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Applied Latent Class Analysis Allan L. McCutcheon, Jacques A. Hagenaars
Publisher: Cambridge University Press

Instead of a standard regression model, consider building a latent class regression. An alternative is to use the Expectation Maximization (EM) algorithm [26], which is also a maximum likelihood approach but ideally suited to problems comprising latent class variables, which is exactly what we have here as the true disease status of each observation is only latently While the DIC is very commonly used in Bayesian analyses, and is straightforward to estimate, it is not without its critics and its reliability in some situations is an active area of statistical research (e.g. Finally, latent class analysis, a statistical method which has been proposed for the assessment of diagnostic tests in the absence of a gold standard, could be used in this context [21]. The data analysis was conducted with SAS 9.2. Latent Class Analysis (LCA) was used to analyze patterns of witnessed community violence among the participants. In addition to standard multinomial logit models, mixed and latent class logit models are used to analyze the data from our stated choice experiment. The goal of the latent class analysis is to simply classify the reentry experiences with system involvement that follows each exit. Latent class analysis was then applied to the individual priorities, to identify separate clusters or subgroups of people. To explore the heterogeneity of APED use patterns, the authors subjected data on use patterns to (a) latent class analysis (LCA), (b) latent trait analysis (LTA), and (c) factor mixture analysis to determine the best model of APED use. Latent class analysis was completed with PROC LCA 1.2.5 beta [12].