hrm-tables-figures

Documents

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).

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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