A comparison of principal component analysis, common factor analysis, and image analysis : a Monte Carlo study
| dc.contributor.author | Woodward, Todd Stephen | en_US |
| dc.date.accessioned | 2024-08-15T20:18:34Z | |
| dc.date.available | 2024-08-15T20:18:34Z | |
| dc.date.copyright | 1993 | en_US |
| dc.date.issued | 1993 | |
| dc.degree.department | Department of Psychology | |
| dc.degree.level | Master of Arts M.A. | en |
| dc.description.abstract | Principal component analysis, common factor analysis, and image analysis are compared on their ability to reproduce loading patterns underlying simulated data sets. Sample size, number of variables per factor, saturation, and percentage of measurement error were systematically varied. Common factor analysis was found to be more accurate at retrieving the population loading pattern when there was no measurement error, but principal component analysis gave the clearest representation of the population loading pattern when there was 25% measurement error or greater. Image analysis severely underestimated the loadings, except when there was no measurement error. The technique differences diminished as the number of variables per factor increased, as population. Loading saturation increased, and as the amount of measurement error decreased. Numerical explanations for these differences are presented, and recommendations for future research are proposed. | en |
| dc.format.extent | 118 pages | |
| dc.identifier.uri | https://hdl.handle.net/1828/20192 | |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | UN SDG 5: Gender Equality | en |
| dc.title | A comparison of principal component analysis, common factor analysis, and image analysis : a Monte Carlo study | en_US |
| dc.type | Thesis | en_US |
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