Due to shifts in society's attitudes toward special education and changes in U.S. federal policies, such as the Individuals with Disabilities Education Act and No Child Left Behind, more students with disabilities (SWDs) are beginning kindergarten in general education classrooms. As a result, schools and districts across the United States have…

Student absenteeism in the earliest years of elementary school has been linked to a range of negative outcomes. Though the literature has examined numerous factors that are associated with children missing school, the role of teachers--especially at the early elementary level--has not been well understood. Given that students spend the majority of…

Attention-deficit/hyperactivity disorder (ADHD) overdiagnosis and overtreatment unnecessarily exposes children to potential harm and contributes to provider and community skepticism toward those with moderate or severe symptoms and significant impairments, resulting in less supportive care. Yet, which sociodemographic groups of children are…

Large-scale education data are collected via complex sampling designs that incorporate clustering and unequal probability of selection. Multilevel models are often utilized to account for clustering effects. The probability weighted approach (PWA) has been frequently used to deal with the unequal probability of selection. In this study, we examine…

This study investigated the extent to which class-specific parameter estimates are biased by the within-class normality assumption in nonnormal growth mixture modeling (GMM). Monte Carlo simulations for nonnormal GMM were conducted to analyze and compare two strategies for obtaining unbiased parameter estimates: relaxing the within-class normality…

Background: It is of grave concern that kindergartners are missing more school than students in any other year of elementary school; therefore, documenting which students are absent and for how long is of upmost importance. Yet, doing so for students with disabilities (SWDs) has received little attention. This study addresses this gap by examining…

Previous analyses of large national datasets have tended to report a negative relationship between parental homework help and student achievement. Yet these studies have not examined heterogeneity in this relationship based on the propensity for a parent to provide homework help. By using a propensity score-based approach, this study investigates…

This study uses data from the Early Childhood Longitudinal Study, 2010-2011 to better understand the rates of grade retention during elementary school and the factors associated with this grade retention. Using matched student-teacher and student-principal data, I examine the student-, teacher-, and school-level factors associated with a student's…

Approximately one in 10 children in the United States is diagnosed with attention-deficit/hyperactivity disorder (ADHD), a disability that can negatively affect academic achievement, yet relatively few children with ADHD are in special education. To better understand factors that determine which students with ADHD are in special education, we…

Nonnormality of univariate data has been extensively examined previously (Blanca et al., 2013; Micceri, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of…

In this article, we review four software packages for implementing propensity score analysis in R: "Matching, MatchIt, PSAgraphics," and "twang." After briefly discussing essential elements for propensity score analysis, we apply each package to a data set from the Early Childhood Longitudinal Study in order to estimate the…

Complex surveys are often used to estimate causal effects regarding the effects of interventions or exposures of interest. Propensity scores (Rosenbaum & Rubin, 1983) have emerged as one popular and effective tool for causal inference in non-experimental studies, as they can help ensure that groups being compared are similar with respect to a…

Growth curve modeling provides a general framework for analyzing longitudinal data from social, behavioral, and educational sciences. Bayesian methods have been used to estimate growth curve models, in which priors need to be specified for unknown parameters. For the covariance parameter matrix, the inverse Wishart prior is most commonly used due…

Using data from the Early Childhood Longitudinal Study Kindergarten Cohort of 1998 (n = 15,070), this study used propensity scores to examine the short- and long-term academic and psychosocial benefits of preschool education for a diverse sample of middle-class children. Compared with children who attended informal care at age 4, preschool…

Background/Context: Chronic school absenteeism is a pervasive problem across the US; in early education, it is most rampant in kindergarten and its consequences are particularly detrimental, often leading to poorer academic, behavioral and developmental outcomes later in life. Though prior empirical research has identified a broad range of…