Determining the Propensity for Academic Dishonesty Using Decision Tree Analysis.
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| Title: | Determining the Propensity for Academic Dishonesty Using Decision Tree Analysis. |
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| Authors: | Wray, Barry A. (AUTHOR), Jones, Adam T. (AUTHOR), Schuhmann, Peter W. (AUTHOR), Burrus, Robert T. (AUTHOR) |
| Source: | Ethics & Behavior. 2016, Vol. 26 Issue 6, p470-487. 18p. 1 Diagram, 13 Charts. |
| Subjects: | Business, Student cheating, Deception, Decision trees, Statistics, Data analysis, Codes of ethics, Undergraduates, Data analysis software, Descriptive statistics |
| Abstract: | This article investigates the propensity for academic dishonesty by university students using the partitioning method of decision tree analysis. A set of prediction rules are presented, and conclusions are drawn. To provide context for the decision tree approach, the partition process is compared with results of more traditional probit regression models. Results of the decision tree analysis complement the probit models in terms of predictive accuracy and confirm results previously found in the literature. In particular, students' moral character--whether they believe cheating is acceptable--is found to be the most important factor in determining the propensity for academic dishonesty. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | This article investigates the propensity for academic dishonesty by university students using the partitioning method of decision tree analysis. A set of prediction rules are presented, and conclusions are drawn. To provide context for the decision tree approach, the partition process is compared with results of more traditional probit regression models. Results of the decision tree analysis complement the probit models in terms of predictive accuracy and confirm results previously found in the literature. In particular, students' moral character--whether they believe cheating is acceptable--is found to be the most important factor in determining the propensity for academic dishonesty. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10508422 |
| DOI: | 10.1080/10508422.2015.1051661 |
