Mixed-methods research — deliberately combining quantitative and qualitative approaches within a single study — has grown from a niche methodological choice into an increasingly respected approach, particularly at the intersection of economics and management where large-sample patterns and rich contextual mechanisms are often both relevant to a complete answer.
Why Combine Methods at All
Quantitative and qualitative methods each have blind spots the other can address. Quantitative analysis can establish that a relationship exists and estimate its magnitude, but often can’t fully explain why. Qualitative analysis can illuminate mechanism and process, but typically can’t establish how widely a pattern generalizes. Mixed-methods designs, done well, use each approach to compensate for the other’s limitations.
Common Mixed-Methods Designs
- Exploratory sequential: Qualitative research first identifies mechanisms or generates hypotheses, followed by quantitative testing of the resulting propositions on a larger sample
- Explanatory sequential: Quantitative analysis first identifies a pattern or relationship, followed by qualitative research explaining the mechanism behind it
- Convergent parallel: Quantitative and qualitative data are collected and analyzed simultaneously, then integrated to provide a fuller picture than either would alone
What Makes Mixed-Methods Research Rigorous, Not Just Eclectic
The defining challenge in mixed-methods research is genuine integration — using findings from one method to meaningfully inform, extend, or explain the other — rather than simply presenting a quantitative section and a qualitative section side by side without connecting them analytically. Reviewers are increasingly attentive to whether integration is substantive or superficial.
Applications at the Economics-Management Intersection
Mixed-methods designs are particularly well suited to questions like: why does a policy intervention work in some firms or regions but not others (quantitative pattern, qualitative mechanism), or how do managers actually make decisions that a large-sample dataset shows correlate with firm performance (quantitative outcome, qualitative process). These questions benefit from both the generalizability of quantitative evidence and the explanatory depth of qualitative inquiry.
Practical Challenges to Plan For
- Resource and timeline demands: Mixed-methods studies typically require more time, funding, and often broader methodological expertise than single-method studies
- Journal and reviewer fit: Not every reviewer is equally trained in both traditions, making it especially important to write clearly for readers who may be more expert in one method than the other
- Genuine integration, not parallel reporting: The analysis and discussion sections need to actively connect the two strands of evidence, not simply present them as separate findings
Structuring a Mixed-Methods Paper for Reviewers
Explicitly naming your mixed-methods design (exploratory sequential, explanatory sequential, convergent) and justifying why that specific structure fits your research question helps reviewers evaluate the design on its own terms, rather than judging it against a single-method standard that doesn’t quite apply.
Where This Approach Is Headed
As datasets become richer and interdisciplinary collaboration between economists and management scholars grows more common, well-integrated mixed-methods research is likely to become an increasingly valued — and increasingly expected — approach for questions that genuinely require both breadth and depth.
For a widely used methodological reference on mixed-methods design, John Creswell’s foundational work on mixed-methods research remains a standard text across social science disciplines.
Designing a mixed-methods study spanning economics and management? Check the journal’s scope and submit your manuscript through the paper submission page.