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Are my colleagues soft on (academic) crime?


Most universities publish a statement addressing academic honesty. Usually, this statement will be a centerpiece of a university honor code; these codes hold that that the pursuit of knowledge requires unwavering honesty among all members of the academic community. The codes typically mention various infractions including plagiarism (word-for-word copying, the mosaic, the paraphrase), cheating on examinations and even various forms of bribery (buying, receiving, or offering some material consideration to obtain a grade).

Honor codes are important in steering students toward a culture of honor in the pursuit of knowledge. Codes at the military academies, Ivy League schools, and such schools as UVA and Washington and Lee frame their entire academic cultures with their honor codes, but such ambitious foundations for academic integrity are the exception. Nonetheless, academic honor codes have garnered interest, and some of this interest likely derives from a perception of an increase in academic dishonesty over the past couple of decades and from a wide range of corporate and public ethical failures. These perceptions, and curiosity about how those perceptions might manifest themselves at a mid-size regional university in the Southeast, encouraged this research. How do faculty perceive the level of academic integrity at the university, and how might those perceptions impact faculty performance, and expectations, in the classroom? How would those perceptions differ across business schools and "general" colleges of arts and science? Findings will be important to the student, faculty and administrator.

Some background on cheating and stakeholder perceptions of cheating is provided in the next section. The third section describes the survey and data collected for this study. Descriptive statistics are provided. We then conduct a traditional cross-sectional study of our data employing a standard limited dependant variable PROBIT model - seeking to discover the importance of faculty perceptions of cheating in describing the detection and punishment of academic dishonesty. We report our results, suggest and conduct a series of tests for robustness, and examine the implications of our findings for the various university stakeholders. We conclude the paper with a summary and a set of encouragements for subsequent research.


McCabe and Trevino (1997) argue that institutions with formal honor codes that "are widely distributed and understood by members of the academic community" are "an integral part of the campus culture," as with UVA and W and L. The converse might also be implied: if academic honesty is not highlighted early and often for the entering college student, it might not become a part of that student's academic "fabric." Kidwell and Wozniak (2003) surveyed students about cheating at a small liberal arts college and found that over 70 percent of those surveyed reported cheating, plagiarism or other forms of academic dishonesty; many reported multiple violations. Other studies (Baird, 1980; Singhal, 1982; Franklyn-Stokes and Newstead, 1995) confirm the same, with over half of students admitting to cheating and a similar portion of faculty reporting that they have observed cheating in their classroom (Stevens and Stevens, 1987; Stern and Havlicek, 1986). Greene and Saxe (1992) suggest that students acknowledge cheating as typical and see "no harm, no foul."

Other studies consider what, exactly, students consider to be cheating and the factors associated with greater amounts of cheating. While students are conflicted about whether many behaviors constitute cheating, prior research generally indicates that students agree that the most obvious cheating behaviors (such as copying answers off your neighbor's paper during an exam) are, indeed, cheating.

Whitley (1998) and Kerkvliet (1994) find that GPA, inordinate focus on grades as opposed to learning, greater perceived grade pressure, fewer hours spent studying, working more hours outside of class, membership in a fraternity or sorority, and too frequent partying and alcohol consumption all contribute to cheating. As well, lower levels of self reported honesty are correlated with greater likelihoods of academic dishonesty.

Research suggests that students do not cheat because they do not understand the nature of cheating--they cheat perhaps because of their own perception of a low likelihood of being caught, or of only modest consequences if they are. Expulsion from the university, a common penalty for all offenses at the military academies, the Ivys and Washington and Lee, is not generally enforced at other universities except in the most egregious examples of academic dishonesty. A movement toward certain expulsion would likely reduce the number of reported cheating incidents and reduce actual cheating.

Environmental factors that impact cheating include perceptions that other students are cheating (Bunn et al., 1992; Mixon and Mixon, 1996; Mixon, 1996) and whether or not clear definitions of cheating are given (Franklyn-Stokes and Newstead, 1995; Burrus et al., 2007). McCabe and Trevino (1993) suggest that student honor codes impact cheating behavior, but note this impact may simply reflect widespread beliefs that honor codes reduce overall cheating.

Mentioned above, the certainty and severity of punishment for cheating are also important factors impacting cheating. Hollinger and Lanza-Kaduce (1996) show that increases in the probability of being caught cheating reduces cheating behavior. As well, Mixon (1996) and Burrus et al. (2007) find that the severity of punishment is an important inverse determinant of the likelihood of cheating.

Faculty perceptions of cheating have not been widely examined. Studies by Wright and Kelly (1974), Barnett and Dalton (1981), and Graham et al. (1994) generally find that students and faculty agree about the most severe or obvious forms of cheating (copying from other student's exams, using cheat sheets, and turning in research that is not your own), but disagree concerning which other behaviors are, indeed, cheating (plagiarism and bibliographical misrepresentation, working with other students on homework when it has been expressly forbidden, using an old test to study without the teacher's knowledge, and getting questions or answers about an exam from someone who has already taken it). Interestingly, while students do admit to cheating, Symaco and Marcelo (2003) find that some behaviors are not as prevalent as previously thought. These activities include remembering as many questions as possible to share with their friends after an exam and looking at another's answer sheet during a quiz.

While students and faculty do not necessarily agree on the behaviors that constitute cheating, Ballew and Roig (1992) showed student perceptions of professors' attitudes were similar to the actual attitudes held by the professors. Professors, however, believed that students were more tolerant of cheating than students reported themselves to be. Smith, Nolan and Dai (1998) focused on faculty perception of the determinants of academic dishonesty. They conclude that classroom environment contributes to the extent and degree of cheating, a result that matches student perceptions.

Since individual faculty are rarely in control of student-specific and campus environmental factors that impact cheating behaviors, these factors generally cannot be used to influence academic honesty. As Smith, Nolan and Dai (1998) point out, however, faculty members do make direct contributions to honor code enforcement.

Given that the certainty and severity of punishment impact student cheating behavior, this study examines the factors that impact this "certainty and severity." Faculty involved in the survey are from a regional university where penalties for cheating are primarily determined by the professor (a "private resolution"); honor cases do not go before an official honor board unless students and faculty cannot agree on whether cheating occurred and/or what the punishment for cheating should be or unless the alleged incident is egregious or pervasive. Faculty are responsible for confronting academic dishonesty and meting out appropriate penalties.


The data for this study were collected early in 2009 in support of the Honor Code Task Force at the University of North Carolina Wilmington. The task force was charged, late in 2007, with studying the UNCW Honor Code and bringing forth recommendations for its improvement.

The entire faculty (including part-time faculty) were asked to participate in a survey that first collected demographic information and then gathered faculty perceptions about student academic honesty at UNCW. Two hundred thirty-eight responses from over 866 faculty members were obtained. Excluding incomplete surveys, 213 usable observations make up our sample.

Faculty members were asked to provide information on their age, the number of years employed at UNCW, their gender, their academic rank, and their academic unit. Respondents were then queried about how often they observe and suspect academic dishonesty and about the types of behaviors that they consider to constitute academic dishonesty.

Respondents were also asked about their perceptions of the certainty and severity of punishment for cheating. They were asked whether the penalties they administer for academic fraud were severe and whether the penalties that other UNCW professors administer were severe. They were asked whether they were personally vigilant in detecting cheating and whether or not their faculty peers were vigilant. Respondents were then asked how vigilant they were in confronting detected cheaters and whether they believed that other UNCW faculty members were vigilant.

Table 1 provides professor-reported demographic information and perceptions of the cheating behavior of students in their classes.

The average faculty respondent's time at UNCW is 7.27 years, around 40 percent of the sample respondents were females, nearly 50 percent were tenured, and 14 percent were business faculty. Table 1 indicates that only 23 percent of faculty respondents were "very familiar" with the campus honor code, 66 percent of faculty suppose that honor code violations were a "moderate" to "major" problem, only 30 percent believe that violations were becoming more frequent. Over three quarters of respondents reported a discussion of academic honesty with their students at the beginning of each semester.

Respondents also report two episodes of observed cheating in their courses per semester but suspect around four academic dishonesty offenses where the average course load was approximately three classes per semester. Thirty-one percent of the sample reported seeing three episodes of cheating and 60 percent suspect three or more episodes of academic dishonesty each semester.

