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Article
Psychology, Applied
Richard N. Landers et al.
Summary: This study compares modern machine learning techniques with ordinary least squares regression in predicting job performance. The results show that the most valuable improvement from using machine learning is by dropping predictors rather than improving prediction accuracy. Machine learning only improves prediction accuracy when the sample size to predictor count ratio is less than approximately 3. The study also finds that several machine learning algorithms, especially elastic net and random forest, show superior predictive accuracy in combining item scores compared to traditional regression. Therefore, the potential value of machine learning is more likely to be realized in unconventional design scenarios and novel data formats.
PERSONNEL PSYCHOLOGY
(2023)
Article
Psychology, Applied
Nan Zhang et al.
Summary: Researchers have investigated the potential of machine learning (ML) to address the issue of subgroup differences by race and gender in personnel selection. The findings indicate that adjusting ML algorithms statistically to reduce subgroup differences can lead to predictive bias, which may unintentionally penalize high-scoring racial minorities. While there are strategies to balance subgroup differences and maintain validity, they are limited in their outcomes. ML may not fully resolve the problem of adverse impact, but it can aid in making incremental improvements.
PERSONNEL PSYCHOLOGY
(2023)
Article
Psychology, Applied
Nick Koenig et al.
Summary: Machine learning (ML) is increasingly used by organizations to improve personnel selection by efficiently scoring narrative information. This article presents six studies showing that ML can accurately and reliably score candidate responses to assessment questions, improving validity without adverse impact. ML algorithms can also generalize across questions and predict multiple outcomes simultaneously. Additionally, ML can enhance job analysis by determining knowledge and skill requirements. These findings highlight the significant impact ML will have on personnel selection.
PERSONNEL PSYCHOLOGY
(2023)
Article
Psychology, Applied
Louis Hickman et al.
Summary: This study addresses the lack of research on the reliability, validity, and generalizability of automated video interviews (AVIs) for screening job applicants by developing and validating machine learning models to assess Big Five personality traits. Results show that AVI personality assessments trained on interviewer-reports exhibit stronger evidence of validity.
JOURNAL OF APPLIED PSYCHOLOGY
(2022)
Article
Psychology, Applied
Hanyi Min et al.
Summary: This study applies collaborative filtering recommender system algorithms to investigate the relationship between job satisfaction and turnover behavior. It finds that more desirable job alternatives positively correlate with employees' future turnover, and the recommender system algorithms can effectively predict employees' movement, especially within the same occupation.
PERSONNEL PSYCHOLOGY
(2022)
Article
Psychology, Applied
Lavanya S. S. Kumar et al.
Summary: This study demonstrates the use of machine learning methods and job analysis data to predict occupational incident rates. The findings highlight the significance of various predictors, supporting past research and expanding understanding of workplace safety outcomes.
PERSONNEL PSYCHOLOGY
(2022)
Article
Psychology, Applied
Sima Sajjadiani et al.
Summary: People are increasingly relying on social media and online forums like Reddit to deal with work-related problems. The way others respond to these posts can greatly affect the emotions and well-being of the person sharing. However, little is known about how the content of the shared posts influences the type of responses received, which obscures the joint role of both the sharer and listener in influencing the outcomes. This study uses computational grounded theorizing and machine learning techniques to analyze a large corpus of work-related conversations on Reddit, developing a theoretical model that sheds light on the dynamics of online social coping in the context of work.
PERSONNEL PSYCHOLOGY
(2022)
Article
Psychology, Applied
Q. Chelsea Song et al.
Summary: Vocational interest measurement plays a significant role in helping individuals make career choices. Traditional scoring methods may not accurately capture specific relations, while machine learning approaches enhance prediction accuracy. This study improves interest inventory-based career choice prediction using machine learning, showing higher accuracy compared to traditional methods.
PERSONNEL PSYCHOLOGY
(2022)
Article
Psychology, Applied
Ivan Hernandez et al.
Summary: This study proposes a framework for integrating various modern natural language processing models to assist researchers in developing valid psychological scales. By using Transformer-based deep neural networks and leveraging the generation and inference capabilities of neural networks, a method for assisting in the development of effective scales is provided.
PERSONNEL PSYCHOLOGY
(2022)
Article
Psychology, Applied
Jeff A. Weekley et al.
JOURNAL OF OCCUPATIONAL AND ORGANIZATIONAL PSYCHOLOGY
(2019)
Article
Business
George C. Banks et al.
JOURNAL OF MANAGEMENT
(2019)
Article
Psychology, Applied
Sima Sajjadiani et al.
JOURNAL OF APPLIED PSYCHOLOGY
(2019)
Article
Psychology, Applied
Andrew B. Speer
PERSONNEL PSYCHOLOGY
(2018)
Article
Psychology, Applied
Michael C. Campion et al.
JOURNAL OF APPLIED PSYCHOLOGY
(2016)
Article
Computer Science, Artificial Intelligence
Tom Fawcett
PATTERN RECOGNITION LETTERS
(2006)
