Literature DB >> 29792115

Using Machine Learning to Advance Personality Assessment and Theory.

Wiebke Bleidorn1, Christopher James Hopwood1.   

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.

Entities:  

Keywords:  Big Data; Big Five; construct validation; machine learning; personality assessment

Mesh:

Year:  2018        PMID: 29792115     DOI: 10.1177/1088868318772990

Source DB:  PubMed          Journal:  Pers Soc Psychol Rev        ISSN: 1532-7957


  16 in total

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2.  Commentary: Principles, Approaches and Challenges of Applying Big Data in Safety Psychology Research.

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4.  Applying Evidence-Centered Design to Measure Psychological Resilience: The Development and Preliminary Validation of a Novel Simulation-Based Assessment Methodology.

Authors:  Sabina Kleitman; Simon A Jackson; Lisa M Zhang; Matthew D Blanchard; Nikzad B Rizvandi; Eugene Aidman
Journal:  Front Psychol       Date:  2022-01-10

5.  Psychosocial Factors Predict the Level of Substance Craving of People with Drug Addiction: A Machine Learning Approach.

Authors:  Hua Gong; Chuyin Xie; Chengfu Yu; Nan Sun; Hong Lu; Ying Xie
Journal:  Int J Environ Res Public Health       Date:  2021-11-19       Impact factor: 3.390

6.  Automatic Assessment of Emotion Dysregulation in American, French, and Tunisian Adults and New Developments in Deep Multimodal Fusion: Cross-sectional Study.

Authors:  Federico Parra; Yannick Benezeth; Fan Yang
Journal:  JMIR Ment Health       Date:  2022-01-24

Review 7.  Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte.

Authors:  Qianyu Hu; Kunfeng Chen; Fei Liu; Mengying Zhao; Feng Liang; Dongfeng Xue
Journal:  Materials (Basel)       Date:  2022-02-02       Impact factor: 3.623

8.  Modeling Latent Topics in Social Media using Dynamic Exploratory Graph Analysis: The Case of the Right-wing and Left-wing Trolls in the 2016 US Elections.

Authors:  Hudson Golino; Alexander P Christensen; Robert Moulder; Seohyun Kim; Steven M Boker
Journal:  Psychometrika       Date:  2021-11-10       Impact factor: 2.290

9.  Health, environmental, and animal rights motives for vegetarian eating.

Authors:  Christopher J Hopwood; Wiebke Bleidorn; Ted Schwaba; Sophia Chen
Journal:  PLoS One       Date:  2020-04-02       Impact factor: 3.240

10.  Artificial neural networks for predicting social comparison effects among female Instagram users.

Authors:  Marta R Jabłońska; Radosław Zajdel
Journal:  PLoS One       Date:  2020-02-25       Impact factor: 3.240

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