Factor Structures of the O*NET Occupational Descriptors

Abstract

The most recent attempt in developing a comprehensive classification system for occupations is the Occupational Information Network (O*NET). The O*NET uses a content model that includes descriptor domains such as, knowledges, skills and generalized work activities which allow jobs to be described in more general rather than occupation-specific terms.

The major purpose of this study was to explore the factor structures underlying the O*NET descriptor domains, specifically knowledges, skills and generalized work activities. The data used for this study were ratings of 1,100 occupations on the aforementioned descriptor domains. In addition, each of the descriptor domains was rated by subject matter experts on an abstract-concrete scale. Based on those ratings, each descriptor was then classified as either 'abstract' or 'concrete.' Once these classifications were made, the ratings on the 1,100 occupations were factor analyzed within the abstract and concrete categories. The research sought to identify which scale, Level or Importance, produced the most stable and differentiated factor structure.

Principal component analyses and principal axes analyses were applied to these data sets. The stability of the factors was estimated by coefficients of congruence.

The results indicate that the Level scales accounted for more variance, whereas, the Importance scales yielded more differentiated factor structures. There were no substantial differences in factor stability for either scale.