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Understanding how and why adding valid predictors can decrease the validity of selection composites: A generalization of Sackett, Dahlke, Shewach, and Kuncel (2017)
Author(s) -
Murphy Kevin R.
Publication year - 2019
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/ijsa.12253
Subject(s) - predictive power , psychology , weighting , predictive validity , selection (genetic algorithm) , multivariate statistics , generalization , incremental validity , criterion validity , range (aeronautics) , statistics , econometrics , clinical psychology , test validity , construct validity , psychometrics , mathematics , artificial intelligence , computer science , engineering , medicine , mathematical analysis , philosophy , epistemology , radiology , aerospace engineering
It is usually assumed that adding more valid predictors will increase the predictive power of a selection test battery. Sackett, Dahlke, Shewach, and Kuncel showed that when selection tests are combined using unit weights, adding a valid predictor can lead to a decrease in validity. Situating the Sackett et al. approach in a more general multivariate framework I show how: (a) it is the tradeoff between predictor validity and predictor intercorrelations, and not the differences in predictor validities that determines whether adding a valid predictor to a composite will cause the validity of that composite to increase or decrease; and (b) this same dynamic applies across a wide range of non‐optimal schemes for weighting predictors and/or criteria.