jom-data-analysis
GitHub用于JOM实证稿件的统计分析与报告，涵盖测量信效度、内生性处理、实验操纵检验及稳健性分析。执行具体估计与结果汇报，不涉研究设计或贡献框架。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jom-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "jom-data-analysis",
"description": "Use when running and reporting the statistical analysis for a Journal of Operations Management (JOM) empirical manuscript — measurement validity for survey constructs, identification and endogeneity for archival operations data, manipulation checks for behavioral-OM experiments, and robustness. Executes and reports the analysis; it does not design the study (jom-methods) or frame the contribution (jom-contribution-framing)."
}
Data Analysis & Validity for Empirical OM (jom-data-analysis)
When to trigger
- Operations data are collected and it is time to estimate and report
- You are unsure whether your estimator matches your design (survey constructs, archival panel, experiment, multilevel/nested plants)
- Reviewers (and the Empirical Research Methods Department) will probe measurement, common-method bias, or endogeneity
- A decision letter says "the analysis does not support the operational inference"
Establish measurement before estimation (survey/behavioral)
For survey-based OM constructs, defend the measurement model first:
- Reliability: Cronbach's alpha and/or composite reliability for each multi-item operations scale.
- CFA: report fit (CFI, TLI, RMSEA, SRMR) and show the hypothesized factor structure beats plausible alternatives (one-factor, combined-factor).
- Convergent & discriminant validity: AVE per construct; AVE > inter-construct squared correlations (or HTMT). Report the correlation matrix with reliabilities on the diagonal.
- Aggregation (plant/team level): justify with ICC(1), ICC(2), r_wg(j) before aggregating respondents.
- Qualitative/IBR: establish trustworthiness — data structure (first-order → themes → dimensions), audit trail, representative evidence so the path from observation to construct is traceable.
Choose the estimator that matches the design
| Operations data structure / claim | Estimator |
|---|---|
| Latent constructs, mediation, full survey model | SEM (covariance-based) or PLS-SEM where prediction/formative |
| Nested data (respondents in plants/firms) | Multilevel / HLM |
| Archival operations panel with unit heterogeneity | Fixed/random effects, high-dimensional FE; cluster-robust SE |
| Causal claim from secondary data | DiD/staggered DiD, IV/2SLS, matching, RD as the design fits |
| Count outcomes (recalls, defects, disruptions) | Poisson / negative binomial |
| Time-to-event (failure, project completion) | Cox / parametric survival |
| Manipulated operational decision | ANOVA/regression with manipulation & attention checks |
Cluster standard errors to the sampling/operational structure (plant, firm, supply tie).
Common-method bias (survey OM)
Report the designed separations from jom-methods first (temporal/source/respondent separation), then statistical evidence: a Harman single-factor test is necessary but weak — prefer a marker variable, an unmeasured latent method factor, or showing interaction effects survive. Multi-respondent dyadic data is the strongest procedural remedy.
Endogeneity (archival OM)
Recalls, supplier ties, lean adoption, and disruptions are rarely exogenous. State the threat (selection, reverse causality, omitted operational confounds), the identification strategy, and its assumptions. Report first-stage strength for IV and parallel-trends/anticipation checks for DiD.
Robustness
- Alternative specifications (controls in/out, alternative operational measures, subsamples by industry/regime).
- Sensitivity to identification assumptions.
- Attrition/missing-data handling (FIML/multiple imputation, not listwise by default).
- Reproducible secondary-data construction consistent with the Wiley Data Availability Statement.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JOM is empirical operations / supply-chain — survey and archival panel data; foreground endogeneity of operational choices and clustered / multilevel inference.
- 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
- OLS on nested plant data ignoring non-independence.
- Causal-steps mediation instead of bootstrapped indirect effects.
- Single-factor test as the sole CMB defense.
- Unaddressed endogeneity in archival operations regressions.
- p-values with no effect sizes or operational magnitude.
Reporting thresholds the Empirical Research Methods reviewers probe
The values below are widely cited conventions, not hard cutoffs; confirm field norms against current methods guidance.
| Diagnostic | Conventional landmark | Wanted alongside it |
|---|---|---|
| Alpha / composite reliability | typically ≥ .70 | source and prior validation of each scale |
| CFA fit (CFI/TLI) | typically ≥ .90–.95 | the model beating one-factor rivals |
| AVE (convergent) | commonly ≥ .50 | AVE > squared correlation, or HTMT |
| IV first-stage F | strong-instrument heuristics | why the instrument is excludable |
Desk-reject and method-check failure patterns
- A single-factor (Harman) test as the only common-method-bias defense.
- OLS on respondents nested in plants with no acknowledgment of non-independence.
- An archival operational-practice regression with no identification strategy for an endogenous practice (lean adoption, supplier selection).
Worked vignette: endogeneity in an operational-practice regression
A study regresses plant defect rates on lean-adoption over a 9-year panel; adopters show 18% fewer defects (illustrative). A referee objects that plants adopting lean may already be better-managed, so selection contaminates the estimate. The JOM-grade response is an identification plan, not a footnote: exploit a staggered corporate mandate as quasi-exogenous timing, run a staggered DiD with plant and year fixed effects, cluster at the plant, and show pre-adoption parallel trends plus no anticipation. Report event-study coefficients so the dynamic effect is visible. If pre-trends are flat and the drop concentrates after the mandate, the inference is credible; if pre-trends slope, soften the claim to association.
Analysis objections reviewers raise, with the fix
- "Construct validity of the survey measures is unestablished." Lead with the full measurement model before any structural estimate.
- "Endogeneity in operational-practice regressions." Name the threat, state the design, and report first-stage strength or pre-trend evidence — do not argue it away.
Output format
【Measurement】alpha/CR, CFA fit, AVE/discriminant (survey) — pass/issues
【Estimator】SEM / HLM / panel-FE / DiD-IV / count / survival / experiment; SE clustering ...
【CMB / identification】designed separation + test; or endogeneity strategy + assumptions ...
【Mediation/Moderation】bootstrap CI / simple slopes reported? ...
【Robustness】...
【Next step】jom-contribution-framing
Version History
- 1839142 Current 2026-07-05 13:52
