Events and Meetings of Italian Statistical Society, Advances in Latent Variables - Methods, Models and Applications

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Local dependence in latent class models: application to voting in elections
Daniel L. Oberski

Last modified: 2013-06-16

Abstract


Demonstrates methods of detecting local dependence in binary data latent class models. Latent class models are applied to five repeated measures of voter turnout in the Dutch Parliamentary elections of 2006 and 2010 obtained from a probability sample of 9510 citizens. Modeling substantive local dependence as separate discrete latent variables while modeling nuisance dependencies as direct effects yields an interpretable model, giving insight into the classification errors present in survey questions about voting. The procedure followed stands in contrast to the “standard” procedure of increasing the number of latent classes until information criteria are satisfactory.

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