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Gateway Industrial-Organizational Psychologists 

SLU's I/O Psychology Program's Professional Activities Group invites GIOP Members for a Forum on Machine Learning

  • 14 Feb 2025
  • 8:30 AM - 10:00 AM
  • Pere Marquette Gallery in DuBourg Hall, 221 N Grand Blvd, St. Louis, MO 63103

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From Predictive Validity to Data Insights: Situational Judgement Tests and Machine Learning in I/O Psychology

with : Dr. Xiaowen Chen (Saint Louis University)


 Date: February 14, 2025

 Cost: Free!

Location: Pere Marquette Gallery in Dubourg Hall, 221 N. Grand Blvd., St. Louis, MO 63103;

Information about paid parking options available at https://www.slu.edu/parking/visitor-parking.php)

Time:  8:30 am to 10:00 am

Networking time will begin at 8:30 and the presentation will begin at 9:00. 

This presentation consists of two parts. The first part focuses on situational judgment tests (SJTs) and their development approaches. SJTs are increasingly utilized in personnel selection and training due to their utility and psychometric advantages over traditional self-report assessments and assessment centers. However, the methods used to develop SJTs play a critical role in determining their predictive validity. The second part of the talk will demonstrate the application of machine learning techniques to extract actionable insights for research and practice in I/O psychology. Attendees will gain an understanding of how these advanced techniques can enhance evidence-based decision-making and contribute to innovative solutions in the workplace.

Xiaowen Chenis an assistant professor at Saint Louis University. Her research focuses on cross-cultural management, personnel selection, assessment development and validation, machine learning, and social justice. She teaches courses in job analysis, performance appraisal, machine learning application in Psychology, Equal Employment Opportunity laws, leadership, and industrial training. In addition to her academic work, she provides consulting services to business and industry.

 

This program is eligible for 1.5 CEU credits.

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