Survey respondents were also asked about the types of behaviors that they considered to be cheating. Table 2 lists behaviors that might constitute cheating and reports the percentage of the sample that believed that the behavior represents academic dishonesty. The table is split into two sections; the top section reports the behaviors that are not consensus cheating behaviors while the bottom reports consensus cheating behaviors. As well, the average number of times (out of 9) that the faculty chose a non-consensus cheating behavior as cheating is 1.2. The average number of times (out of 11) that a faculty member chose a consensus behavior as cheating is 9.18.

Table 3 reports faculty perceptions about their own policing of cheating and the policing of cheating of others. As a general rule, faculty members believed that other professors were soft on crime while they were not.


Examining the factors that influence the certainty and severity of punishment, we estimate the following equation using a probit specification:

CER/[SEV.sub.i] = [[beta].sub.0] + [[beta].sub.1]([TIME.sub.i]) + [[beta].sub.2]([FEMALE.sub.i]) + [[beta].sub.3]([TENURE.sub.i]) + [[beta].sub.4]([BUSINESS.sub.i]) + [[beta].sub.5]([OBS.sub.i]) + [[beta].sub.6]([HARSHIN.sub.i]) + [[beta].sub.7]([KNOWHC.sub.i]) + [[beta].sub.8]([DEGREED.sub.i]) + [[beta].sub.9]([CHEATWOR.sub.i]) + [[beta].sub.10]([DISCUSS.sub.i]) + [[beta].sub.11]([CERO.sub.i]) + [[beta].sub.12]([SEVO.sub.i]) + [e.sub.i],

The model is run twice with the independent variable in the first model being the faculty member's own perceptions of whether they are "moderately" to "very" vigilant in detecting cheating (as opposed to "slightly" or "not at all vigilant") and, in the second, whether the penalties they assign are "moderate" to "severe" (as opposed to "mild"). Other variables included as independent variables on the right hand side of the model are defined in Tables 1-3 save for HARSHIN, which is the ratio of number of harsh definitions of cheating to the number of normal definitions of cheating (see Table 2), CERO, which represents the faculty perceptions of the vigilance of other faculty in confronting cheating, and SEVO, denoting faculty perceptions of the severity of punishment of other faculty (see Table 3).

Ex ante, faculty members that observe more cheating, have increased knowledge of the honor code, believe that cheating is a problem and believe that cheating is getting worse are expected to confront cheating with increased frequency and to be more severe in the punishments meted. Faculty members who believe that behaviors typically not identified with cheating are, indeed, cheating are also expected to be more vigilant at confronting cheating and to have harsher penalties. Hence, we anticipate positive and significant coefficient estimates on OBS, HARSHIN, KNOWCH, DEGREED, and CHEATWOR. We also expect a positive sign on DISCUSS for the severity of punishment model, but not necessarily for the certainty model, as faculty usually spell out how cheaters will be punished on the first day of class but rarely discuss how they will be detected and confronted. We have no priors on TIME, FEMALE, TENURE, and BUSINESS, as no earlier studies have been conducted on these variables as they relate to faculty perceptions of cheating.

Finally, we are conflicted about whether the certainty that other faculty members confront cheating, CERO, and the severity of the punishments that other faculty dish out, SEVO, will have positive or negative impacts on the self-reported vigilance of detection and severity of punishment for the surveyed faculty. On the one hand, faculty might be encouraged to be tougher on crime if they believe their peers are tough on crime, or they may be tougher on crime to compensate for the shortcomings of their peers.


Considering those variables about which no prior expectations were made, female professors perceive themselves as tougher on crime than their male counterparts, as FEMALE is positive and significant in both the certainty and severity models. Tenured faculty persons, on the other hand, believe that they are better at confronting classroom crime than their untenured colleagues, but having tenure doesn't impact self-reported severity of punishment.

Other results are generally consistent with our expectations. Instructors who discuss academic integrity during the first days of class are also more likely to report severity in punishments and vigilance in confronting cheaters. Faculty members who are relatively harsh in their definitions of cheating and those who believe that the degree of campus cheating is high are more likely to report severity in assigning punishments while the probability of reporting vigilance in confronting cheating is higher for faculty who are relatively familiar with the honor code.

