技能 数据科学 JMR统计分析与报告指南

JMR统计分析与报告指南

v20260724
jmr-data-analysis
本技能指南为用户提供一套针对顶级营销期刊(如JMR)的数据分析与报告流程。内容涵盖了根据不同的研究设计(实验、面板、因果推断等)选择合适的统计估计量(如ANOVA、DiD、工具变量、中介效应)。核心要求是确保报告符合期刊的严格规范,包括精确的p值、标准误和效应量,并将所有的统计结果有效地转化为具体的商业和管理洞察。
获取技能
397 次下载
概览

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 digitsnot 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

Resources

信息
Category 数据科学
Name jmr-data-analysis
版本 v20260724
大小 7.05KB
更新时间 2026-07-28
语言