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Meta‐Analysis of Clerical Performance Predictors: Still stable after all these years
Author(s) -
Whetzel Deborah L.,
McCloy Rodney A.,
Hooper Amy,
Russell Teresa L.,
Waters Shonna D.,
Campbell Wanda J.,
Ramos Robert A.
Publication year - 2011
Publication title -
international journal of selection and assessment
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.812
H-Index - 61
eISSN - 1468-2389
pISSN - 0965-075X
DOI - 10.1111/j.1468-2389.2010.00533.x
Subject(s) - psychology , predictive validity , meta analysis , job performance , stability (learning theory) , population , generalization , robustness (evolution) , external validity , econometrics , applied psychology , social psychology , statistics , developmental psychology , mathematics , job satisfaction , computer science , demography , machine learning , medicine , mathematical analysis , biochemistry , chemistry , sociology , gene
Pearlman, Schmidt, and Hunter (1980) published a seminal paper on the validity of various measures for predicting performance in clerical jobs. They concluded that for both job and training performance criteria, 10 types of tests (e.g., perceptual speed, clerical aptitude, verbal ability) predicted performance across 5 clerical job families. This paper describes a psychometric meta‐analysis of validity studies using similar measures for clerical jobs conducted since 1980 to examine the stability of Pearlman and colleagues' validity estimates. This paper contributes to the literature by investigating the long‐term stability of validity generalization estimates over a period of several decades. Clerical jobs provide a compelling case study because these jobs have changed considerably due to the increased use of computers and technology in the office environment. Results showed that the mean population estimates in the present study were consistent with, or higher than, those obtained by Pearlman and colleagues, thus demonstrating the long‐term stability of meta‐analytic estimates of validity. The relative stability of the validity estimates also supports the robustness of g as a predictor, even as jobs change over time.