Predicting Latent Class Scores for Subsequent Analysis

• DOI: 10.1007/s11336-012-9248-6
• Publication Date: 2012-02-06T19:27:53Z
• Authors: Janne Petersen; Karen Bandeen-Roche; Esben Budtz-Jørge...; Klaus Groes Larsen
• Keywords: 0101 mathematics; 01 natural sciences

AbstractLatent class regression models relate covariates and latent constructs such as psychiatric disorders. Though full maximum likelihood estimation is available, estimation is often in three steps: (i) a latent class model is fitted without covariates; (ii) latent class scores are predicted; and (iii) the scores are regressed on covariates. We propose a new method for predicting class scores that, in contrast to posterior probability-based methods, yields consistent estimators of the parameters in the third step. Additionally, in simulation studies the new methodology exhibited only a minor loss of efficiency. Finally, the new and the posterior probability-based methods are compared in an analysis of mobility/exercise.