Advisor

Mundfrom, Daniel J.

Committee Member

Schaffer, Jay

Committee Member

Perrett, Jamis

Committee Member

Pulos, Steven

Department

Applied Statistics & Research Methods

Institution

University of Northern Colorado

Type of Resources

Text

Place of Publication

Greeley (Colo.)

Publisher

University of Northern Colorado

Date Created

12-1-2009

Genre

Thesis

Extent

141 pages

Digital Origin

Born digital

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.

Degree type

PhD

Degree Name

Doctoral

Language

English

Local Identifiers

Piccone_unco_0161N_10019

Rights Statement

Copyright is held by author.

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