hrm-tables-figures

name	hrm-tables-figures
description	Use when exhibits are the bottleneck for a Human Resource Management (Wiley "HRM") manuscript — the descriptives/correlation table, the model build-up, interaction and simple-slope plots, the theoretical-model figure, and (for qualitative work) the data-structure figure. Builds reader-ready exhibits; it does not run the analysis (hrm-data-analysis).

Tables & Figures (hrm-tables-figures)

When to trigger

• The correlation table is missing means, SDs, reliabilities, or has inconsistent decimals
• A significant interaction is reported in text but never plotted
• The regression/HLM tables dump every coefficient with no model build-up logic
• The theoretical model in the intro does not match the hypotheses being tested
• A qualitative paper has rich quotes but no data-structure figure

The exhibits HRM expects (and the conventions referees enforce)

HRM follows management/applied-psychology table norms (APA-aligned house style). The standard set:

Exhibit	Must contain
Table 1 — descriptives & correlations	Means, SDs, full correlation matrix, scale reliabilities (α) on the diagonal; significance noted; level-appropriate (within/between if multilevel)
Table 2+ — regression / HLM / SEM	Nested model build-up (controls → main effects → interactions); unstandardized and/or standardized coefficients with SEs; model fit (R², ΔR², pseudo-R², CFI/RMSEA for SEM); df and N at each level
Interaction plot	Simple slopes at ±1 SD, axes labeled in construct units, the moderator legend clear, region of significance where relevant
Theoretical-model figure	Boxes and arrows mapping one-to-one to the numbered hypotheses
Mediation figure	Path coefficients on the diagram; indirect effect + bootstrap CI reported
Qualitative data-structure figure	First-order codes → second-order themes → aggregate dimensions (Gioia-style)

Make exhibits carry the argument, not just the numbers

• The correlation table is the credibility table. Reviewers read it first; reliabilities below ~.70, a correlation near 1.0 between "distinct" constructs (discriminant-validity red flag), or a mean at a scale ceiling all undermine the paper before the hypotheses are tested.
• Build models, don't dump them. A nested progression shows the incremental variance the focal effect explains over controls — that ΔR²/Δ-2LL is the contribution made visible.
• Always plot a supported interaction. A coefficient is not interpretable as "the effect strengthens"; the plot is. Label axes in real construct units, not z-scores, so an HR reader can see the practical magnitude.
• The model figure is a contract. Every arrow must be a hypothesis and every hypothesis an arrow; mismatches read as sloppiness or HARKing.
• Translate magnitude for practice. Where possible, annotate the practically meaningful difference (e.g., the predicted productivity gap between low- and high-HPWS units) so the exhibit serves HRM's practice mandate.

Formatting discipline

• Self-contained titles and notes: a reader should understand each exhibit without the text (N, level, what significance markers mean, abbreviations defined).
• Consistent decimals (typically two) and consistent variable names across all tables and the text.
• Report effect sizes and CIs, not only stars; do not let asterisks substitute for interpretation.
• Place exhibits per Wiley/ScholarOne submission conventions; keep figures legible in greyscale.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers (the usual source of body-vs-appendix drift). Full map: execution-with-mcp. HRM is empirical HR — multilevel survey data, field experiments, and panels; multilevel inference and many-outcome corrections matter most.

• Tables: etable (multi-model columns) or did_summary_to_latex straight from the result_id.
• Figures: plot_from_result / enhanced_event_study_plot / event_study_table — axis units and the SE/clustering note baked in.
• Every note names the estimator + clustering and states the effect size in interpretable units.

See a full fitted-result → exhibit chain in the JF execution walkthrough.

Checklist

• Table 1 has M, SD, correlations, and reliabilities on the diagonal
• Regression/HLM/SEM tables show a nested model build-up with fit and ΔR²/Δfit
• Every supported interaction is plotted with labeled, construct-unit axes
• The theoretical-model figure maps one-to-one to the hypotheses
• Mediation diagrams show paths and indirect-effect bootstrap CIs
• Qualitative papers include a first-order → themes → dimensions data structure
• Titles/notes are self-contained; decimals and variable names consistent
• Effect sizes / CIs reported; practitioner magnitude annotated where possible

Anti-patterns

• Missing reliabilities: a correlation table with no α on the diagonal
• Coefficient dump: one mega-table with no model build-up
• Unplotted interaction: a claimed moderation never shown graphically
• Figure–hypothesis mismatch: arrows that don't correspond to numbered hypotheses
• Star-only reporting: asterisks instead of effect sizes and CIs
• Z-score axes: interaction plots no HR reader can map to practice
• Orphan exhibits: tables that cannot be read without the surrounding text

Output format

【Journal】Human Resource Management (Wiley "HRM")
【Skill】hrm-tables-figures
【Table 1】M/SD/correlations/reliabilities present? [Y/N]
【Model tables】nested build-up + fit + ΔR²/Δfit? [Y/N]
【Interactions】all supported ones plotted, construct-unit axes? [Y/N]
【Model figure】one-to-one with hypotheses? [Y/N]
【Mediation/qual】path CIs / data-structure figure present? [Y/N]
【Magnitude】practitioner-meaningful annotation added? [Y/N]
【Next skill】hrm-writing-style