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About Dr. Q. Chelsea Song

Dr. Chelsea Song is an Assistant Professor in Industrial-Organizational Psychology at Purdue University. Her research focuses on personnel selection, specifically, enhancing diversity in the workplace, vocational interests, personality, and person-environment fit, and big data and machine learning methods. Dr. Song’s research has been published in Journal of Applied Psychology, Personnel Psychology, and Journal of Personality and Social Psychology, among others, and has been featured in popular outlets such as Forbes. Her work has received recognition such as the Meredith P. Crawford Fellowship in I-O Psychology from HumRRO, and has been supported by the Robert P. Larsen Grant for Research in Career Development and the Department of Defense. Dr. Song currently serves on the editorial boards of the Journal of Applied Psychology and Organizational Research Methods.

You can find her CV here

Main Research Interests

Dr. Song’s research focuses on recruitment and selection (e.g., hiring), and aims to answer questions such as (a) what predicts workplace outcome, (b) how to measure them, and (c) how to make hiring decisions. Specific topics include:

  • Diversity in hiring (enhancing race & gender diversity via hiring; adverse impact reduction)
  • Vocational interests, personality development, and person-environment fit
  • Research methods/advanced quantitative methods (multi-objective optimization, machine learning/Big Data)

Dr. Song’s research seeks to enhance diversity in hiring outcomes and reduce adverse impact using an innovative quantitative approach. Her work on individual differences and person-environment fit uses multilevel and longitudinal modeling to study how vocational interests and personality development in early adulthood, as well as how person-occupation fit (in terms of both vocational interest fit and gender fit) help to determine individual job performance and job satisfaction. Finally, in the area of research methods, she is interested in the application of machine learning and multi-objective optimization methods in the study of the workplace.

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