# Jedpsych Tables Figures

> 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.

- Skill: `thedixitjain/jedpsych-tables-figures` (Agent Skill)
- Install (CLI): `npx skillmds add thedixitjain/jedpsych-tables-figures`
- Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/jedpsych-tables-figures/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: thedixitjain (https://skillmd.com/u/thedixitjain)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/thedixitjain/jedpsych-tables-figures

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# 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

1. **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.
2. **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).
3. **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.
4. **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.
5. **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`](../../../shared-resources/empirical-methods/execution-with-mcp.md). 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) 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](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).
## 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`](../../resources/external_tools.md) — `papaja`, `ggplot2`, plotting and APA-table tooling
- [`../../resources/official-source-map.md`](../../resources/official-source-map.md) — APA 7th style and masked-review requirement

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**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Journal-of-Educational-Psychology-Skills/skills/jedpsych-tables-figures/SKILL.md`