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…

Exploring how people represent natural categories is a key step toward developing a better understanding of how people learn, form memories, and make decisions. Much research on categorization has focused on artificial categories that are created in the laboratory, since studying natural categories defined on high-dimensional stimuli such as…

The central question we addressed was whether mothers' adjustment might vary systematically by the developmental stages of their children. In an Internet-based study of over 2,200 mostly well-educated mothers with children ranging from infants to adults, we examined multiple aspects of mothers' personal well-being, parenting, and perceptions of…

Developmental science is replete with studies on the impact of mothers on their children, but little is known about what might best help caregivers to function well themselves. In an initial effort to address this gap, we conducted an Internet-based study of over 2,000 mostly well-educated mothers, seeking to illuminate salient risk and protective…

Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify…