Self-Reports of Student Cheating

Research in Economic Education In this section, the Journal of Economic Education publishes original theoretical and empirical studies of economic education dealing with the analysis and evaluation of teaching methods, learning, attitudes and interests, materials, or processes. PETER KENNEDY, Section Editor

Self-Reports of Student Cheating: Does a Definition of Cheating Matter? Robert T. Burrus, KimMarie McGoldrick, and Peter W. Schuhmann

Abstract: The authors examine student cheating based on implicit and explicit definitions of cheating. Prior to being provided a definition of cheating, students reported whether they had cheated. Students were then provided a definition of cheating and asked to rereport their cheating behaviors. Results indicate that students do not understand what constitutes cheating and are much more likely to report cheating postdefinition. In addition, both pre- and postdefinition cheating behaviors are more prevalent for students with lower GPAs and for those who perceive more cheating by student peers. Alcohol consumption, seeing another student cheat, fraternity/sorority membership, and athletic membership also increase the likelihood of cheating. These findings are consistent with previous studies. On the basis of a sample of students who provided cheating data after a definition of cheating is communicated, the authors find that students who believe that punishment for cheating is relatively severe are less likely to report cheating and that students at institutions with well-publicized honor codes are less likely to admit to cheating compared with students at nonhonor code institutions. Key words: cheating, crime, honor code JEL codes: A22, K42 Authors of most studies have found that classroom cheating occurs frequently, with as many as 50 to 75 percent of students admitting to cheating (Baird 1980; Robert T. Burrus (e-mail: [email protected]) and Peter W. Schuhmann are associate professors of economics at the University of North Carolina at Wilmington, and KimMarie McGoldrick is an associate professor of economics at the University of Richmond. Copyright © 2007 Heldref Publications

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Singhal 1982; Meade 1992; Franklyn-Stokes and Newstead 1995) and 50 to 70 percent of faculty reporting that they have observed cheating in their classroom (Stevens and Stevens 1987; Stern and Havlicek 1986). Study results suggest that many factors influence student cheating. These study results indicate that individual student characteristics1 and their academic environment2 affect cheating behavior. Programs such as student honor codes have also been shown to affect cheating behavior, perhaps because honor codes reduce the perception that other students are cheating (McCabe and Trevino 1993). Cheating among college students has been examined through formal economic modeling. In particular, student cheating is likened to other types of criminal behavior, in which the rational agent weighs the costs and benefits of a criminal action (Becker 1968). Bunn, Caudill, and Gropper (1992) acknowledged, however, two main differences between societal crimes like theft and classroom crime. First, professors can affect the costs of crime in ways that police cannot. Professors can disperse students, make different versions of exams, and can monitor behavior much more closely than can local enforcement officers—activities that have been confirmed to reduce student cheating (Hollinger and LanzaKaduce 1996). Second, because exam answers are similar to public goods, the victims of cheating are not necessarily made worse off by the cheaters. It is not surprising that Houston (1986) found a positive relation between cheaters and the degree of acquaintance of the victim. In this study, we consider a third difference between societal crime and classroom cheating. Societal laws are usually explicit whereas there is often a degree of ambiguity concerning the behaviors that are considered student cheating. In an attempt to clarify what constitutes cheating, institutions often provide a statement of cheating behaviors that includes specific definitions. For example, the definition of cheating that was presented to students for the current study was directly quoted from the student handbook of one of the two institutions considered and stated that cheating is defined as any one of the following: (a) the submission of work that is not one’s own; (b) the giving or receiving of illegal aid from other persons or materials or from materials brought into the classroom by you (such as looking at someone else’s paper or “cheat sheets”); (c) the use of prior knowledge of the contents of the test or quiz without authorization from the instructor— “Knowledge of the contents” can include conversation about the test with students who already have completed it or unauthorized viewing of the test paper. Even with this provided definition, some students failed to consider discussing a take-home exam with other students as cheating unless the instructor explicitly defined the behavior as cheating. Gardner, Roper, and Gonzalez (1988) found that surveys underestimate student cheating as cheaters often claim that they do not cheat. This finding is, perhaps, a result of students failing to recognize the full set of behaviors that constitute cheating. The degree of interpretation ambiguity in cheating definitions has been noted in Wright and Kelly (1974); Barnett and Dalton (1981); Graham et al. (1994). Those study results generally indicate 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 one’s own) but disagree 4

