( p  = 0.007). There was a small non-significant increase in social support (F (2, 53) =2.88, p  = 0.065) during the first year of membership (see Table 2 and Figs. 1 and 2 ).

DJG loneliness for all participants over first year of membership at LAC club ( n  = 28).

*Represents significant difference compared to baseline ( p  < 0.01)

UCLA loneliness score for all participants over first year of membership at LAC club ( n  = 28).

*Indicates log values of the variable at 6-months were significantly different from baseline ( p  < 0.01)

In total, 11 participants attended the two focus groups, six people who participated in PA clubs (four women) and five who participated in social clubs (all women). All focus group participants were either retired ( n  = 9) or semi-retired ( n  = 2). The mean age of participants was 67 years (see Table 2 for further details). Most of the participants (82%) had been members of a LAC for less than 2 years and two females in the social group had been members of LAC clubs for 5 and 10 years respectively.

Analysis of the focus group transcripts identified two themes relating to social benefits of group participation; i) Social resources and ii) Social wellbeing (see Fig. 3 ). Group discussion suggested that membership of a LAC provides access to more social resources through greater and diverse social contact and opportunity. It is through this improvement in social resources that social wellbeing may improve.

Themes arising from focus group discussion around the benefits of LAC membership

Social resources

The social resources theme referred to an increase in the availability and variety of social connections that resulted from becoming a member of a LAC. The social nature of the groups enabled an expansion and diversification of members’ social network and improved their sense of social connectedness. There was widespread agreement in both the focus groups that significant life events, especially retirement, illness or death of spouse and moving house changes one’s social resources. Membership of the LAC had benefits especially at these times and these events were often motivators to join such a club. Most participants found that their social resources declined after retirement and even felt that they were grieving for the loss of their work.

“ I just saw work as a collection of, um, colleagues as opposed to friends. I had a few good friends there. Most were simply colleagues or acquaintances …. [interviewer- Mmm.] ..Okay, you’d talk to them every day. You’d chatter in the kitchen, oh, pass banter back and forth when things are busy or quiet, but... Um, in terms of a friendship with those people, like going to their home, getting to know them, doing other things with them, very few. But what I did miss was the interaction with other people. It had simply gone….. But, yeah, look, that, the, yeah, that intervening period was, oh, a couple of months. That was a bit tough…. But in that time the people in LAC and the people in U3A…. And the other dance group just drew me into more things. Got to know more people. So once again, yeah, reasonable group of acquaintances.” (Male, PAFG)

Group members indicated general agreement with these two responses, however one female found she had a greater social life following retirement due to the busy nature of her job.

Within the social resources theme, three subthemes were identified, i) Opportunity for social connectedness, ii) Opportunity for friendships, and iii) Opportunity for social responsibility/leadership . Interestingly, these subthemes were additional to the information gathered in the survey. This emphasises the power of the inductive nature of the qualitative exploration employed in the focus groups to broaden the knowledge in this area.

The most discussed and expanded subtheme in both focus groups was Opportunity for social connectedness , which arose through developing new connections, diversifying social connections, sharing interests and experiences with others and peer learning. Participants in both focus groups stated that being a member of LAC facilitated their socialising and connecting with others to share ideas, skills and to do activities with, which was especially important through times of significant life events. Furthermore, participants in each of the focus groups valued developing diverse connections:

“ Yeah, I think, as I said, I finished up work and I, and I had more time for wa-, walking. So I think a, in meeting, in going to this group which, I saw this group of women but then someone introduced me to them. They were just meeting, just meeting a new different set of people, you know? As I said, my work people and these were just a whole different group of women, mainly women. There’s not many men. [Interviewer: Yes.]….. Although our leader is a man, which is ironic and is about, this man out in front and there’s about 20 women behind him, but, um, so yeah, and people from different walks of life and different nationalities there which I never knew in my work life, so yeah. That’s been great. So from that goes on other things, you know, you might, uh, other activities and, yeah, people for coffee and go to the pictures or something, yeah. That’s great.” (Female, PAFG)

Simply making new connections was the most widely discussed aspect related to the opportunity for social connectedness subtheme, with all participants agreeing that this was an important benefit of participation in LAC groups.

“Well, my experience is very similar to everybody else’s…….: I, I went from having no social life to a social life once I joined a group.” (Female, PAFG)

There was agreement in both focus groups that these initial new connections made at a LAC are strengthened through development of deeper personal connections with others who have similar demographics and who are interested in the same activities. This concurs with the Social Identity Theory [ 58 ] discussed previously.

“and I was walking around the lake in Ballarat, like wandering on my own. I thought, This is ridiculous. I mean, you’ve met all those groups of women coming the opposite way, so I found out what it was all about, so I joined, yeah. So that’s how I got into that.[ Interviewer: Yeah.] Basically sick of walking round the lake on my own. [Interviewer: Yeah, yeah.] So that’s great. It’s very social and they have coffee afterwards which is good.” (female, PAFG)

The subtheme Opportunity for development of friendships describes how, for some people, a number of LAC members have progressed from being just initial social connections to an established friendship. This signifies the strength of the connections that may potentially develop through LAC membership. Some participants from each group mentioned friendships developing, with slightly more discussion of this seen in the social group.

