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It's the students, stupid: how perceptions of student reporting impact cheating.

I. Introduction

While universities nationwide continue to revamp policies, increase penalties, and work harder to detect cheating, large numbers of students continue to admit to cheating and large numbers of faculty witness cheating in their classrooms. (Baird, 1980; Burrus, McGoldrick, and Schuhmann, 2007; Frankyn-Stokes and Newstead, 1995; Kidwell and Wozniak, 2003; Meade, 1992; Singhal, 1982; Stem and Havlicek, 1986; Stevens and Stevens, 1987). Our research suggests that it may be time for students to take a leading role in promoting academic honesty.

To understand academic dishonesty, many studies have sought to compare student and faculty definitions of cheating. While there does exist agreement between students and faculty in defining obvious instances of academic dishonesty (using cheat sheets or glancing on another student's paper during an exam), students and faculty do not agree on other types of potential dishonesty (discussing an exam with a student who has not taken it and using an old test for exam preparation) (Barnett and Dalton, 1981; Graham et al. 1994, Wright and Kelley 1974).

Even though students may not define cheating in agreement with their professors, students generally know what faculty members expect. Roig and Ballew (1994) showed student perceptions of professors' attitudes were very similar to the actual attitudes held by the professors. Because there is already a large overlap in definitions of cheating, simply rewriting policies to include a stricter definition of cheating is unlikely to reduce academic dishonesty in any significant way.

Many additional studies have sought to determine student-specific and campus factors that influence why students cheat. Individual student characteristics that impact academic dishonesty include age, gender, grade point average, and membership in a fraternity or sorority (Baird, 1980; Burrus, McGoldrick, and Schuhmann, 2007; Lambert, Ellen, and Taylor, 2003; Leming, 1980; McCabe and Trevino, 1997).

Contextual or environmental factors found to impact cheating include perceptions that other students are cheating (Bunn, Caudill, and Gropper, 1992; Greene and Saxe, 1992; Mixon, 1996; Mixon and Mixon, 1996), and whether or not clear definitions of cheating are given (Burrus, McGoldrick, and Schuhmann, 2007; Franklyn-Stokes and Newstead, 1995). Other contextual factors that influence cheating behavior include the certainty of being caught cheating, the severity of punishment, and various other countermeasures to discourage cheating. Kibler (1993) concludes that "students are less likely to cheat the more they feel they are likely to get caught," while Burrus, McGoldrick, and Schuhmann (2007) and Mixon (1996) show that the severity of punishment is an important, negative determinant of cheating behavior. In addition, Hollinger and Lanza-Kaduce (2009) show that increased countermeasure activities, such as scrambled test answers and multiple proctors, are effective in reducing cheating while assigned seats and rules dictating that students cannot leave an exam are not effective.

Another extremely important cheating deterrent is the implementation of a traditional honor code. Though many "honor codes" are better thought of as policies administrated through faculty and administrator channels, McCabe and Trevino (1993) show that campuses with traditional honor codes (unproctored exams, an honor pledge, the encouragement or requirement of student reporting of cheating, and student-run honor boards) experience decreased self-reported cheating.

Traditional honor codes may work best because they cultivate a culture where students will not tolerate academic dishonesty. McCabe, Trevino and Butterfield (2001) further the research on honor codes by finding that there is additional peer reporting of cheating at schools with traditional honor codes. Thus, students at these universities perceive that cheaters are more likely to be caught, and academic dishonesty is reduced.

The large size of many universities makes the implementation of a traditional honor code difficult, but a culture of the disapproval of cheating may still decrease academic dishonesty. McCabe and Trevino (1997), for example, find that peer disapproval of cheating exerts a significant negative impact on cheating behavior. They conclude that administrative efforts to create a culture of academic integrity should focus on student's expectations of their fellow students.

