When using multiple imputation, users often want to know how many imputations they need. An old answer is that 2-10 imputations usually suffice, but this recommendation only addresses the efficiency of point estimates. You may need more imputations if, in addition to efficient point estimates, you also want standard error (SE) estimates that would…

The purpose of this paper is to extend and evaluate methods of "Bayesian historical borrowing" applied to longitudinal data with a focus on parameter recovery and predictive performance. Bayesian historical borrowing allows researchers to utilize information from previous data sources and to adjust the extent of borrowing based on the…

The purpose of this paper is to extend and evaluate methods of "Bayesian historical borrowing" applied to longitudinal data with a focus on parameter recovery and predictive performance. Bayesian historical borrowing allows researchers to utilize information from previous data sources and to adjust the extent of borrowing based on the…

Respondent attrition is a common problem in national longitudinal panel surveys. To make full use of the data, weights are provided to account for attrition. Weight adjustments are based on sampling design information and data from the base year; information from subsequent waves is typically not utilized. Alternative methods to address bias from…

Researchers, policymakers, and practitioners have recently aligned efforts to reduce school absenteeism, particularly during kindergarten when excessive absences are highest out of all elementary grades. Little is known, however, about whether the way in which students get to school might influence if they go to school. To address this gap, this…

This case study uses structural equation modeling to examine the predictive validity of the Read Aloud Profile-Together, a measure of the distinct behaviors of parents and children during shared book reading, in relation to preschool children's early reading competency. Using secondary data analysis, this case study includes 800 parent-child pairs…

Extensive research has conclusively demonstrated that children's social class is one of the most significant predictors--if not the single most significant predictor--of their educational success. This three-part study combines a statistical analysis of early skills gaps among a recent cohort of children and changes in them over time with a…

Disparities in science achievement across race and gender have been well documented in secondary and postsecondary school; however, the science achievement gap in the early years of elementary school remains understudied. We present findings from the recently released Early Childhood Longitudinal Study, Kindergarten Class of 2010-2011 that…

Background/Context: Despite decades of research, the persistence of the gap in student achievement between disadvantaged minority students and their middle-class peers remains unexplained. Purpose/Objective: The purpose of the current article is to propose a new model of the achievement gap. Research Design: Data were analyzed from three…

We examined the age of onset, over-time dynamics, and mechanisms underlying science achievement gaps in U.S. elementary and middle schools. To do so, we estimated multilevel growth models that included as predictors children's own general knowledge, reading and mathematics achievement, behavioral self-regulation, sociodemographics, other child-…

We examined the age of onset, over-time dynamics, and mechanisms underlying science achievement gaps in U.S. elementary and middle schools. To do so, we estimated multilevel growth models that included as predictors children's own general knowledge, reading and mathematics achievement, behavioral self-regulation, sociodemographics, other child-…

Applied early adolescent researchers often sample students (Level 1) from within classrooms (Level 2) that are nested within schools (Level 3), resulting in data that requires multilevel modeling analysis to avoid Type 1 errors. Although several articles have been published to assist researchers with analyzing sample data nested at two levels, few…

Has the academic achievement gap between high-income and low-income students changed over the last few decades? If so, why? And what can schools do about it? Researcher Sean F. Reardon conducted a comprehensive analysis of research to answer these questions and came up with some striking findings. In this article, he shows that income-related…

In longitudinal multilevel studies, especially in educational settings, it is fairly common that participants change their group memberships over time (e.g., students switch to different schools). Participant's mobility changes the multilevel data structure from a purely hierarchical structure with repeated measures nested within individuals and…

This paper studies the effect of the Head Start program on children's achievements in reading and math tests during their first 4 years of schooling after completing the program. Using nationally representative data from the Early Childhood Longitudinal Study, I found large measurement error in the parental reports of Head Start attendance, which…