Use of the O*NET Descriptors in Numerical Occupational Classification: An Exploratory Study

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Title: Use of the O*NET Descriptors in Numerical Occupational Classification: An Exploratory Study
Author: Levine, Jonathan D
Advisors: Dr. Mark Wilson, Committee Member
Dr. D. Drewes, Committee Member
Dr. Mike Wogalter, Committee Member
Dr. J.W. Cunningham, Committee Chair
Abstract: 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 &#8212; variable set combinations assigned occupations to their parent Standard Occupational Classification (SOC) groups. The d2 &#8212; 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.
Date: 2003-09-08
Degree: PhD
Discipline: Psychology
URI: http://www.lib.ncsu.edu/resolver/1840.16/5102


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