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jams-data-analysis
GitHub用于JAMS期刊稿件的数据分析与报告，匹配设计选择估计量（SEM/HLM/回归等），报告效应量与不确定性，并将统计结果转化为具有管理意义的商业指标。
Trigger Scenarios
数据收集完毕需进行估算和报告时
不确定估计量是否匹配研究设计或数据结构时
审稿人质疑分析支持性或缺少效应量时
仅报告显著性而缺失管理幅度时
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jams-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "jams-data-analysis",
"description": "Use when running and reporting the analysis for a Journal of the Academy of Marketing Science (JAMS) manuscript — selecting the estimator that matches the design (SEM\/PLS, HLM, regression\/econometrics, experiments, meta-analysis), reporting effect sizes and uncertainty, and translating estimates into managerial magnitudes. Executes and reports; jams-methods designs the study and jams-contribution-framing states the payoff."
}
Data Analysis & Reporting (jams-data-analysis)
When to trigger
- Data are collected and it is time to estimate and report
- You are unsure whether the estimator matches the design or the data structure
- A reviewer says "the analysis does not support the inference" or "report effect sizes"
- Significance is reported but the managerial magnitude is missing
Choose the estimator that matches the design
| Design / claim | Estimator |
|---|---|
| Latent constructs + structural paths (survey) | Covariance-based SEM (Mplus / lavaan / AMOS); PLS-SEM when prediction or formative constructs dominate |
| Nested data (consumers in stores, firms in industries) | HLM / multilevel models; random intercepts/slopes; report ICC |
| Mediation (process) | Bootstrapped indirect effects (PROCESS / lavaan), bias-corrected CIs; report the indirect effect, not just Baron–Kenny steps |
| Moderation / moderated mediation | Interaction term + simple slopes; conditional indirect effects (index of moderated mediation) |
| Experiment (factorial) | ANOVA / regression; estimated marginal means; planned contrasts; effect sizes per cell |
| Panel / observational causal | FE / DiD (modern staggered estimators); cluster-robust SE |
| Endogenous marketing regressor | IV/2SLS or Gaussian-copula control function; report first stage / instrument strength |
| Discrete choice / demand | Logit/probit; random-coefficient (mixed) logit |
| Meta-analysis | Random-effects effect-size synthesis; moderator meta-regression; publication-bias diagnostics |
Match SE clustering to the sampling/assignment structure (participant, store, market, firm).
JAMS reporting conventions
- APA results style. Report exact statistics (coefficients, SEs or t-values, CIs, exact p where shown). Avoid asterisk-only tables where the journal asks for precision; let the magnitude, not the star count, carry the result.
- Effect sizes and uncertainty, always. Standardized coefficients, R²/f², η²/Cohen's d, or odds ratios as the model requires — significance without magnitude is not a JAMS result.
- SEM reporting: measurement model first (loadings, AVE, CR, discriminant validity), then the structural model (standardized paths, R² for endogenous constructs, overall fit: CFI, TLI, RMSEA, SRMR).
- PLS reporting: loadings/weights, CR, AVE, HTMT, R², Q² (predictive relevance), and f²; bootstrap the path significances.
Translate every result into a managerial magnitude
This is the JAMS-distinguishing step. For each headline result, write a ledger row before drafting the results paragraph:
| Result | Theory point it supports | Required statistic | Managerial magnitude |
|---|---|---|---|
| Main path / treatment effect | which hypothesis / mechanism is confirmed | std. coef. + CI / d | sales lift, share, CLV, margin, retention, brand-equity points |
| Mediation (process) | which mechanism carries the effect | indirect effect + bias-corrected CI | why the process matters for the decision |
| Moderation (contingency) | when the effect strengthens/reverses | interaction + simple slopes | the managerial guardrail / segmentation rule |
| Robustness / alternative model | which threat (CMV, endogeneity) is reduced | same discipline as the main result | whether the conclusion's direction/size holds |
If the managerial-magnitude column is empty, the result is not yet ready for a JAMS results section.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JAMS is empirical marketing with much survey-based SEM; the chain below serves causal / quasi-experimental designs and many-outcome corrections.
- 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.
Checklist
- Estimator matches design and data structure; SE clustering correct
- SEM: measurement model reported before structural; full fit indices given
- PLS: HTMT, R², Q², f² reported; paths bootstrapped
- Mediation via bootstrapped indirect effects with bias-corrected CIs
- Moderation: simple slopes + index of moderated mediation where relevant
- Effect sizes and uncertainty reported throughout (APA style)
- Every headline result has a managerial-magnitude translation
- Robustness addresses the design's specific threat (CMV / endogeneity / pre-trends)
Robustness that targets the design's real threat
Generic robustness ("we also ran model B") rarely persuades JAMS reviewers; the robustness must answer the specific threat to the genre's inference:
- Survey/SEM: rule out CMV with a marker-variable / CFA-marker model and report whether paths survive; test an alternative measurement specification; show results hold on a holdout or second sample.
- Secondary data: placebo tests, alternative instruments, pre-trend/parallel-trends evidence, sensitivity to the identifying assumption, and alternative fixed-effect structures.
- Experiment: replication across stimuli/samples, a confound-ruling-out study, and a test of the alternative-mechanism account.
- Meta-analysis: sensitivity to coding decisions, trim-and-fill / PET-PEESE for publication bias, and influence diagnostics for outlier studies.
State, for each robustness check, which threat it neutralizes — a list of checks with no mapped threat reads as box-ticking.
Anti-patterns
- Baron–Kenny causal-steps mediation instead of bootstrapped indirect effects
- Reporting fit indices but no standardized paths or R²
- Significance with no effect size and no managerial magnitude
- Ignoring nesting (consumers within stores) and clustering
- A weak/untested instrument, or endogeneity waved away
- Asterisk tables that hide the size of the effect
- Robustness checks listed with no statement of which threat each addresses
Output format
【Design】survey-SEM / PLS / HLM / experiment / panel-causal / choice / meta
【Estimator】matches design? SE clustering: [...]
【Measurement (if SEM/PLS)】AVE/CR/discriminant + fit/HTMT: pass/fix
【Effect sizes + uncertainty】reported (APA)? pass/fix
【Mediation/moderation】bootstrapped indirect / simple slopes: done?
【Managerial-magnitude ledger】every headline result translated? yes/fix
【Robustness】design-specific threat addressed: [...]
【Next skill】jams-contribution-framing
Version History
- 1839142 Current 2026-07-05 13:58
