Loglinear Latent Variable Models for Longitudinal Categorical Data

Jacques A. Hagenaars ()
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Jacques A. Hagenaars: Tilburg University, Department of Methodology and Statistics

Chapter Chapter 1 in Longitudinal Research with Latent Variables, 2010, pp 1-36 from Springer

Abstract: Abstract Errors and unreliability in categorical data in the form of independent or systematic misclassifications may have serious consequences for the substantive conclusions. This is especially true in the analysis of longitudinal data where very misleading conclusions about the underlying processes of change may be drawn that are completely the result of even very small amounts of misclassifications. Latent class models offer unique possibilities to correct for all kinds of misclassifications. In this chapter, latent class analysis will be used to show the possible distorting influences of misclassifications in longitudinal research and how to correct for them. Both simple and more complicated analyses will be dealt with, discussing both systematic and independent misclassifications.

Keywords: Structural Equation Model; Latent Class; Latent Class Analysis; Rotation Group; Latent Class Model (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-11760-2_1

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DOI: 10.1007/978-3-642-11760-2_1

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