Collecting both qualitative and quantitative data is the easy part of mixed methods research. The harder, more consequential step is integration — actually bringing the two strands together into findings that say something neither one could say alone. Studies that collect both but analyze and report them side by side, without ever connecting them, aren’t really doing mixed methods integration at all. This guide covers the main techniques researchers use to do that connecting well.
Convergent parallel designs: collect separately, merge on purpose
In a convergent parallel design, quantitative and qualitative data are collected and analyzed concurrently and independently, then deliberately merged during interpretation (Bilgic et al., 2026; Adhikari & Timsina, 2024). The value of doing this is triangulation in the fullest sense — not just confirming one strand with the other, but being able to see convergence, complementarity, or genuine divergence between what the numbers show and what participants actually said (Östlund, Kidd, Wengström, & Rowa-Dewar, 2011; Adhikari & Timsina, 2024; Bailey, Hole, Plumb, & Caskey, 2022).
For that merge to actually work, the two data instruments need to be designed around the same underlying constructs from the start — a survey scale and an interview question that are notionally both “about trust” but built independently, with no shared conceptual anchor, are much harder to genuinely integrate later (Fetters, Curry, & Creswell, 2013; Haynes-Brown & Fetters, 2021).
Joint displays: where integration actually happens
The most concrete integration technique is the joint display — a table, matrix, or figure that puts quantitative results and qualitative findings side by side, deliberately, so patterns and contradictions between them become visible rather than staying buried in two separate results sections (Haynes-Brown & Fetters, 2021; Guetterman, Fàbregues, & Sakakibara, 2021; Guetterman, Fetters, & Creswell, 2015). The most common formats are side-by-side comparison matrices and “statistics-by-themes” tables, which cross-tabulate numerical findings directly against qualitative categories (Guetterman et al., 2021; Guetterman et al., 2015; Fetters & Tajima, 2022).
Building a joint display isn’t just a presentation choice — the act of constructing one is itself analytic. Laying quantitative and qualitative findings next to each other creates the exact conditions for noticing a meta-inference (something only visible from combining both strands) or a discrepancy (where the two strands actually disagree) that wouldn’t have surfaced from either strand read on its own (Haynes-Brown & Fetters, 2021).
A structured approach: the Pillar Integration Process
For researchers who want a more standardized procedure than “build a table and see what emerges,” the Pillar Integration Process offers a four-stage protocol: listing findings from each strand, comparing them, checking them against each other, and integrating what survives that comparison into a final set of “pillars” — synthesized findings genuinely built from both strands rather than either one alone (Johnson, Grove, & Clarke, 2019; Haynes-Brown & Fetters, 2021). This kind of structured protocol is particularly useful when a study has enough separate findings, from both strands, that an unstructured “just look for patterns” approach risks missing things.
What makes integration credible
Across this literature, the studies flagged as methodologically strong share a few things: an explicit rationale for why integration matters for that specific research question, a clear procedural diagram showing when and how the strands were combined, and integrated findings reported as meta-inferences — conclusions that specifically required both strands — rather than two sets of findings simply placed next to each other in the same paper (Johnson et al., 2019; Hirose & Creswell, 2022; Bailey et al., 2022).
The common thread
Whether the technique is a convergent parallel design, a joint display, or the Pillar Integration Process, the underlying discipline is the same: integration has to be planned and executed deliberately. Collecting two kinds of data and hoping a connection between them becomes obvious at write-up time is not integration — it’s parallel reporting. The methods that work all force the comparison to happen explicitly, on the page, where it can actually be checked.
Mixed methods work in Byleron QDA: because your qualitative coding stays queryable alongside document-level metadata, building a statistics-by-themes joint display means pulling structured comparisons straight from your coded project — not manually re-assembling two separate analyses into one table by hand.
References
- Adhikari, R., & Timsina, T. P. (2024). An Educational Study Focused on the Application of Mixed Method Approach as a Research Method. OCEM Journal of Management, Technology & Social Sciences. https://doi.org/10.3126/ocemjmtss.v3i1.62229
- Bailey, P. K., Hole, B., Plumb, L. A., & Caskey, F. (2022). Mixed-methods research in nephrology. Kidney International. https://doi.org/10.1016/j.kint.2022.01.027
- Bilgic, E., Kahlke, R., Poth, C., Abbas, M., Chopra, S., & Ngo, Q. N. (2026). Reimagining Convergent Designs: Advancing Qualitatively-Driven Adaptive Designs Through Concurrent Analysis Strategies. Journal of Mixed Methods Research. https://doi.org/10.1177/15586898261453199
- Fetters, M., Curry, L., & Creswell, J. (2013). Achieving integration in mixed methods designs — principles and practices. Health Services Research, 48(6 Pt 2), 2134–2156. https://doi.org/10.1111/1475-6773.12117
- Fetters, M., & Tajima, C. (2022). Joint Displays of Integrated Data Collection in Mixed Methods Research. International Journal of Qualitative Methods, 21. https://doi.org/10.1177/16094069221104564
- Guetterman, T. C., Fàbregues, S., & Sakakibara, R. (2021). Visuals in joint displays to represent integration in mixed methods research: A methodological review. Methods in Psychology. https://doi.org/10.1016/j.metip.2021.100080
- Guetterman, T. C., Fetters, M., & Creswell, J. (2015). Integrating Quantitative and Qualitative Results in Health Science Mixed Methods Research Through Joint Displays. The Annals of Family Medicine, 13, 554–561. https://doi.org/10.1370/afm.1865
- Haynes-Brown, T. K., & Fetters, M. (2021). Using Joint Display as an Analytic Process: An Illustration Using Bar Graphs Joint Displays From a Mixed Methods Study of How Beliefs Shape Secondary School Teachers’ Use of Technology. International Journal of Qualitative Methods, 20. https://doi.org/10.1177/1609406921993286
- Hirose, M., & Creswell, J. (2022). Applying Core Quality Criteria of Mixed Methods Research to an Empirical Study. Journal of Mixed Methods Research, 17, 12–28. https://doi.org/10.1177/15586898221086346
- Johnson, R. E., Grove, A., & Clarke, A. (2019). Pillar Integration Process: A Joint Display Technique to Integrate Data in Mixed Methods Research. Journal of Mixed Methods Research, 13, 301–320. https://doi.org/10.1177/1558689817743108
- Östlund, U., Kidd, L., Wengström, Y., & Rowa-Dewar, N. (2011). Combining qualitative and quantitative research within mixed method research designs: A methodological review. International Journal of Nursing Studies, 48, 369–383. https://doi.org/10.1016/j.ijnurstu.2010.10.005