Exploratory sequential design (Mixed methods #4)

Exploratory sequential design is a mixed-methods strategy built to turn qualitative insight into a quantitative test of how broadly those insights hold up. The core workflow starts with collecting and analyzing qualitative data, then using what emerges from that analysis to build a quantitative instrument—typically a questionnaire—so researchers can check whether findings transfer to a wider or different population. That “qual-to-quant” bridge matters because it directly addresses a common criticism of qualitative research: small, case-focused studies can be rich, but they’re often seen as too limited to inform broader understanding.

The design is especially useful when either (1) a phenomenon has already been studied qualitatively and the goal is to assess generalizability or transferability, or (2) the phenomenon is under-researched and there aren’t established variables or measurement tools to include in a quantitative instrument. In the first scenario, qualitative results from an initial sample can be tested against a broader group to see whether people interpret or experience the phenomenon similarly. In the second scenario, researchers may not even know what to measure in quantitative terms—because the construct is not well defined in existing literature. The transcript illustrates this with a study of Polish migrants’ English language identity, focusing on how English relates to a sense of self. Because English language identity was treated as under-researched, the researcher couldn’t confidently select questionnaire variables or decide which items to include. Instead, an in-depth qualitative phase—interviews and reflective journals—was used to develop an understanding of what “English language identity” involves. Only after that conceptual groundwork was done was a questionnaire constructed to investigate whether more people endorsed similar ideas as the original qualitative participants.

Procedurally, the approach unfolds in stages: qualitative data collection (for example, interviews), qualitative analysis to identify the construct’s components, and then translating those components into questionnaire variables or items. A quantitative phase follows, with data analysis aimed at comparing results across phases. The key check is whether the quantitative findings align with the qualitative results—suggesting that the phenomenon’s elements identified in the qualitative work can be observed beyond the initial sample.

The main trade-offs are practical and methodological. Researchers need competence in both qualitative and quantitative methods, and the design is time-consuming because the quantitative phase can’t begin until qualitative analysis is complete. It is also often emergent: plans may start as purely qualitative, and only later does the need (or opportunity) arise to test transferability. That emergence creates proposal and timeline risks—details of the second phase may be uncertain at the outset, and additional months may be required to develop, conduct, and analyze the quantitative study. Even so, the transcript frames the design’s strength as outweighing these challenges, largely because it helps build a stronger, more defensible argument that qualitative insights can inform broader measurement and understanding.