JOURNAL OF ECONOMIC EDUCATION

concerning which other behaviors are 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). This ambiguity affects behavior as indicated by Franklyn-Stokes and Newstead (1995), who found an inverse relationship between the perception of a behavior as cheating and the likelihood that students will cheat in that way. They concluded that clear and consistent definitions of acceptable and unacceptable behaviors would likely decrease undesirable academic behaviors. In a framework similar to those employed by Bunn, Caudill, and Gropper (1992), Mixon and Mixon (1996), and Mixon (1996), we sought to determine the degree to which a provision of a definition of cheating influenced cheating behavior, ceteris paribus. We extended previous research by considering cheating reported by the student respondent prior to provision of a formal definition and cheating reported after provision of a definition. Using probit specifications, we modeled the probability of cheating. A comparison of reported cheating instances before and after the aforementioned definition was provided sheds light on the issue of whether a formal definition of cheating affects the relationships between cheating and the explanatory variables. An important issue in all studies that rely on survey data is that survey responses are likely to contain reporting errors. This is especially true in the case of selfreported cheating behavior by students. In an attempt to ensure truth in reporting, Kerkvliet (1994) employed a randomized response technique to nearly 200 principles of economics students and found that 42 percent of students reported cheating, using a randomized response technique, whereas only 25 percent reported cheating, using a direct question survey. Kerkvliet pointed out that the randomized response survey method decrease sample size as only a portion of the surveyed students answered the sensitive cheating questions. By providing a definition of cheating to all surveyed students, our study sought to elicit more accurate cheating data without decreasing the size of the sample. However, we acknowledge that lack of complete anonymity may have introduced biases noted by Kerkvliet. This research is, therefore, important for two reasons. First, if colleges and universities are serious about minimizing cheating, they must first understand the disparity between student and faculty definitions of cheating, and, next, they must seek cost-effective means to reduce the disparity. Second, because our results indicated that many students were unaware of what cheating is, research investigating cheating behavior via student surveys may be forming erroneous conclusions in cases where a formal definition of cheating was not provided. DATA AND SURVEY DESIGN In spring 2000, over 384 principles of economics students at two U.S. universities, The University of Richmond (UR) and the University of North Carolina Wilmington (UNCW), were surveyed to gather information on cheating.3 Participation in the survey was voluntary and anonymous. One unique aspect in the comparison of these two institutions is that UR has a very formal and Winter 2007

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integrated honor system in which students face frequent reminders of the importance of academic honesty whereas UNCW does not. After removing incomplete surveys, a maximum of 300 (UR, 106; UNCW, 194) usable observations composed our sample.4 In the survey, students were asked to provide demographic and general scholastic information and their perceptions on academic dishonesty and the certainty and severity of punishment for cheating. Students were also asked about the frequency of their personal cheating, using their own standards of what constitutes cheating and were asked again following the provision of the definition of cheating given above. Variable names, definitions, and descriptive statistics are reported in Table 1 for the entire sample and by institution. Using the midpoint of the grade point average (GPA) categories, we found the average student GPA to be nearly 3.0. Twenty-nine percent of respondents were members of a fraternity or sorority (FRATSOR), and 10 percent were athletes (ATHL). Students reported drinking an average of six alcoholic beverages (ALCOHOL) each week. Eighty-three percent of students believed they were honest (HONEST). Seventy-one percent of respondents had witnessed cheating at the college level (SEECHEAT), but only 20 percent had witnessed someone getting caught for cheating (CCAUGHT). Students were also asked about their perceptions concerning the degree to which cheating occurs on their campus, the certainty and severity of punishment for cheating, and their attitudes concerning the appropriateness of cheating. Fiftyfive percent of students believed that cheating occurred only on occasion or never (OCCASION), whereas 13 percent believed that cheating occurred very often (VOFTEN). Only 15 percent believed that the probability of being caught cheating was 25 percent and above (25CAUGHT 50CAUGHT 75CAUGHT). Forty-eight percent of students believed that between 0 and 25 percent of student cheaters got caught (0CAUGHT). Forty-four percent of students thought that punishment for cheating at their college was severe (SEVPUNISH). As expected, students reported significantly more cheating after the formal definition of cheating was provided (PNCHEAT), with an average of 3.32 incidences in the last 12 months compared with the predefinition number (NCHEAT) of 1.8; thus, students tended to underreport cheating behavior when a definition of cheating was not given. The percentage of students reporting at least one incident of cheating (CHEATER) also increased from 39 percent to 53 percent after the definition was provided (PCHEATER). The increase in the percentage of students reporting cheating was similar to the increase in cheating behavior discovered by the use of randomized response surveys (Kerkvliet 1994). Interpretation of these results is confounded by the fact that students in the two institutions are mixed in their interpretations of what behaviors constitute cheating. Prior to the provision of the definition of cheating, our survey elicited opinions on the behaviors that students considered as cheating. We note that some of these behaviors may not constitute cheating in all circumstances and that the policies of individual instructors may change the context of what constitutes cheating in a particular class. Nonetheless, the responses to these questions provided insight into student’s perceptions. Although a majority of students (93 percent) defined glancing at the test or quiz of the students sitting next to them to compare their answers as cheating 6


