Mixed methods research is most powerful when designed intentionally, carried out iteratively, and supported by connected tools. NVivo allows researchers to code qualitative responses and uncover recurring themes, while XLSTAT analyzes those structured outputs alongside demographic or survey data in Excel. Together, they help researchers move seamlessly from thematic insights to statistical validation, creating a more holistic, data-driven understanding of complex questions.
Mixed methods research involves any study where qualitative and quantitative data are both used. Since no one method provides the answer to every question, researchers combine approaches to view their research questions from different angles. The synthesis of insights from multiple analyses is intended to provide a more holistic understanding of complex concepts and phenomena.
But what does it mean to do mixed methods research? It’s a mistake to think of mixed methods as simply combining qualitative and quantitative analysis without a robust, intentional research strategy. In a recent webinar, Lumivero’s Efthalia Anagnostou and Roehl Sybing demonstrated how researchers can make the most of their data in a mixed methods study using NVivo for qualitative coding and XLSTAT for statistical analysis.
Watch the webinar on-demand or continue reading to learn five principles that make mixed methods research powerful and accessible to any researcher with the right tools.
Combining qualitative and quantitative research just for the sake of complexity doesn’t make for a robust study. A mixed methods study is most appropriate when your research inquiry needs multiple forms of analyses.
In the webinar, Roehl and Thalia presented a sample study using synthetic data regarding the perceptions of farmers. The goal was to identify common concerns among farmers and what characteristics those farmers had such as where they lived, how old they were, and what kind of crops they grew.
“The first reason is that we want to fill a research gap, whether it’s theoretical or methodological…when you look at your data from different methods, different approaches, you can address limitations that a single approach can’t tackle by itself,” said Roehl.
In this example, free-text responses from farmers highlighted themes such as drought concern, reliance on local knowledge, and government dependence. These insights were first coded in NVivo to identify recurring themes then converted into a structured format for statistical analysis. That combination allows for investigation into how demographic attributes, like age or location, were associated with particular viewpoints. The example showed why mixed methods is best suited for research questions that cannot be answered by one method alone.
Mixed methods studies require planning from the outset rather than adding a second method after data collection has begun. A clear design establishes how the qualitative and quantitative components interact, when each will be used, and what role they play in answering the research question. Without that structure, the findings risk becoming fragmented rather than integrated.
An intentional design also involves selecting from different possible frameworks. Researchers may choose a convergent design, where qualitative and quantitative data are collected simultaneously, or a sequential design, where one approach follows the other.
For example, a sequential design might begin with qualitative analysis to generate hypotheses and then move into quantitative testing. The critical factor is aligning the design with the research goals, rather than treating mixed methods as a simple combination of techniques.
Mixed methods research is rarely a linear process. Insights gained from one round of analysis often raise new questions that require further exploration, either qualitatively or quantitatively. Iteration strengthens the study by allowing researchers to refine their approach, revisit earlier assumptions, and generate findings that are more nuanced and reliable.
The iterative nature of mixed methods is especially valuable for applied research. A study might identify initial themes in qualitative data, test those themes through quantitative methods, and then return to participants for clarification or follow-up. Each stage builds on the previous one, producing more robust insights than a single pass of data collection and analysis.
Software plays an important role in helping researchers manage and connect different types of data. The right mixed methods research tool can handle both qualitative and quantitative analyses, integrate with other tools, reduce the risk of siloed findings and simplify the process of drawing connections. The right combination of tools supports efficiency and clearer synthesis across data types.
Researchers can use NVivo to code qualitative data, quantify the results, and then export them into a spreadsheet format compatible with XLSTAT. This allows statistical testing and visualization of themes identified in the qualitative analysis.
The advantage of this workflow is its efficiency: NVivo quickly transforms open-ended responses into structured variables, and XLSTAT provides the statistical power in Microsoft Excel to analyze them further. For researchers managing large datasets or complex designs, these connections save time, reduce errors, and ensure findings can move seamlessly from one stage to the next.
Collaboration is often key in mixed methods projects. Researchers with different methodological backgrounds bring unique insights to both the design and interpretation of the study. By combining expertise, teams can strengthen the analysis, challenge assumptions, and produce interpretations that account for diverse perspectives.
One effective way to structure this collaboration is by dividing focus areas: for instance, one researcher may take the lead on qualitative thematic analysis, while another focuses on quantitative statistical testing. When both perspectives are applied to the same research question in tandem, the result is richer, more holistic insights than either could achieve alone.
This kind of partnership demonstrates one of the broader strengths of mixed methods research: it encourages dialogue between different traditions of inquiry. While qualitative researchers may focus on meaning, context, and interpretation, quantitative researchers bring expertise in measurement and statistical inference. Together, these perspectives can produce findings that are more balanced and relevant to diverse audiences.
Whether you're using our intuitive interface or taking advantage of our AI tools for academic research, NVivo can help you maximize the insights from your qualitative data. XLSTAT adds powerful, Excel-based statistical analysis to help make quantitative research quick, easy, and convenient. Whatever your research needs, Lumivero’s research solutions can support you at every step of the research process. Buy NVivo and XLSTAT today to get started.