While faculty members often believe that they are the only line of defense against academic misconduct, the literature above suggests that students may play the most important role. Our study considers one specific role that students play in preserving academic integrity: the detection and reporting of cheating. While students may not police the entire portfolio of behaviors that faculty consider to be dishonest, they may be more likely to detect the most brazen forms of cheating. Thus, administrator encouragements for student reporting of cheating may be a necessary element in combating academic dishonesty. Our research, based on student surveys, reinforces the importance of peer reporting and the student's role in preventing academic dishonesty.

The paper proceeds as follows. The next section describes the data used in the study and provides descriptive statistics. We then estimate a model of student cheating--seeking to discover the importance of student and faculty policing of cheating in preventing academic dishonesty. Next, we report our results, suggest and conduct a series of tests for robustness, and examine the implications of our findings, followed by conclusions.

II. Data

During February of 2009, an on-line survey was administered to 5,000 students in an effort to revise the honor policy at the University of North Carolina Wilmington (UNCW), a medium-sized, public, four-year institution in the southeast United States. At the time of the survey (and currently), UNCW did not have a traditional honor code. While students were under an obligation to report honor violations, there was little education about the policy, there was no formal student honor board, exams were proctored, and students were not asked to sign an honor pledge. Faculty members were in charge of detecting cheating, confronting students about cheating, and determining the severity of punishment in the event of cheating. The outcomes of these faculty-driven cheating inquiries were not generally reported to anyone; thus, it was possible for students to cheat multiple times without any accountability at the university level.

The survey was conducted electronically to provide for anonymity and sent to a random sample of students, not specific classes. There is potential for self-selection bias but not administration bias. Self-selection is minimized and truthfulness encouraged by survey anonymity based on Kerkvilet (1994). The goals of the survey administration were several: i) to determine the pervasiveness of cheating (1) on the campus, ii) to determine attitudes about the pervasiveness of cheating, iii) to quantify student perceptions about the certainty and severity of punishment for cheating, iv) to inquire about the types of behaviors that students consider to be cheating, and v) whether or not the existing honor code was, indeed, part of the university's culture. Of the 333 students that responded to the survey, 229 students gave complete, usable observations (a 6.67% response rate and a 4.6% usable completion rate, respectively).

Students were asked to indicate their perceptions about the "degree to which honor code violations occur at UNCW" as either major, moderate, minor, or not a problem. Respondents were also asked to "describe the penalties you believe most faculty members assign students for academic dishonesty" as severe, moderate, mild, no penalties seem to be assigned, or have no idea. Students were also asked, in separate questions, to describe the vigilance of faculty toward detecting cheating, the vigilance of students toward reporting cheating, and the vigilance of faculty toward confronting academic dishonesty after it is detected as very vigilant, moderately vigilant, slightly vigilant, or not vigilant at all.

The demographic characteristics and the cheating behaviors and perceptions of our sample are provided in Table 1. Female respondents (female) comprised around 67% of our sample. (2) The average age of the students in the sample (age) may seem high at 23.54 years when compared to traditional undergraduate institutions; however, the sample includes graduate students (grad). Twenty-two percent of respondents have not declared a major and are associated with the 'university college' (univcollege), 48% have majors in the college of arts and sciences (artssciences), 17% are in the business school (business), and 12% are in the other professional schools (nursing and education).

To gauge student cheating, students were asked to report the number of times they committed academic dishonesty during their careers at UNCW. To promote truthfulness in reporting, students were reminded that the survey was anonymous. The validity of our sample is supported by the fact that the amount of cheating on the campus is consistent with other studies; fifty-nine percent (59%) of students admitted to committing academic dishonesty on at least one occasion, and cheating students indicate an average of 1.21 incidences of cheating during their college careers. (3)

Table 2 reports student perceptions of the certainty and severity of cheating. Seventy-six percent (76%) of the sample indicate they believe faculty members are vigilant in detecting cheating (vigilantfac), while only 14% believe that other students are vigilant in detection (vigilantstu). Sixty-six percent (66%) of student respondents indicate that punishment for cheating is moderate to severe (severe). Because specific examples of penalties were not given in the question, the results indicate students' perceptions about the penalties they believe will be assessed in the event of academic honesty. When asked about the extent of the academic honesty problem, only thirty-eight percent (38%) of respondents thought it was a moderate or major problem (cheatproblem). Eighty-six percent (86%) of the sample state that a majority of the faculty discuss the honor code at the beginning of a course (discussed), and 75% believes that faculty will confront cheaters if cheating is detected (facultyconfront).

