amj-methods
GitHub针对AMJ稿件，协助匹配研究设计与理论问题。涵盖因果、纵向、档案及定性方法选择，解决共同方法偏差、内生性、测量效度及分析层级对齐等关键痛点，提升设计严谨性与期刊适配度。
触发场景
安装
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill amj-methods -g -y
SKILL.md
Frontmatter
{
"name": "amj-methods",
"description": "Use when the research design and method are the bottleneck for an Academy of Management Journal (AMJ) manuscript — matching design (archival, survey, experiment, multi-method, field) and level of analysis to the theoretical question. Designs the study; it does not run the estimation or validity checks (amj-data-analysis)."
}
Research Design & Methods (amj-methods)
When to trigger
- The design may not match the theory's level, timing, or causal claim
- Data are single-source, single-wave, and self-reported (common-method bias risk)
- The theory is causal but the design is cross-sectional/correlational
- Constructs lack established, validated measures
- A reviewer says "the design cannot test this hypothesis" or "endogeneity is unaddressed"
Match the design to the question
AMJ explicitly welcomes all empirical methods — qualitative, quantitative, field, laboratory, meta-analytic, and mixed. The bar is fit and rigor, not a single preferred method, and qualitative designs are held to an equally demanding standard (the Eisenhardt multiple-case approach and the Gioia methodology for grounded qualitative rigor are the field's reference points).
| Theoretical claim | Design that earns it |
|---|---|
| Causal effect of a manipulable cause | Experiment (lab/field/online), or natural experiment |
| Process unfolding over time | Multi-wave panel; longitudinal/lagged design |
| Firm/strategy outcomes from archival cause | Panel archival with fixed effects + endogeneity strategy |
| Cross-level mechanism (e.g., team→indiv.) | Multilevel/nested data with HLM-appropriate structure |
| Rich, novel, or contested phenomenon | Qualitative or multi-method (often paired with a study 2) |
A two-study design (e.g., field study for generalizability + experiment for causal mechanism) is a common AMJ strength — it answers both internal and external validity.
Designing against the threats AMJ cares about
- Common-method bias (CMB): separate sources for predictor and outcome; temporal separation across waves; objective/archival outcomes where possible. Procedural remedies beat statistical fixes (the Podsakoff et al. guidance is the standard reference). Plan this before collecting data.
- Endogeneity (archival): anticipate omitted variables, reverse causality, and selection. Plan an identification strategy (instrument, natural experiment, panel fixed effects, difference-in-differences, Heckman/2SLS, propensity matching) and the assumptions each requires.
- Measurement: use validated multi-item scales; pilot new measures; plan a CFA. State the level at which each construct is measured and how cross-level data are aggregated (with justification: ICC, r_wg, aggregation theory).
- Sampling and power: justify the sampling frame, response rate, and statistical power for the focal and interaction effects (interactions need more power).
Level-of-analysis discipline
State the level for theory, measurement, and analysis, and keep them aligned. If theory is at the team level but data are individual, justify aggregation; if effects are cross-level, the analysis must 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. AMJ is empirical management — panel, multilevel, DiD, IV, and field/lab experiments; the chain below serves that lane, while grounded-theory / qualitative work uses its own standards.
detect_design→recommend→ fit withas_handle=true→audit_resultto enumerate the checks the design owes.- Panel / staggered DiD:
callaway_santanna/sun_abraham+bacon_decompositionhonest_did_from_result. IV:effective_f_test+anderson_rubin_ci. RDD:rdrobust+mccrary_test.
- Experiments: randomization-based inference and
romano_wolffor 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
- Design can actually test each hypothesis (causal claims have causal leverage)
- CMB addressed by procedural design (separate sources/time), not just a post-hoc test
- Endogeneity strategy specified for archival/observational causal claims
- Constructs use validated measures; new measures piloted; CFA planned
- Level of analysis consistent across theory, measurement, and analysis; aggregation justified
- Sampling frame, response rate, and power (including for interactions) justified
- Where feasible, a second study triangulates the causal mechanism
Anti-patterns
- Cross-sectional causal claims: "X causes Y" from one-wave correlational data.
- CMB as afterthought: relying solely on a Harman single-factor test instead of designed separation.
- Ignored endogeneity: archival "effect" with an obviously endogenous regressor and no strategy.
- Mismatched levels: theorizing at the team level, testing with disaggregated individual data via OLS.
- Unvalidated home-grown scales with no evidence of reliability or construct validity.
- Underpowered interactions presented as null "boundary conditions."
Output format
【Design】experiment / panel-archival / multilevel survey / qualitative / multi-method
【Hypothesis-design fit】each H testable? notes ...
【CMB plan】procedural remedies ...
【Endogeneity strategy】(if archival) instrument / NE / FE / DiD / matching ...
【Measures】validated? new (piloted)? CFA planned?
【Levels】theory / measurement / analysis aligned? aggregation justification ...
【Power & sampling】frame, N, power for interactions ...
【Next step】amj-data-analysis
版本历史
- 1839142 当前 2026-07-05 12:14