“we all have a good old chat, you know, and, and it’s all about friendship as well.” (female, SocialFG)

The subtheme Opportunity for social responsibility or leadership was mentioned by two people in the active group, however it was not brought up in the social group. This opportunity for leadership is linked with the development of a group identity and desiring to contribute meaningfully to a valued group.

“with our riding group, um, you, a leader for probably two rides a year so you’ve gotta prepare for it, so some of them do reccie rides themselves, so, um, and also every, uh, so that’s something that’s, uh, a responsibility.” (male, PAFG)

Social wellbeing

The social resources described above seem to contribute to a number of social, wellbeing outcomes for participants. The sub themes identified for Social wellbeing were , i) Increased social support, ii) Reduced loneliness, iii) Improved home relationships and iv) Improved social skills.

Increased social support

Social support was measured quantitatively in the survey (no significant change over time for new members) and identified as a benefit of LAC membership during the focus group discussions. However, only one of the members of the active group mentioned social support directly.

‘it’s nice to be able to pick up the phone and share your problem with somebody else, and that’s come about through LAC. ……‘Cos before that it was through, with my family (female, PAFG)

There was some agreement amongst participants of the PA group that they felt this kind of support may develop in time but most of them had been members for less than 2 years.

“[Interviewer: Yeah. Does anyone else have that experience? (relating to above quote)]” There is one lady but she’s actually the one that I joined with anyway. [Interviewer: Okay.] But I, I feel there are others that are definitely getting towards that stage. It’s still going quite early days. (female1, PAFG) [Interviewer: I guess it’s quite early for some of you, yeah.] “yeah” (female 2, PAFG)

Social support through sharing of skills was mentioned by one participant in the social group also, with agreement indicated by most of the others in the social focus group.

Discussion in the focus groups also touched on the subthemes Reduced loneliness and Improved home relationships, which were each mentioned by one person. And focus groups also felt that group membership Improved social skills through opening up and becoming more approachable (male, PAFG) or enabling them to become more accepting of others’ who are different (general agreement in Social FG).

This case study integrated results from a one-year longitudinal survey study and focus group discussions to gather rich information regarding the potential changes in social wellbeing that older adults may experience when joining community organisations offering group activities. The findings from this study indicate that becoming a member of such a community organisation can be associated with a range of social benefits for older adults, particularly related to reducing loneliness and maintaining social connections.

Joining a LAC was associated with a reduction in loneliness over 1 year. This finding is in line with past group-intervention studies where social activity groups were found to assist in reducing loneliness and social isolation [ 49 ]. This systematic review highlighted that the majority of the literature explored the effectiveness of group activity interventions for reducing severe loneliness or loneliness in clinical populations [ 49 ]. The present study extends this research to the general older adult population who are not specifically lonely and reported to be of good general health, rather than a clinical focus. Our findings are in contrast to results from an evaluation of a community capacity-building program aimed at reducing social isolation in older adults in rural Australia [ 59 ]. That program did not successfully reduce loneliness or improve social support. The lack of change from pre- to post-program in that study was reasoned to be due to sampling error, unstandardised data collection, and changes in sample characteristics across the programs [ 59 ]. Qualitative assessment of the same program [ 59 ] did however suggest that participants felt it was successful in reducing social isolation, which does support our findings.

Changes in loneliness were not a main discussion point of the qualitative component of the current study, however some participants did express that they felt less lonely since joining LACVI and all felt they had become more connected with others. This is not so much of a contrast in results as a potential situational issue. The lack of discussion of loneliness may have been linked to the common social stigma around experiencing loneliness outside certain accepted circumstances (e.g. widowhood), which may lead to underreporting in front of others [ 45 ].

Overall, both components of the study suggest that becoming a member of an activity group may be associated with reductions in loneliness, or at least a greater sense of social connectedness. In addition to the social nature of the groups and increased opportunity for social connections, another possible link between group activity and reduced loneliness is an increased opportunity for time out of home. Previous research has found that more time away from home in an average day is associated with lower loneliness in older adults [ 60 ]. Given the significant health and social problems that are related to loneliness and social isolation [ 13 , 14 , 15 ], the importance of group involvement for newly retired adults to prevent loneliness should be advocated.

In line with a significant reduction in loneliness, there was also a trend ( p  = 0.056) toward an increase in social support from baseline to 12 months in the survey study. Whilst suggestive of a change, it is far less conclusive than the findings for loneliness. There are a number of possible explanations for the lack of statistically significant change in this variable over the course of the study. The first is the small sample size, which would reduce the statistical power of the study. It may be that larger studies are required to observe changes in social support, which are possibly only subtle over the course of 1 year. This idea is supported by a year-long randomised controlled trial with 90 mildly-depressed older adults who attended senior citizen’s club in Norway [ 37 ]. The study failed to see any change in general social support in the intervention group compared to the control over 1 year. Additional analysis in that study suggested that people who attended the intervention groups more often, tended to have greater increases in SS ( p  = 0.08). The researchers stated that the study suffered from significant drop-out rates and low power as a result. In this way, it was similar to our findings and suggests that social support studies require larger numbers than we were able to gain in this early exploratory study. Another possible reason for small changes in SS in the current study may be the type of SS measured. The scale used gathered information around functional support or support given to individuals in times of need. Maybe it is not this type of support that changes in such groups but more specific support such as task-specific support. It has been observed in other studies and reviews that task-specific support changes as a result of behavioural interventions (e.g. PA interventions) but general support does not seem to change in the time frames often studied [ 61 , 62 , 63 ].

