Mixed Methods Research 混合方法研究
Released已發布Design and conduct mixed methods research using convergent, explanatory sequential, or exploratory sequential strategies with genuine integration of qualitative and quantitative strands. Use this skill when the user needs to choose a mixed methods design, integrate qualitative and quantitative data at design, methods, or interpretation levels, justify mixing on pragmatist grounds, or when they ask 'which mixed methods design should I use', 'how do I integrate qual and quant findings', or 'is running both qual and quant enough to be mixed methods'.
學術研究技能：Mixed Methods Research 分析與應用。
Overview概述
Mixed methods research combines qualitative and quantitative approaches within a single study or program of inquiry to leverage the strengths of both. Grounded in pragmatism, it selects methods based on what works best for the research question. The defining feature is not merely using both approaches but genuinely integrating them at design, methods, or interpretation levels to produce insights neither approach could achieve alone.
When to Use使用時機
- A single approach (qual or quant alone) cannot adequately address the research question
- Quantitative results need qualitative explanation (why did the effect occur?)
- Qualitative findings need quantitative testing (does the pattern generalize?)
- Complex phenomena require both breadth (quant) and depth (qual) of understanding
When NOT to Use不適用時機
- When the research question can be fully addressed by one approach
- When the researcher lacks competence in either qualitative or quantitative methods
- When resources (time, funding, team) cannot support both strands adequately
- When the paradigmatic assumptions of qual and quant are irreconcilable for the specific study
Assumptions前提假設
IRON LAW: Mixed methods requires GENUINE INTEGRATION — running qual
and quant in parallel without connecting findings is NOT mixed methods,
it is two separate studies stapled together. Integration must occur at
design, methods, or interpretation level.
Key assumptions:
- Pragmatism as the philosophical foundation — what works for the research question determines the method
- Both qualitative and quantitative data have legitimate claims to knowledge
- Integration is the defining feature — not merely combining, but connecting, merging, or embedding
- The research question drives design choice, not methodological allegiance
Framework 框架
Step 1: Select the Mixed Methods Design
| Design | Structure | Purpose |
|---|---|---|
| Convergent | QUAL + QUANT simultaneously | Compare and merge findings for completeness or validation |
| Explanatory Sequential | QUANT → qual | Use qual to explain, elaborate, or contextualize quant results |
| Exploratory Sequential | QUAL → quant | Use qual to develop instruments, variables, or typologies tested by quant |
| Embedded | qual within QUANT (or vice versa) | One strand supports the other within a larger design |
Use uppercase to indicate the dominant strand; lowercase for the supporting strand.
Step 2: Implement Each Strand with Rigor
Apply full methodological rigor to each strand independently. Qualitative strand follows qualitative quality criteria (credibility, transferability). Quantitative strand follows quantitative criteria (validity, reliability). Do not compromise one strand for the other.
Step 3: Integrate the Strands
Integration strategies by level:
| Level | Strategy | Example |
|---|---|---|
| Design | Embedding one strand within the other | Qual interviews within an RCT |
| Methods | Building one strand from the other | Qual themes become survey items |
| Interpretation | Joint display, merging, or narrative weaving | Side-by-side comparison table |
Step 4: Draw Meta-Inferences
Synthesize findings from both strands into meta-inferences that transcend what either strand alone could produce. Address convergence, complementarity, or divergence between strands.
Output Format輸出格式
Gotchas注意事項
- A joint display table is the gold standard for demonstrating integration — if you cannot produce one, integration may be absent
- Do NOT privilege one strand over the other unless the design explicitly calls for it (e.g., QUANT-dominant explanatory sequential)
- Mixing paradigms requires philosophical justification — pragmatism is common but not the only option (dialectical pluralism is another)
- Explanatory sequential requires the QUANT phase to be complete before designing the qual phase — you cannot design both at the start
- Sample sizes differ between strands: the quant sample follows power analysis, the qual sample follows saturation or information richness
- Reviewers often critique "quasi-mixed" studies where the two strands never actually connect — make integration explicit
References參考資料
- Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.
- Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving integration in mixed methods designs. Health Services Research, 48(6pt2), 2134-2156.
- Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research. Sage.