Validity Generalization Results for Computer Programmers.

Using both the Bayesian validity generalization procedure presented in Schmidt, Hunter, Pearlman, and Shane and a second, recently developed Bayesian procedure, this study shows that most of the between-study variance in four distributions of observed validity coefficients of the Programmer Aptitude...

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Bibliographic Details
Published in:Journal of Applied Psychology Vol. 65; no. 6; pp. 643 - 662
Main Authors: Schmidt, Frank L., Gast-Rosenberg, Ilene, Hunter, John E.
Format: Article
Published: American Psychological Association Dec80
Subjects:
Online Access:View this record in EBSCOhost
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        10.1037/0021-9010.65.6.643
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        atl: Validity Generalization Results for Computer Programmers.
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          Schmidt, Frank L.
          Gast-Rosenberg, Ilene
          Hunter, John E.
        affil:
          U.S. Office of Personnel Management, Washington, D.C.
          George Washington University
          Michigan State University
      su:
        Computer programmers
        Electronic data processing personnel
        Ability testing
        Educational tests & measurements
        Psychological tests
        Estimation theory
        Statistical sampling
        Analysis of variance
      sug:
        subj:
          Computer programmers
          Electronic data processing personnel
          Ability testing
          Educational tests & measurements
          Psychological tests
          Estimation theory
          Statistical sampling
          Analysis of variance
      ab: Using both the Bayesian validity generalization procedure presented in Schmidt, Hunter, Pearlman, and Shane and a second, recently developed Bayesian procedure, this study shows that most of the between-study variance in four distributions of observed validity coefficients of the Programmer Aptitude Test for measures of proficiency on the job is artifactual in nature. The average percentage of variance accounted for by artifacts was 69 using the Schmidt et al. procedure (without the Fisher's z transformation) and 65 using the new procedure. These figures were lower (39% and 41%, respectively) for the single distribution of training criterion validities. Results for both procedures indicated that validities were generalizable to new settings in four of five cases. Further analyses indicated that corrections for sampling error alone were sufficient to support the conclusions of validity generalizability. These findings taken together indicate that validity generalization is a robust phenomenon.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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