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Kush, Joseph M.; Konold, Timothy R.; Bradshaw, Catherine P. – Grantee Submission, 2021
Multilevel structural equation (MSEM) models allow researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This paper…
Descriptors: Sampling, Structural Equation Models, Factor Structure, Monte Carlo Methods
Marsh, Herbert W.; Ludtke, Oliver; Nagengast, Benjamin; Trautwein, Ulrich; Morin, Alexandre J. S.; Abduljabbar, Adel S.; Koller, Olaf – Educational Psychologist, 2012
Classroom context and climate are inherently classroom-level (L2) constructs, but applied researchers sometimes--inappropriately--represent them by student-level (L1) responses in single-level models rather than more appropriate multilevel models. Here we focus on important conceptual issues (distinctions between climate and contextual variables;…
Descriptors: Foreign Countries, Classroom Environment, Educational Research, Research Design

Clarkson, Douglas B. – Psychometrika, 1979
The jackknife by groups and modifications of the jackknife by groups are used to estimate standard errors of rotated factor loadings for selected populations in common factor model maximum likelihood factor analysis. Simulations are performed in which t-statistics based upon these jackknife estimates of the standard errors are computed.…
Descriptors: Error of Measurement, Factor Analysis, Factor Structure, Mathematical Models

Loo, Robert – Perceptual and Motor Skills, 1983
In examining considerations in determining sample sizes for factor analyses, attention was given to the effects of outliers; the standard error of correlations, and their effect on factor structure; sample heterogeneity; and the misuse of rules of thumb for sample sizes. (Author)
Descriptors: Correlation, Error of Measurement, Evaluation Methods, Factor Analysis
Vasu, Ellen S.; Elmore, Patricia B. – 1975
The effects of the violation of the assumption of normality coupled with the condition of multicollinearity upon the outcome of testing the hypothesis Beta equals zero in the two-predictor regression equation is investigated. A monte carlo approach was utilized in which three differenct distributions were sampled for two sample sizes over…
Descriptors: Correlation, Error of Measurement, Factor Structure, Hypothesis Testing