Predicting latent class scores for subsequent analysis

TY - JOUR

T1 - Predicting latent class scores for subsequent analysis

AU - Petersen, J

AU - Bandeen-Roche, K

AU - Budtz-Jørgensen, Esben

AU - Larsen, KG

PY - 2012/4

Y1 - 2012/4

N2 - Latent 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.

AB - Latent 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.

M3 - Journal article

SN - 0033-3123

VL - 77

SP - 244

EP - 262

JO - Psychometrika

JF - Psychometrika

IS - 2

ER -