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Loglinear Latent Variable Models for Longitudinal Categorical Data

In: Longitudinal Research with Latent Variables

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  • Jacques A. Hagenaars

    (Tilburg University, Department of Methodology and Statistics)

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.

Suggested Citation

  • Jacques A. Hagenaars, 2010. "Loglinear Latent Variable Models for Longitudinal Categorical Data," Springer Books, in: Kees van Montfort & Johan H.L. Oud & Albert Satorra (ed.), Longitudinal Research with Latent Variables, chapter 0, pages 1-36, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-11760-2_1
    DOI: 10.1007/978-3-642-11760-2_1
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