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Direct linkde Winter, J. C. F.; Dodou, D.; Wieringa, P. A. – Multivariate Behavioral Research, 2009
Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes ("N"), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for "N" below 50. Simulations were carried out to estimate the minimum required "N" for different…
Descriptors: Sample Size, Factor Analysis, Enrollment, Evaluation Methods
Peer reviewedMarsh, Herbert W.; Hau, Kit-Tai; Balla, John R.; Grayson, David – Multivariate Behavioral Research, 1998
Whether "more is ever too much" for the number of indicators per factor in confirmatory factor analysis was studied by varying sample size and indicators per factor in 35,000 Monte Carlo solutions. Results suggest that traditional rules calling for fewer indicators for smaller sample size may be inappropriate. (SLD)
Descriptors: Factor Structure, Monte Carlo Methods, Research Methodology, Sample Size
Peer reviewedBenson, Jeri; Bandalos, Deborah L. – Multivariate Behavioral Research, 1992
Factor structure of the Reactions to Tests (RTT) scale measuring test anxiety was studied by testing a series of confirmatory factor models including a second-order structure with 636 college students. Results support a shorter 20-item RTT but also raise questions about the cross-validation of covariance models. (SLD)
Descriptors: College Students, Factor Analysis, Factor Structure, Higher Education
