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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
ISBN: 0521594510, 9780521594516
Publisher: Cambridge University Press
Format: pdf
Page: 478

We applied latent class analysis to measures of affection and conflict in 2,698 older parent–child relationships in 6 developed nations: England, Germany, Israel, Norway, Spain, and the United States (Southern California). The study used latent class analysis to associate child maltreatment and behavioral disorder. If single binary questions are used, then belief in reincarnation is more often reported than if a forced-choice question is used which offers respondents alternatives to belief in reincarnation (e.g. Nursing Medical Article: Using latent class analysis to model prescription medications in the measurement of falling among a community elderly population. Cambridge, England: Cambridge University Press. We compare two techniques that are widely used in the analysis of life course trajectories: latent class analysis and sequence analysis. 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. This study applied latent class analysis (LCA) to identify subgroups of female juvenile offenders based on their self-report of offending profiles (N=133). In contrast, two studies using multiple group latent class analysis (i.e. 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 can be applied to determine the sensitivity and specificity of a new test when no standard exists. 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. Structural equation model and its applications (2nd ed.) Beijing, China: Educational Science Publishing House. Mccutcheon (Eds.), Applied latent class analysis (pp. Cambridge, UK: Cambridge University Press.