Browsing by Author "Dr. J.W. Cunningham, Committee Chair"

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Factor Structures of the O*NET Occupational Descriptors
(2002-11-22) Clark, Carri L.; Dr. J.W. Cunningham, Committee Chair; Dr. Mark Wilson, Committee Member; Dr. Don Drewes, Committee Member
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.
Use of the O*NET Descriptors in Numerical Occupational Classification: An Exploratory Study
(2003-09-08) Levine, Jonathan D; Dr. Mark Wilson, Committee Member; Dr. D. Drewes, Committee Member; Dr. Mike Wogalter, Committee Member; Dr. J.W. Cunningham, Committee Chair
The present study explored the usefulness of four O*NET descriptor domains, Knowledge, Skills, Abilities, and Generalized Work Activities, as tools in occupational classification and in estimating O*NET Job Zones reflecting the education, training, experience requirements (i.e., O*NET Job Zones) of occupations. First, assignment analyses were carried out to compare how accurately different association measure — variable set combinations assigned occupations to their parent Standard Occupational Classification (SOC) groups. The d2 — O*NET domain factor combination yielded a high assignment rate and was used in all subsequent analyses. Second, unstructured and structured numerical cluster analyses were carried out at different levels of the SOC. An unstructured, overall analysis produced some clusters that matched well with Major Groups in the SOC's hierarchical structure. Subsequent structured analyses within groups at two different levels of the SOC produced many clusters that matched well with both SOC Minor Groups and Broad Occupational Groups, oftentimes yielding 100 percent membership overlap. Although numerical occupational clustering based on the O*NET domain factors did not produce a meaningful alternative to the SOC, it did provide some empirical support for the SOC at the Major Group, Minor Group, and Broad Occupation levels. Finally, an a priori matrix classification approach employing SOC Major Groups and O*NET Job Zones did not yield a meaningful occupational structure. Regression and discriminant analyses were carried out to explore whether O*NET domain factors would be useful in estimating job zone assignments for occupations. Initial regression analyses with O*NET domain factors as the independent variables and Job Zone as the dependent variable yielded an R value of .80 (p < .001). A discriminant analysis provided further evidence that O*NET descriptors may be useful in assigning Job Zones to new and emerging occupations.