Loglinear Latent Variable Models for Longitudinal Categorical Data
DOI: 10.1007/978-3-642-11760-2_1
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摘要: Errors and unreliability in categorical data the form of independent or systematic misclassifications may have serious consequences for substantive conclusions. This is especially true analysis longitudinal where very misleading conclusions about underlying processes change be drawn that are completely result even small amounts misclassifications. Latent class models offer unique possibilities to correct all kinds In this chapter, latent will used show possible distorting influences research how them. Both simple more complicated analyses dealt with, discussing both
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