ManyLabs16

<p>The data set contains standardized mean differences (y) and standard errors (se)
from 55 replication studies of the framing effect originally described by
tversky1981framing;textualRoBMA. These studies were part of the
Many Labs 2 project examining variation in replicability across samples and settings
klein2018many;textualRoBMA.</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

ManyLabs16 function

<p>A data.frame with 2 columns and 55 observations.</p>

Format

55 effect sizes from Many Labs 2 replication studies of
tversky1981framing;textualRoBMA framing effects — ManyLabs16

55 effect sizes from Many Labs 2 replication studies of
tversky1981framing;textualRoBMA framing effects

ManyLabs16: 55 effect sizes from Many Labs 2 replication studies of tversky1981framing;textualRoBMA framing effects

Description

Usage

Arguments

Value

Format

References