What is mixed methods research and how to do it?
Introduction to mixed methods research
For more best practices see our method overview
Definition of mixed methods research
Mixed methods research is an approach that combines both qualitative and quantitative research methods and techniques into a single study. This integration allows for a more comprehensive understanding of a research problem by leveraging the strengths of both methodological paradigms. Qualitative data provides depth and context, exploring the underlying reasons, opinions, and motivations, while quantitative data offers measurable, statistical evidence. By combining these methods, you can triangulate findings, validate results, and address research questions more robustly. Mixed methods research is particularly useful in complex studies where neither qualitative nor quantitative approaches alone would suffice. This approach can be implemented sequentially, concurrently, or iteratively, depending on the research design.
Strategies for a mixed-methods approach
Depending on the research question you are investigating, there are different ways of integrating a mixed methods approach into your research design. An important factor is the order in which you apply the quantitave and qualitative methods. Each of the following three options are valid and you have to weigh the pros and cons of each carefully before engaging in the research. We advise that you do at least one peer debriefing session to iteratively work on your research design before starting with the sampling of participants and gathering of data.
- Qualitative first: An exploratory design starts with qualitative research to explore phenomena to build a theory and is followed by quantitative research to test the theory (potentially already framed as hypothesis ready for a statistical test) generated from qualitative findings. This is also the classic research cycle if it is not performed as mixed-methods in one single study. While confirmatory case studies also exist, more often than not, theory testing is done with a quantitative approach while theory building likely takes a qualitative route. So quantitative studies often follow from qualitative ones.
- Quantitative first: An explanatory design begins with quantitative research, followed by qualitative methods to explain the statistical results in more detail. Often times you will measure an anomally in the data where it is not immediately obvious where it came from and why. Performing semi-structured interviews with the participants can help shed some light on the findings.
- Interatively: Sometimes it can be benefitial to run both methodologies in an iterative fashion. Qualitative research is often iterative in nature anyway with theoretical sampling driving the data gathering. A quantitative approach can accompany this iterative workflow where in each iteration you compile some statistics about your study so that in the next iteration you can inquire about any interesting or surprising results of the quantitative analysis of the last iteration. This can be a very powerful strategy. However you should be aware that this kind of study is also more labor intensive. But because it can be very rewarding it is worth considering making the iterations a bit longer than you would normally have them in a purely qualitative study.
- Concurrently: If there is little interaction between the two methods to be expected, you can also run them cuncurrently to save time. However from our experience, in most cases an iterative strategy is preferable due to the opportunity of additional explanations and insights through the interactions between the methods and the iterative learning about the phenomenon which can enable you to ask better questions to your participants and see more patterns in the data.
Methodological rigor
As we have outlined on our article on research rigor, there exist two competing paradigms for maintaining and evaluating the quality and rigor of the research conducted. Each of the two paradigms is closely related to either quantitative or qualitative research. This means, for a mixed-methods approach that prioritizes qualitative and quantitative methods equally, it is advisable to address both of the paradigms for rigor. This includes ensuring validity, reliability, and trustworthiness. The best practices and methods require to fulfill corresponding dimensions of each of the paradigms is often similar: careful sampling, triangulation, and the use of consistent data collection procedures. However the framing will be different and you should be aware of the precise differences between the terms to ensure clear communication and get your paper past peer review. Mixed methods studies should also consider the potential biases that may arise from integrating different methodologies and strive to minimize them. Some methods are also closely tied to a specific epistemological stance which might make them incompatible with other methods that do not share this view of knowledge and truth.
Conclusion on mixed methods research
The mixed methods approach is a valuable and flexible tool, bridging the gap between numerical data and human experiences to generate more holistic and actionable knowledge. The mixed methods approach enables you to gain a more comprehensive and nuanced understanding of a research question, addressing its multifaceted nature from different angles. It requires careful design though, since your study will judged by peer reviewers who could be experts in either methodological paradigm.
The presentation of such a study also comes with its own challenges, both in terms of evaluating the results and interpreting the data and drawing the right connections. The interpretation of your data should provide a coherent narrative that links the qualitative and quantitative findings, highlighting how they complement each other. When reporting, clearly describe the mixed methods design, the rationale for using it, and how the integration of data provided a more comprehensive understanding of the research problem. This transparency enhances the credibility of the study and allows for replication in future research.
The presentation of such a study also comes with its own challenges, both in terms of evaluating the results and interpreting the data and drawing the right connections. The interpretation of your data should provide a coherent narrative that links the qualitative and quantitative findings, highlighting how they complement each other. When reporting, clearly describe the mixed methods design, the rationale for using it, and how the integration of data provided a more comprehensive understanding of the research problem. This transparency enhances the credibility of the study and allows for replication in future research.
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