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…

It is common to include multiple dependent variables (DVs) in single-case experimental design (SCED) meta-analyses. However, statistical issues associated with multiple DVs in the multilevel modeling approach (i.e., possible dependency of error, heterogeneous treatment effects, and heterogeneous error structures) have not been fully investigated.…

Respondent attrition is a common problem in national longitudinal panel surveys. To make full use of the data, weights are provided to account for attrition. Weight adjustments are based on sampling design information and data from the base year; information from subsequent waves is typically not utilized. Alternative methods to address bias from…

The impact of misspecifying covariance matrices at the second and third levels of the three-level model is evaluated. Results indicate that ignoring existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased when covariance is either modeled or ignored. If the research interest…

Increasing the correlation between the independent variable and the mediator ("a" coefficient) increases the effect size ("ab") for mediation analysis; however, increasing a by definition increases collinearity in mediation models. As a result, the standard error of product tests increase. The variance inflation caused by…

The design of research studies utilizing binary multilevel models must necessarily incorporate knowledge of multiple factors, including estimation method, variance component size, or number of predictors, in addition to sample sizes. This Monte Carlo study examined the performance of random effect binary outcome multilevel models under varying…

In this study the authors investigated the use of 5 (i.e., Claudy, Ezekiel, Olkin-Pratt, Pratt, and Smith) R[squared] correction formulas with the Pearson r[squared]. The authors estimated adjustment bias and precision under 6 x 3 x 6 conditions (i.e., population [rho] values of 0.0, 0.1, 0.3, 0.5, 0.7, and 0.9; population shapes normal, skewness…