Heterogeneity is commonplace in meta-analysis. When heterogeneity is found, researchers often aim to identify predictors that account for at least part of such heterogeneity by using mixed-effects meta-regression models. Another potentially relevant goal is to focus on the amount of heterogeneity as a function of one or more predictors, but this…

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…

Studies combined in a meta-analysis often have differences in their design and conduct that can lead to heterogeneous results. A random-effects model accounts for these differences in the underlying study effects, which includes a heterogeneity variance parameter. The DerSimonian-Laird method is often used to estimate the heterogeneity variance,…

Fluency is an important construct in clinical assessment and in cognitive taxonomies. In the Cattell-Horn-Carroll (CHC) model, Fluency is represented by several narrow factors that form a subset of the long-term memory encoding and retrieval (Glr) broad factor. The CHC broad classification of Fluency was evaluated in five data sets, and the CHC…

Meta-analyses are typically used to estimate the overall/mean of an outcome of interest. However, inference about between-study variability, which is typically modelled using a between-study variance parameter, is usually an additional aim. The DerSimonian and Laird method, currently widely used by default to estimate the between-study variance,…

Heterogeneity in meta-analysis is most commonly estimated using a moment-based approach described by DerSimonian and Laird. However, this method has been shown to produce biased estimates. Alternative methods to estimate heterogeneity include the restricted maximum likelihood approach and those proposed by Paule and Mandel, Sidik and Jonkman, and…

The testing effect is a robust empirical finding in the research on learning and instruction, demonstrating that taking tests during the learning phase facilitates later retrieval from long-term memory. Early evidence came mainly from laboratory studies, though in recent years applied educational researchers have become increasingly interested in…

Structural equation modeling (SEM) is one of the most popular multivariate statistical techniques in Turkish educational research. This study elaborates the SEM procedures employed by 75 educational research articles which were published from 2010 to 2015 in Turkey. After documenting and coding 75 academic papers, categorical frequencies and…

The objectives of the study were to identify best practices in online doctoral programming and to synthesise these practices into a framework for developing online doctoral programmes. The field of online doctoral studies is nascent and presents challenges for conventional forms of literature review. The literature was therefore reviewed using a…

When classifying the evidence base of practices, special education scholars typically appraise study quality to identify and exclude from consideration in their reviews unacceptable-quality studies that are likely biased and might bias review findings if included. However, study quality appraisals used in the process of identifying evidence-based…

A meta-analyses was undertaken to determine the factors that are most related with reducing the achievement gap. The meta-analysis included 30 studies that examined attempts to bridge the achievement gap between White students on one hand and Black and Latino students on the other. The results indicate that several factors are associated with a…

Modern statistics are considered as a branch of psychometrics and the question of how the central problems of statistics can be resolved using psychometric methods is investigated. Methods developed in the fields of test theory, scaling, and factor analysis are related to principle problems of modern statistical theory and method. (Author/JKS)

Reviews methods for handling missing data in a research study. Model-based methods, such as maximum likelihood using the EM algorithm and multiple imputation, hold more promise than ad hoc methods. Although model-based methods require more specialized computer programs and assumptions about the nature of missing data, these methods are appropriate…

Missing data analyses have received considerable recent attention in the methodological literature, and two "modern" methods, multiple imputation and maximum likelihood estimation, are recommended. The goals of this article are to (a) provide an overview of missing-data theory, maximum likelihood estimation, and multiple imputation; (b) conduct a…

Less than optimum strategies for missing values can produce biased estimates, distorted statistical power, and invalid conclusions. After reviewing traditional approaches (listwise, pairwise, and mean substitution), selected alternatives are covered including single imputation, multiple imputation, and full information maximum likelihood…