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Strauss, Christian L. L. – ProQuest LLC, 2022
In many psychological and educational applications, it is imperative to obtain valid and reliable score estimates of multilevel processes. For example, in order to assess the quality and characteristics of high impact learning processes, one must compute accurate scores representative of student- and classroom-level constructs. Currently, there…
Descriptors: Scores, Factor Analysis, Models, True Scores
St. Clair, Suzanne W. – ProQuest LLC, 2011
The current study evaluated the performance of traditional versus modern MDTs in the estimation of fixed-effects and variance components for data missing at the second level of an hierarchical linear model (HLM) model across 24 different study conditions. Variables manipulated in the analysis included, (a) number of Level-2 variables with missing…
Descriptors: Hierarchical Linear Modeling, Data Analysis, Sample Size, Computation
Rickles, Jordan Harry – ProQuest LLC, 2012
In this study I present, demonstrate, and test a method that extends the Stuart and Rubin (2008) multiple control group matching strategy to a multisite setting. Three primary phases define the proposed method: (1) a design phase, in which one uses a two-stage matching strategy to construct treatment and control groups that are well balanced along…
Descriptors: Probability, Hierarchical Linear Modeling, Computation, Outcomes of Treatment
Stovall, Holly – ProQuest LLC, 2012
Over the past decade educational research has been stimulated by new legislation such as the No Child Left Behind Act. Increasing emphasis is being placed on accurately quantifying the success of treatment programs through student achievement scores, so precise estimation is vital for establishing the efficacy of new methodology. Ranked set…
Descriptors: Sampling, Educational Research, Hierarchical Linear Modeling, Nonparametric Statistics
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