Most mixed-methods papers never actually mix. The numbers get a results section, the themes get a results section, and somewhere in the discussion a single paragraph asserts that the two line up. That paragraph is usually the thinnest, least defensible part of the whole study, and a sharp reviewer knows it. The problem is not that the researcher lacked insight. It is that the integration happened in their head and never made it onto the page where anyone could check it.
A joint display fixes that. It is the visual anchor of mixed-methods integration: a table that sets a quantitative result next to the qualitative evidence on the same row, so the two strands can be read together instead of a hundred pages apart. Do it well and you produce a third thing that neither strand shows on its own, the meta-inference, the integrated reading that only appears once the numbers and the narratives are side by side.
Why the display is also an audit trail
Putting the strands on the same row does more than look tidy. It creates a record a reviewer can follow and verify. Anyone reading the table can see exactly which result you paired with which evidence, and what conclusion you drew from the pair. There is no leap of faith between the analysis and the claim, because the join is right there in a cell, traceable back to the data behind it. That transparency is what turns integration from an assertion into something you can defend.
There is no single correct shape for the table. The right structure follows your design, and three of them cover most studies.
1. Convergent design: the construct-level display
In a convergent design you collect both strands at the same time, analyze them separately, then bring them together to see whether they agree, disagree, or complete each other. The display that fits this design lines them up construct by construct: one row per idea, a column for what the numbers show, a column for what the narratives show, and a final column for the integrated reading.
Picture a workforce-training study that measured confidence, wages, engagement, and completion, and also interviewed the trainees. Line them up construct by construct and the integration becomes concrete:
| Construct | What the numbers show | What the narratives show | Integrated meaning (meta-inference) |
|---|---|---|---|
| Confidence | Average confidence rose from 3.8 to 7.4 across the cohort | Trainees describe finally believing they could do the work | Confirmed. The rise is a real gain, not a rating artifact. |
| Wages | 184 of 240 trainees saw a wage rise at 12 months | Most who did not point to job offers that fell through late | Explained. Flat wages trace to the job market, not the program. |
| Engagement | Week-4 attendance dipped 18 percent across sites | Trainees mention feeling lost in the middle weeks | Diagnosed. A curriculum-pacing problem, and a fixable one. |
Notice what the last column buys you. The confidence row is confirmation, two strands agreeing. The wages row is explanation, qualitative evidence accounting for a flat number. The engagement row is diagnosis, a problem the survey flagged and the interviews located. None of those readings live in either strand alone.
2. Explanatory sequential design: statistics by themes
In an explanatory sequential design the numbers come first, and you gather qualitative data afterward to explain the patterns, the outliers, and the groups the statistics turned up. The display for this design sorts the qualitative evidence underneath the quantitative categories it explains.
Say a survey of clinics produced a relationship-quality score, and you split the clinics into the top and bottom quartiles. You then place representative quotes under each group. The low-scoring clinics describe people guarding information and protecting themselves; the high-scoring clinics describe open doors and quick, blame-free problem solving. Set side by side, the quotes explain the score. The number told you the groups differ. The evidence tells you why, and points straight at what to change.
3. Intervention design: the side-by-side case display
Sometimes a trial comes back null. Overall, the intervention shows no statistical difference, and a numbers-only report would stop there and call it a failure. A case-level display keeps you from stopping too soon. You put each case, a clinic, a site, a classroom, on its own row, set its outcome change next to a short read of how it actually ran the intervention, and the picture separates into two stories.
One clinic moves its screening rate from 42 percent to 68 percent; the notes describe an adaptive team that ran weekly huddles and cleared its own bottlenecks. Another slips from 51 to 49 percent; the notes describe a rigid hierarchy, leadership that waved off staff ideas, and heavy turnover. The flat average hid both. The case display recovers them and hands you the real finding: this intervention works where a team has the room to adapt it, and stalls where it does not.
Four rules for a display that holds up
Whichever design you are in, the same four habits keep a joint display defensible. Label the sources clearly, so a reader can tell at a glance which columns are quantitative, standardized scales and percentages, and which are qualitative, codes and quotes. Align the structure to your design, so the table mirrors how the study actually flowed. Always synthesize to a meta-inference, and never let two columns of data sit side by side without a stated conclusion, because that empty final column is exactly where reviewers stop trusting you. And keep every cell traceable, so each number and each quote can be followed back to its source.
Where MM Studio builds the display for you
Here is the catch. Built by hand in a word processor, a joint display is slow and brittle. The quotes are in one file, the statistics are in another, the meta-inference is whatever you can hold in your memory, and the moment a code or a number changes, the table is out of date and nobody knows. That friction is the reason so many studies fall back on the one-paragraph claim instead.
ReliCheck MM Studio treats the joint display as a native output of the analysis rather than a document you assemble afterward. Your quantitative results and your coded qualitative evidence sit in the same workspace, so aligning a number with the theme that explains it happens where the analysis already is. The meta-inference gets its own column by design, which keeps the integrated reading in front of you instead of stranded in the discussion. And because every cell stays linked to the result or the quote beneath it, the audit trail a reviewer wants is built as you work, not reconstructed under deadline.
Integration is the whole promise of mixed methods, and for most researchers it has been the part that stays weakest because the tools made it hardest. Put the two strands on the same row, insist on the meta-inference, and keep the trail intact, and the join stops being the thinnest paragraph in your paper and becomes the strongest. MM Studio is built so that the place your methods come together is also the most defensible part of the study, which is exactly where it should be.
ReliCheck MM Studio builds convergent, explanatory, and case-level joint displays from your linked quantitative results and coded qualitative evidence, with a meta-inference column and a cell-level audit trail. See it at mmstudio.relichecksurvey.com.