Binary outcomes are often analyzed in cluster randomized trials (CRTs) using logistic regression and cluster robust standard errors (CRSEs) are routinely used to account for the dependent nature of nested data in such models. However, CRSEs can be problematic when the number of clusters is low (e.g., < 50) and, with CRTs, a low number of…

Normative studies are needed to obtain norms for comparing individuals with the reference population on relevant clinical or educational measures. Norms can be obtained in an efficient way by regressing the test score on relevant predictors, such as age and sex. When several measures are normed with the same sample, a multivariate regression-based…

A log-linear model (LLM) is a well-known statistical method to examine the relationship among categorical variables. This study investigated the performance of LLM in detecting differential item functioning (DIF) for polytomously scored items via simulations where various sample sizes, ability mean differences (impact), and DIF types were…

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…

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…

The purpose of this study is to illustrate the multilevel meta-analysis of results from single-subject experimental designs of different types, including AB phase designs, multiple-baseline designs, ABAB reversal designs, and alternating treatment designs. Current methodological work on the meta-analysis of single-subject experimental designs…

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…

The aim of this paper is to provide an introduction of new imputation algorithms for estimating missing values from official statistics in larger data sets of data pre-processing, or outliers. The goal is to propose a new algorithm called IRMI (iterative robust model-based imputation). This algorithm is able to deal with all challenges like…

Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their…

Clustered data (e.g., students within schools) are often analyzed in educational research where data are naturally nested. As a consequence, multilevel modeling (MLM) has commonly been used to study the contextual or group-level (e.g., school) effects on individual outcomes. The current study investigates the use of an alternative procedure to…

Nearly all quantitative analyses in higher education draw from incomplete datasets-a common problem with no universal solution. In the first part of this paper, we explain why missing data matter and outline the advantages and disadvantages of six common methods for handling missing data. Next, we analyze real-world data from 5,905 students across…

In this paper, we document a study that involved applying a multiple imputation technique with chained equations to data drawn from the 2007 iteration of the TIMSS database. More precisely, we imputed missing variables contained in the student background datafile for Tunisia (one of the TIMSS 2007 participating countries), by using Van Buuren,…

In organizational studies involving multiple levels, the association between a covariate and an outcome often differs at different levels of aggregation, giving rise to widespread interest in "contextual effects models." Such models partition the regression into within- and between-cluster components. The conventional approach uses each…