The last two columns of Tables 1 and 2 also divide the sample by whether or not students admitted to at least one incident of academic dishonesty (cheaters) and those who self-reported no incidents of cheating (non-cheaters). One of the most striking differences between the two groups, seen in Table 2, is the difference in perception about how vigilant peers are about reporting cheaters. Non-cheaters are much more likely to believe their peers are vigilant about reporting cheating than cheaters and cheating students are generally younger in age and do not believe that penalties are severe (compared to the noncheaters). There were no other significant differences between cheaters and non-cheaters in this initial analysis.

III. Model

Decision making about cheating is two-fold. First, students choose whether or not to cheat; this decision is made at the "extensive" margin. Next, students choose how often to cheat at the "intensive" margin. In order to examine the effect of peer reporting on if a student violates the expected conduct of students by cheating at least once (the extensive margin) and the number of times that dishonest students cheat (the intensive margin), the following equation was estimated using a probit model (for the extensive margin), and ordinary-least squares, poisson, and negative binomial regression techniques (for the intensive margin):


For the probit analysis of whether or not a student decides to violate the expectation of academic integrity by cheating at least once, the dependent variable cheat is a binary variable indicating a student has admitted to cheating at least once. The model that examines the number of academic dishonesty offences uses cheat, defined as either total cheating over the college career or the number of cheats per year, as the dependent variable. Only age is a quantitative variable while other variables in the estimations are binary indicator variables. The variables univcollege, artssciences, nursing, and education are a series of dummy variables indicating the school in which the respondent is majoring; the omitted category is business.

Consistent with the literature, we anticipate a negative coefficient estimate for female (Baird, 1980; Iyer and Eastman, 2006; Kerkvilet, 1994; Lambert, Ellen, and Taylor, 2003; McCabe and Trevino, 1997; Stem and Havlicek, 1986). We also anticipate negative coefficient estimates for age and grad as these students are, perhaps, more motivated to learn than to feign learning and may have an increased moral development (Newstead, Franklyn-Stokes, and Armstead, 1996; Klein, Levenburg, McKendall, and Mothersell, 2007; Whitley, 1998). We also posit that older students may perceive higher opportunity costs for cheating if they are caught since they are closer in time to the job market.

The literature is mixed on the differences in students' cheating according to their chosen major. Brown (1996) does not find an impact in the choice of major on student cheating. In reference to business majors, Meade (1992), McCabe and Trevino (1995), and Park (2003) find that business students cheat more than other students, and Klein, Levenburg, McKendall, and Mothersell (2007) and Roig and Ballew (1994) find that business students have more loose interpretation of the kinds of behaviors that constitute cheating. On the other hand, Iyer and Eastman (2006) and Klein, Levenburg, McKendall, and Mothersell (2007) do not find that business students exhibit significant differences in cheating behavior. Given these studies, we have no prior expectations on if arts and science, nursing, and education students will exhibit significant differences in cheating compared to business majors with one notable exception: we anticipate that students with declared majors are less likely to risk cheating than younger students who may lack a clear direction and vision of their futures. Thus, we anticipate that undeclared students will cheat more than business students; the coefficient estimate is predicted as positive for univcollege.

