jmr-data-analysis

Research

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.

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Source SKILL.md: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/Journal-of-Marketing-Research-Skills/skills/jmr-data-analysis/SKILL.md

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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) or benjamini_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 exact suggest_function for each.
Exhibits: etable / did_summary_to_latex from 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