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Using the Scenario Method to Analyze Cheating Behaviors

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An Erratum to this article was published on 05 December 2012

Abstract

Using student self-reported cheating admissions and answers from a hypothetical cheating scenario, this paper analyzes the effects of individual and situational factors on potential cheating behavior. Results confirm several conclusions about student factors that are related to cheating. The probability of cheating is associated with younger students, lower GPAs, alcohol consumption, fraternity/sorority membership, and having cheated in high school. Student perceptions of the certainty and severity of punishment appear to have a negative and significant impact on the probability of cheating on in-class assignments. Students who report a belief that cheating is never acceptable appear to be significantly less likely to cheat in any circumstance. This study illustrates the context-dependent nature of academic dishonesty, and the associated difficulty in understanding the relationships between measurable factors and cheating behavior.

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Notes

  1. While cheating at the university level is rampant, Jensen et al. (2002) show that high school students cheat more than college students.

  2. Crown and Spiller (1998) provide a review of individual and situational factors related to college cheating.

  3. McCabe and Trevino (1997) suggest that fraternity/sorority membership could be treated as a contextual factor rather than an individual factor, as fraternities/sororities provide the context through which cheating norms and skills are transmitted, as well as access to resources that facilitate cheating.

  4. To protect anonymity and allow for the assignment of bonus points, survey respondents were given a code word at the end of the survey. Students were instructed to write the code word on their final exam for bonus points.

  5. The survey instrument also included two simple questions to gauge whether or not students were reading the questions and responses carefully, placed roughly one-third and two-thirds through the survey. Students who did not provide logical answers to these questions were also removed from the sample.

  6. As correlations between attribute levels could confound the estimation of the effect of attributes on the stated probability of cheating, the scenarios were developed such that the correlations between factors were minimized across the 12 scenarios. The range of Pearson correlation coefficients in the final design was -0.20 – 0.298. The average of the absolute values of the 21 Pearson correlation coefficients was 0.15.

  7. In other words, we assume that “probably would not cheat” can be interpreted as “might cheat”.

  8. The null hypothesis of no relationship between the any of the predictor variable and the outcome is rejected at the 1 % significance level for all models using Chi-Square tests.

  9. Multicollinearity between the witness others cheating variable and student beliefs about the prevalence of cheating may affect the results of Models 3 and 6.

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Acknowledgments

Support for this research was provided by a grant from the Cameron School of Business at the University of North Carolina Wilmington.

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Authors and Affiliations

  1. Department of Economics and Finance, The University of North Carolina Wilmington, Wilmington, NC, 28403, USA

    Peter W. Schuhmann, Robert T. Burrus, Preston D. Barber & J. Edward Graham

  2. Department of Accountancy and Business Law, The University of North Carolina Wilmington, Wilmington, NC, 28403, USA

    M. Fara Elikai

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  1. Peter W. Schuhmann
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  2. Robert T. Burrus
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Correspondence to Peter W. Schuhmann.

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Schuhmann, P.W., Burrus, R.T., Barber, P.D. et al. Using the Scenario Method to Analyze Cheating Behaviors. J Acad Ethics 11, 17–33 (2013). https://doi.org/10.1007/s10805-012-9173-4

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