IDENTIFYING OPTIMAL PREDICTOR COMPOSITES AND TESTING FOR GENERALIZABILITY ACROSS JOBS AND PERFORMANCE FACTORS

Autor: Jeffrey J. Mchenry, Lauress L. Wise, John P. Campbell
Rok vydání: 1990
Předmět:
Zdroj: Personnel Psychology. 43:355-366
ISSN: 1744-6570
0031-5826
DOI: 10.1111/j.1744-6570.1990.tb01563.x
Popis: The initial examination of validity generalization in the Army Selection and Classification Project used data from a concurrent validation sample of 4,039 job incumbents drawn from a representative sample of nine jobs. The available data consisted of 24 predictor scores and five job performance factor scores on each individual. The major objectives were to determine (a) the degree of validity generalization across the major components of performance, with the job held constant, and (b) the degree of validity generalization across jobs within each major performance factor. After reducing the predictor set by eliminating variables that added no information, a modified confirmatory analysis was used to test the hypotheses that one equation would fit the data from all performance components and that one equation would fit the data from all jobs, given a particular performance component. The major findings were that different predictor equations were needed for each of the five criterion factors. For generalization across jobs, within each criterion factor, one equation fit the data for four of the five performance components. Different prediction equations were required for the component that reflects proficiency on the technical tasks specific to each job.
Databáze: OpenAIRE
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