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hrm-data-analysis

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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).

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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 / claimEstimatorWhat referees will check
Individuals nested in units; cross-level effectsHLM / mixed models (random intercepts/slopes)Variance decomposition; ICC justifying multilevel; correct level for each predictor
Latent constructs + structural pathsSEM (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-levelTheorized mechanism, not inference from significance alone; 1-1-1 vs. 2-1-1 structure stated
Moderation / interactionProduct terms; simple slopes; interaction plotCentering; region of significance; power; theory for the slope change
HR system → firm performance (panel)Fixed-effects / DiD / IVEndogeneity strategy; clustered SEs; pre-trends if DiD
Meta-analysisRandom-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

  • Estimator matches the data structure (nesting modeled; latent constructs in SEM)
  • ICC reported and the multilevel choice justified
  • CFA fit + discriminant validity established before structural interpretation
  • Centering choice stated and matched to the effect being tested
  • Indirect effects via bootstrap CIs; mechanism theorized a priori
  • Endogeneity addressed; SEs clustered at the right level
  • Effect sizes translated into practitioner-meaningful magnitudes
  • Qualitative: transparent data structure + trustworthiness account
  • Data-availability statement prepared per Wiley policy

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