Abstract
We implemented an online anonymous survey targeted to current and former students, where the interviewed indicate whether and to what extent they cheated during written university examinations. We find that 61% of respondents have cheated once or more. Cheaters are more likely to report that their classmates and friends cheated and that in general people can be trusted. Two different cheating styles emerge: ‘social cheaters,’ who self-report that they have violated the rules interacting with others; ‘individualistic’ cheaters, who self-report that they have used prohibited materials. Only social cheaters exhibit higher levels of trust compared to individualistic cheaters.
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Data availability
The data that support the findings of this study are available from the corresponding author upon request.
Notes
Since the definition of cheating varies across studies (in some cases encompassing only cheating in written examinations—see, e.g., Harpp and Hogan (1993)—and in other cases also including plagiarism—see, e.g., Griffin et al. 2015), comparisons of magnitudes of cheating rates across studies are not very informative, although magnitudes themselves are suggestive of a highly widespread phenomenon. Most statistics show that more than half of students have engaged in academic dishonesty at least once (Jones 2011), but some studies report much higher peaks, around 75% (Baird 1980).
This culture is present in our sample as well. In our questionnaire, we included a question on reporting others’ cheating (Question Q9; see Appendix). We found that just 0.7% of those who witnessed dishonest behavior chose to report it to the professor.
One may argue that the choice of individual versus social cheating can be also driven by the size of social network in the classroom. Yet, we cannot control for social networks in university courses. Also, there is high heterogeneity in the structure of social networks at university across faculties and across Bachelor’s vs. Master’s courses, mainly due to (1) the number of students attending the lectures and (2) how many other courses students share and attend together. It is possible that higher trust in others correlates with a larger social network in the classroom and hence that the relationship between trust and social cheating is also mediated by the extent of the social network.
To elicit generalized trust, we use the question from the World Value Survey (Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?) and from the US General Social Survey (Do you think most people would try to take advantage of you if they got the chance, or would they try to be fair?).
To elicit merit, we defined the following question: ‘Generally speaking, do you think that merit is rewarded in the public sector and in the private sector’. To elicit risk attitude, we used the question from the SOEP (‘Do you consider yourself a person who is willing to take risks or a person that avoids taking risks? Mark one of the underlying numbers, where 0 means “absolutely not willing to take risks” and 10 means “totally willing to take risks”’).
Note that the sum of the two frequencies is slightly higher than that of the variable ‘ever cheated’ because in the questionnaire it was possible to report more than one way of cheating. Overall, 61.4% of the subjects report any form of cheating. Specifically, 48.3% report just one type of cheating, while 13.1% report both types of cheating.
As a robustness check, we defined the index from a factor analysis based on raw variable Q12, Q13 and Q14. This index is highly correlated (0.69) with the one used in this analysis, and results based on it are in line with our benchmark findings. Evidence is available upon request.
The ‘Merit rewarded first job’ dummy is equal to one if variable Q17 is higher than 6; the ‘Not religious’ dummy is equal to one if variable Q13 reports ‘No, I am atheist/agnostic’. All the other dummy variables are set equal to one if the corresponding variable indicates any of the two ‘Yes’ options.
The result may also depend on the fact that Italy is one of the EU countries with the highest youth unemployment rate (32.2% against the EU average of 15.2%. Source: Eurostat).
The sanction is varying across universities and departments. Typical sanctions include automatic failure at the examination, skipping the following examination session, and a dishonorable mention to the faculty head.
If the degree still has to be obtained, i.e., if the respondent is a student, we keep his or her current age. This change affects 21.19% of the respondents that on average are 29 years old.
In 2016, across all universities and fields, the average graduate was 26.1 years old, female in 59.2% of cases, and immigrate in 3.5%. Source (Accessed February 8 2019): http://www2.almalaurea.it/en/cgi-php/lau/sondaggi/intro.php?lang=en&config=profilo. We only notice some geographical concentration, with 46.9% of the answers coming from three regions (Friuli-Venezia Giulia, Lazio and Veneto) whose population accounts for about 20% of the total population in Italy. In a separate robustness check, we perform our analysis using sample weights proportional to the population size of each region (source: ISTAT). This way statistics on the distribution of the observations are consistent with the actual distribution of the population. The results, available upon request, confirm our key findings.
The two types of cheating are not mutually exclusive. Overall, 13.1% of the respondents report to implement both types of cheating. We do not find different results when separately considering single-type cheaters.
There are, however, few exceptions in the gendered pattern of academic cheating, pointing out no difference between males and females (Naghdipour and Emeagwali 2013), or males cheating less than females (Kervliet 1994). Interestingly, McCabe et al. (2012) note that female students (in some majors such as engineering) appear to have narrowed the gap with their male counterparts in cheating. The authors believe that this pattern can be attributed to female students trying to play by ‘men’s rules’ to be successful in that major, with engineering as a historically male-dominated field.
Further information, not used for this analysis, refers to the frequency of detection of the dishonest behavior by instructors: in 89% of the cases respondents report that instructor(s) never noticed the dishonest behavior.
Interactive cheating, however, may also occur from someone who has more knowledge to someone who needs help. The person with better knowledge may not be interested in committing cheating for her own benefit.
We do not have any information about how seats are assigned at university examinations, although we recognize that this could be an interesting input in the analysis of individualistic versus social cheating for future research.
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The authors thank three anonymous reviewers and the participants to the 2018 SABE-IAREP Conference in London for useful discussion.
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Bucciol, A., Cicognani, S. & Montinari, N. Cheating in university exams: the relevance of social factors. Int Rev Econ 67, 319–338 (2020). https://doi.org/10.1007/s12232-019-00343-8
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DOI: https://doi.org/10.1007/s12232-019-00343-8