| name | jms-methods |
| description | Use when the research design is the bottleneck for a Journal of Management Studies (JMS) manuscript — matching design (qualitative case/ethnography, process/longitudinal, survey, archival, experiment, multi-method) to the theoretical question, with qualitative rigor treated as first-class. Designs the study; it does not run the estimation or trustworthiness checks (jms-data-analysis). |
Research Design & Methods (jms-methods)
When to trigger
- The design may not match the theory's level, timing, or causal claim
- A qualitative study's case selection, saturation, or analytic procedure is under-specified
- Quantitative data are single-source, single-wave, self-reported (common-method bias risk)
- The theory is causal but the design is cross-sectional/correlational
- A reviewer says "the design cannot test/show this" or "the method is not rigorous enough"
Match the design to the question — pluralism with rigor
JMS welcomes all rigorous designs and is, distinctively, a friendly home for qualitative and process work — but rigor must clear a top-tier bar regardless of method. Choose the design the question demands:
| Theoretical claim / question | Design that earns it |
|---|
| How/why a phenomenon emerges or works | Inductive multi-case (Eisenhardt) or ethnography |
| How something unfolds over time | Process / longitudinal (temporal bracketing, visual mapping) |
| Causal effect of a manipulable cause | Experiment (lab / field / online) or natural experiment |
| Whether & how much, with generalisation | Survey (multi-wave) or panel archival |
| Cross-level mechanism (firm → individual) | Multilevel / nested design (HLM-appropriate) |
| Contested, novel, or richly contextual | Multi-method (e.g., qual study 1 + quant study 2) |
Designing qualitative rigor (first-class at JMS)
- Case/site selection is theoretical, not convenient: state the sampling logic (extreme, polar, theoretically replicating). Justify the number of cases and why they let the theory travel.
- Data sources triangulated: interviews + archives + observation; report counts (informants, hours, documents) and the period.
- Analytic procedure stated: which approach (Gioia, Eisenhardt cross-case, grounded theory, narrative/temporal bracketing) and how codes became constructs.
- Trustworthiness in the qualitative idiom: member checking, an audit trail, negative-case analysis, inter-coder agreement where appropriate — not p-values.
Designing against the threats JMS reviewers cite (quantitative)
- Common-method bias: separate sources / temporal separation across waves; objective or archival outcomes where possible. Procedural design beats a post-hoc Harman test (the Podsakoff guidance is standard).
- Endogeneity (archival/survey): anticipate omitted variables, reverse causality, selection; plan an identification strategy (panel FE, DiD, IV/2SLS, natural experiment, matching) and state each one's assumptions.
- Measurement: validated multi-item scales; pilot new measures; plan a CFA; state the level each construct is measured at and justify any aggregation (ICC, r_wg).
- Power & sampling: justify the frame, response rate, and power — interactions need more power than main effects.
Level-of-analysis discipline
State the level for theory, measurement, and analysis, and keep them aligned. If theory is at the firm level but data are individual, justify aggregation; for cross-level effects, model the nesting — do not run OLS on nested data.
Execution bridge (StatsPAI / Stata MCP)
For the empirical / causal lane, estimate and audit rather than only specify. Full
map: execution-with-mcp. JMS mixes qualitative and quantitative management research; the chain below is for the quantitative-empirical lane.
detect_design → recommend → fit with as_handle=true → audit_result to
enumerate the checks the design owes.- Panel / staggered DiD:
callaway_santanna / sun_abraham + bacon_decomposition
honest_did_from_result. IV: effective_f_test + anderson_rubin_ci. RDD:
rdrobust + mccrary_test.
- Experiments: randomization-based inference and
romano_wolf for the many-outcome
family-wise correction reviewers expect.
Match the toolchain to the reviewer pool, and report the effect size the venue
wants. A run end-to-end (synthetic data, real returns) is in the
JF execution walkthrough.
Checklist
Anti-patterns
- Qualitative-by-default vagueness: "we did a case study" with no selection logic or analytic procedure
- Cross-sectional causal claims: "X causes Y" from one-wave correlational data
- CMB as afterthought: relying on a single Harman test instead of designed separation
- Ignored endogeneity: an obviously endogenous regressor with no identification strategy
- Mismatched levels: theorising at the firm level, testing disaggregated individual data via OLS
- Method theatre: a fashionable estimator or qualitative label not justified by the question
Output format
【Design】qual multi-case / ethnography / process / experiment / survey / panel-archival / multi-method
【Question-design fit】can the design answer each claim? notes …
【Qualitative rigor】(if qual) case selection · sources+counts · analytic procedure · trustworthiness
【CMB / endogeneity】(if quant) procedural remedy · identification strategy
【Measures】validated? new (piloted)? CFA planned?
【Levels】theory / measurement / analysis aligned? aggregation justified?
【Next step】jms-data-analysis
