| name | hrm-data-analysis |
| description | Use when estimation and analysis are the bottleneck for a Human Resource Management (Wiley "HRM") manuscript — fitting HLM/SEM, testing mediation and cross-level moderation, defending aggregation, and qualitative coding rigor. Runs and validates the analysis; it does not design the study (hrm-methods). |
Data Analysis (hrm-data-analysis)
When to trigger
- You have nested data (employees in units/firms) and need the right multilevel model
- A mediation/moderation hypothesis needs a defensible test (not just a significant indirect effect)
- A measurement model (CFA) must establish discriminant validity before structural tests
- A reviewer challenges the aggregation, the estimator, or asks for robustness
- Qualitative data need a transparent, auditable coding and trustworthiness account
Match the estimator to the data structure
| Data / claim | Estimator | What referees will check |
|---|
| Individuals nested in units; cross-level effects | HLM / mixed models (random intercepts/slopes) | Variance decomposition; ICC justifying multilevel; correct level for each predictor |
| Latent constructs + structural paths | SEM (with measurement model first) | CFA fit (CFI/TLI ≥ ~.95, RMSEA ≤ ~.06, SRMR ≤ ~.08); discriminant validity (AVE > shared variance) |
| Mediation (the HR black box) | Bootstrap indirect effect CIs; multilevel mediation if cross-level | Theorized mechanism, not inference from significance alone; 1-1-1 vs. 2-1-1 structure stated |
| Moderation / interaction | Product terms; simple slopes; interaction plot | Centering; region of significance; power; theory for the slope change |
| HR system → firm performance (panel) | Fixed-effects / DiD / IV | Endogeneity strategy; clustered SEs; pre-trends if DiD |
| Meta-analysis | Random-effects (e.g., Hunter–Schmidt / HVZ) | Coding reliability; heterogeneity (I², Q); publication-bias checks; moderator analysis |
Multilevel and SEM discipline (HRM's bread and butter)
- Justify going multilevel. Report ICC(1)/ICC(2); if essentially zero between-unit variance, a multilevel model is not warranted — say so.
- Group-mean center lower-level predictors when testing within-unit effects; grand-mean center for cross-level; state which and why (the choice changes the meaning of the coefficient).
- Measurement before structure. Run the CFA and establish discriminant validity before interpreting structural paths; a saturated SEM with a poor measurement model is not evidence.
- Mediation is a theory claim. Report the indirect effect with bias-corrected bootstrap CIs, but the mechanism must have been theorized a priori; do not back-fill the mechanism from a significant indirect path.
- Aggregation evidence travels with the analysis. r_wg, ICC(1), ICC(2) belong in the results, tied to the composition model from
hrm-methods.
Robustness and transparency HRM expects
- Report alternative specifications (controls in/out, alternative operationalizations of the HR system) and show the focal effect is stable.
- Address endogeneity for adoption/performance claims (FE, DiD, IV) and report clustered standard errors at the assignment level.
- Provide effect sizes in practitioner-meaningful terms (e.g., a 1-SD increase in HPWS is associated with X% higher productivity) — HRM rewards results an HR leader can act on.
- For qualitative work, give a transparent audit trail: data structure (first-order codes → second-order themes → aggregate dimensions), coding reliability or consensus process, and trustworthiness (member checks, triangulation).
- Follow Wiley's data-availability policy: include a data-availability statement and prepare materials for sharing where permitted (检索于 2026-06；以官网为准).
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. HRM is empirical HR — multilevel survey data, field experiments, and panels; multilevel inference and many-outcome corrections matter most.
- 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.
Checklist
Anti-patterns
- OLS on nested data: ignoring clustering and inflating significance
- Structure before measurement: interpreting SEM paths with a failing CFA
- Mediation by significance: claiming a mechanism from a significant indirect effect never theorized
- Centering silence: not stating group- vs. grand-mean centering in multilevel models
- p-value-only results: no effect sizes, no practitioner translation
- Aggregation without evidence: a unit-level construct with no r_wg/ICC
- Opaque qualitative coding: themes with no audit trail or reliability account
Output format
【Journal】Human Resource Management (Wiley "HRM")
【Skill】hrm-data-analysis
【Data structure】nested / latent-SEM / panel / meta / qualitative
【Estimator】HLM / SEM / FE-DiD-IV / bootstrap mediation / RE meta
【Measurement】CFA fit + discriminant validity status
【Multilevel】ICC reported; centering choice
【Mediation/moderation】indirect-effect CIs; interaction plot; a-priori mechanism?
【Robustness】alt specs / endogeneity / clustered SEs
【Practitioner magnitude】effect translated to an actionable number
【Data policy】availability statement prepared? 检索于 2026-06；以官网为准
【Next skill】hrm-contribution-framing