Importantly, faculty members who believe that other faculty are tough on crime report being tougher themselves. This proposition holds except that the perception that others impose tough sentences for cheating does not significantly impact the certainty of confronting cheating. Our results generally show that the decisions to confront cheating and impose severe penalties are not really related, but faculty members are strongly influenced by the behavior of their peers.

Model results are provided in Table 4.


Most studies on student cheating find that students commit academic infractions if they perceive that other students are cheating. Perceptions matter. Anecdotally, some schools that trumpet their honor codes derive benefits from an extracurricular impression that the schools' students are cut from a different cloth; others might enjoy similar enhancements following a similar path, with overall improvements in the schools' reputations being one of the results.

In this paper, we find that professors are increasingly vigilant in policing student cheating and assigning harsher penalties if they believe that their peers are tough on crime--even though they generally believe that they are harsher on academic crime than their peers. This finding has important policy implications. First, while it is generally noted that honor codes help to create a culture of academic integrity among students, embracing that culture by faculty may encourage greater vigilance in detecting and punishing cheaters. Second, as the literature shows, the fostering of an academic community in which faculty are engaged in ensuring academic integrity will likely lead to fewer incidences of cheating. Third, the precise manner with which a university, a school of business or an individual faculty member might contribute to this "culture of academic integrity" is not immediately evident, and will invite further research.


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Barnett, D. C., and J. C. Dalton (1981). Why college students cheat. Journal of College Student Personnel 22 (6): 545-51.

Bunn, D. N., S. B. Caudill, and D. M. Gropper (1992). Crime in the classroom: An economic analysis of undergraduate student cheating behavior. Journal of Economic Education 23 (Summer): 197-207.

Franklyn-Stokes, A., and S. E. Newstead (1995). Undergraduate cheating: Who does what why? Studies in Higher Education 20 (2):159-72.

Graham, M. A., J. Monday, K. O'Brien and S. Steffen (1994). Cheating at small colleges: An examination of student and faculty attitudes and behaviors. Journal of College Student Development 35 (4): 255-60.

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Hollinger, R. C., and L. Lanza-Kaduce (1996). Academic dishonesty and the perceived effectiveness of countermeasures: An empirical survey of cheating at a major public university. NASPA Journal 33 (4): 292-306.

Kerkvliet, J. (1994). Cheating by economics students: A comparison of survey results. Journal of Economic Education 27 (2): 121-33.

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McCabe, D. L., and L. K. Trevino (1997). Individual and contextual influences on academic dishonesty: A multicampus investigation. Research in Higher Education, 38 (3): 379-96.

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Mixon, Franklin G., Jr. (1996). Crime in the classroom: An extension. Journal of Economic Education, 27 (3): 195200.

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Roig, M. and C. Ballew (1992). Attitudes toward cheating by college students and professors. Paper presented at the Annual Meeting of the Eastern Psychological Association, Boston, MA. 1992.

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Robert T. Burrus, UNC Wilmington

J. Edward Graham, UNC Wilmington

Mike Walker, UNC Wilmington
Table 1: Demographic Variables and Perceptions of Cheating

Variable   Definition                                       Average or
Name                                                        Proportion

Time       Time at Institution.                                7.28

Female     Dummy variable: 1=female; 0=other                   0.42

Tenure     Dummy variable: 1=tenured; 0=other                  0.50

Business   Dummy variable: 1=professor in business             0.14
           school; 0=other

Obs        Number of observed episodes of cheating per         1.99

Knowhc     Dummy variable: 1=professor knows honor code        0.23
           very well

Degreed    Dummy variable: 1=professor believes that           0.66
           honor code violations are a moderate to major
           problem on the campus

Cheatwor   Dummy variable: professor believes that             0.30
           cheating behaviors are getting worse

Discuss    Dummy variable: professor discusses the honor       0.78
           code on the first day of class.