There were many social wellbeing benefits such as increased social connectivity identified in focus group discussion, but the specific theme of social support was rarely mentioned. It may be that general social support through such community groups may take longer than 1 year to develop. There is evidence that strong group ties are sequentially positively associated between social identification and social support [ 34 ], suggesting that the connections formed through the groups may lead increased to social support from group members in the future. This is supported by results from the focus group discussions, where one new member felt she could call on colleagues she met in her new group. Other new members thought it was too soon for this support to be available, but they could see the bonds developing.

Other social wellbeing changes

In addition to social support and loneliness that were the focus of the quantitative study, the focus group discussions uncovered a number of other benefits of group membership that were related to social wellbeing (see Fig. 3 ). The social resources theme was of particular interest because it reflected some of the mechanisms that appeared enable social wellbeing changes as a result of being a member of a LAC but were not measured in the survey. The main social resources relating to group membership that were mentioned in the focus groups were social connectedness, development of friendships and opportunity for social responsibility or leadership. As mentioned above, there was wide-spread discussion within the focus groups of the development of social connections through the clubs. Social connectedness is defined as “the sense of belonging and subjective psychological bond that people feel in relation to individuals and groups of others.” ([ 25 ], pp1). As well as being an important predecessor of social support, greater social connectedness has been found to be highly important for the health of older adults, especially cognitive and mental health [ 26 , 32 , 34 , 35 , 64 ]. One suggested theory for this health benefit is that connections developed through groups that we strongly identify with are likely to be important for the development of social identity [ 34 ], defined by Taifel as: “knowledge that [we] belong to certain social groups together with some emotional and value significance to [us] of this group membership” (Tajfel, 1972, p. 31 in [ 58 ] p 2). These types of groups to which we identify may be a source of “personal security, social companionship, emotional bonding, intellectual stimulation, and collaborative learning and……allow us to achieve goals.” ([ 58 ] p2) and an overall sense of self-worth and wellbeing. There was a great deal of discussion relating to the opportunity for social connectedness derived through group membership being particularly pertinent following a significant life event such as moving to a new house or partners becoming unwell or dying and especially retirement. This change in their social circumstance is likely to have triggered the need to renew their social identity by joining a community group. Research with university students has shown that new group identification can assist in transition for university students who have lost their old groups of friends because of starting university [ 65 ]. In an example relevant to older adults, maintenance or increase in number of group memberships at the time of retirement reduced mortality risk 8 years later compared to people who reduce their number of group activities in a longitudinal cohort study [ 66 ]. This would fit with the original Activity Theory of ageing; whereby better ageing experience is achieved when levels of social participation are maintained, and role replacement occurs when old roles (such as working roles) must be relinquished [ 67 ]. These connections therefore appear to assist in maintaining resilience in older adults defined as “the ability to maintain or improve a level of functional ability (a combination of intrinsic physical and mental capacity and environment) in the face of adversity” (p29, [ 5 ]). Factors that were mentioned in the focus groups as assisting participants in forming connections with others were shared interest, learning from others, and a fun and accepting environment. It was not possible to assess all life events in the survey study. However, since the discussion from the focus groups suggested this to be an important motivator for joining clubs and potentially a beneficial time for joining them, it would be worth exploring in future studies.

Focus group discussion suggested that an especially valuable time for joining such clubs was around retirement, to assist with maintaining social connectivity. The social groups seem to provide social activity and new roles for these older adults at times of change. It is not necessarily important for all older adults but maybe these ones identify themselves as social beings and therefore this maintenance of social connection helps to continue their social role. Given the suggested importance of social connectivity gained through this organisation, especially at times of significant life events, it would valuable to investigate this further in future and consider encouragement of such through government policy and funding. The majority of these types of clubs exist for older adults in general, but this study emphasises the need for groups such as these to target newly retired individuals specifically and to ensure that they are not seen as ‘only for old people’.

