Multilevel and Latent Variable Modeling with Composite Links and Exploded Likelihoods

Composite links and exploded likelihoods are powerful yet simple tools for specifying a wide range of latent variable models. Applications considered include survival or duration models, models for rankings, small area estimation with census information, models for ordinal responses, item response models with guessing, randomized response models, unfolding models, latent class models with random effects, multilevel latent class models, models with log-normal latent variables, and zero-inflated Poisson models with random effects. Some of the ideas are illustrated by estimating an unfolding model for attitudes to female work participation. We wish to thank The Research Council of Norway for a grant supporting our collaboration.     

Semi‐parametric proportional hazards models with crossed random effects for psychometric response times

The semi‐parametric proportional hazards model with crossed random effects has two important characteristics: it avoids explicit specification of the response time distribution by using semi‐parametric models, and it captures heterogeneity that is due to subjects and items. The proposed model has a proportionality parameter for the speed of each test taker, for the time intensity of each item, and for subject or item characteristics of interest. It is shown how all these parameters can be estimated by Markov chain Monte Carlo methods (Gibbs sampling). The performance of the estimation procedure is assessed with simulations and the model is further illustrated with the analysis of response times from a visual recognition task.     

Is episodic memory performance more vulnerable to depressive affect in older adulthood?

This study examined how age, depressive symptoms, demographic variables, frailty, and health factors jointly influence episodic memory across the lifespan in two large, diverse samples. Hierarchical regression analyses from both samples showed that depressive symptoms negatively impacted episodic memory performance with the effect being more pronounced for older adults. Health and frailty tended not to be associated with episodic memory. However, the main effect of depressive symptoms tended to remain significant over and above other predictors, while the interaction with age was weakened with the addition of demographic variables. The unique contribution of this study is demonstrating that the relationship between depressive symptoms and episodic memory is moderated by age across relatively large non-clinical lifespan samples of adults. The findings indicate the importance of measuring and studying depressive symptoms during the course of aging in order to better understand the complex relationship between age, affect, physical functioning, and memory.     

A latent variable model for discrete multivariate psychometric waiting times

A version of the discrete proportional hazards model is developed for psychometrical applications. In such applications, a primary covariate that influences failure times is a latent variable representing a psychological construct. The Metropolis-Hastings algorithm is studied as a method for performing marginal likelihood inference on the item parameters. The model is illustrated with a real data example that relates the age at which teenagers first experience various substances to the latent ability to avoid the onset of such behaviors.We thank Michael Newton and Daode Huang for their helpful comments and suggestions.