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Review of Educational Research Comprehensiveness & Balance

brycewang-stanfordbybrycewang-stanford
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Learning & Documentation

Evaluates research reviews and meta-analyses for exhaustiveness, risk of bias, heterogeneity, and sensitivity to meet top-tier academic standards.

Designed specifically for academic manuscripts targeting the Review of Educational Research (RER) and other top-tier journals, this skill stress-tests literature reviews and meta-analyses against omission, bias, and fragile conclusions. It enforces rigorous research standards by replacing vote-counting with credibility-weighted synthesis, diagnosing publication bias and heterogeneity, modeling dependent effect sizes, and ensuring search saturation across published and grey literature.

Key Features

01Tests literature search saturation and applies the named-omission test to eliminate critical research blind spots
02Prevents vote-counting by weighting study findings according to standardized risk-of-bias appraisals and design quality
03Provides robust meta-analytic diagnostic checks including I², tau², Egger's tests, trim-and-fill, and leave-one-out sensitivity
041,090 GitHub stars
05Reconciles conflicting empirical findings using estimand discipline and steelmanning rival theoretical camps
06Generates standardized verification checklists covering heterogeneity, publication bias, and dependent effects modeling

Use Cases

01Synthesizing conflicting empirical literature in narrative reviews without resorting to naive vote-counting
02Preparing an education or social science systematic review manuscript for submission to Review of Educational Research (RER)
03Auditing a meta-analysis for publication bias, dependent effect inflation, and sensitivity before peer review