Studies indicate that students who believe that other students are cheating are more likely to cheat; the anticipated sign for cheatproblem is, therefore, positive. The literature also suggests that students respond to reminders of the need for academic integrity and to the certainty and severity of punishment; thus, discussed, vigilantfac, vigilantstu, and faculty confront are anticipated to have negative signs (Burrus, McGoldrick, and Schuhmann, 2007; McCabe, Trevino, and Butterfield, 2001; Mixon 1996; Mixon and Mixon, 1996).

IV. Estimation Results

The estimation results are shown in Table 3; robust standard errors were used for each model estimation. The first three columns in the table model the number of times that students cheat over their collegiate careers or during an academic year. The first column (1), reports the OLS estimation results using annual cheats. Because academic honesty takes place in discrete increments, the dependent variable (annual cheats) for regressions reported in column (2) is rounded to the nearest integer and estimated using negative-binomial techniques. As a robustness check, we also estimate negative-binomial regressions using the self-reported number of times a student cheated as the dependent variable with exposure correction for the number of years in school. The results of the negative-binomial estimation are presented in column (3). (4) Column (4) provides probit model estimates to explain the determinants of cheating at the extensive margin, the decision to abdicate the position of complete honesty. We note that the coefficients should not be interpreted as being comparable in magnitude.

Our estimated results are remarkably robust across the three models of the number of incidences of academic dishonesty, and the estimated coefficients generally had the predicted signs; in cases where the signs were not anticipated, the estimate was not statistically significant. Across the first three model runs, only cheatproblem was positive and significant throughout, a result suggesting that students cheat more if they believe that their peers are cheating. Also across the first three estimations, examining the intensive margin, age, severe, and vigilantstu have negative and significant coefficient estimates, as predicted.

Of particular interest is the consistent and highly significant negative relationship between vigilantstu and a student's cheating activity compared to the insignificant vigilantfac. In all three regressions the coefficients are significantly different. Most studies that seek to understand whether or not students respond to the certainty of punishment when deciding if to cheat and how much to cheat simply report whether or not students believe that they will be caught cheating. These studies do not attempt to distinguish between faculty and student efforts to detect cheating. Our results indicate that despite the best efforts of professors, university officials, and other various prevention techniques, fear of being turned in by fellow students may be the best prevention measure. (5) The vigilance of faculty has little effect empirically on the self-reported cheating behaviors of students but peer monitoring is a significant deterrent. The belief that fellow students are at least moderately vigilant about monitoring and reporting cheating reduces cheating by over 60%. (6)

Another interesting result is the insignificance of discussed over the model runs. McCabe and Trevino (1993) speculated that administrator and faculty reminders of the necessities of academic integrity might lead to decreased cheating even on a campus that did not have a formal honor code. Our results suggest that student-driven initiatives (honor boards, etc.) might be more beneficial in creating a culture of honesty than honor-code discussions by the faculty.

Finally, we noted that the literature is mixed on whether business students cheat more than other students. Adding to the mixed findings, our results indicate that there is not a significant difference between students who have declared a business major (the omitted category) and students who have declared majors in other disciplines. There is, however, a difference between business students and students who have not declared a major; specifically, undeclared students cheat more frequently than business students. (7)

The probit model estimates are similar; student detection is significant in influencing the decision to abdicate the moral "high ground" by committing academic honesty, the extensive margin, while faculty efforts are not. There are, however, some notable differences. Age does not impact the decision to cheat, only how much, while enrollment as a graduate student reduces the likelihood a student decides to cheat at least once. In addition, the belief that cheating is a problem on campus does not influence the extensive margin but does influence the intensive margin of how much to cheat. As well, the probit model reveals that undeclared students are not more likely to cheat than business majors.

V. Conclusion

Reducing the level of cheating and creating an academic culture of integrity on college campuses is a difficult endeavor of which student involvement is a critical component. Many methods have been cited to reduce cheating; student surveys have found that students believe prevention measures such as scrambled test questions, small classes, multiple proctors, and strict penalties are effective (Hollinger and Lanza-Kaduce, 2009).

