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O*NET™ database for the identification of assessment instruments or selection techniques to use when measuring aptitude requirements associated with selecting and placing employees. The design used to investigate the relationship between the O*NET™ job analysis results and potential assessment variables was predicated on the job component validation (JCV) model (McCormick, 1979).
Job component validation is one method to identify potential selection tests in situations in which it is not feasible to conduct other types of validation studies, primarily because of a lack of sufficient numbers of employees in the occupations for which selection procedures are to be developed. JCV involves two main hypotheses: (1) if various jobs have a given component in common, the attributes needed to fulfill the requirements of that component would be the same across the various jobs; and (2) the validity of a predictor of a job requirement defined by a job component is consistent across jobs.
The first step in the JCV process is the development and use of an objective job analysis procedure to document critical information about work behaviors and required worker characteristics for the job or occupation in question. Next, the JCV process examines the relationships between these specific job and worker characteristics and well-defined aptitude and ability characteristics. It was hypothesized that the O*NET™ data—in particular, the occupational analyst data—could be used as a source of job analysis information in the JCV process. The Position Analysis Questionnaire (PAQ) database, together with the O*NET™ analyst database, was used to see whether O*NET™ information could be used to accurately predict the General Aptitude Test Battery (GATB) and estimates of the Wonderlic test scores contained in the PAQ database. Using 249 occupations, a generally high level of accuracy was obtained in predicting these scores (e.g., cross-validated multiple correlation coefficients of .88 for predicting verbal aptitude, .82 for clerical perception, .64 for manual dexterity, and .81 for the Wonderlic). In addition, generalized work activities rationally linked to PAQ dimensions produced multiple correlations of similar magnitude as those generalized work activities empirically selected through a cross-validated regression