Comparing Fits of Latent Trait and Latent Class Models Applied to Sparse Binary Data: An Illustration with Human Resource Management Data

Lilian M. De Menezes and Ana Lasaosa

Journal of Applied Statistics, 2007, vol. 34, issue 3, 303-319

Abstract: This paper addresses the problem of comparing the fit of latent class and latent trait models when the indicators are binary and the contingency table is sparse. This problem is common in the analysis of data from large surveys, where many items are associated with an unobservable variable. A study of human resource data illustrates: (1) how the usual goodness-of-fit tests, model selection and cross-validation criteria can be inconclusive; (2) how model selection and evaluation procedures from time series and economic forecasting can be applied to extend residual analysis in this context.

Keywords: Multivariate statistics; latent variable models; forecast encompassing; human resource management (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1080/02664760601004908

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