This paper was presented at the 21st Nordic Conference on Mathematical Statistics, Rebild, Denmark, June 2006 (NORDSTAT 2006).
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Latent Variable Modelling: A Survey†
Article first published online: 5 DEC 2007
DOI: 10.1111/j.1467-9469.2007.00573.x
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How to Cite
SKRONDAL, A. and RABE-HESKETH, S. (2007), Latent Variable Modelling: A Survey. Scandinavian Journal of Statistics, 34: 712–745. doi: 10.1111/j.1467-9469.2007.00573.x
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Publication History
- Issue published online: 5 DEC 2007
- Article first published online: 5 DEC 2007
- Received December 2006, in final form August 2007
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Keywords:
- factor analysis;
- GLLAMM;
- item–response theory;
- latent class;
- latent trait;
- latent variable;
- measurement error;
- mixed effects model;
- multilevel model;
- random effect;
- structural equation model
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.