Tables & Figures (commres-tables-figures)

SkillDev tools

Use when building tables and figures for a Communication Research (CR) manuscript so exhibits are self-contained, accessible, and formatted to APA. CR is quantitative, so exhibits must show effect sizes and uncertainty, not just stars. Designs exhibits; it does not run the analysis.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Tables & Figures (commres-tables-figures) skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in Communication-Research-Skills/skills/commres-tables-figures/SKILL.md and read by ahel’s review.

Exhibits are where a CR reviewer checks whether the result is real and whether the mechanism holds. Because CR is a quantitative, APA-reporting journal, every table and figure must show effect sizes and uncertainty, not significance stars alone, and follow APA formatting.

When to trigger

  • Designing the main results table/figure or a key descriptive/measurement exhibit
  • Deciding what belongs in the article vs. the online supplement
  • A reviewer found an exhibit unclear, mislabeled, or not self-contained
  • Presenting mediation/moderation results readably

Principles

  1. Self-contained. A reader should understand each exhibit from its title, column/axis labels, and note alone — without hunting through the text. State units, sample, N, and what the estimate is.
  2. Show magnitude and uncertainty. Report effect sizes (d, η²ₚ, standardized β, R²) with CIs or SDs. Coefficient/forest plots, marginal-effects and predicted-value plots, mediation path diagrams, and interaction (simple-slopes) plots communicate better than a wall of coefficients.
  3. APA format. Table/figure numbering, titles, and notes per APA; consistent decimals; report the statistics APA expects (M, SD, test statistic with df, effect size, exact p).
  4. Measurement exhibits. For SEM/CFA, show the measurement model (loadings, fit indices); for content analysis, report category frequencies and intercoder reliability by category.
  5. Accessible. Colorblind-safe palettes; legible in grayscale; no chartjunk, no 3D, no needless color. Reviewers must parse it quickly.
  6. Main text vs. supplement. Keep the few exhibits that carry the argument in the article; move full specifications, robustness grids, scale items, and codebooks to the online supplement (also anonymized) — this also helps you fit the page budget.
  7. Reproducible. Each exhibit is generated by the master script; numbers match the deposited materials (see commres-transparency-and-data).

Discipline-specific exhibits

  • Experiment cell means with CIs; ANOVA effect-size tables.
  • Mediation/moderated-mediation path diagrams with indirect-effect estimates and bootstrap CIs.
  • Simple-slopes / Johnson–Neyman plots for interactions.
  • SEM/CFA measurement-model tables (loadings, AVE/CR, fit indices).
  • Content-analysis reliability tables (alpha by category) and category-frequency exhibits.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers (the usual source of body-vs-supplement drift). Full map: execution-with-mcp. Communication Research is experiment- and survey-heavy; emphasize randomization inference, mediation done right, and family-wise corrections.

  • 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.

Anti-patterns

  • Tables that require the prose to be intelligible (not self-contained)
  • Significance stars with no effect size, SD, or interval (an APA-reporting failure at CR)
  • A mediation table without bootstrap CIs, or an interaction with no simple-slopes display
  • Cramming every robustness check into the main text (blows the page budget — use the supplement)
  • Color-only encoding that fails in grayscale or for colorblind readers
  • Exhibit numbers/values that don't match the deposited code output

Exhibit pass for Communication Research

Treat this skill as an executable review pass, not a prose hint. First lock the communication process, the measured constructs, the study design, and the inferential claim; then judge whether the manuscript answers CR's real reader: a quantitatively trained communication scientist who weighs theory, measurement validity, identification, and effect interpretation.

  • Do the pass: For every table or figure, state the estimand or object, the sample/N, the uncertainty display, the effect size shown, and one sentence the exhibit proves.
  • Return a ledger: give claim / evidence / risk / manuscript location rows so the next agent edits rather than rediscovers the issue.
  • Sibling guard: Journal of Communication, Human Communication Research, New Media & Society. If a sibling owns the contribution, re-route before polishing format.
  • Stop condition: do not give submission-ready advice until resources/official-source-map.md has been checked and the manuscript has one concrete fix for the largest venue-specific risk.

Output format

【Main exhibit】what it shows + why a figure/table
【Self-contained?】title + labels + note + N/units present? [Y/N]
【Magnitude + uncertainty?】effect size + CI/SD shown (not stars only)? [Y/N]
【APA format?】numbering/titles/notes per APA? [Y/N]
【Accessible?】grayscale-legible + colorblind-safe? [Y/N]
【Article vs supplement】split decided, page-budget impact noted
【Reproducible?】generated by master script, matches materials? [Y/N]
【Next】commres-writing-style

Supplementary resources

Signals

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Item type
skill
Key
commres-tables-figures
Source
github.com/brycewang-stanford/awesome-journal-skills