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hrm-tables-figures
GitHub针对HRM稿件，生成符合APA规范的描述性统计、回归模型、交互效应及理论模型图表。确保数据呈现逻辑清晰、信效度合规，提升读者可读性与审稿通过率。
Trigger Scenarios
相关性表格缺失均值、标准差或信度指标
报告显著交互作用但未绘制图形
回归表格缺乏模型构建逻辑
理论模型与假设不匹配
质性研究缺少数据结构图
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill hrm-tables-figures -g -y
SKILL.md
Frontmatter
{
"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) ordid_summary_to_latexstraight from theresult_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
Version History
- 1839142 Current 2026-07-05 13:18
