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jedpsych-tables-figures
GitHub专为JEP期刊设计表格与图表，遵循APA第7版规范。聚焦多层/SEM模型结果、效应量及置信区间可视化。强调去匿名化以符合盲审要求，区分正文与补充材料，确保数据可解释、无障碍且自包含。
Trigger Scenarios
设计主结果表或图（模型结果、中介、增长轨迹）
决定内容放入正文还是在线补充材料
图表不符合APA规范、不清晰或泄露作者身份
需要可视化轨迹、方差成分及不确定性
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jedpsych-tables-figures -g -y
SKILL.md
Frontmatter
{
"name": "jedpsych-tables-figures",
"description": "Use when building tables and figures for a Journal of Educational Psychology manuscript. JEP uses APA 7th-edition style and expects exhibits that report multilevel\/SEM model results, effect sizes with uncertainty, and growth trajectories clearly, and that are anonymized for masked review. Designs exhibits; it does not run the analysis."
}
Tables & Figures (jedpsych-tables-figures)
In the Journal of Educational Psychology, exhibits must carry the quantitative argument for nested, model-based results: multilevel/SEM estimates, effect sizes with confidence intervals, mediation paths, and growth trajectories. They follow APA 7th-edition conventions and — because review is masked — must not reveal author identity (school names, project sites, identifying acknowledgments). A good JEP figure makes the learning effect, its uncertainty, and its mechanism legible at a glance.
When to trigger
- Designing the main results table/figure (model results, mediation, growth)
- Deciding what goes in the article vs. online supplemental material
- A reviewer found an exhibit unclear, non-APA, or identity-revealing
- Visualizing trajectories, variance components, and uncertainty (not just means)
Principles
- Show model results, effect sizes, and uncertainty. Tables report estimates with standard errors and confidence intervals, variance components/ICC for multilevel models, and fit indices for SEM — not just stars. Figures display trajectories or effects with CIs, not bare bar-of-means.
- Self-contained + APA 7th. Titles, notes, variable definitions, Ns at each level, and units make each exhibit intelligible alone; follow APA 7th table/figure formatting (including a clear note row).
- Make the effect interpretable. Where possible, annotate the educational meaning (months of progress, percentile shift, percent variance explained) so the magnitude is legible to readers and policy audiences.
- Earn the space. Push secondary exhibits (full covariance matrices, every robustness model, measurement details) to online supplemental material; keep the article focused on the contribution.
- Anonymized + reproducible + accessible. No identifying site/school names in exhibits or notes (masked review); values generated by the shared analysis script; colorblind-safe and grayscale-legible.
Worked micro-example — the main results exhibits (illustrative)
For the cluster-randomized reading trial, two exhibits carry the argument the prose summarizes.
Table 1. Two-level model of transfer comprehension.
Rows: intercept, treatment (classroom level), pretest covariate,
variance components (student, classroom), ICC.
Columns: estimate, SE, 95% CI, standardized effect (g).
Note: defines levels and Ns (48 classrooms, 1,089 students), the
outcome metric, and that intervals are 95% CIs; no site names.
Figure 1. Adjusted transfer-comprehension by condition, with mediation.
Geometry: classroom means + 95% CI (dot/interval), NOT a bar of means;
inset path diagram for the monitoring mediator (a, b, indirect).
Annotation: g = 0.23, 95% CI [0.06, 0.40]; ~2.0 months of progress.
Source: rendered by the deposited R script so values match Table 1.
Exhibit triage — article vs. online supplemental material
| Exhibit | Home | Reason |
|---|---|---|
| Primary multilevel model + effect size with CI | main text | this is the contribution |
| Mediation/moderation path result | main text | the mechanism is theory-central at JEP |
| Full SEM covariance / measurement model | supplement | needed for rigor, not the headline |
| Every robustness specification | supplement | summarize in one main-text sentence |
| Item-level measure detail / fidelity tables | supplement | credibility, not the main claim |
Exhibit-stage reviewer pushback and the venue fix
- "Table reports only stars" → add SE, CI, and a standardized effect column; this is the post-reform expectation.
- "Bar chart hides the spread" → switch to dot/interval with 95% CIs; show cluster means where N allows.
- "No ICC / variance components shown" → report them; reviewers check that nesting was modeled.
- "Figure names the school district" → strip identifying labels for masked review.
- "Figure values don't match Table 1" → regenerate both from the single deposited script.
Exhibit calibration anchors
- Because JEP results are model-based, the table is where the nesting (ICC, variance components) and the effect size with its CI actually live; design it to stand alone if an editor reads only the exhibits.
- A growth figure should show trajectories with uncertainty bands, not just endpoint means; a mediation figure should make the indirect path and its CI visible.
- Masked review is easy to break in exhibits — site names, IRB identifiers, or a recognizable program logo in a figure can de-anonymize the paper; scrub them.
- Accessibility is part of credibility: colorblind-safe palettes and grayscale-legible encodings.
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. JEdPsych mixes field/lab experiments and observational school data; multilevel (student-in-class-in-school) 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.
Anti-patterns
- Bar plots of means that hide distribution, uncertainty, and nesting
- Tables reporting only stars/p-values with no effect size, SE, or CI
- Omitting ICC / variance components for a multilevel result
- Identity-revealing labels (school, district, site) during masked review
- Exhibit values that don't match the shared analysis script
Output format
【Main exhibit】what it shows + why a table/figure
【Model detail】effect size + CI + variance components/ICC (or SEM fit)? [Y/N]
【Educational meaning】magnitude annotated (months/percentile/variance)? [Y/N]
【APA 7th + self-contained + anonymized?】[Y/N]
【Article vs supplement】split decided
【Reproducible + accessible?】matches script, grayscale/colorblind-safe? [Y/N]
【Next】jedpsych-writing-style
Supplementary resources
../../resources/external_tools.md—papaja,ggplot2, plotting and APA-table tooling../../resources/official-source-map.md— APA 7th style and masked-review requirement
Version History
- 1839142 Current 2026-07-05 13:36
