Wang2025

<p>The data set contains Hedges' g effect sizes, standard errors,
sample sizes for experimental and control groups, and various study characteristics
including grade level, type of course, duration, learning model, role of ChatGPT,
and area of ChatGPT application. The meta-analysis examined the effect of ChatGPT
on students' learning performance, learning perception, and higher-order thinking
wang2025effectRoBMA.</p>

datasets

A framework for estimating ensembles of meta-analytic, meta-regression, and
multilevel models (assuming either presence or absence of the effect, heterogeneity,
publication bias, and moderators). The RoBMA framework uses Bayesian model-averaging to
combine the competing meta-analytic models into a model ensemble, weights
the posterior parameter distributions based on posterior model probabilities
and uses Bayes factors to test for the presence or absence of the
individual components (e.g., effect vs. no effect; Bartoš et al., 2022,
<doi:10.1002/jrsm.1594>; Maier, Bartoš & Wagenmakers, 2022,
<doi:10.1037/met0000405>; Bartoš et al., 2025, <doi:10.1037/met0000737>).
Users can define a wide range of prior distributions for the effect size, heterogeneity,
publication bias (including selection models and PET-PEESE), and moderator components.
The package provides convenient functions for summary, visualizations, and fit diagnostics.

Franti<c5><a1>ek Barto<c5><a1>

RoBMA

Robust Bayesian Meta-Analyses

František Bartoš

Maximilian Maier

Eric-Jan Wagenmakers

Joris Goosen

Matthew Denwood

Martyn Plummer

Wang2025 function

<p>A data.frame with 12 columns and 70 observations.</p>

Format

70 effect sizes from a meta-analysis of ChatGPT's impact on student learning
by wang2025effect;textualRoBMA — Wang2025

70 effect sizes from a meta-analysis of ChatGPT's impact on student learning
by wang2025effect;textualRoBMA

Wang2025: 70 effect sizes from a meta-analysis of ChatGPT's impact on student learning by wang2025effect;textualRoBMA

Description

Usage

Arguments

Value

Format

References