The standard methodology for many large-scale assessments in education involves regressing latent variables on numerous contextual variables to estimate proficiency distributions. To reduce the number of contextual variables used in the regression and improve estimation, we propose and evaluate principal component analysis on the covariance matrix…

We consider Bayesian estimation of a hierarchical linear model (HLM) from small sample sizes. The continuous response Y and covariates C are partially observed and assumed missing at random. With C having linear effects, the HLM may be efficiently estimated by available methods. When C includes cluster-level covariates having interactive or other…

Bayesian algorithms have been used successfully in the social and behavioral sciences to analyze dichotomous data particularly with complex structural equation models. In this study, we investigate the use of the Polya-Gamma data augmentation method with Gibbs sampling to improve estimation of structural equation models with dichotomous variables.…

Whenever you think you have an idea of how something works, you have a mental model. That is, in effect, a layman's way of talking about having an hypothesis. The hypothesis needs to be tested for how closely it fits reality--and reality is the data collected from an experiment. So the data is collected on the few and compared with a few…

Value-added models have been widely used to assess the contributions of individual teachers and schools to students' academic growth based on longitudinal student achievement outcomes. There is concern, however, that ignoring the presence of missing values, which are common in longitudinal studies, can bias teachers' value-added scores.…

Status quo approaches to teacher evaluation have recently come under increasing criticism. They typically assign most teachers the highest available score, provide minimal feedback for improvement, and have little connection with student achievement growth and the quality of instruction that leads to higher student growth. A more comprehensive…

A large-scale analysis was conducted of problem behavior by observing 20 individuals living in residential facilities. Data were converted into scatter plot formats. When the data were transformed into aggregate "control charts," 12 of 15 sets of data revealed 30-minute intervals during which problem behavior was more likely to occur.…

Data in social and behavioral sciences typically possess heavy tails. Structural equation modeling is commonly used in analyzing interrelations among variables of such data. Classical methods for structural equation modeling fit a proposed model to the sample covariance matrix, which can lead to very inefficient parameter estimates. By fitting a…

Outlines differences between classical statistics and exploratory data analysis. Provides examples in the use of the exploratory techniques. (YP)

This study assesses the extent of residential segregation among 15,246 people diagnosed with psychiatric disabilities and receiving Medicaid (MA) in Philadelphia, and an identically sized group of MA recipients serving as matched controls. Results indicate that overall levels of residential segregation among this group were modest at their most…

Global economic transformations have never been as dramatic as in the twentieth century. Most countries have experienced radical changes in the standards of income per capita, technology, fertility, mortality, income inequality and the extent of democracy in the course of the past century. It is the goal of many disciplines--economics, history,…