meta-analysis

Cluster-Bootstrapping a meta-analytic selection model

In this post, we will sketch out what we think is a promising and pragmatic method for examining selective reporting while also accounting for effect size dependency. The method is to use a cluster-level bootstrap, which involves re-sampling clusters of observations to approximate the sampling distribution of an estimator. To illustrate this technique, we will demonstrate how to bootstrap a Vevea-Hedges selection model.

POMADE

Power for Meta-Analysis of Dependent Effects

Single case design research in Special Education: Next generation standards and considerations

Single case design has a long history of use for assessing intervention effectiveness for children with disabilities. Although these designs have been widely employed for more than 50 years, recent years have been especially dynamic in terms of …

Power approximations for overall average effects in meta-analysis of dependent effect sizes

Meta-analytic models for dependent effect sizes have grown increasingly sophisticated over the last few decades, which has created challenges for a priori power calculations. We introduce power approximations for tests of average effect sizes based …

Investigating narrative performance in children with developmental language disorder: A systematic review and meta-analysis

__Purpose__: Speech-language pathologists (SLPs) typically examine narrative performance when completing a comprehensive language assessment. However, there is significant variability in the methodologies used to evaluate narration. The primary aims …

Multi-level meta-analysis of single-case experimental designs using robust variance estimation

Single-case experimental designs (SCEDs) are used to study the effects of interventions on the behavior of individual cases, by making comparisons between repeated measurements of an outcome under different conditions. In research areas where SCEDs …

Meta-Analysis with robust variance estimation: Expanding the range of working models

In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to include all dependent effect sizes in a single …

Cluster wild bootstrapping to handle dependent effect sizes in meta-analysis with a small number of studies

The most common and well-known meta-regression models work under the assumption that there is only one effect size estimate per study and that the estimates are independent. However, meta-analytic reviews of social science research often include …

Examining the effects of social stories on challenging behavior and prosocial skills in young children: A systematic review and meta-analysis

Social stories are a commonly used intervention practice in early childhood special education. Recent systematic reviews have documented the evidence-base for social stories, but findings are mixed. We examined the efficacy of social stories for …

Variance component estimates in meta-analysis with mis-specified sampling correlation

\[ \def\Pr{{\text{Pr}}} \def\E{{\text{E}}} \def\Var{{\text{Var}}} \def\Cov{{\text{Cov}}} \] In a recent paper with Beth Tipton, we proposed new working models for meta-analyses involving dependent effect sizes. The central idea of our approach is to use a working model that captures the main features of the effect size data, such as by allowing for both between- and within-study heterogeneity in the true effect sizes (rather than only between-study heterogeneity).