Meta-analyses of educational research findings frequently involve statistically dependent effect size estimates. Meta-analysts have often addressed dependence issues using ad hoc approaches that involve modifying the data to conform to the assumptions of models for independent effect size estimates, such as by aggregating estimates to obtain one…

Meta-analytic studies of second language (L2) learning typically employ a classic approach to meta-analysis. Although the classic approach can clarify findings, a multivariate, multilevel meta-analysis (3M) approach increases transparency by accounting for (a) dependencies in the evidence presented by primary studies, (b) methodological…

In two-level designs, the total sample is a function of both the number of Level 2 clusters and the average number of Level 1 units per cluster. Traditional multilevel power calculations rely on either the arithmetic average or the harmonic mean when estimating the average number of Level 1 units across clusters of unbalanced size. The current…

In cluster randomized evaluations, a treatment or intervention is randomly assigned to a set of clusters each with constituent individual units of observations (e.g., student units that attend schools, which are assigned to treatment). One consideration of these designs is how many units are needed per cluster to achieve adequate statistical…

In cluster randomized evaluations, a treatment or intervention is randomly assigned to a set of clusters each with constituent individual units of observations (e.g., student units that attend schools, which are assigned to treatment). One consideration of these designs is how many units are needed per cluster to achieve adequate statistical…

Power in multilevel models remains an area of interest to both methodologists and substantive researchers. In two-level designs, the total sample is a function of both the number of level-2 (e.g., schools) clusters and the average number of level-1 (e.g., classrooms) units per cluster. Traditional multilevel power calculations rely on either the…

Previous research found bias in the estimate of the overall fixed effects and variance components using multilevel meta-analyses of standardized single-case data. Therefore, we evaluate two adjustments in an attempt to reduce the bias and improve the statistical properties of the parameter estimates. The results confirm the existence of bias when…

Many experimental designs in educational and behavioral research involve at least one level of clustering. Clustering affects the precision of estimators and its impact on statistics in cross-sectional studies is well known. Clustering also occurs in longitudinal designs where students that are initially grouped may be regrouped in the following…

Multilevel modeling (MLM) is a statistical technique for analyzing clustered data. Despite its long history, the technique and accompanying computer programs are rapidly evolving. Given the complexity of multilevel models, it is crucial for researchers to provide complete and transparent descriptions of the data, statistical analyses, and results.…

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…

The latent growth model (LGM) in structural equation modeling (SEM) may be extended to allow for the modeling of associations among multiple latent growth trajectories, resulting in a multiple domain latent growth model (MDLGM). While the MDLGM is conceived as a more powerful multivariate analysis technique, the examination of its methodological…

Multivariate analysis of variance (MANOVA) is a statistical method used to examine group differences on multiple outcomes. This article reports results of a review of MANOVA in gifted education journals between 2011 and 2017 (N = 56). Findings suggest a number of conceptual and procedural misunderstandings about the nature of MANOVA and its…

Research based on single-case designs (SCD) are frequently utilized in educational settings to evaluate the effect of an intervention on student behavior. Visual analysis is the primary method of evaluation of SCD, despite research noting concerns regarding reliability of the procedure. Recent research suggests that characteristics of the graphic…

Educational stakeholders are keen to know the magnitude and importance of different interventions. However, the way evidence is communicated to support understanding of the effectiveness of an intervention is controversial. Typically studies in education have used the standardised mean difference as a measure of the impact of interventions. This…

Differential item functioning (DIF) analyses have been used as the primary method in large-scale assessments to examine fairness for subgroups. Currently, DIF analyses are conducted utilizing manifest methods using observed characteristics (gender and race/ethnicity) for grouping examinees. Homogeneity of item responses is assumed denoting that…