Assessing the quality and usefulness of factor-analytic applications to personality measures: A study with the statistical anxiety scale

Abstract

Factor analysis (FA) is the most widely used modeling approach for developing and assessing psychometric personality measures. Furthermore, the appropriateness of an FA application of this type is generally judged on the sole basis of model-data fit, a criterion which is clearly insufficient. This article proposes a multi-faceted approach for assessing (a) the strength and replicability of the factorial solution, (b) the accuracy and effectiveness of the factor score estimates, and (c) the closeness to unidimensionality in measures that were initially designed to be single-trait. The proposal was applied to a measure of statistical anxiety, the SAS, and the main results were the following: (a) both the unidimensional and the oblique solutions were well defined and replicable, and they led to accurate factor score estimates; and (b) unidimensional-based scores were effective over the full practical range of trait values whereas the ranges of the more specific factors in the oblique solution were narrower. It is submitted that the use of the proposal and the accompanying criteria has important advantages and can help to raise standards in FA applications in personality.