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Harring, Jeffrey R.; Weiss, Brandi A.; Li, Ming – Educational and Psychological Measurement, 2015
Several studies have stressed the importance of simultaneously estimating interaction and quadratic effects in multiple regression analyses, even if theory only suggests an interaction effect should be present. Specifically, past studies suggested that failing to simultaneously include quadratic effects when testing for interaction effects could…
Descriptors: Structural Equation Models, Statistical Analysis, Monte Carlo Methods, Computation
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Holden, Jocelyn E.; Kelley, Ken – Educational and Psychological Measurement, 2010
Classification procedures are common and useful in behavioral, educational, social, and managerial research. Supervised classification techniques such as discriminant function analysis assume training data are perfectly classified when estimating parameters or classifying. In contrast, unsupervised classification techniques such as finite mixture…
Descriptors: Discriminant Analysis, Classification, Computation, Behavioral Science Research
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Stuive, Ilse; Kiers, Henk A. L.; Timmerman, Marieke E.; ten Berge, Jos M. F. – Educational and Psychological Measurement, 2008
This study compares two confirmatory factor analysis methods on their ability to verify whether correct assignments of items to subtests are supported by the data. The confirmatory common factor (CCF) method is used most often and defines nonzero loadings so that they correspond to the assignment of items to subtests. Another method is the oblique…
Descriptors: Assignments, Simulation, Construct Validity, Factor Analysis
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Yoo, Jin Eun – Educational and Psychological Measurement, 2009
This Monte Carlo study investigates the beneficiary effect of including auxiliary variables during estimation of confirmatory factor analysis models with multiple imputation. Specifically, it examines the influence of sample size, missing rates, missingness mechanism combinations, missingness types (linear or convex), and the absence or presence…
Descriptors: Monte Carlo Methods, Research Methodology, Test Validity, Factor Analysis