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Applied Latent Class Analysis book

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

Applied Latent Class Analysis


Applied.Latent.Class.Analysis.pdf
ISBN: 0521594510,9780521594516 | 478 pages | 12 Mb


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




Latent class analysis was used to classify children into four profiles of classroom engagement: free play, individual instruction, group instruction, and scaffolded learning. [I like this tidbit: 2% of respondents said they'd never heard of global warming]. Next, the researchers applied latent class growth analysis to estimate the smoking trajectory for each individual. Classifications of these subgroups were based on their psychosocial characteristics (e.g., substance use). Richard Dembo⇓; Rhissa Baseline data collected in two brief intervention projects (BI-Court and Truancy Project) were used to assess similarities and differences in subgroups of at-risk youth. Latent class analysis was used to cluster people according to their beliefs, attitudes, etc. Multigroup latent class analysis identified two BI-Court subgroups of youth and three truant subgroups. 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. The researchers then applied latent class growth analysis to determine the smoking trajectory for the students, measuring how smoking behaviors changed over time. 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. 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. A Multigroup Exploratory Latent Class Analysis. To determine the underlying causes that were more likely to lead to PMV, we applied LCA to group separate co-morbidity diagnoses into no more than 10 clusters of in-patients who had undergone PMV.