| Abstract: | We describe a unique application of a synthetic validation technique to a selection system development project in a large organization. Job analysis data were collected from 4,725 job incumbents and 619 supervisors, and were used to identify 11 job families and 27 job components. We developed 12 tests to predict performance on these job components and conducted a concurrent validation study to collect test and job component data for 1,926 incumbents. We created a test composite for each job component and then chose a test battery for each job family based on its relevant job components. Synthetic validity coefficients were computed for each test battery and compared to traditional validity coefficients that were computed within job families with large sample sizes. The synthetic validity coefficient was very close to the within-family validity coefficient for most job families and was within the bounds of sampling error for all job families. Validities tended to be highest when predictors were weighted according to the number of job components to which they were relevant and job component criterion measures were unit weighted. |
