First Advisor
Mundfrom, Daniel J.
Document Type
Dissertation
Date Created
12-1-2009
Department
College of Education and Behavioral Sciences, Applied Statistics and Research Methods, ASRM Student Work
Abstract
Three computational solutions to the number of factors problem were investigated over a wide variety of typical psychometric situations using Monte Carlo simulated population matrices with known characteristics. The standard error scree, the minimum average partials test, and the technique of parallel analysis were evaluated head-to-head for accuracy. The question of using principal components-based eigenvalues versus common factors-based eigenvalues in the analyses was also investigated. As a benchmark, the commonly used eigenvalues-greater-than-one criterion was included. Across all conditions, the principal components-based version of parallel analysis was found to most accurately recover dimensionality using sample correlation matrices drawn from populations with known, simple factor structures. The high degree of accuracy observed for this method suggests that a workable solution to the age-old number of factors problem may be close at hand.
Abstract Format
html
Keywords
Statistics; Quantitative Psychology; Factor Analysis; Minimum Average Partial Test; Number of Factors; Parallel Analysis; Principal Component Analysis; Standard Error Scree Test
Extent
141 pages
Local Identifiers
Piccone_unco_0161N_10019
Rights Statement
Copyright is held by author.
Recommended Citation
Piccone, Adam Vincent, "Comparison of three computational procedures for solving the number of factors problem in exploratory factor analysis" (2009). Dissertations. 228.
https://digscholarship.unco.edu/dissertations/228