Strengths and limitations

The use of mixed –methodologies, combining longitudinal survey study analysed quantitatively, with a qualitative exploration through focus group discussions and thematic analysis, was a strength of the current study. It allowed the researchers to not only examine the association between becoming a member of a community group on social support and loneliness over an extended period, but also obtain a deeper understanding of the underlying reasons behind any associations. Given the variability of social support definitions in research [ 17 ] and the broad area of social wellbeing, it allowed for open exploration of the topic, to understand associations that may exist but would have otherwise been missed. Embedding the research in an existing community organisation was a strength, although with this also came some difficulties with recruitment. Voluntary coordination of the community groups meant that informing new members about the study was not always feasible or a priority for the volunteers. In addition, calling for new members was innately challenging because they were not yet committed to the club fully. This meant that so some people did not want to commit to a year-long study if they were not sure how long they would be a member of the club. This resulted in slow recruitment and a resulting relatively low sample size and decreased power to show significant statistical differences, which is a limitation of the present study. However, the use of Linear Mixed Models for analysis of the survey data was a strength because it was able to include all data in the analyses and not remove participants if one time point of data was missing, as repeated measures ANOVAs would do. The length of the study (1 year) is another strength, especially compared to previous randomised controlled studies that are typically only 6–16 weeks in length. Drop-out rate in the current study is very low and probably attributable to the benefits of working with long-standing organisations.

The purpose of this study was to explore in detail whether there are any relationships between joining existing community groups for older adults and social wellbeing. The lack of existing evidence in the field meant that a small feasibility-type case study was a good sounding-board for future larger scale research on the topic, despite not being able to answer questions of causality. Owing to the particularistic nature of case studies, it can also be difficult to generalise to other types of organisations or groups unless there is a great deal of similarity between them [ 68 ]. There are however, other types of community organisations in existence that have a similar structure to LACVI (Seniors centres [ 36 , 40 ], Men’s Sheds [ 38 ], University of the Third Age [ 34 , 69 ], Japanese salons [ 70 , 71 ]) and it may be that the results from this study are transferable to these also. This study adds to the literature around the benefits of joining community organisations that offer social and physical activities for older adults and suggests that this engagement may assist with reducing loneliness and maintaining social connection, especially around the time of retirement.

Directions for future research

Given that social support trended toward a significant increase, it would be useful to repeat the study on a larger scale in future to confirm this. Either a case study on a similar but larger community group or combining a number of community organisations would enable recruitment of more participants. Such an approach would also assist in assessing the generalisability of our findings to other community groups. Given that discussions around social benefits of group membership in the focus groups was often raised in conjunction with the occurrence of significant life events, it would be beneficial to include a significant life event scale in any future studies in this area. The qualitative results also suggest that it would be useful to investigate whether people who join community groups in early years post retirement gain the same social benefits as those in later stages of retirement. Studies investigating additional health benefits of these community groups such as physical activity, depression and general wellbeing would also be warranted.

With an ageing population, it is important to investigate ways to enable older adults to age successfully to ensure optimal quality of life and minimisation of health care costs. Social determinants of health such as social support, loneliness and social contact are important contributors to successful ageing through improvements in cognitive health, quality of life, reduction in depression and reduction in mortality. Unfortunately, older adults are at risk of these social factors declining in older age and there is little research investigating how best to tackle this. Community groups offering a range of activities may assist by improving social connectedness and social support and reducing loneliness for older adults. Some factors that may assist with this are activities that encourage sharing interests, learning from others, and are conducted in a fun and accepting environment. Such groups may be particularly important in developing social contacts for newly retired individuals or around other significant life events such as moving or illness of loved ones. In conclusion, ageing policy and strategies should emphasise participation in community groups especially for those recently retired, as they may assist in reducing loneliness and increasing social connections for older adults.

Abbreviations

Focus group

Life Activities Club

Life Activities Clubs Victoria

Linear mixed model

Physical activity

World Health Organisation

United Nations. In: P.D. Department of Economic and Social Affairs, editor. World Population Ageing 2015. New York: United Nations; 2015.

Google Scholar  

World Health Organisation. Global Health and Ageing. 2011 [cited 2014 March 25]; Available from: http://www.who.int/ageing/publications/global_health/en/ .

Balogun JA, et al. Age-related changes in balance performance. Disabil Rehabil. 1994;16(2):58–62.

Article   CAS   Google Scholar  

Singh MAF. Exercise comes of age: rationale and recommendations for a geriatric exercise prescription. J Gerontol Ser A Biol Med Sci. 2002;57(5):M262–82.

Article   Google Scholar  

World Health Organisation. World report on ageing and health. Geneva: World Health Organisation; 2015.

Rowe JW, Kahn RL. Successful Aging1. The Gerontologist. 1997;37(4):433–40.

Depp CA, Jeste DV. Definitions and predictors of successful aging: a comprehensive review of larger quantitative studies. Am J Geriatr Psychiatry. 2006;14(1):6–20.

Song M, Kong E-H. Older adults’ definitions of health: a metasynthesis. Int J Nurs Stud. 2015;52(6):1097–106.

Tate RB, Lah L, Cuddy TE. Definition of successful aging by elderly Canadian males: the Manitoba follow-up study. The Gerontologist. 2003;43(5):735–44.

Phelan EA, et al. Older Adults’ views of “successful aging”—how do they compare with Researchers’ definitions? J Am Geriatr Soc. 2004;52(2):211–6.

Haslam SA, et al. Social cure, what social cure? The propensity to underestimate the importance of social factors for health. Soc Sci Med. 2018;198:14–21.

Holt-Lunstad J, Smith TB, Layton JB. Social relationships and mortality risk: a meta-analytic review. PLoS Med. 2010;7(7):e1000316.

