meta-analysis-psych

To compute Hedges' g for a meta-analysis, derive Cohen's d from common summary statistics like means, standard deviations, and sample sizes, then apply a small-sample correction. This Skill automates that calculation directly from input effect-size and variance columns.

Running a random-effects meta-analysis with DerSimonian-Laird pooling requires inputting study-level effect sizes and variances. This Skill uses numpy and scipy to calculate pooled estimates, heterogeneity metrics like Q and I², and confidence intervals deterministically.

Checking publication bias in a psychology meta-analysis involves running an Egger test, PET-PEESE regression correction, and p-curve analysis. This Skill performs all these evaluations alongside funnel-plot oriented diagnostics to probe biases across multiple studies.

Quantifying heterogeneity using I² and τ² in a meta-analysis is part of the DerSimonian-Laird random-effects pooling process. This Skill calculates these metrics along with the Q statistic and reports them with confidence intervals and p-values.

Yes, you can generate a forest plot with a summary diamond from your study-level dataset. This Skill uses matplotlib to produce visualizations directly from the pooled Hedges' g estimates and heterogeneity computations.

For a quantitative synthesis of multiple psychology studies, you need a study-level dataset with input effect-size and variance columns. This Skill processes these deterministic computations using pandas data structures to output pooled estimates and statistical tests.