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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
Comparing Empirically Keyed And Random Forest Scoring Models In Biodata Assessments, Mathijs Affourtit, Kristin S. Allen, Craig M. Reddock, Paul M. Fursman
Comparing Empirically Keyed And Random Forest Scoring Models In Biodata Assessments, Mathijs Affourtit, Kristin S. Allen, Craig M. Reddock, Paul M. Fursman
Personnel Assessment and Decisions
Effective pre-hire assessments impact organizational outcomes. Recent developments in machine learning provide an opportunity for practitioners to improve upon existing scoring methods. This study compares the effectiveness of an empirically keyed scoring model with a machine learning, random forest model approach in a biodata assessment. Data was collected across two organizations. The data from the first sample (N=1,410), was used to train the model using sample sizes of 100, 300, 500, and 1,000 cases, whereas data from the second organization (N=524) was used as an external benchmark only. When using a random forest model, predictive validity …
The Machines Aren’T Taking Over (Yet): An Empirical Comparison Of Traditional, Profiling, And Machine Learning Approaches To Criterion-Related Validation, Kristin S. Allen, Mathijs Affourtit, Craig M. Reddock
The Machines Aren’T Taking Over (Yet): An Empirical Comparison Of Traditional, Profiling, And Machine Learning Approaches To Criterion-Related Validation, Kristin S. Allen, Mathijs Affourtit, Craig M. Reddock
Personnel Assessment and Decisions
Criterion-related validation (CRV) studies are used to demonstrate the effectiveness of selection procedures. However, traditional CRV studies require significant investment of time and resources, as well as large sample sizes, which often create practical challenges. New techniques, which use machine learning to develop classification models from limited amounts of data, have emerged as a more efficient alternative. This study empirically investigates the effectiveness of traditional CRV with a variety of profiling approaches and machine learning techniques using repeated cross-validation. Results show that the traditional approach generally performs best both in terms of predicting performance and larger group differences between candidates …
Crowdsourcing Job Satisfaction Data: Examining The Construct Validity Of Glassdoor.Com Ratings, Richard N. Landers, Robert C. Brusso, Elena M. Auer
Crowdsourcing Job Satisfaction Data: Examining The Construct Validity Of Glassdoor.Com Ratings, Richard N. Landers, Robert C. Brusso, Elena M. Auer
Personnel Assessment and Decisions
Researchers, practitioners, and job seekers now routinely use crowdsourced data about organizations for both decision-making and research purposes. Despite the popularity of such websites, empirical evidence regarding their validity is generally absent. In this study, we tackled this problem by combining two curated datasets: (a) the results of the 2017 Federal Employee Viewpoint Survey (FEVS), which contains facet-level job satisfaction ratings from 407,789 US federal employees, and which we aggregated to the agency level, and (b) current overall and facet ratings of job satisfaction of the federal agencies contained within FEVS from Glassdoor.com as scraped from the Glassdoor application programming …