Other studies encourage the use of honor codes. Traditional honor codes (unproctored exams, signed honor pledges, student honor boards, and encouragements to report cheating) have been shown to be effective in reducing cheating by creating a culture of academic integrity and by the encouragement of reporting the cheating of their peers. Even on campuses that do not have traditional honor codes, our results suggest that students can greatly influence the cheating behavior of other students. We show that both the decision to cheat and the number of episodes of cheating are impacted by the student's perceptions of other students' willingness to report cheating. Though students do not generally believe that their peers are vigilant in detecting cheating, student cheating behavior is very sensitive to peer detection.

Interestingly, our study also shows that the perception that faculty persons will detect and confront cheaters was not a significant deterrent to cheating behavior. Among many other possible reasons, students may provide more of a deterrent to cheating than faculty persons simply because they far outnumber faculty members.

In conclusion, we note that perceptions about what peers are doing often drives college cheating. Administrators serious about reducing the cheating on their campuses should encourage a culture of academic integrity that focuses on the students. A culture of honesty is all about the students.


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1. For the purpose of this study, cheating generically refers to all forms of academic dishonesty, including cheating (such as copying another student's exam), or plagiarism.

(2.) When using survey data, sample bias is always a concern. Even though our sample population is slightly older than the general student population and there was a higher percentage of females our sample, we do not believe these differences significantly bias our results.

(3.) Burrus, McGoldrick, and Schuhmann (2007) indicate that these student responses may understate actual cheating since cheating was not defined for these students.

(4.) These estimations were also computed using corrections for zero inflated data but the results were essentially the same and a Vuong test failed to select a model. The models were also run using Poisson estimation but only as a robustness check as concerns about overispersion suggest negative binomial estimation is more appropriate. For simplicity of presentation the zero-inflated results and Poisson results are omitted.

(5.) While there is some concern that students who are cheating are more aware of other students watching them this should positively bias the coefficient in our model and does not invalidate the negative relationship. However, the survey data indicates that it is the students who are not cheating who believe their peers are watching them.

(6.) This is the marginal effect of vigilant peers on annuals cheats.

(7.) In various regression models with univcollege as the missing variable (as opposed to business), arts and sciences students, nursing students and education students all exhibited significantly less cheating than the undeclared students. Business majors, however, were the only group that cheated significantly less than undeclared students over all the count data model runs.

by Robert T. Burrus, Jr. *, Adam T. Jones **, Bill Sackley ***, and Mike Walker ****

* University of North Carolina Wilmington, E-mail:

** Corresponding author: University of North Carolina Wilmington, Department of Economics and Finance, 601 S. College Rd, Wilmington, NC 28405, Email:

*** University of North Carolina Wilmington, E-mail:

**** University of North Carolina Wilmington, E-mail:

Sample Statistics for Demographic Variables

                                                      Full Sample

Var            Description                           Mean     SD

age            Student age                           23.54    5.26
female         Female (+)                              .66     .47
univcollege    University College (Undeclared) (+)     .22     .41
artssciences   College of Arts and Sciences (+)        .48     .50
business       School of Business (+)                  .17     .38
nursing        School of Nursing (+)                   .02     .15
education      School of Education (+)                 .10     .31

               Full Sample   Cheaters   Non-Cheaters

Var            Min    Max      Mean         Mean

age            17     36      22.87       24.48 **
female          0      1        .67          .65
univcollege     0      1        .24          .19
artssciences    0      1        .46          .51
business        0      1        .18          .17
nursing         0      1        .02          .052
education       0      1        .10          .12

NOTES: (+) binary variables; ** significant difference in the mean age
of cheaters and non-cheaters at the 5% level;  there is no significant
difference in the proportions of cheaters and non-cheaters for any of
the listed variables.