Richard A, et al. Loneliness is adversely associated with physical and mental health and lifestyle factors: results from a Swiss national survey. PLoS One. 2017;12(7):1–18.

Luo Y, et al. Loneliness, health, and mortality in old age: a national longitudinal study. Soc Sci Med. 2012;74(6):907–14.

Luo Y, Waite LJ. Loneliness and mortality among older adults in China. J Gerontol B Psychol Sci Soc Sci. 2014;69(4):633–45.

de Jong Gierveld J, Van Tilburg T, Dykstra PA. Loneliness and social isolation. In: Vangelisti A, Perlman D, editors. Cambridge handbook of personal relationships. Cambridge: Cambridge University Press; 2006. p. 485-500.

Williams P, Barclay L, Schmied V. Defining social support in context: a necessary step in improving research, intervention, and practice. Qual Health Res. 2004;14(7):942–60.

Valtorta N, Hanratty B. Loneliness, isolation and the health of older adults: do we need a new research agenda? J R Soc Med. 2012;105(12):518–22.

Jylhä M. Old age and loneliness: cross-sectional and longitudinal analyses in the Tampere longitudinal study on aging. Can J Aging La Revue can du vieil. 2010;23(2):157–68.

Huxhold O, Miche M, Schüz B. Benefits of having friends in older ages: differential effects of informal social activities on well-being in middle-aged and older adults. J Gerontol B Psychol Sci Soc Sci. 2014;69(3):366–75.

Berkman L, Syme S. Social networks, host resistance and mortality: a nine year follow-up study of alameda county residents. Am J Epidemiol. 1979;185(11):1070–88.

House JS, Landis KR, Umberson D. Social relationships and health. Science. 1988;241(4865):540–5.

Pynnonen K, et al. Does social activity decrease risk for institutionalization and mortality in older people? J Gerontol B Psychol Sci Soc Sci. 2012;67(6):765–74.

Broadhead WE, et al. The epidemiologic evidence for a relationship between social support and health. Am J Epidemiol. 1983;117(5):521–37.

Haslam C, et al. Social connectedness and health. Encyclopedia of geropsychology. 2017:2174–82. https://doi.org/10.1007/978-981-287-080-3_46-1 .

Haslam C, Cruwys T, Haslam SA. “The we’s have it”: evidence for the distinctive benefits of group engagement in enhancing cognitive health in aging. Soc Sci Med. 2014;120:57–66.

Uebelacker LA, et al. Social support and physical activity as moderators of life stress in predicting baseline depression and change in depression over time in the Women’s Health Initiative. Soc Psychiatry Psychiatr Epidemiol. 2013;48(12):1971–82.

Tajvar M, et al. Social support and health of older people in middle eastern countries: a systematic review. Australas J Ageing. 2013;32(2):71–8.

Dalgard OS, Bjork S, Tambs K. Social support, negative life events and mental health. Br J Psychiatry. 1995;166(1):29–34.

Cohen S, Wills TA. Stress, social support, and the buffering hypothesis. Psychol Bull. 1985;98(2):310–57.

Gilmour H. Social participation and the health and well-being of Canadian seniors. Health Rep. 2012;23(4):1B.

Glei DA, et al. Participating in social activities helps preserve cognitive function: an analysis of a longitudinal, population-based study of the elderly. Int J Epidemiol. 2005;34(4):864–71.

Lee SH, Kim YB. Which type of social activities may reduce cognitive decline in the elderly?: a longitudinal population-based study. BMC Geriatr. 2016;16(1):165.

Haslam C, et al. Group ties protect cognitive health by promoting social identification and social support. J Aging Health. 2016;28(2):244–66.

Haslam C, et al. Groups 4 health: evidence that a social-identity intervention that builds and strengthens social group membership improves mental health. J Affect Disord. 2016;194:188–95.

Bøen H. Characteristics of senior Centre users -- and the impact of a group programme on social support and late-life depression. Norsk Epidemiologi. 2012;22(2):261–9.

Bøen H, et al. A randomized controlled trial of a senior Centre group programme for increasing social support and preventing depression in elderly people living at home in Norway. BMC Geriatr. 2012;12:20.

Golding BG. Social, local, and situated: recent findings about the effectiveness of older Men’s informal learning in community contexts. Adult Educ Q. 2011;61(2):103.

Life Activities Clubs. Life Activities Clubs. About Us. . 2014 [cited 2014 January 13, 2014]; Available from: http://www.life.org.au/aboutus .

Hutchinson SL, Gallant KA. Can senior Centres be contexts for aging in third places? J Leis Res. 2016;48(1):50–68.

Millard J. The health of older adults in community activities. Work Older People. 2017;21(2):90–9.

Creswell JW, Plano-Clark VL. Designing and conducting mixed methods research. Thousand Oaks, Calif: SAGE Publications; 2007. p. c2007.

Loeb S, Penrod J, Hupcey J. Focus groups and older adults: tactics for success. J Gerontol Nurs. 2006;32(3):32–8.

PubMed   Google Scholar  

Broadhead W, et al. The Duke-UNC functional social support questionnaire: measurement of social support in family medicine patients. Med Care. 1988;26(7):709–23.

