Title of the ESTIMATION AND MODEL SELECTION FOR Dissertation FINITE MIXTURES OF LATENT INTERACTION MODELS Jui-Chen Hsu, Doctor of Philosophy, 2011 Directed by Professor Gregory R. Hancock, Department of Measurement, Statistics and Evaluation Professor Jeffrey R. Harring, Department of Measurement, Statistics and Evaluation Latent interaction models and mixture models have received considerable ...

An important interest in mixture modeling is the investigation of what types of individuals belong to each latent class by relating classes to covariates, concurrent outcomes and distal outcomes, also known as auxiliary variables. This article presents results from real data examples and simulations to show how various factors, such as the degree to which people are classified correctly into la...

This chapter gives an applied introduction to latent profile and class analysis (LPA/LCA). LPA/LCA are model-based methods for clustering individuals in unobserved groups. Their primary goals probing whether and, if so, how many classes can be identified the data estimating their proportional size response profiles. Moreover, membership serve as a predictor or outcome external variables. Substa...

Title of Dissertation: EXAMINING DIFFERENTIAL ITEM FUNCTIONING FROM A LATENT CLASS PERSPECTIVE Karen Samuelsen, Doctor of Philosophy, 2005 Dissertation directed by: C. Mitchell Dayton Department of Measurement, Statistics and Evaluation Current approaches for studying differential item functioning (DIF) using manifest groups are problematic since these groups are treated as homogeneous in natur...

We propose a model for a point-referenced spatially correlated ordered categorical response and methodology for estimation of model parameters. Models and methods for spatially correlated continuous response data are widespread, but models for spatially correlated categorical data, and especially ordered multicategory data, are less developed. Bayesian models and methodology have been proposed ...

Histologic tumor grade is a strong predictor of risk of recurrence in breast cancer. Nevertheless, tumor grade readings by pathologists are susceptible to intraand inter-observer variability due to its subject nature. Because of this limitation, histologic tumor grade is not included in the breast cancer stating system. Latent class models have been considered for analysis of such discrete diag...

A latent transition analysis (LTA) model was described with a mixture Rasch model (MRM) as the measurement model. Unlike the LTA, which was developed with a latent class measurement model, the LTA-MRM permits within-class variability on the latent variable, making it more useful for measuring treatment effects within latent classes. A simulation study indicated that model recovery using the LTA...

Hierarchical latent class (HLC) models are tree-structured Bayesian networks where leaf nodes are observed while internal nodes are latent. There are no theoretically well justified model selection criteria for HLC models in particular and Bayesian networks with latent nodes in general. Nonetheless, empirical studies suggest that the BIC score is a reasonable criterion to use in practice for le...