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    Predicting faking in interviews with automated text analysis and personality

    Access Status
    Fulltext not available
    Authors
    Holtrop, Djurre
    Van Breda, Ward
    Oostrom, Janneke
    De Vries, Reinout
    Date
    2019
    Type
    Conference Paper
    
    Metadata
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    Citation
    Holtrop, D. and van Breda, W. and Oostrom, J. and de Vries, R. 2019. Predicting faking in interviews with automated text analysis and personality, in Proceedings of the EAWOP Congress, May 29-Jun 1 2019. Turin: European Association of Work and Organizational Psychology (eawop).
    Source Conference
    EAWOP 2019
    Faculty
    Faculty of Business and Law
    School
    Future of Work Institute
    URI
    http://hdl.handle.net/20.500.11937/75891
    Collection
    • Curtin Research Publications
    Abstract

    INTRODUCTION/PURPOSE: Some assessment companies are already applying automated text-analysis to job interviews. We aimed to investigate if text-mining software can predict faking in job interviews. To our knowledge, we are the first to examine the predictive validity of text-mining software to detect faking. DESIGN/METHOD: 140 students from the University of Western Australia were instructed to behave as an applicant. First, participants completed a personality questionnaire. Second, they were given 12 personality-based interview questions to read and prepare. Third, participants were interviewed for approximately 15-20 minutes. Finally, participants were asked to—honestly—indicate to what extent they had verbally (α=.93) and non-verbally (α=.77) faked during the interview. Subsequently, the interview text transcripts (M[words]=1,755) were automatically analysed with text-mining software in terms of personality-related words (using a program called Sentimentics) and 10 other hypothesised linguistic markers (using LIWC2015). RESULTS: Overall, the results showed very modest relations between verbal faking and the text-mining programs’ output. More specifically, verbal faking related to the linguistic categories ‘affect’ (r=.21) and ‘positive emotions’ (r=.21). Altogether, the personality-related words and linguistic markers predicted a small amount of variance in verbal faking (R2=.17). Non-verbal faking was not related to any of the text-mining programs’ output. Finally, self-reported personality was not related to any of the faking behaviours. LIMITATIONS/PRACTICAL IMPLICATIONS: The present study shows that linguistic analyses with text-mining software is unlikely to detect fakers accurately. Interestingly, verbal faking was only related to positive affect markers. ORIGINALITY/VALUE: This puts the use of text-analysis software on job interviews in question.

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