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

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dc.contributor.advisor Dr. Mark Wilson, Committee Member en_US
dc.contributor.advisor Dr. D. Drewes, Committee Member en_US
dc.contributor.advisor Dr. Mike Wogalter, Committee Member en_US
dc.contributor.advisor Dr. J.W. Cunningham, Committee Chair en_US Levine, Jonathan D en_US 2010-04-02T19:07:51Z 2010-04-02T19:07:51Z 2003-09-08 en_US
dc.identifier.other etd-09032003-134601 en_US
dc.description.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. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject occupational classification en_US
dc.subject taxonomy en_US
dc.subject job classification en_US
dc.subject O*NET en_US
dc.subject job analysis en_US
dc.subject occupational analysis en_US
dc.subject SOC en_US
dc.subject cluster analysis en_US
dc.subject standard occupational classification en_US
dc.subject occupation en_US
dc.title Use of the O*NET Descriptors in Numerical Occupational Classification: An Exploratory Study en_US PhD en_US dissertation en_US Psychology en_US

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