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jmr-data-analysis
GitHub专为JMR稿件设计的数据分析与报告技能。匹配研究设计与估计量，严格执行JMR报告规范（精确p值、标准误、效应量），涵盖行为与计量分析细节，确保结果可复现并支持推断。
Trigger Scenarios
数据收集完毕需进行估计和报告时
不确定估计量是否与实验设计匹配时
必须遵守JMR精确统计报告规则时
审稿人指出分析无法支持推断或缺乏效应量时
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmr-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "jmr-data-analysis",
"description": "Use when running and reporting the analysis for a Journal of Marketing Research (JMR) manuscript — selecting the estimator that matches the design, and meeting JMR's hard journal-level reporting mandate of exact p-values, standard errors, and effect sizes, plus replication-ready disclosure. Executes and reports; jmr-methods designs the study and jmr-contribution-framing states the payoff."
}
Data Analysis & Reporting (jmr-data-analysis)
When to trigger
- Data are collected (experimental or observational) and it is time to estimate and report
- You are unsure whether your estimator matches your design
- You must conform to JMR's exact-statistics reporting rules
- A reviewer says "the analysis does not support the inference" or "report effect sizes"
JMR's hard reporting mandate (journal-level)
JMR enforces statistics reporting more explicitly than generic top journals. Empirical papers must report:
- Actual p-values to three digits — not thresholds (no "p < .05"), not asterisks.
- Standard errors of parameter estimates in tables.
- Effect sizes — and a discussion of practical magnitude, not just significance.
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.
Choose the estimator that matches the design
| 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).
Behavioral analysis specifics
- Report manipulation- and attention-check results before the main effect.
- Mediation: bootstrap indirect effects with bias-corrected CIs (e.g., 5,000 resamples); for moderated mediation report the conditional indirect effect.
- Moderation: report the interaction coefficient, plot simple slopes, and give effect sizes per cell.
Modeling / econometric specifics
- Report identification diagnostics (first-stage strength, parallel-trends/pre-trends, balance, overidentification) as relevant.
- Report structural parameter estimates with standard errors; show fit and counterfactuals where the contribution rests on them.
Result-to-claim ledger
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.
Replication & robustness (AMA transparency policy)
- Provide enough detail (in-text, Web Appendix, or online supplements) for a reasonably trained researcher to replicate; be ready to share code, instruments/stimuli, and materials on request, and to provide data/materials before final acceptance.
- Put robustness — alternative specifications, subsamples, alternative measures, additional studies — in the 'W'-prefixed Web Appendix, keeping the print paper within 50 pages.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JMR mixes experiments, structural models, and quasi-experiments; the chain below serves the experimental and reduced-form lanes, while structural demand estimation uses its own toolkit.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg— report the adjusted threshold. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley; multilevel data → cluster at the right level. - Re-fit off one handle:
audit_result(result_id)lists the missing checks and the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the appendix. See the executed chain in the JF execution walkthrough.
Anti-patterns
- Reporting "p < .05" or asterisks instead of exact three-digit p-values.
- Tables with no standard errors; significance without effect sizes.
- Causal-steps (Baron-Kenny) mediation instead of bootstrapped indirect effects.
- Ignoring clustering / non-independence; a weak or untested instrument.
- A leading zero before the decimal, or more than three decimal places.
- Results paragraphs that report significance but no practical magnitude or marketing interpretation.
Output format
[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
Resources
Version History
- 1839142 Current 2026-07-05 13:49
