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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

Latent class analysis (LCA) combines information from multiple imperfect measures of an unmeasurable latent condition to estimate sensitivity (Se) and specificity (Sp) of each measure. Citation: Walter SD, Riddell CA, Rabachini T, Villa LL, Franco EL (2013) Accuracy of p53 Codon 72 Polymorphism Status Determined by Multiple Laboratory Methods: A Latent Class Model Analysis. The methods we presented here to analyse the p53 data can be applied in many other situations where multiple tests exist, but where none of them is a gold standard. It is common in latent class analysis to fit models with different numbers of classes and compare them by Bayesian information criterion (BIC) and choose the model with the smallest BIC values [37,38]. Doi:10.1371/journal.pone.0056430. BRFSS interviewed 3,999 respondents. Applications from the behavioral and social sciences The book is divided into techniques for analyzing cross-cultural data within the generalized-latent-variable approach: multiple-group confirmatory factor analysis and multiple-group structural equation modeling; multi-level analysis; latent class analysis; and item-response theory. A website features some of the data sets and syntax commands used in the book. Latent Class Analysis (Quantitative Applications in the Social. Applied Quantitative Analysis in Education and the Social Sciences. Latent Class Scaling Analysis (Quantitative Applications in the Social Sciences). With simple algebraic sensitivity analysis (SA), one uses researcher-specified RS The authors applied LCA to a validation study of a new substance use disorder (SUD) screener and a larger prevalence study. A latent class regression (LCR) model, using 9 BRFSS HRQOL indicators, was used to determine latent classes of HRQOL for RI adults and to model the relationship between latent class membership and covariates.