A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class models

Daniel Oberski (), Geert Kollenburg () and Jeroen Vermunt ()

Advances in Data Analysis and Classification, 2013, vol. 7, issue 3, 267-279

Abstract: Binary data latent class analysis is a form of model-based clustering applied in a wide range of fields. A central assumption of this model is that of conditional independence of responses given latent class membership, often referred to as the “local independence” assumption. The results of latent class analysis may be severely biased when this crucial assumption is violated; investigating the degree to which bivariate relationships between observed variables fit this hypothesis therefore provides vital information. This article evaluates three methods of doing so. The first is the commonly applied method of referring the so-called “bivariate residuals” to a Chi-square distribution. We also introduce two alternative methods that are novel to the investigation of local dependence in latent class analysis: bootstrapping the bivariate residuals, and the asymptotic score test or “modification index”. Our Monte Carlo simulation indicates that the latter two methods perform adequately, while the first method does not perform as intended. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Conditional dependence; Latent variable models; Score test; Lagrange multiplier test; Modification index; Bivariate residuals; 62F40; 62F03; 62F99; 62H15; 62H17; 62H30 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s11634-013-0146-2

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