An introduction to mixed methods research

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.

Mixed methods research question examples
  • 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)?

When to use 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:

  • 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.

Research example
You want to research cycling safety in high-traffic areas of Amsterdam. If you’re interested in the frequency of accidents and where they occur, this could be a straightforward quantitative analysis. If you’re interested in the nature of complaints submitted by cyclists, or their perceptions about cycling in particular areas, then a qualitative approach may fit best.

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

For example, you could use a mixed methods design to investigate whether areas perceived as dangerous have high accident rates, or to explore why specific areas are more dangerous for cyclists than others.

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.

Mixed methods research designs

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:

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.

Example: Convergent parallel design
In your research on cycling safety in Amsterdam, you undertake both sides of your research simultaneously:

  • 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.

When you finish your data collection and analysis, you then compare results and tie your findings together.

Embedded

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.

Example: Embedded design
As part of a quantitative study testing whether the number of cyclist complaints about an area correlates with the number of accidents, you could “embed” a series of qualitative interviews with cyclists who submitted complaints to further strengthen your argument. The bulk of your research remains quantitative.

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.

Example: Explanatory sequential
You analyze the accident statistics first and draw preliminary conclusions about which areas are most dangerous. Based on these findings, you conduct interviews with cyclists in high-accident areas and analyze complaints qualitatively.

You can utilize the qualitative data to explain why accidents occur on specific roads, and take a deep dive into particular problem areas.

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.

Example: Exploratory sequential design
You first interview cyclists to develop an initial understanding of problem areas, and draw preliminary conclusions. Then you analyze accident statistics to test whether cyclist perceptions line up with where accidents occur.

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Advantages of mixed methods research

“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.

Disadvantages of mixed methods research

Workload

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.

Frequently asked questions

What’s the difference between quantitative and qualitative methods?

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.

What is mixed methods research?

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

What is data collection?

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.

What is triangulation in research?

Triangulation means using multiple methods to collect and analyze data on the same subject. By combining different types or sources of data, you can strengthen the validity of your findings.

What are the main types of mixed methods research designs?

These are four of the most common mixed methods designs:

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

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Tegan George

Tegan is an American based in Amsterdam, with master's degrees in political science and education administration. While she is definitely a political scientist at heart, her experience working at universities led to a passion for making social science topics more approachable and exciting to students. A well-designed natural experiment is her favorite type of research, but she also loves qualitative methods of all varieties.