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Generalized latent variables models with non-linear effects

Rizopoulos, Dimitris and Moustaki, Irini (2008) Generalized latent variables models with non-linear effects. British Journal of Mathematical and Statistical Psychology, 61 (2). pp. 415-438. ISSN 0007-1102

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Identification Number: 10.1348/000711007X213963


Until recently, item response models such as the factor analysis model for metric responses, the two-parameter logistic model for binary responses and the multinomial model for nominal responses considered only the main effects of latent variables without allowing for interaction or polynomial latent variable effects. However, non-linear relationships among the latent variables might be necessary in real applications. Methods for fitting models with non-linear latent terms have been developed mainly under the structural equation modelling approach. In this paper, we consider a latent variable model framework for mixed responses (metric and categorical) that allows inclusion of both non-linear latent and covariate effects. The model parameters are estimated using full maximum likelihood based on a hybrid integration–maximization algorithm. Finally, a method for obtaining factor scores based on multiple imputation is proposed here for the non-linear model.

Item Type: Article
Official URL:
Additional Information: © 2008 British Psychological Society
Divisions: Statistics
Subjects: Q Science > QA Mathematics
Date Deposited: 05 Jun 2008 13:51
Last Modified: 16 May 2024 00:50

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