From amj-skills
Runs and reports statistical analysis for AMJ manuscripts: measurement validity (CFA, reliability), estimator selection (HLM, SEM, panel, experiments), common-method bias, endogeneity, and robustness checks.
How this skill is triggered — by the user, by Claude, or both
Slash command
/amj-skills:amj-data-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- Data are collected and it is time to estimate and report
AMJ reviewers expect the measurement model to be defended first:
| Data structure / claim | Estimator |
|---|---|
| Latent constructs, mediation, full model | Structural equation modeling (SEM) |
| Nested data (indiv. in teams/firms) | Multilevel / hierarchical linear modeling (HLM) |
| Panel with unit heterogeneity | Fixed/random effects; cluster-robust SE |
| Manipulated cause | ANOVA/regression with manipulation & attention checks |
| Endogenous archival regressor | 2SLS/IV, DiD, Heckman, propensity matching (per design) |
| Count/limited dependent variable | Poisson/negative binomial, logit/probit, Tobit as fits |
Match clustering of standard errors to the sampling/nesting structure.
Report the designed separations from amj-methods first; then provide statistical evidence: a Harman single-factor test is necessary but weak — prefer a marker-variable approach, an unmeasured latent method factor (CFA), or showing interaction effects survive (interactions are hard to inflate by CMB). The Podsakoff et al. framework is the expected reference for both procedural and statistical remedies.
【Measurement】alpha/CR, CFA fit (CFI/TLI/RMSEA/SRMR), AVE/discriminant: pass/issues
【Estimator】SEM / HLM / panel-FE / experiment / IV-DiD; SE clustering ...
【CMB evidence】designed separation + statistical test ...
【Mediation/Moderation】bootstrap CI / simple slopes reported? ...
【Endogeneity】strategy executed; assumptions discussed ...
【Robustness】[...]
【Open issues for reviewers】[...]
【Next step】amj-contribution-framing
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin amj-skillsDesigns the causal research design for AMJ-management manuscripts — matching method (archival, survey, experiment, multi-method) to theoretical questions. Addresses common-method bias, endogeneity, and measurement threats before data collection.
Runs and validates SEM/CFA, HLM/multilevel, regression, mediation/moderation, and meta-analytic estimation for JOM manuscripts. Use when estimation and results are the bottleneck.
Selects estimator matching research design (SEM/PLS, HLM, experiments, meta-analysis), reports effect sizes/uncertainty, and translates results into managerial magnitudes for JAMS manuscripts.