Scale errors are intriguing phenomena in which a child tries to perform an object-specific action on a tiny object. Several viewpoints explaining the developmental mechanisms underlying scale errors exist; however, there is no unified account of how different factors interact and affect scale errors, and the statistical approaches used in the…

Network meta-analysis has been an essential methodology of systematic reviews for comparative effectiveness research. The restricted maximum likelihood (REML) method is one of the current standard inference methods for multivariate, contrast-based meta-analysis models, but recent studies have revealed the resultant confidence intervals of average…

When meta-analyzing heterogeneous bodies of literature, meta-regression can be used to account for potentially relevant between-studies differences. A key challenge is that the number of candidate moderators is often high relative to the number of studies. This introduces risks of overfitting, spurious results, and model non-convergence. To…

This study explores the performance of classical methods for detecting publication bias--namely, Egger's regression test, Funnel Plot test, Begg's Rank Correlation and Trim and Fill method--in meta-analysis of studies that report multiple effects. Publication bias, outcome reporting bias, and a combination of these were generated. Egger's…

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 multiple effect size estimates per primary study, leading to dependence in the estimates. Some…

Publication bias and other forms of outcome reporting bias are critical threats to the validity of findings from research syntheses. A variety of methods have been proposed for detecting selective outcome reporting in a collection of effect size estimates, including several methods based on assessment of asymmetry of funnel plots, such as the…

The focus of the current study is on handling the dependence among multiple regression coefficients representing the treatment effects when meta-analyzing data from single-case experimental studies. We compare the results when applying three different multilevel meta-analytic models (i.e., a univariate multilevel model avoiding the dependence, a…

Dependent effect sizes are ubiquitous in meta-analysis. Using Monte Carlo simulation, we compared the performance of 2 methods for meta-regression with dependent effect sizes--robust variance estimation (RVE) and 3-level modeling--with the standard meta-analytic method for independent effect sizes. We further compared bias-reduced linearization…

The purpose of this study is to illustrate the multilevel meta-analysis of results from single-subject experimental designs of different types, including AB phase designs, multiple-baseline designs, ABAB reversal designs, and alternating treatment designs. Current methodological work on the meta-analysis of single-subject experimental designs…

Publication selection bias is a serious challenge to the integrity of all empirical sciences. We derive meta-regression approximations to reduce this bias. Our approach employs Taylor polynomial approximations to the conditional mean of a truncated distribution. A quadratic approximation without a linear term, precision-effect estimate with…

Replication studies allow for making comparisons and generalizations regarding the effectiveness of an intervention across different populations, versions of a treatment, settings and contexts, and outcomes. One method for making these comparisons across many replication studies is through the use of meta-analysis. A recent innovation in…