De Jong Gierveld J, Van Tilburg T. Living arrangements of older adults in the Netherlands and Italy: Coresidence values and behaviour and their consequences for loneliness. J Cross Cult Gerontol. 1999;14(1):1–24.

de Jong-Gierveld J, Kamphuls F. The development of a Rasch-type loneliness scale. Appl Psychol Meas. 1985;9(3):289–99.

Tilburg Tv, Leeuw Ed. Stability of scale quality under various data collection procedures: a mode comparison on the ‘De Jong-Gierveld loneliness scale. Int J Public Opin Res. 1991;3(1):69–85.

Hughes ME, et al. A short scale for measuring loneliness in large surveys - results from two population-based studies. Res Aging. 2004;26(6):655–72.

Dickens AP, et al. Interventions targeting social isolation in older people: a systematic review. BMC Public Health. 2011;11:647.

Hawkley LC, et al. Loneliness predicts increased blood pressure: 5-year cross-lagged analyses in middle-aged and older adults. Psychol Aging. 2010;25(1):132.

Netz Y, et al. Loneliness is associated with an increased risk of sedentary life in older Israelis. Aging Ment Health. 2013;17(1):40–7.

Australian Bureau of Statistics. Australian and New Zealand Standard Classification of Occupations, 2013, Version 1.2. Canberra: Australian Bureau of Statistics; 2013.

Australian Bureau of Statistics, Australian Standard Geographical Classification (ASGC). Canberra: Australian Bureau of Statistics; 2011.

Ware JE, Kosinski M, Keller SD. A 12-item short-form health survey - construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220–33.

Sanson-Fisher RW, Perkins JJ. Adaptation and validation of the SF-36 health survey for use in Australia. J Clin Epidemiol. 51(11):961–7.

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.

Patton MQ. Qualitative research & evaluation methods. 4 ed. Thousand Oaks, California: Sage Publications; 2015.

Haslam SA, et al. Social identity, health and well-being: an emerging agenda for applied psychology. Appl Psychol. 2009;58(1):1–23.

Bartlett H, et al. Preventing social isolation in later life: findings and insights from a pilot Queensland intervention study. Ageing Soc. 2013;33(07):1167–89.

Petersen J, et al. Time out-of-home and cognitive, physical, and emotional wellbeing of older adults: a longitudinal mixed effects model. PLoS One. 2015;10(10):e0139643.

Lindsay Smith G, et al. The association between social support and physical activity in older adults: a systematic review. Int J Behav Nutr Phys Act. 2017;14(1):56.

Sallis JF, et al. The development of scales to measure social support for diet and exercise behaviors. Prev Med. 1987;16(6):825–36.

Oka R, King A, Young DR. Sources of social support as predictors of exercise adherence in women and men ages 50 to 65 years. Womens Health Res Gender Behav Policy. 1995;1:161–75.

CAS   Google Scholar  

Greenaway KH, et al. From “we” to “me”: group identification enhances perceived personal control with consequences for health and well-being. J Pers Soc Psychol. 2015;109(1):53–74.

Iyer A, et al. The more (and the more compatible) the merrier: multiple group memberships and identity compatibility as predictors of adjustment after life transitions. Br J Soc Psychol. 2009;48(4):707–33.

Steffens NK, et al. Social group memberships in retirement are associated with reduced risk of premature death: evidence from a longitudinal cohort study. BMJ Open. 2016;6(2):e010164.

Havighurst RJ. Successful aging. Gerontol. 1961;1:8–13.

Yin R. Case study research: design and methods. Beverly Hills, CA: Sage publishing; 1994.

Merriam SB, Kee Y. Promoting community wellbeing: the case for lifelong learning for older adults. Adult Educ Q. 2014;64(2):128–44.

Hikichi H, et al. Social interaction and cognitive decline: results of a 7-year community intervention. Alzheimers Dement: Translat Res Clin Interv. 2017;3(1):23–32.

Hikichi H, et al. Effect of a community intervention programme promoting social interactions on functional disability prevention for older adults: propensity score matching and instrumental variable analyses, JAGES Taketoyo study. J Epidemiol Community Health. 2015;69(9):905–10.

Download references

The primary author contributing to this study (GLS) receives PhD scholarship funding from Victoria University. The other authors were funded through salaries at Victoria University.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available due the ethics approval for this study not allowing open access to the individual participant data but are available from the corresponding author on reasonable request.

Author information

Authors and affiliations.

Institute for Health and Sport, Victoria University, Melbourne, Australia

Gabrielle Lindsay-Smith, Grant O’Sullivan, Rochelle Eime, Jack Harvey & Jannique G. Z. van Uffelen

School of Health and Life Sciences, Federation University, Ballarat, Australia

Rochelle Eime & Jack Harvey

Department of Movement Sciences, Physical Activity, Sports and Health Research Group, KU Leuven - University of Leuven, Leuven, Belgium

Jannique G. Z. van Uffelen

You can also search for this author in PubMed   Google Scholar

Contributions

GLS, RE and JVU made substantial contributions to the conception and design of the study. GLS and GOS supervised data collection for the surveys (GLS) and focus groups (GOS and GLS). GLS, GOS, RE, JH and JVU were involved in data analysis and interpretation. All authors were involved in drafting, the manuscript and approved the final version.

