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Lamsal, Sunil – ProQuest LLC, 2015
Different estimation procedures have been developed for the unidimensional three-parameter item response theory (IRT) model. These techniques include the marginal maximum likelihood estimation, the fully Bayesian estimation using Markov chain Monte Carlo simulation techniques, and the Metropolis-Hastings Robbin-Monro estimation. With each…
Descriptors: Item Response Theory, Monte Carlo Methods, Maximum Likelihood Statistics, Markov Processes
Valdez, Guadalupe Flores – ProQuest LLC, 2017
Research on attachment in adult relationships has included minimal studies to encompass a Hispanic population. Mexican Americans have specific characteristics grounded in historical, demographic, and family contexts which are different from other groups. Therefore, cross-cultural validation of instruments to assess family relationships increases…
Descriptors: Undergraduate Students, Attachment Behavior, Interpersonal Relationship, Factor Analysis
Coughlin, Kevin B. – ProQuest LLC, 2013
This study is intended to provide researchers with empirically derived guidelines for conducting factor analytic studies in research contexts that include dichotomous and continuous levels of measurement. This study is based on the hypotheses that ordinary least squares (OLS) factor analysis will yield more accurate parameter estimates than…
Descriptors: Comparative Analysis, Least Squares Statistics, Maximum Likelihood Statistics, Factor Analysis
Yang, Ji Seung – ProQuest LLC, 2012
Nonlinear multilevel latent variable modeling has been suggested as an alternative to traditional hierarchical linear modeling to more properly handle measurement error and sampling error issues in contextual effects modeling. However, a nonlinear multilevel latent variable model requires significant computational effort because the estimation…
Descriptors: Hierarchical Linear Modeling, Computation, Maximum Likelihood Statistics, Mathematics
Jeon, Minjeong – ProQuest LLC, 2012
Maximum likelihood (ML) estimation of generalized linear mixed models (GLMMs) is technically challenging because of the intractable likelihoods that involve high dimensional integrations over random effects. The problem is magnified when the random effects have a crossed design and thus the data cannot be reduced to small independent clusters. A…
Descriptors: Hierarchical Linear Modeling, Computation, Measurement, Maximum Likelihood Statistics