Bayesian methods for latent trait modelling of longitudinal data.

Published

Journal Article (Review)

Latent trait models have long been used in the social science literature for studying variables that can only be measured indirectly through multiple items. However, such models are also very useful in accounting for correlation in multivariate and longitudinal data, particularly when outcomes have mixed measurement scales. Bayesian methods implemented with Markov chain Monte Carlo provide a flexible framework for routine fitting of a broad class of latent variable (LV) models, including very general structural equation models. However, in considering LV models, a number of challenging issues arise, including identifiability, confounding between the mean and variance, uncertainty in different aspects of the model, and difficulty in computation. Motivated by the problem of modelling multidimensional longitudinal data, this article reviews the recent literature, provides some recommendations and highlights areas in need of additional research, focusing on methods for model uncertainty.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB

Published Date

  • October 2007

Published In

Volume / Issue

  • 16 / 5

Start / End Page

  • 399 - 415

PubMed ID

  • 17656454

Pubmed Central ID

  • 17656454

Electronic International Standard Serial Number (EISSN)

  • 1477-0334

International Standard Serial Number (ISSN)

  • 0962-2802

Digital Object Identifier (DOI)

  • 10.1177/0962280206075309

Language

  • eng