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Loglinear Latent Variable Models for Longitudinal Categorical Data

  • Jacques A. HagenaarsEmail author
Chapter

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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of Methodology and StatisticsTilburg UniversityTilburgThe Netherlands

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