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Rubin, Donald B.; Thayer, Dorothy T. – Psychometrika, 1982
The details of EM algorithms for maximum likelihood factor analysis are presented for both the exploratory and confirmatory models. An example is presented to demonstrate potential problems in other approaches to maximum likelihood factor analysis. (Author/JKS)
Descriptors: Algorithms, Factor Analysis, Matrices, Maximum Likelihood Statistics
Kreft, Ita G. G.; Kim, Kyung-Sung – 1990
A detailed comparison of four computer programs for analyzing hierarchical linear models is presented. The programs are: VARCL; HLM; ML2; and GENMOD. All are compiled, stand-alone, and specialized. All use maximum likelihood (ML) estimation for decomposition of the variance into different parts; and in all cases, computing the ML estimates…
Descriptors: Algorithms, Comparative Analysis, Computer Software, Computer Software Evaluation
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Millsap, Roger E.; Meredith, William – Psychometrika, 1988
An extension of component analysis to longitudinal or cross-sectional data is presented. Components are derived under the restriction of invariant and/or stationary compositing weights. Multiple occasion and multiple group analyses, the computing algorithm, component pattern and structure matrices, and an example are discussed. (TJH)
Descriptors: Algorithms, Componential Analysis, Cross Sectional Studies, Longitudinal Studies
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de Leeuw, Jan; Kreft, Ita G. G. – Journal of Educational and Behavioral Statistics, 1995
Practical problems with multilevel techniques are discussed. These problems relate to terminology, computer programs employing different algorithms, and interpretations of the coefficients in either one or two steps. The usefulness of hierarchical linear models (HLMs) in common situations in educational research is explored. While elegant, HLMs…
Descriptors: Algorithms, Computer Software, Definitions, Educational Research