Plagiarism detection and prevalence in online, text-based, structured interviews

Faking is a common issue with traditional self-report assessments in personnel selection (Levashina et al., 2014).

The major concern with faking is that it may affect construct and criterion-related validity (Tett & Simonet, 2021). Concerningly, some research reports the prevalence of self-report faking to be as high as 30-50% depending on the assumed faking severity (Griffith et al., 2007). 

In this paper, we examine a parallel adversarial input type in modern text/chat-based interviews: plagiarism. Plagiarism poses a threat similar to faking in self-reports impacting construct and criterion-related validity. Furthermore, both plagiarism and faking impact fairness. The rank order of applicants may be altered by both practices, thereby changing the hiring decisions (Levashina et al., 2014). 

While not studied exclusively in the selection space, plagiarism has been a major concern for the education sector and extensively studied in the literature (Park, 2003). One aspect that has received considerable attention is gender differences in plagiarism. Results remain inconclusive, with some evidence that men are more likely to plagiarize than women (Jereb, et al, 2018; Negre et al., 2015). 

We also explore differences in plagiarism rates across different job families and device types (i.e., mobile vs. desktop).

Data from over 200,000 candidates (56% female) who applied to various organizations across the world. Candidates participated in an online chat-based structured interview, answering 5-7 open-ended questions on the Sapia Chat Interview™ platform. Over 1 million individual textual answers were checked against answers from past candidates (over 6.4 million answers) for plagiarism. Plagiarism detection calculates the Jaccard similarity coefficient between the new submission and all existing answers, and answers resulting in a Jaccard coefficient (Wang et al., 2013) over 0.75 were marked as plagiarized and flagged for hiring manager review.

Results show that 3.28% of candidates plagiarized at least one answer, which is significantly lower than the up-to 30-50% of candidates estimated to be faking self-report measures (Griffith et al., 2007).

Consistent with previous findings on self-report faking, males plagiarized significantly more than females. Plagiarism rates also differed significantly across role families, with the highest level of plagiarism observed among candidates who applied to ‘Call center sales’ roles and the lowest plagiarism rates observed for ‘Graduate’ roles. Additionally, we found candidates answering on a mobile phone plagiarized significantly higher than those using a desktop computer.

This work represents an important first step in investigating plagiarism detection in online, open-text chat interviews. While the prevalence is much lower than faking in self-reports, there are still fairness implications, especially given men are more likely to plagiarism than women. This is why it is so important to flag candidates who plagiarize so the hiring manager is made aware and can manually review their responses.

References:

Griffith, R. L., Chmielowski, T., & Yoshita, Y. (2007). Do applicants fake? An examination of the frequency of applicant faking behavior. Personnel Review, 36(3), 341–355.

Jereb, E., Urh, M., Jerebic, J., & Šprajc, P. (2018). Gender differences and the awareness of plagiarism in higher education. Social Psychology of Education : An International Journal, 21(2), 409–426. 

Levashina, J., Weekley, J. A., Roulin, N., & Hauck, E. (2014). Using Blatant Extreme Responding for Detecting Faking in High-stakes Selection: Construct validity, relationship with general mental ability, and subgroup differences. International Journal of Selection and Assessment, 22(4), 371–383.

Negre, J. S., Forgas, R. C., & Trobat, M. F. O. (2015). Academic Plagiarism among Secondary and High School Students: Differences in Gender and Procrastination. Comunicar. Media Education Research Journal, 23(1).

Park, C. (2003). In Other (People’s) Words: Plagiarism by university students–literature and lessons. Assessment & Evaluation in Higher Education, 28(5), 471–488.

Tett, R., & Simonet, D. (2021). Applicant Faking on Personality Tests: Good or Bad and Why Should We Care? Personnel Assessment and Decisions, 7(1).

Wang, S., Qi, H., Kong, L., & Nu, C. (2013). Combination of VSM and Jaccard coefficient for external plagiarism detection. 2013 International Conference on Machine Learning and Cybernetics, 04, 1880–1885.

About Author

Laura Belfield
Head of Marketing

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