By Yonathan Bard
ISBN-10: 0120782502
ISBN-13: 9780120782505
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Simultaneous factor analysis in several populations. Psychometrika, 36, 409–426. , & Muthén, B. (2006). Analyzing criminal trajectory profiles: Bridging multilevel and group-based approaches using growth mixture modeling. Manuscript submitted for publication. , & van de Pol, F. (2002). Latent Markov chains. In J. A. Hagenaars & A. L. ), Applied latent class analysis (pp. 304–341). Cambridge, UK: Cambridge University Press. , Turnbull, B. , McCulloch, C. , & Slate, E. Journal of the American Statistical Association, 97, 53–65.
The answers to the above questions will be illustrated with the following simulation study. 4. The smaller the MSE of α1 the easier it is to recover the heterogeneity in the population. 19) where μcj and βcj are between level random effects with variance v and covariance 0. We vary the parameter v across the models. 8 in class 2. The residual variable εij is a zero mean normally distributed variable with variance θ = 1. The covariate Xij is also a standard normal random variable. We vary the status of the C variable across the models.
Indb 26 10/17/07 1:15:41 PM Chapter 2 Multilevel Mixture Models Tihomir Asparouhov Muthén & Muthén Bengt Muthén University of California, Los Angeles Introduction Multilevel statistical models allow researchers to evaluate the effects of individuals’ shared environment on an individual’s outcome of interest. Finite mixture models allow the researchers to question the homogeneity of the population and to classify individuals into smaller and more homogeneous latent subpopulations. Structural equation models allow the researchers to explore relations between observed variables and latent constructs.
Nonlinear Parameter Estimation by Yonathan Bard
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