From jmr-skills
Selects estimators matching experimental or observational designs and enforces JMR's exact-statistics reporting mandate (p-values, SEs, effect sizes).
How this skill is triggered — by the user, by Claude, or both
Slash command
/jmr-skills:jmr-data-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- Data are collected (experimental or observational) and it is time to estimate and report
JMR enforces statistics reporting more explicitly than generic top journals. Empirical papers must report:
AMA results-reporting style: no leading zero before the decimal (write .97, p = .032), and no more than three decimal places. Apply this to every table and in-text statistic.
| Design / claim | Estimator |
|---|---|
| Experiment (factorial, between/within) | ANOVA / regression; estimated marginal means; planned contrasts |
| Behavioral mediation | Bootstrapped indirect effects (e.g., PROCESS), bias-corrected CIs |
| Moderation / moderated mediation | Interaction term + simple slopes; conditional indirect effects |
| Panel / observational causal | FE / DiD (modern staggered estimators); cluster-robust SE |
| Endogenous regressor | IV/2SLS, control function; report first stage and instrument tests |
| Discrete choice / demand | Logit/probit; random-coefficient (BLP-style) demand |
| Heterogeneity | Hierarchical Bayes / mixture models |
| Counts / limited DV | Poisson/NB, Tobit, as the outcome requires |
Cluster standard errors to the sampling/assignment structure (e.g., by participant, store, or market).
For each table or study, write one ledger row before drafting results:
| Result | Claim it supports | Required statistic | Practical meaning |
|---|---|---|---|
| Main treatment or model estimate | What marketing decision, mechanism, or theory point changes? | Exact p-value, standard error, CI/effect size | Unit change, percentage lift, WTP/profit/customer impact |
| Mediation/process result | Which mechanism is supported and which rival is weaker? | Indirect effect with CI; moderation where relevant | Why the process matters for managers or theory |
| Robustness / alternative model | Which threat is reduced? | Same reporting discipline as main result | Whether conclusion changes in magnitude or direction |
| Counterfactual / simulation | What marketplace decision follows? | Parameter uncertainty and sensitivity | Managerial action implied by the estimate |
If the practical-meaning column is empty, the result is not ready for a JMR results paragraph. JMR reviewers expect precision, but they also expect a marketing payoff.
[Target] JMR
[Genre] behavioral / modeling-econometric
[Estimator] matches design? SE clustering ...
[Exact stats] p three-digit / SEs / effect sizes: pass/fix
[AMA number style] no leading zero, <= 3 decimals: pass/fix
[Identification or process] diagnostics reported
[Result-to-claim ledger] claim + practical meaning complete
[Replication] Web Appendix + code/materials ready
[Next skill] jmr-contribution-framing
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin jmr-skillsRuns and reports statistical analysis for Journal of Marketing manuscripts, selecting the right estimator for big-tent designs and enforcing JM's exact p-value, standard-error, and effect-size reporting mandate.
Selects estimator matching research design (SEM/PLS, HLM, experiments, meta-analysis), reports effect sizes/uncertainty, and translates results into managerial magnitudes for JAMS manuscripts.
Matches research design to causal claims for JMR manuscripts: experimental design, causal identification (IV/DiD/RDD/matching), and structural estimation. Adapts to JMR's rigor and replication expectations.