Using Machine Learning to Advance Personality Assessment and Theory. (May 2019)

• Record Type: Journal Article
• Authors: Bleidorn, Wiebke; Hopwood, Christopher James
• Is Part Of: Personality and social psychology review. Volume 23:Number 2(2019)
• Page Start: 190
• Page End: 203
• Publication Date: 2019-05
• Subjects: personality assessment -- machine learning -- Big Five -- construct validation -- Big Data; Personality -- Periodicals; Social psychology -- Periodicals; 155.05
• DOI: 10.1177/1088868318772990
• Languages: English
• ISSNs: 1088-8683

Abstract

Machine learning has led to important advances in society. One of the most exciting applications of machine learning in psychological science has been the development of assessment tools that can powerfully predict human behavior and personality traits. Thus far, machine learning approaches to personality assessment have focused on the associations between social media and other digital records with established personality measures. The goal of this article is to expand the potential of machine learning approaches to personality assessment by embedding it in a more comprehensive construct validation framework. We review recent applications of machine learning to personality assessment, place machine learning research in the broader context of fundamental principles of construct validation, and provide recommendations for how to use machine learning to advance our understanding of personality.