Tables & Figures (asq-tables-figures)
SkillAI & modelsUse when building and refining exhibits for an Administrative Science Quarterly (ASQ) manuscript, qualitative data structures and data-to-theory tables, process models, and quantitative tables. Designs exhibits; it does not run the analysis behind them.
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
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 (asq-tables-figures) skill
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in Administrative-Science-Quarterly-Skills/skills/asq-tables-figures/SKILL.md and read by ahel’s review.
When to trigger
- Your exhibits are dense, generic, or do not reveal the data structure
- Qualitative: you have quotes but no data-to-theory table or process model figure
- Quantitative: tables are over-stuffed or hide the result that matters
- Reviewers cannot reconstruct how your data became theory from the exhibits
Principle: exhibits do theoretical work
At ASQ, exhibits are part of the argument, not decoration. A reader should be able to grasp the contribution from the figures and tables alone. Build them in tandem with asq-data-analysis.
Qualitative exhibits
- Data structure figure. Show first-order codes → second-order themes → aggregate dimensions (Gioia-style), or an equivalent cross-case/process display. This is often the single most scrutinized exhibit in an inductive ASQ paper.
- Data-to-theory table. Columns: theoretical construct/dimension → representative quotes/evidence (proof quotes) → analytic note. This makes the inference auditable.
- Process model figure. For process theory, a clean phase/feedback diagram with arrows that mean something (sequence, transformation, feedback), labeled with your constructs.
- Case/site table. Comparative table of cases on key dimensions (for multiple-case designs).
- Power quotes live in the body; proof quotes live in tables — keep the body readable.
Quantitative exhibits
- Descriptives & correlations table (means, SD, correlations) — standard and complete.
- Main regression table. Build models cumulatively (baseline → controls → focal effects → interactions). Avoid wall-to-wall columns; show the models that test the theory.
- Interaction plots. Plot significant moderations; a marginal-effects/simple-slopes figure beats a coefficient alone.
- Robustness can be summarized compactly or moved to an appendix; the body shows the result that matters.
Craft standards (both)
- Each exhibit is self-contained: title, units, notes, significance conventions, and source defined in the note.
- Figures are clean and legible in grayscale; no chart-junk; consistent fonts and labels.
- Number and reference every exhibit in text; the text interprets, it does not merely repeat the table.
- Keep exhibits anonymized for ASQ's double-blind review (no author-revealing site names or acknowledgments in figure sources).
- Follow APA style for citations in notes (ASQ adopted APA in January 2025), with SAGE table conventions; manuscripts go in via ScholarOne (Word or PDF, 12-pt Times New Roman, double-spaced). Exhibits count toward length, and ASQ rewards "high intellectual value per page" — keep the whole manuscript near the suggested 35–45 pages of text (over-long files are unsubmitted before review). Verify current details at journals.sagepub.com/author-instructions/asq.
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. ASQ wants a clean causal or well-identified observational design behind an organizational-theory contribution; reduced-form estimation fits the chain below, interpretive work does not.
- 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
- Qual: data-structure figure present and faithful to the coding
- Qual: data-to-theory / evidence table lets a reader audit the inference
- Qual: process model figure (if process theory) with meaningful arrows
- Quant: descriptives + correlations table complete
- Quant: main table built cumulatively; interactions plotted
- Every exhibit is self-contained (title, notes, units, significance key)
- Text interprets exhibits rather than restating them
- Formatting matches current ASQ/SAGE guidelines (verify on official page)
Anti-patterns
- A "wall of coefficients" table where the key result is buried among dozens of columns
- Qualitative papers with quotes but no data-structure figure or evidence table
- Process figures whose arrows have no defined meaning
- Exhibits that require the body text to be interpretable (not self-contained)
- Chart-junk, illegible grayscale, inconsistent decimals/labels
- Reporting interaction coefficients without a plot
Output format
【Exhibit list】figures + tables planned
【Data-to-theory exhibit】present? (qual) / cumulative main table? (quant)
【Process model】present? (if process theory)
【Self-containment】all notes/units/keys complete?
【Formatting】matches ASQ/SAGE guidelines (verify)
【Next step】asq-writing-style
Signals
- GitHub stars
- 1k
- Forks
- 155
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
asq-tables-figures- Source
- github.com/brycewang-stanford/awesome-journal-skills
github.com/brycewang-stanford/awesome-journal-skills
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