Using Big Data and Machine Learning in Personality Measurement: Opportunities and Challenges. Issue 5 (21st September 2020)

• Record Type: Journal Article
• Authors: Alexander, Leo; Mulfinger, Evan; Oswald, Frederick L.
• Editors: Rauthmann, John
• Other Names: Rauthmann John F. guestEditor.
• Is Part Of: European journal of personality. Volume 34:Issue 5(2020)
• Page Start: 632
• Page End: 648
• Publication Date: 2020-09-21
• Subjects: big data -- machine learning -- personality measurement; Personality -- Periodicals; 155.2
• DOI: 10.1002/per.2305
• Languages: English
• ISSNs: 0890-2070

Abstract

Abstract: This conceptual paper examines the promises and critical challenges posed by contemporary personality measurement using big data. More specifically, the paper provides (i) an introduction to the type of technologies that give rise to big data, (ii) an overview of how big data is used in personality research and how it might be used in the future, (iii) a framework for approaching big data in personality science, (iv) an exploration of ideas that connect psychometric reliability and validity, as well as principles of fairness and privacy, to measures of personality that use big data, (v) a discussion emphasizing the importance of collaboration with other disciplines for personality psychologists seeking to adopt big data methods, and finally, (vi) a list of practical considerations for researchers seeking to move forward with big data personality measurement and research. It is expected that this paper will provide insights, guidance, and inspiration that helps personality researchers navigate the challenges and opportunities posed by using big data methods in personality measurement. © 2020 European Association of Personality Psychology