Simulation studies are the basic tools of quantitative methodologists used to obtain empirical solutions to statistical problems that may be impossible to derive through direct mathematical computations. The successful execution of many simulation studies relies on the accurate generation of correlated multivariate data that adhere to a particular…

Over the last four decades, meta-analysis has proven to be a vital analysis strategy in educational research for synthesizing research findings from different studies. When synthesizing studies in a meta-analysis, it is common to assume that the true underlying effect varies from study to study, as studies will differ in design, participants,…

High dimensional multivariate data, where the number of variables approaches or exceeds the sample size, is an increasingly common occurrence for social scientists. Several tools exist for dealing with such data in the context of univariate regression, including regularization methods such as Lasso, Elastic net, Ridge Regression, as well as the…

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…

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in…

Multilevel modeling has grown in use over the years as a way to deal with the nonindependent nature of observations found in clustered data. However, other alternatives to multilevel modeling are available that can account for observations nested within clusters, including the use of Taylor series linearization for variance estimation, the design…

In a traditional regression-discontinuity design (RDD), units are assigned to treatment on the basis of a cutoff score and a continuous assignment variable. The treatment effect is measured at a single cutoff location along the assignment variable. This article introduces the multivariate regression-discontinuity design (MRDD), where multiple…

The purpose of this study was to explore what may be contributing to differences in performance in mathematics on the Trends in International Mathematics and Science Study 2007. This was done by using a mixture item response theory modeling approach to first detect latent classes in the data and then to examine differences in performance on items…

Interactive learning environments can provide learners with opportunities to explore rich, real-world problem spaces, but the nature of these problem spaces can make assessing learner progress difficult. Such assessment can be useful for providing formative and summative feedback to the learners, to educators, and to the designers of the…

Six stepwise multiple-comparison procedures for repeated-measures means were compared for their overall familywise rates of Type I error when multisample sphericity and multivariate normality were not satisfied. Robust stepwise procedures were identified by Keselman, Keselman, and Shaffer (1991) with respect to three definitions of power. (SLD)

Several methods for dealing with incomplete multivariate data and ways to examine the effectiveness of these methods are discussed. It is concluded that pairwise and listwise deletions are among the least effective methods in terms of approximating the results, whereas estimates based on correlational procedures generally produce the most accurate…

Classification studies are important for practitioners who need to identify individuals for specialized treatment or intervention. When interventions are irreversible or misclassifications are costly, information about the proficiency of different classification procedures becomes invaluable. This study furnishes information about the relative…

Meta-analytic methods were used to summarize results of Monte Carlo (MC) studies investigating the robustness of various statistical procedures for testing within-subjects effects in split-plot repeated measures designs. Through a literature review, accessible MC studies were identified, and characteristics (simulation factors) and outcomes (rates…

This paper discussed methods currently under study for use with multiple-response data. Besides using Bonferroni inequality methods to control type one error rate over a set of inferences involving multiple response data, a recently proposed methodology of plotting the p-values resulting from multiple significance tests was explored. Proficiency…