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Ting Dai; Yang Du; Jennifer Cromley; Tia Fechter; Frank Nelson – Journal of Experimental Education, 2024
Simple matrix sampling planned missing (SMS PD) design, introduce missing data patterns that lead to covariances between variables that are not jointly observed, and create difficulties for analyses other than mean and variance estimations. Based on prior research, we adopted a new multigroup confirmatory factor analysis (CFA) approach to handle…
Descriptors: Research Problems, Research Design, Data, Matrices
Victor Snipes Swaim – ProQuest LLC, 2009
Numerous procedures have been suggested for determining the number of factors to retain in factor analysis. However, previous studies have focused on comparing methods using normal data sets. This study had two phases. The first phase explored the Kaiser method, Scree test, Bartlett's chi-square test, Minimum Average Partial (1976&2000),…
Descriptors: Factor Analysis, Factor Structure, Maximum Likelihood Statistics, Evaluation Methods
Peer reviewedMulaik, Stanley A. – Psychometrika, 1976
Discusses Guttman's index of indeterminacy in light of alternative solutions which are equally likely to be correct and alternative solutions for the factor which are not equally likely to be chosen. Offers index which measures a different aspect of the same indeterminacy problem. (ROF)
Descriptors: Correlation, Factor Analysis, Factor Structure, Matrices
Peer reviewedDunlap, William P.; Cornwell, John M. – Multivariate Behavioral Research, 1994
The fundamental problems that ipsative measures impose for factor analysis are shown analytically. Normative and ipsative correlation matrices are used to show that the factor pattern induced by ipsativity will overwhelm any factor structure seen with normative factor analysis, making factor analysis not interpretable. (SLD)
Descriptors: Correlation, Factor Analysis, Factor Structure, Matrices

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