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…

Educational analysts studying achievement and other educational outcomes frequently encounter an association between initial status and growth, which has important implications for the analysis of covariate effects, including group differences in growth. As explicated by Allison (1990), where only two time points of data are available, identifying…

The present study investigates different approaches to adding covariates and the impact in fitting mixture item response theory models. Mixture item response theory models serve as an important methodology for tackling several psychometric issues in test development, including the detection of latent differential item functioning. A Monte Carlo…

Evaluators of education interventions are increasingly designing studies to detect impacts much smaller than the 0.20 standard deviations that Cohen (1988) characterized as "small." While the need to detect smaller impacts is based on compelling arguments that such impacts are substantively meaningful, the drive to detect smaller impacts…

Previous research has demonstrated that differential item functioning (DIF) methods that do not account for multilevel data structure could result in too frequent rejection of the null hypothesis (i.e., no DIF) when the intraclass correlation coefficient (?) of the studied item was the same as the ? of the total score. The current study extended…

In the 2009 Programme for International Student Assessment, the Flemish community of Belgium outscored its French community in reading, with low achievers accounting for a large proportion of the score gaps. In this study, between-community comparisons based on the Blinder-Oaxaca decomposition method showed that the Flemish community benefits…

Mixtures of item response theory (IRT) models have been proposed as a technique to explore response patterns in test data related to cognitive strategies, instructional sensitivity, and differential item functioning (DIF). Estimation proves challenging due to difficulties in identification and questions of effect size needed to recover underlying…

One of the vexing problems in the analysis of SSD is in the assessment of the effect of intervention. Serial dependence notwithstanding, the linear model approach that has been advanced involves, in general, the fitting of regression lines (or curves) to the set of observations within each phase of the design and comparing the parameters of these…

This study is aimed at addressing the criticisms that have been leveled at the currently available statistical procedures for analyzing single subject designs (SSD). One of the vexing problems in the analysis of SSD is in the assessment of the effect of intervention. Serial dependence notwithstanding, the linear model approach that has been…

In this article, three multilevel models for meta-analysis are examined. Hedges and Olkin suggested that effect sizes follow a noncentral "t" distribution and proposed several approximate methods. Raudenbush and Bryk further refined this model; however, this procedure is based on a normal approximation. In the current research literature, this…

The general purpose of this study was to examine the efficiency of the Precision Efficacy Analysis for Regression (PEAR) method for choosing appropriate sample sizes in regression studies used for precision. The PEAR method, which is based on the algebraic manipulation of an accepted cross-validity formula, essentially uses an effect size to…

In a Monte Carlo analysis of single-subject data, Type I and Type II error rates were compared for various statistical tests of the significance of treatment effects. Data for 5,000 subjects in each of 6 treatment effect size groups were computer simulated, and 2 types of treatment effects were simulated in the dependent variable during…

The analysis of interaction effects in multiple regression has received considerable attention in recent years, but problems with the valid identification of moderating variables have been noted by researchers. G. McClelland and C. Judd (1993), in their discussion of the statistical difficulties of detecting interactions and moderating effects,…

The focus of this research was to determine the efficacy of a new method of selecting sample sizes for multiple linear regression. A Monte Carlo simulation was used to study both empirical predictive power rates and empirical statistical power rates of the new method and seven other methods: those of C. N. Park and A. L. Dudycha (1974); J. Cohen…

This Monte Carlo study explored relationships among standard and unstandardized regression coefficients, structural coefficients, multiple R_ squared, and significance level of predictors for a variety of linear regression scenarios. Ten regression models with three predictors were included, and four conditions were varied that were expected to…