Mechanical Versus Clinical Data Combination in Selection and Admissions Decisions: A Meta-Analysis

In employee selection and academic admission decisions, holistic (clinical) data combination methods continue to be relied upon and preferred by practitioners in our field. This meta-analysis examined and compared the relative predictive power of mechanical methods versus holistic methods in predict...

Full description

Saved in:
Bibliographic Details
Published in:Journal of applied psychology 2013-11, Vol.98 (6), p.1060-1072
Main Authors: Kuncel, Nathan R., Klieger, David M., Connelly, Brian S., Ones, Deniz S.
Format: Article
Language:English
Subjects:
Academic Achievement
Admissions
Biological and medical sciences
College admissions
Data Interpretation, Statistical
Decision Making
Educational psychology
Employees
Experts
Forecasts
Fundamental and applied biological sciences. Psychology
Human
Humans
Job Performance
Judgment
Meta-analysis
Occupational psychology
Organization
Orientation. Evaluation
Orientation. Selection. Evaluation
Personnel Selection
Personnel Selection - standards
Personnel Training
Power
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
School Admission Criteria
Statistical Validity
Student Admission Criteria
Studies
Systematic review
Training
Validity
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In employee selection and academic admission decisions, holistic (clinical) data combination methods continue to be relied upon and preferred by practitioners in our field. This meta-analysis examined and compared the relative predictive power of mechanical methods versus holistic methods in predicting multiple work (advancement, supervisory ratings of performance, and training performance) and academic (grade point average) criteria. There was consistent and substantial loss of validity when data were combined holistically-even by experts who are knowledgeable about the jobs and organizations in question-across multiple criteria in work and academic settings. In predicting job performance, the difference between the validity of mechanical and holistic data combination methods translated into an improvement in prediction of more than 50%. Implications for evidence-based practice are discussed.
ISSN:0021-9010
1939-1854
DOI:10.1037/a0034156