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Applied Latent Class Analysis by Allan L. McCutcheon, Jacques A. Hagenaars

Applied Latent Class Analysis Allan L. McCutcheon, Jacques A. Hagenaars ebook
Publisher: Cambridge University Press
ISBN: 0521594510, 9780521594516
Page: 478
Format: pdf

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. It contains articles about statistical models, classification, cluster analysis, multidimensional scaling, multivariate analysis, latent variables, knowledge extraction from temporal data, financial and economic applications, and missing values. Latent class analysis was used to classify children into four profiles of classroom engagement: free play, individual instruction, group instruction, and scaffolded learning. In applying the proposed approach to an epidemiological study that examined the relationship between environmental polychlorinated biphenyl (PCB) exposure and the risk of endometriosis, we identified a highly significant overall effect of PCB concentrations on the risk of endometriosis. Latent class analysis was used to identify sub-groups within the group of young people who were in JWT. We develop a latent class modeling approach for joint analysis of high-dimensional semicontinuous biomarker data and a binary disease outcome. 4.5 In all three studies, securing samples of young people (and employers) was problematic. The researchers then applied latent class growth analysis to determine the smoking trajectory for the students, measuring how smoking behaviors changed over time. Applying latent class analysis to this data, six classes were identified as clinically interpretable and relevant subgroups of prescription opioid abusers. Optimum decision points for In applying this new agent system to diagnosis of acute myocardial infarction (AMI) we demonstrated that at an optimum clustering distance the number of classes is minimized with efficient training on the neural network. Latent class analysis (LCA) was used to identify distinct patterns of known risk factors for suicide among the decedents and to classify these decedents by these patterns. Three data sets have been extensively validated prior to neural network analysis using receiver-operator curve (ROC analysis), Latent Class Analysis, and a multinomial regression approach.