Mixed-methods isn't "a survey plus some interviews" — it's a deliberate design where qualitative and quantitative strands inform each other in a specific, named structure. Without that integration, a committee will read it as two disconnected mini-studies stapled together.
| Design | Sequence | Best Fit |
|---|---|---|
| Convergent | Qual and quant collected at the same time, merged at interpretation | Comparing or corroborating findings from two angles |
| Explanatory sequential | Quant first, then qual to explain unexpected results | Statistical findings that need contextual explanation |
| Exploratory sequential | Qual first, then quant to test or generalize what emerged | Building/testing an instrument from qualitative themes |
The defining feature of mixed-methods — and the part committees scrutinize most — is the integration point: the specific place where qualitative and quantitative strands are brought together and interpreted jointly, not just presented side by side. This might be a joint display (a table merging qual themes against quant results), a narrative weaving section, or a data transformation (turning qual themes into quant variables, or vice versa).
A joint display is the easiest integration tool to defend. A table or matrix showing how a qualitative theme aligns (or doesn't) with a quantitative finding gives your committee a concrete artifact of integration — far more convincing than a paragraph asserting that the two strands "support each other."
A named design, justified rationale, and a real integration point — not two studies stapled together.
No — many mixed-methods studies are deliberately quant-dominant or qual-dominant depending on the research priority. What matters is being explicit about the weighting and the reason for it, not pretending both strands are equal when they aren't.
Yes — this is common. We focus on building the integration point and the joint analysis that ties your existing datasets together, rather than redoing data collection.
Qualitative and quantitative sample sizes follow different logics — saturation for one, statistical power for the other. We write separate, appropriately justified rationales for each rather than forcing one standard onto both.