We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct…

Educational Data Mining is an emerging discipline which seeks to develop methods to explore large amounts of data from educational settings, in order to understand students' behavior, interests and results in a better way. In recent years there have been various works related to this specialty and multiple data mining techniques derived from this…

In the human sciences, ability tests or psychological inventories are often repeatedly conducted to measure growth. Standard item response models do not take into account possible autocorrelation in longitudinal data. In this study, the authors propose an item response model to account for autocorrelation. The proposed three-level model consists…

This study examines the choice of a university major with a special focus on the type of major chosen in the first year and success/failure at the end of this first year, using a sample of 2284 students in Flanders, Belgium. Extending previous research, the effects of prior subject uptake, occupational interests, prior mathematics and Dutch…

Maintaining comparability of test scores is a major challenge faced by testing programs that have almost continuous administrations. Among the potential problems are scale drift and rapid accumulation of errors. Many standard quality control techniques for testing programs, which can effectively detect and address scale drift for small numbers of…