This article discusses how several hypotheses about change in discrete variables can be tested on data obtained in a longitudinal study. A first class of hypotheses pertain to the invariance of certain characteristics of marginal distributions. A second class of hypotheses derive from...

Conclusions about changes in categorical characteristics based on observed panel data can be incorrect when (even a small amount of) measurement error is present. Random measurement errors, referred to as independent classification errors, usually lead to over-estimation of the total amount of...

Latent class analysis (LCA) is an extremely useful and flexible technique for the analysis of categorical data, measured at the nominal, ordinal, or interval level (the latter with fixed or estimated scores). It is, first, a general measurement model, a particular kind of latent structure model...

A basic assumption of latent structure models is that of local independence: given the score on the latent variable, the scores on the manifest variables are independent of each other. This basic assumption is violated when test-retest effects, response consistency effects, correlated response...

Dieses Buch behandelt Modelle zur Analyse kategorialer Daten. Kategoriale Daten sind Variablen, die eine begrenzte Anzahl von Ausprägungen (Kategorien) haben. Bei vielen der in Umfrageforschung und amtlicher Statistik erhobenen Merkmale handelt es sich um kategoriale Daten. Beispiele wären...