Latent Variable Modelling: A Survey*

Authors

  • ANDERS SKRONDAL,

    1. Department of Statistics and The Methodology Institute, London School of Economics and Division of Epidemiology, Norwegian Institute of Public Health
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  • SOPHIA RABE-HESKETH

    1. Graduate School of Education and Graduate Group in Biostatistics, University of California, Berkeley and Institute of Education, University of London
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  • *

    This paper was presented at the 21st Nordic Conference on Mathematical Statistics, Rebild, Denmark, June 2006 (NORDSTAT 2006).

Anders Skrondal, Department of Statistics, London School of Economics, Houghton Street, London WC2A 2AE, UK.
E-mail: a.skrondal@lse.ac.uk

Abstract

Abstract.  Latent variable modelling has gradually become an integral part of mainstream statistics and is currently used for a multitude of applications in different subject areas. Examples of ‘traditional’ latent variable models include latent class models, item–response models, common factor models, structural equation models, mixed or random effects models and covariate measurement error models. Although latent variables have widely different interpretations in different settings, the models have a very similar mathematical structure. This has been the impetus for the formulation of general modelling frameworks which accommodate a wide range of models. Recent developments include multilevel structural equation models with both continuous and discrete latent variables, multiprocess models and nonlinear latent variable models.

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