From journal-of-management-skills
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.
How this skill is triggered — by the user, by Claude, or both
Slash command
/journal-of-management-skills:jmgmt-data-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- The model is fit but a reviewer questions the measurement model or fit indices
JOM houses some of the field's leading research-methods scholars and runs methods reviews, so analysis is read by an unusually demanding audience. The expectation is a transparent measurement model before the structural model, effect sizes and confidence intervals alongside tests (not p-stars alone), and analysis choices that match the level and design set in jmgmt-methods. Report enough that the analysis is reconstructable from the paper and the (anonymized) data transparency table.
jmgmt-methods through to the estimates (first-stage strength, exclusion logic).Because JOM reviewers are methods-literate, anticipate the standard robustness asks rather than waiting for them: report the focal result under alternative specifications (with/without controls, alternative operationalizations), show it is not driven by influential cases, and — for archival work — report the endogeneity-corrected estimate alongside the naive one so the reader sees how much the correction moves the coefficient. Park the full robustness battery in the online supplement and summarize it in a sentence in the main text; the 50-page limit makes the supplement essential, not optional.
A multilevel paper claims a cross-level moderation: team climate strengthens the individual-level link between role clarity and performance. The weak version reports a significant level-2 × level-1 product term and stops. The JOM-grade version reports the ICC(1) = .18 that justifies HLM, grand-mean-centers the level-2 moderator and group-mean-centers the level-1 predictor (stating why), fits a random-slope model, reports the cross-level interaction with its CI, and plots the simple slopes of role clarity at high vs. low team climate. The plot, not the p-value, is what convinces a reviewer the moderation is real and correctly modeled.
JOM's masked review and its data transparency table mean the analysis must be describable in enough detail to reconstruct without revealing the authors. Report the software and key package versions, the estimator and its options (e.g., MLR estimation, bootstrap resamples, centering choices), and how missing data were handled (FIML vs. listwise vs. multiple imputation). Where a method has researcher degrees of freedom — moderator coding in a meta-analysis, item parceling in SEM — state the choice and show the result is not an artifact of it. This is what turns "trust me" into "check me" for a methods-literate referee.
【Branch】SEM/CFA / HLM / regression / meta-analysis
【Measurement】loadings, CR/ω, AVE, discriminant (HTMT?) ...
【Fit / model comparison】CFI/TLI/RMSEA(CI)/SRMR; alt model ...
【Focal effects】coef + effect size + CI (no asterisk-only)
【Mediation/moderation】bootstrap CI / simple slopes / J-N ...
【Multilevel】ICC, centering, random slopes ...
【Meta】k, N, corrected effect, 80% CV, I², bias checks ...
【Next step】jmgmt-contribution-framing
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin journal-of-management-skillsGuides analysis and reporting for Journal of Applied Psychology manuscripts, covering SEM, multilevel models, mediation/moderation, and meta-analysis with proper effect sizes and confidence intervals.
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.
Executes and stress-tests econometric, SEM/PLS, analytical-model, or ML analyses for JMIS manuscripts. Handles identification, endogeneity, construct validity, and robustness checks.