Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. However, model-based clustering methods for categorical data are less standard. Latent class analysis is a commonly used method for model-based clustering of binary data and/or categorical data, but due to an assumed local independence structure there may not be a correspon...

The purpose of the present study was to compare the results of Configural Frequency Analysis (CFA) and solutions derived from Latent Class Analysis (LCA) to evaluate concordances and disconcordances regarding their outcomes. Explorative LCA was applied to the Big Five dataset described in Lautsch & Thöle (this issue). The comparative analyses of the LCA solutions and the CFA results demonstrate...

A spatial latent class analysis model that extends the classic latent class analysis model by adding spatial structure to the latent class distribution through the use of the multinomial probit model is introduced. Linear combinations of independent Gaussian spatial processes are used to develop multivariate spatial processes that are underlying the categorical latent classes. This allows the l...

Latent class analysis has been used to model measurement error, to identify flawed survey questions and to estimate mode effects. Using data from a survey of University of Maryland alumni together with alumni records, we evaluate this technique to determine its usefulness for detecting bad questions in the survey context. Two sets of latent class analysis models are applied in this evaluation: ...

The data base availability and the possibility to perform survey permit to the researcher to explore new segmentation model trying to involve methodological statistics and explanatory variables. Recent literature focused on values and lifestyles as predictors of consumer purchase (Kamakura, Mazzon, 1991). We purpose to build a survey based on questionnaire to analyze the influences of explanato...

We exploit the potential of latent class analysis in order to propose an innovative framework for financial portfolio development. By stressing the latent nature of the most important financial variables, the expected return and the risk, we are able to introduce a methodological dimension in relevant steps of portfolio analysis. First, we provide a test for the number of possible investment ch...