Empirical analysis of variation in demographic events within the population is facilitated by using longitudinal survey data because of the richness of covariate measures in such data, but there is wave-on-wave dropout. When attrition is related to the event, it precludes consistent estimation of the impacts of covariates on the event and on event…

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…

Several statistical models have been developed in educational measurement to determine and track the performance of students in longitudinal studies. An example of a model designed for such purpose is the latent transition analysis (LTA) model. The LTA model (Graham, Collins, Wugalter, Chung, & Hansen 1991) is a type of autoregressive model…

We address measurement error bias in propensity score (PS) analysis due to covariates that are latent variables. In the setting where latent covariate X is measured via multiple error-prone items W, PS analysis using several proxies for X--the W items themselves, a summary score (mean/sum of the items), or the conventional factor score (i.e.,…

Analysts of discrete data often face the challenge of managing the tendency of inflation on certain values. When treated improperly, such phenomenon may lead to biased estimates and incorrect inferences. This study extends the existing literature on single-value inflated models and develops a general framework to handle variables with more than…

In order to investigate causality in situations where random assignment is not possible, propensity scores can be used in regression adjustment, stratification, inverse-probability treatment weighting, or matching. The basic concepts behind propensity scores have been extensively described. When data are longitudinal or missing, the estimation and…

This report is based on an activity completed by 89 Grade 6 students carrying out a statistical investigation with a focus on posing an initial question given a context and then refining it for a chosen data set. Having reached a conclusion based on evidence from their data analysis, students were given additional information on the context and…

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…

Since the seminal introduction of the propensity score (PS) by Rosenbaum and Rubin, PS-based methods have been widely used for drawing causal inferences in the behavioral and social sciences. However, the PS approach depends on the ignorability assumption: there are no unobserved confounders once observed covariates are taken into account. For…

The Goodenough-Harris Drawing Test (GHDT) is a non-verbal assessment designed to infer young children's levels of intellectual development and understanding via the collection of three human figure drawings (HFDs)--one each of a man, a woman and a self-portrait. This paper presents findings from a research project that applied the Rasch model for…

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…

The goal of the present study is to examine the stability of time attitudes profiles across a one-year period as well as the association between time attitudes profiles and several variables. These variables include attitudes towards alcohol, context of alcohol use, consumption of a full drink, and subjective life expectancy. We assessed the…

What factors best explain the low incidence of skills training in a late industrial society like Russia? This research undertakes a multilevel analysis of the role of occupational structure in the probability of training. The explanatory power of occupation-specific determinants and skills polarization are evaluated, using a representative 2012…