Agent Skills
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jedpsych-data-analysis
GitHub用于JEP论文的数据分析，遵循嵌套结构、教育意义效应量、机制检验及JARS披露规范。触发场景包括运行分析、应对审稿人要求、协调预注册与探索性分析、准备可复现脚本。
Trigger Scenarios
运行主分析和支持性分析
审稿人要求多层模型或效应量
协调预注册分析与探索性跟进
准备可共享的分析脚本和代码本
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jedpsych-data-analysis -g -y
SKILL.md
Frontmatter
{
"name": "jedpsych-data-analysis",
"description": "Use when analyzing and reporting results for a Journal of Educational Psychology manuscript. JEP expects analyses that respect nesting (multilevel\/SEM\/growth models), report educationally meaningful effect sizes with confidence intervals, test mechanisms (mediation\/moderation), and follow JARS with full disclosure. Guides analysis norms; it does not fabricate results."
}
Data Analysis (jedpsych-data-analysis)
The Journal of Educational Psychology holds analyses to the standards of a rigorous psychological research journal operating in nested educational settings. The recurring requirements are: model the nesting (students in classes in schools), report effect sizes with confidence intervals that are educationally interpretable, test the mechanism (mediation/moderation), and disclose fully under JARS. Analysis scripts and data are expected to be shareable and reproducible.
When to trigger
- Running and reporting the main and supporting analyses
- A reviewer asked for multilevel modeling, effect sizes, mechanism tests, or disclosure
- Reconciling preregistered analyses with exploratory follow-ups
- Preparing analysis scripts and a codebook for deposit
Reporting norms JEP expects
- Respect the nesting. Use multilevel (hierarchical linear) models, SEM, or growth models that account for students nested in classrooms/schools. Cluster-robust or random-effects inference is expected; ignoring clustering deflates standard errors and is a standard JEP rejection reason.
- Educationally meaningful effect sizes + uncertainty. Report a standardized effect (e.g., Hedges's g, a multilevel d, R²/variance explained, or a growth-rate difference) with a confidence interval, and interpret it in learning terms (e.g., months of progress, percentile shift) — not just p-values and stars.
- Test the mechanism. JEP is theory-driven: where the hypothesis includes a learning/motivational process, fit the mediation (with appropriate multilevel mediation methods) or moderation, not only the total effect.
- Full disclosure (JARS). Report how sample size was determined, all conditions and measures, all exclusions/attrition (with reasons and counts), missing-data handling (e.g., FIML/multiple imputation), and model specification. Confirmatory vs. exploratory must be clearly separated.
- Appropriate inference. Justify the model; report assumptions/diagnostics and fit indices for SEM; correct for multiple comparisons across many outcomes; consider robustness to alternative specifications.
Robustness and missing data
- Show the result survives reasonable alternative specifications (covariate sets, model form, with/without exclusions). Handle attrition and missingness with principled methods (FIML, MI) and report rates by arm.
Worked micro-example (illustrative numbers)
A preregistered cluster-randomized reading-comprehension trial (48 classrooms, ~1,100 students). The confirmatory analysis is a two-level model with a pretest covariate and a preregistered mediation test.
Confirmatory (preregistered) — primary effect
Two-level model (students within classrooms), pretest-adjusted:
classroom-level treatment effect on transfer comprehension
g = 0.23, 95% CI [0.06, 0.40]; ICC = 0.14; ~2.0 months of progress.
Inference uses random classroom intercepts; SEs respect clustering.
Confirmatory (preregistered) — mechanism
Multilevel mediation: monitoring gain mediates ~40% of the effect,
indirect 95% CI [0.02, 0.13] (excludes 0).
Sensitivity: holds with/without the preregistered attrition exclusions
(g 0.23 → 0.21), and under FIML for missing posttests.
Exploratory (labeled): larger effect for initially low-comprehension
readers (ATI); reported as exploratory, flagged for future confirmation.
Why this passes JEP scrutiny: the model respects nesting; the effect carries a CI and an educational interpretation; the mechanism is tested, not asserted; the sensitivity line pre-empts the "fragile-to- exclusions" reviewer; and the ATI is honestly demoted to exploratory.
Analysis-stage reviewer pushback and the venue fix
| Reviewer pushback | What it signals here | JEP fix |
|---|---|---|
| "You ignored clustering" | deflated SEs from nesting | refit a multilevel/random-effects model; report the ICC |
| "Effect size, and what does it mean for learning?" | post-reform interpretability bar | add a CI and an educational metric (months/percentile) |
| "Mechanism untested" | total effect without theory | fit the preregistered multilevel mediation/moderation |
| "Which analyses were preregistered?" | forking-paths suspicion | give the disclosure table; relabel post hoc as exploratory |
| "How was attrition handled?" | missing-data validity | report rates by arm; use FIML/MI; show robustness |
Calibration anchors
- One well-powered, properly nested effect with a tight CI and a clear educational interpretation beats a pile of stars from a model that treated students as independent — the latter is a routine JEP reject.
- Prefer estimation language ("the intervention raised transfer comprehension by g = 0.23, ~2 months of progress, 95% CI [...]") to dichotomous "significant/not."
- Mechanism evidence is what makes the paper educational psychology rather than evaluation; budget the mediation/moderation test as a first-class result, not an afterthought.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. 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.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg— report the adjusted threshold. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley; multilevel data → cluster at the right level. - Re-fit off one handle:
audit_result(result_id)lists the missing checks and the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the supplement. See the executed chain in the JF execution walkthrough.
Anti-patterns
- Treating nested students as independent (single-level OLS on clustered data)
- p-values and stars with no effect size, CI, or educational interpretation
- Reporting a total intervention effect with no test of the theorized mechanism
- Selective reporting of conditions, measures, or exclusions (undisclosed flexibility)
- Ad hoc deletion of missing data with no principled method or robustness check
Output format
【Model】multilevel / SEM / growth — nesting respected? [Y/N]
【Main result】effect size + CI + educational interpretation
【Mechanism】mediation/moderation tested as hypothesized? [Y/N/NA]
【Disclosure】N-determination + all exclusions/attrition + all measures (JARS)? [Y/N]
【Confirmatory vs exploratory】clearly separated? [Y/N]
【Reproducible】scripts + codebook + missing-data method? [Y/N]
【Next】jedpsych-tables-figures
Supplementary resources
../../resources/external_tools.md—lme4/nlme,lavaan/Mplus,mediation,metafor,effectsize, missing-data tools../../resources/official-source-map.md— JARS statistical and disclosure requirements
Version History
- 1839142 Current 2026-07-05 13:35