Corresponding author

Correspondence to Gabrielle Lindsay-Smith .

Ethics declarations

Ethics approval and consent to participate.

Ethics approval to conduct this study was obtained from the Victoria University Human Research Ethics Committee (HRE14–071 [survey] and HRE15–291 [focus groups]). All survey participants provided informed consent to partake in the study prior to undertaking the first survey by clicking ‘agree’ in the survey software (online participants) or signing a written consent form (paper participants) after they had read all participant information relating to the study. Focus group participants also completed a written consent form after reading and understanding all participant information regarding the study.

Consent for publication

Focus group participants have given permission for their quotes to be used in de-identified form prior to undertaking the research.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Lindsay-Smith, G., O’Sullivan, G., Eime, R. et al. A mixed methods case study exploring the impact of membership of a multi-activity, multicentre community group on social wellbeing of older adults. BMC Geriatr 18 , 226 (2018). https://doi.org/10.1186/s12877-018-0913-1

Download citation

Received : 19 April 2018

Accepted : 10 September 2018

Published : 24 September 2018

DOI : https://doi.org/10.1186/s12877-018-0913-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Social engagement
• Group activity

BMC Geriatrics

ISSN: 1471-2318

• Submission enquiries: [email protected]
• General enquiries: [email protected]

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.

Conduct research from conception to analysis with ATLAS.ti.

Download a free trial of ATLAS.ti to see how our software can aid you in the research process.

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.

Look at your data from all angles with ATLAS.ti.

Analyze your data quickly and efficiently with ATLAS.ti. Click here for a free trial.

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.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Nepal J Epidemiol
• v.12(1); 2022 Mar

The Growing Importance of Mixed-Methods Research in Health

Sharada prasad wasti.

1,2 School of Human and Health Sciences, University of Huddersfield, United Kingdom

Padam Simkhada

3 Centre for Midwifery, Maternal and Perinatal Health, Bournemouth University, Bournemouth, United Kingdom

Edwin R. van Teijlingen

Brijesh sathian.

4 Geriatrics and long term care Department, Rumailah Hospital, Hamad Medical Corporation, Doha, Qatar

Indrajit Banerjee

5 Sir Seewoosagur Ramgoolam Medical College, Belle Rive, Mauritius

All authors have made substantial contributions to all of the following: (1) the conception and design of the study (2) drafting the article or revising it critically for important intellectual content, (3) final approval of the version to be submitted

There is no conflict of interest for any author of this manuscript.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sector.

This paper illustrates the growing importance of mixed-methods research to many health disciplines ranging from nursing to epidemiology. Mixed-methods approaches requires not only the skills of the individual quantitative and qualitative methods but also a skill set to bring two methods/datasets/findings together in the most appropriate way. Health researchers need to pay careful attention to the ‘best’ approach to designing, implementing, analysing, integrating both quantitative (number) and qualitative (word) information and writing this up in a way offers greater insights and enhances its applicability. This paper highlights the strengths and weaknesses of mixed-methods approaches as well as some of the common mistakes made by researchers applying mixed-methods for the first time.

Quantitative and qualitative research methods each address different types of questions, collect different kinds of data and deliver different kinds of answers. Each set of methods has its own inherent strengths and weaknesses, and each offers a particular approach to address specific types of research questions (and agendas). Health disciplines such as dentistry, nursing, speech and language therapy, and physiotherapy often use either quantitative or qualitative research methods on their own. However, there is a steadily growing literature showing the advantages of mixed-methods research is used in the health care and health service field [ 1-2 ]. Although we have advocated the use of mixed-methods in this journal eight years ago [ 3 ], there is still not enough mixed-methods research training in the health research field, particularly for health care practitioners, such as nurses, physiotherapists, midwives, and doctors, wanting to do research. Mixed-methods research has been popular in the social sciences since the twentieth century [ 4 ], and it has been growing in popularity among healthcare professionals [ 5 ], although it is still underdeveloped in disciplines such nursing and midwifery [ 6 , 7 ].

Underpinning philosophies

To help understand that mixed-methods research is not simply employing two different methods in the same study, one needs to consider their underpinning research philosophies (also called paradigms). First, quantitative research is usually underpinned by positivism. This includes most epidemiological studies; such research is typically based on the assumption that there is one single real world out there that can be measured. For example, quantitative research would address the question “What proportion of the population of India drinks coffee?” Secondly, qualitative research is more likely to be based on interpretivism. This includes research based on interviews and focus groups, research which us is typically based on the assumption that we all experience the world differently. Since we all live in a slightly different world in our heads the task of qualitative research is to analyse the interpretations of the people in the sample. For example, qualitative research would address the question “How do people experience drinking coffee in India?”, and “What does drinking coffee mean to them?”

