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
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
Source: brycewang-stanford/Awesome-Journal-Skills → Human-Resource-Management-Skills/skills/hrm-tables-figures/SKILL.md
1---2name: hrm-tables-figures3description: 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).4---5
6
7# Tables & Figures (hrm-tables-figures)
8
9## When to trigger
10
11- The correlation table is missing means, SDs, reliabilities, or has inconsistent decimals
12- A significant interaction is reported in text but never plotted
13- The regression/HLM tables dump every coefficient with no model build-up logic
14- The theoretical model in the intro does not match the hypotheses being tested
15- A qualitative paper has rich quotes but no data-structure figure
16
17## The exhibits HRM expects (and the conventions referees enforce)
18
19HRM follows management/applied-psychology table norms (APA-aligned house style). The standard set:
20
21| Exhibit | Must contain |
22|---------|--------------|
23| **Table 1 — descriptives & correlations** | Means, SDs, full correlation matrix, **scale reliabilities (α) on the diagonal**; significance noted; level-appropriate (within/between if multilevel) |
24| **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 |
25| **Interaction plot** | Simple slopes at ±1 SD, axes labeled in construct units, the moderator legend clear, region of significance where relevant |
26| **Theoretical-model figure** | Boxes and arrows mapping **one-to-one** to the numbered hypotheses |
27| **Mediation figure** | Path coefficients on the diagram; indirect effect + bootstrap CI reported |
28| **Qualitative data-structure figure** | First-order codes → second-order themes → aggregate dimensions (Gioia-style) |
29
30## Make exhibits carry the argument, not just the numbers
31
32- **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.
33- **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.
34- **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.
35- **The model figure is a contract.** Every arrow must be a hypothesis and every hypothesis an arrow; mismatches read as sloppiness or HARKing.
36- **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.
37
38## Formatting discipline
39
40- Self-contained titles and notes: a reader should understand each exhibit without the text (N, level, what significance markers mean, abbreviations defined).
41- Consistent decimals (typically two) and consistent variable names across all tables and the text.
42- Report **effect sizes and CIs**, not only stars; do not let asterisks substitute for interpretation.
43- Place exhibits per Wiley/ScholarOne submission conventions; keep figures legible in greyscale.
44
45## Execution bridge (StatsPAI / Stata MCP)
46
47Generate exhibits from the fitted result, not by retyping numbers (the usual source of
48body-vs-appendix drift). Full map: [`execution-with-mcp`](../../../shared-resources/empirical-methods/execution-with-mcp.md). HRM is empirical HR — multilevel survey data, field experiments, and panels; multilevel inference and many-outcome corrections matter most.
49
50- **Tables:** `etable` (multi-model columns) or `did_summary_to_latex` straight from the
51 `result_id`.
52- **Figures:** `plot_from_result` / `enhanced_event_study_plot` / `event_study_table` —
53 axis units and the SE/clustering note baked in.
54- **Every note** names the estimator + clustering and states the effect size in
55 interpretable units.
56
57See a full fitted-result → exhibit chain in the [JF execution walkthrough](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).
58## Checklist
59
60- [ ] Table 1 has M, SD, correlations, and reliabilities on the diagonal
61- [ ] Regression/HLM/SEM tables show a nested model build-up with fit and ΔR²/Δfit
62- [ ] Every supported interaction is plotted with labeled, construct-unit axes
63- [ ] The theoretical-model figure maps one-to-one to the hypotheses
64- [ ] Mediation diagrams show paths and indirect-effect bootstrap CIs
65- [ ] Qualitative papers include a first-order → themes → dimensions data structure
66- [ ] Titles/notes are self-contained; decimals and variable names consistent
67- [ ] Effect sizes / CIs reported; practitioner magnitude annotated where possible
68
69## Anti-patterns
70
71- **Missing reliabilities**: a correlation table with no α on the diagonal
72- **Coefficient dump**: one mega-table with no model build-up
73- **Unplotted interaction**: a claimed moderation never shown graphically
74- **Figure–hypothesis mismatch**: arrows that don't correspond to numbered hypotheses
75- **Star-only reporting**: asterisks instead of effect sizes and CIs
76- **Z-score axes**: interaction plots no HR reader can map to practice
77- **Orphan exhibits**: tables that cannot be read without the surrounding text
78
79## Output format
80
81```text
82【Journal】Human Resource Management (Wiley "HRM")
83【Skill】hrm-tables-figures
84【Table 1】M/SD/correlations/reliabilities present? [Y/N]
85【Model tables】nested build-up + fit + ΔR²/Δfit? [Y/N]
86【Interactions】all supported ones plotted, construct-unit axes? [Y/N]
87【Model figure】one-to-one with hypotheses? [Y/N]
88【Mediation/qual】path CIs / data-structure figure present? [Y/N]
89【Magnitude】practitioner-meaningful annotation added? [Y/N]
90【Next skill】hrm-writing-style
91```
92
93---
94
95**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Human-Resource-Management-Skills/skills/hrm-tables-figures/SKILL.md`
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