Table 2: Behaviors that are Considered Cheating by Faculty

Variable                    Definition                     Proportion

AskH         Asking for help from a classmate on the           0.05
             assigned homework, paper or project

Backex       Writing formulas or other information on the      0.075
             back of an exam as soon as it is received

Comhw        Comparing homework answers                        0.10

Oldt         Studying from old exams                           0.24

Chpaper      Having someone else check over a written          0.03

Manip        Visiting a professor to influence a grade         0.24

Study        Studying with another student for and exam         0

Badcite      Using only citations that confirm your point      0.08
             of view

Text         Text messaging during a lecture                   0.37

TotalHarsh   Number of times a faculty member confirmed a      1.19
             minority cheating behavior (less than half     (average)
             of the respondents also consider the
             behavior as cheating)

Excuse       Using a false excuse to get out of taking an      0.80
             exam or turning in an assignment

Glance       Looking at another student's exam                 0.94

Allgla       Allowing a student to look on an exam             0.97

Askth        Asking a student about a take home exam           0.54

Askin        Asking about the content of an exam from a        0.69
             student who has already taken it

Givein       Giving information about an exam                  0.81

Falcites     Adding citations to a bibliography when           0.77
             those cites don't appear in the paper

Nocites      Failing to properly cite a source                 0.87

Cheat        Using a cheat sheet                               0.95

Calc         Programming formulas into a calculator            0.88

Attend       Signing an attendance sheet for someone who       0.96
             is not in class

TotalNorm    Number of times a faculty member confirmed a      9.19
             consensus cheating behavior (more than half    (average)
             of the respondents also consider the
             behavior as cheating)

Table 3: Attitudes Toward Severity of Punishment and Certainty of

Variable                     Definition                     Proportion

Vvconfp    Professor is very vigilant in detecting and         0.45
           confronting cheating

Mvconfp    Professor is moderately vigilant in detecting       0.36
           and confronting cheating

Snconfp    Professor is either slightly vigilant or not        0.19
           vigilant in detecting and confronting cheating

Sevp       Professor inflicts severe punishments for           0.20

Msevp      Professor inflicts moderately severe                0.54
           punishments for cheating

Mildsevp   Professor inflicts mild punishments for             0.26

Vvconfo    Others are very vigilant in detecting and           0.15
           confronting cheating

Mvconfo    Others are moderately vigilant in detecting         0.44
           and confronting cheating

Snconfo    Others are either slightly vigilant or not          0.41
           vigilant in detecting and confronting cheating

Sevo       Others inflict severe punishments for cheating      0.02

Msevo      Others inflict moderately severe punishments        0.28
           for cheating

Mildsevo   Others inflict mild punishments for cheating        0.70

Cer        Vvconfp+Mvconfp                                     0.81

Sev        Sevp + Msevp                                        0.74

Cero       Vvconfo + Mvconfo                                   0.61

Sevo       Sevo + Msevo                                        0.30

TABLE 4: Probit Results

                     Model 1 (Y=CER)          Model 2 (Y=SEV)

Variable         Coefficient   b/St.Er.   Coefficient   b/St.Er.

Constant           -0.83       -2.06 **     -1.88      -4.49 ***
TIME               -0.04       -0.98         0.05       1.49
FEMALE              0.55        2.08 **      0.46       1.95 **
TENURE              0.62        1.90 *      -0.15      -0.53
BUSINESS            0.27        0.84         0.22       0.71
OBS                 0.03        0.40        -0.02      -0.40
HARSHIN             0.37        0.48         1.36       1.75 *
KNOWHC              0.76        2.07 **      0.47       1.37
DEGREED             0.20        0.73         0.70       2.74 ***
CHEATWOR            0.14        0.46         0.12       0.45
DISCUSS             0.48        1.65 *       0.84       3.21 ***
CERO                1.63        5.77 ***     1.16       3.46 ***
SEVO                -0.21      -0.71         0.84       3.37 ***
Log likelihood           -75.012                -84.9804
Restricted               104.33                -122.705
Pseudo R-sq                0.281011              75.44912

* significant at the 10% level ** significant at the 5% level ***
significant at the 1% level
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Author:Burrus, Robert T.; Graham, J. Edward; Walker, Mike
Publication:Journal of Economics and Economic Education Research
Geographic Code:1USA
Date:Sep 1, 2011
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