Mixed-methods research brings together questions from two different philosophies in what is being referred to as the third path [ 8 ], third research paradigm [ 9 , 10 ], the third methodology movement [ 11 , 12 ] and pragmatism [ 5 ]. The two paradigms differ in key underlying assumptions that ultimately lead to choices in research methodology and methods and often give a breadth by answering more complicated research questions [ 4 ]. The roles of mixed-methods are clear in an understanding of the situation (the what), meaning, norms, values (the why or how) within a single research question which combine the strength of two different method and offer multiple ways of looking at the research question [ 13 ]. Epidemiology sits strongly in the quantitative research corner, with a strong emphasis on large data sets and sophisticated statistical analysis. Although the use of mixed methods in health research has been discussed widely researchers raised concerns about the explanation of why and how mixed methods are used in a single research question [ 5 ].

The relevance of mixed-methods in health research

The overall goal of the mixed-methods research design is to provide a better and deeper understanding, by providing a fuller picture that can enhance description and understanding of the phenomena [ 4 ]. Mixed-methods research has become popular because it uses quantitative and qualitative data in one single study which provides stronger inference than using either approach on its own [ 4 ]. In other words, a mixed-methods paper helps to understand the holistic picture from meanings obtained from interviews or observation to the prevalence of traits in a population obtained from surveys, which add depth and breadth to the study. For example, a survey questionnaire will include a limited number of structured questions, adding qualitative methods can capture other unanticipated facets of the topic that may be relevant to the research problem and help in the interpretation of the quantitative data. A good example of a mixed-methods study, it one conducted in Australia to understand the nursing care in public hospitals and also explore what factors influence adherence to nursing care [ 14 ]. Another example is a mixed-methods study that explores the relationship between nursing care practices and patient satisfaction. This study started with a quantitative survey to understand the general nursing services followed by qualitative interviews. A logistic regression analysis was performed to quantify the associations between general nursing practice variables supplemented with a thematic analysis of the interviews [ 15 ]. These research questions could not be answered if the researchers had used either qualitative or quantitative alone. Overall, this fits well with the development of evidence-based practice.

Despite the strengths of mixed-methods research but there is not much of it in nursing and other fields [ 7 ]. A recent review paper shows that the prevalence of mixed-methods studies in nursing was only 1.9% [ 7 ]. Similarly, a systematic review synthesised a total of 20 papers [ 16 ], and 16 papers [ 17 ] on nursing-related research paper among these only one mixed-methods paper was identified. Worse, a further two mixed-methods review recently revealed that out of 48 [ 18 , 19 ] synthesised nursing research papers, not one single mixed-methods paper was identified. This clearly depicts that mixed-methods research is still in its infancy stage in nursing but we can say there is huge scope to implement it to understand research questions on both sides of coin [ 4 ]. Therefore, there is a great need for mixed-methods training to enhance the evidence-based decision making in health and nursing practices.

Strengths and weaknesses of mixed-methods

There are several challenges in identifying expertise of both methods and in working with a multidisciplinary, interdisciplinary, or transdisciplinary team [ 20 ]. It increases costs and resources, takes longer to complete as mixed-methods design often involves multiple stages of data collection and separate data analysis [ 4 , 5 ]. Moreover, conducting mixed-methods research does not necessarily guarantee an improvement in the quality of health research. Therefore, mixed-methods research is only appropriate when there are appropriate research questions [ 4 , 6 ].

Identifying an appropriate mixed-methods journal can also be challenging when writing mixed-methods papers [ 21 ]. Mixed-methods papers need considerably more words than single-methods papers as well as sympathetic editors who understand the underlying philosophy of a mixed-methods approach. Such papers, simply require more words. The mixed-methods researcher must be reporting two separate methods with their own characteristics, different samples, and ways of analysing, therefore needs more words to describe both methods as well as both sets of findings. Researcher needs to find a journal that accepts longer articles to help broaden existing evidence-based practice and promote its applicability in the nursing field [ 22 ].

Common mistakes in applying mixed-methods

Not all applied researchers have insight into the underlying philosophy and/or the skills to apply each set of methods appropriately. Younas and colleagues’ review identified that around one-third (29%) of mixed-methods studies did not provide an explicit label of the study design and 95% of studies did not identify the research paradigm [ 7 ]. Whilst several mixed-methods publications did not provide clear research questions covering both quantitative and qualitative approaches. Another common issue is how to collect data either concurrent or sequential and the priority is given to each approach within the study where equal or dominant which are not clearly stated in writing which is important to mention while writing in the methods section. Similarly, a commonly overlooked aspect is how to integrate both findings in a paper. The responsibility lies with the researcher to ensure that findings are sufficiently plausible and credible [ 4 ]. Therefore, intensive mixed-methods research training is required for nursing and other health practitioners to ensure its appropriate.

The way forward

Despite the recognised strengths and benefits of doing mixed-methods research, there is still only a limited number of nursing and related-health research publications using such this approach. Researchers need training in how to design, conduct, analyse, synthesise and disseminate mixed-methods research. Most importantly, they need to consider appropriate research questions that can be addressed using a mixed methods approach to add to our knowledge in evidence-based practice. In short, we need more training on mixed-methods research for a range of health researchers and health professionals.

Acknowledgement