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…

This study proposes a two-stage causal modeling with instrumental variables to mitigate selection bias, provide correct standard error estimates, and address nonnormal and missing data issues simultaneously. Bayesian methods are used for model estimation. Robust methods with Student's "t" distributions are used to account for nonnormal…

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…

Growth curve models are widely used in social and behavioral sciences. However, typical growth curve models often assume that the errors are normally distributed although non-normal data may be even more common than normal data. In order to avoid possible statistical inference problems in blindly assuming normality, a general Bayesian framework is…

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…

Bayesian alternatives to frequentist propensity score approaches have recently been proposed. However, few studies have investigated their covariate balancing properties. This article compares a recently developed two-step Bayesian propensity score approach to the frequentist approach with respect to covariate balance. The effects of different…

Private and public investments in early childhood education have expanded significantly in recent years. Despite this heightened investment, we have little empirical evidence on whether children today enter school with different skills than they did in the late nineties. Using two large, nationally representative data sets, this article documents…

This study assessed how socioeconomic status (SES), race/ethnicity, and birth weight interacted to predict differential patterns of body mass index (BMI) growth among U.S. children born in the early 1990s. Three BMI growth trajectories emerged--one above the 50th percentile across the age range of 5 to 14, one in which children rapidly became…

The purpose of this study is to explore Bayesian model averaging in the propensity score context. Previous research on Bayesian propensity score analysis does not take into account model uncertainty. In this regard, an internally consistent Bayesian framework for model building and estimation must also account for model uncertainty. The…

Propensity score analysis (PSA) has been used in a variety of settings, such as education, epidemiology, and sociology. Most typically, propensity score analysis has been implemented within the conventional frequentist perspective of statistics. This perspective, as is well known, does not account for uncertainty in either the parameters of the…

This article examines the multilevel linear crossed random effects growth model for estimating teacher and school effects from repeated measurements of student achievement. Results suggest that even a small degree of unmodeled nonlinearity can result in a substantial upward bias in the magnitude of the teacher effect, which raises concerns about…

This paper provides a history of ETS's role in developing assessment instruments and psychometric procedures for measuring change in large-scale national assessments funded by the Longitudinal Studies branch of the National Center for Education Statistics. It documents the innovations developed during more than 30 years of working with…

This study addressed concerns about the potential for differential gains in reading during the first 2 years of formal schooling (K-1) versus the next 2 years of schooling (1st-3rd grade). A multilevel piecewise regression with a node at spring 1st grade was used in order to define separate regressions for the two time periods. Empirical Bayes…

This study attempts to identify different patterns of cognitive growth in kindergarten and first grade associated with selected subpopulations. The results are based upon a nationally representative sample of fall kindergartners who were retested in the spring of their kindergarten year and then again in the fall and spring of their first grade…