Based on the literature about self-disclosure, it was hypothesized that different groups of subjects differ in their pattern of self-disclosure with respect to different areas of social interaction. An extended latent-trait latent-class model was proposed to describe these general patterns of self-disclosure. The model was used to analyze the data of 1,113 subjects, tested on extraversion and w...

Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions of unknowns, including the latent variables. In this article, we propose a broad class of semipa...

Popular longitudinal models allow for prediction of growth trajectories in alternative ways. In latent class growth models (LCGMs), person-level covariates predict membership in discrete latent classes that each holistically define an entire trajectory of change (e.g., a high-stable class vs. late-onset class vs. moderate-desisting class). In random coefficient growth models (RCGMs, also known ...

In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search G...

In educational studies, the use of computer-based assessments leads to the collection of multiple outcomes to assess student performance. The student-specific outcomes are correlated and often measured in different scales, such as continuous and count outcomes. A multivariate zero-inflated model with random effects is proposed and adapted for the challenging situation where the multiple outcome...

INTRODUCTION The aim of this study was to develop an empirical approach to classifying patients with transthyretin amyloidosis (ATTR) based on clinical signs and symptoms. METHODS Data from 971 symptomatic subjects enrolled in the Transthyretin Amyloidosis Outcomes Survey were analyzed using a latent class analysis approach. Differences in health status measures for the latent classes were ex...

We consider the task of inferring causal relations in brain imaging data with latent confounders. Using a priori knowledge that randomized experimental conditions cannot be effects of brain activity, we derive statistical conditions that are sufficient for establishing a causal relation between two neural processes, even in the presence of latent confounders. We provide an algorithm to test the...

Using a basic latent class model for the analysis of three-way threemode data (i.e. raters by objects by attributes) to cluster raters is often problematic because the number of conditional probabilities increases rapidly when extra latent classes are added. To solve this problem, Meulders et al. (J Classification 19:277– 302, 2002) proposed a constrained latent class model in which object-attr...