Sample Statistics for Cheating and Perceptions of Cheating Variables

      Var         Description

cheats            Annual Cheats
vigilantfac       Faculty are vigilant about detection (+)
vigilantstu       Peers vigilant about reporting (+)
severe            Believe penalties are modest or severe (+)
cheatproblem      Believe cheating is a moderate or major problem (+)
discussed         Academic honesty discussed at start of semester (+)
facultyconfront   Faculty vigilant about confronting after
                    detection (+)

                            Full Sample         Cheaters   Cheaters

      Var         Mean    SD      Min    Max      Mean       Mean

cheats            .71     1.09     0      7       1.21        0
vigilantfac       .76      .43     0      1        .77        .76
vigilantstu       .14      .35     0      1        .08       .23 *
severe            .66      .47     0      1        .62        .72
cheatproblem      .38      .49     0      1        .40        .36
discussed         .86      .35     0      1        .87        .85
facultyconfront   .75      .43     0      1        .72        .8

NOTES: (+) binary variables; * significant difference in the
proportions of cheaters and non-cheaters at the 1 % level.

Regression Results

                          (1)               (2)
Dependent Variable   Annual Cheats     Annual cheats

Estimation                OLS        Negative Binomial

Age                   -0.0305 ***     -0.0341 **
                     (-2.76)         (-2.12)
female                -0.151          -0.0737
                     (-0.93)         (-0.43)
grad                  -0.176          -0.580 **
                     (-1.31)         (-2.11)
severe                -0.476 **       -0.464 ***
                     (-2.49)         (-2.65)
cheatproblem           0.397 ***       0.351 **
                      (2.75)          (2.39)
discussed              0.219           0.251
                      (1.26)          (1.27)
vigilantfac           -0.0114          0.0133
                     (-0.05)          (0.06)
vigilantstu           -0.496 ***      -0.850 ***
                     (-3.30)         (-2.85)
facultyconfront        0.186           0.109
                      (0.77)          (0.50)
univcollege            0.536 **        0.399 *
                      (2.20)          (1.83)
artssciences           0.0382          0.0654
                      (0.26)          (0.34)
nursing               -0.0715         -0.0655
                     (-0.30)         (-0.17)
education              0.00280        -0.0972
                      (0.01)         (-0.37)
constant               1.332 ***       0.526
                      (3.69)          (1.13)

Exposure Variable

N                       229            229
[R.sup.2]              0.174         0.076

                            (3)               (4)
Dependent Variable     Total cheats         Cheater

Estimation           Negative Binomial      Probit

Age                   -0.0606 ***         -0.0334
                     (-3.23)             (-1.63)
female                -0.322 *             0.00399
                     (-1.78)              (0.02)
grad                  -0.356              -0.614 **
                     (-1.22)             (-2.20)
severe                -0.585 ***          -0.403 *
                     (-3.17)             (-1.91)
cheatproblem           0.450 ***           0.0377
                      (2.66)              (0.20)
discussed              0.130               0.210
                      (0.63)              (0.83)
vigilantfac           -0.0632              0.280
                     (-0.31)              (1.21)
vigilantstu           -0.814 **           -0.771 ***
                     (-2.32)             (-3.01)
facultyconfront        0.271              -0.267
                      (1.32)             (-1.10)
univcollege            0.577 **            0.0287
                      (2.32)              (0.10)
artssciences           0.140               0.00265
                      (0.69)              (0.01)
nursing               -0.134               0.308
                     (-0.31)              (0.47)
education              0.110               0.0818
                      (0.33)              (0.22)
constant               0.987 *             1.240 **
                      (1.86)              (2.01)

Exposure Variable    Years Enrolled

N                       229                 229
[R.sup.2]              0.063               0.088

t statistics in parentheses

* p < .1, ** p < .05, *** p < .O1
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Author:Burrus, Robert T., Jr.; Jones, Adam T.; Sackley, Bill; Walker, Mike
Publication:American Economist
Geographic Code:1USA
Date:Mar 22, 2013
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