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Research Article

Applicant Faking of Personality Inventories in College Admission: Applicants’ Shift From Honest Responses Is Unsystematic and Related to the Perceived Relevance for the Profession

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Pages 758-769 | Received 25 Feb 2019, Accepted 12 Jun 2019, Published online: 12 Aug 2019
 

Abstract

This study aims to further our understanding of applicant faking twofold. First, it challenges viewing applicant faking as a systematic shift from otherwise honest responses. To this end, systematic item parameter shifts are proposed and tested across applicants and incumbents. Second, items’ perceived relevance in a college admission process as real-life selection is scrutinized: Are applicants influenced by items’ perceived relevance for the education, the prospective profession, or both? Participants (n = 840) filled out a Big Five inventory in a college admission process, and were retested 10 months later under standard instructions. Furthermore, they rated items’ perceived relevancies for educational and professional purposes. Linear partial credit models were used to model item parameter shifts across response conditions. All proposed patterns of systematic shifts fit the data worse than unsystematic shifts. Unsystematic shifts were greater for lower response categories and items that were perceived as more relevant for the profession. The perceived relevance for education moderated the latter effect. Taken together, applicant faking should not be understood as applicants systematically shifting from their otherwise honest responses. Although applicants elevate their responses, it is not in a systematic way. Furthermore, determinants of applicant faking should be differentiated across real-life selection settings.

Acknowledgments

I would like to thank Barbara Pflanzl, Florian H. Müller and Corinna Koschmieder, my dear friends and colleagues who have helped me on this project. It was a pleasure to work alongside you! Thank you!

This article has earned the Center for Open science badges for Open Materials and Open Data. The data and materials are openly accessible at https://osf.io/e3scf.

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