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FRATSOR (%) ALCOHOL (n) ATHL (%) HONEST (%) SNHONEST (%) SEECHEAT (%) OCCASION (%) PERIODIC (%) VOFTEN (%) CCAUGHT (%) 75CAUGHT (%) 50CAUGHT (%) 25CAUGHT (%) 0CAUGHT (%) DKCAUGHT (%) SEVPUNISH (%) MODPUNISH (%) MILDPUNISH (%) DKPUNISH (%) NCHEAT (n)

GPA

Variable

Definition

G.P.A. intervals “below 1.75,” “1.75 to 1.99,” “2.0 to 2.249,” “2.5 to 2.749,” “2.75 to 2.99,” “3.00 to 3.249,” “3.25 to 3.499,” “3.5 to 3.749,” and “7.75 to 4.00” are coded as 1.625, 1.875, 2.125, 2.375, 2.625, 2.875, 3.125, 3.375, 3.625, and 3.875, respectively. 1 fraternity/sorority member; 0 nonmember number of alcoholic drinks consumed per week—“11 or more” is coded as 11 (not %) 1 athlete; 0 nonathlete 1 student considers that he/she is honest; 0 otherwise 1 student considers that he/she is somewhat honest or not honest; 0 otherwise 1 student has witnessed cheating; 0 other 1 cheating occurs only on occasion or never; 0 other 1 cheating is periodic; 0 other 1 cheating occurs very often; 0 other 1 student has witnessed a cheater get caught; 0 other 1 over 75% of cheaters get caught; 0 other 1 between 50 and 75% of cheaters get caught; 0 other 1 between 25 and 50% of cheaters get caught; 0 other 1 between 0 and 25% of cheaters get caught; 0 other 1 don’t know percentage of cheaters that get caught; 0 other 1 punishment for cheating is severe; 0 other 1 punishment for cheating is moderate; 0 other 1 punishment for cheating is mild; 0 other 1 punishment for cheating is unknown; 0 other Number of episodes of cheating episodes reported prior to the provision of a definition of cheating

TABLE 1. Variable Names and Definitions

28.67 6.09 10.00 83.00 17.00 71.33 54.67 32.67 12.67 19.67 3.67 2.33 9.33 48.33 36.33 44.33 29.67 7.00 19.00 1.80

2.98

Total

44.34 5.53 7.22 83.51 16.50 76.29 50.52 34.02 15.46 23.71 3.61 3.09 9.28 43.30 40.72 35.57 32.47 9.28 22.68 1.61

2.94

UNCW

(Table continues)

20.10 7.11 15.09 82.08 17.93 62.26 62.26 30.19 7.55 12.26 3.77 0.94 9.43 57.55 28.30 60.38 24.53 2.83 12.26 2.15

3.05

UR

8


Number of cheating episodes reported post the provision of a definition of cheating 1 indicated cheating before the definition of cheating was provided; 0 other 1 indicated cheating after the definition of cheating was provided; 0 other 1 glancing at another student’s quiz or exam is cheating; 0 this behavior is not cheating 1 asking a classmate a question about a take-home exam is cheating; 0 this behavior is not cheating 1 using a test or quiz from a previous semester to study is cheating; 0 this behavior is not cheating 1 comparing homework answers with a classmate’s prior to class is cheating; 0 this behavior is not cheating 1 asking for help from a classmate on the assigned homework is cheating; 0 this behavior is not cheating 1 student attends the University of Richmond; 0 student attends the University of North Carolina Wilmington

Definition

Note. GPA grade point average; UR University of Richmond; UNCW University of North Carolina-Wilmington.

UR (%)

ASKHW (%)

COMPHW (%)

OLDTEST (%)

TAKEHOME (%)

CHEATER (%) PCHEATER (%) LOOKPAPER (%)

PNCHEAT (n)

Variable

TABLE 1. Continued

35.33

2.33

9.33

19.33

42.33

38.7 53.0 93.00

3.32

Total

N/A

4.72

15.09

31.13

74.53

35.85 42.45 95.28

2.99

UR

N/A

1.03

6.19

12.89

24.74

40.21 58.76 91.75

3.50

UNCW

(LOOKPAPER), only 42 percent believed that asking a classmate a question about a take-home exam (TAKEHOME) was cheating. Nineteen percent believed that studying from an old exam was cheating (OLDTEST), whereas 9 percent and 2 percent, respectively, believed that comparing homework answers with a classmate’s prior to class (COMPHW) and asking for help from a classmate on the assigned homework (ASKHW) were cheating behaviors. We found that a statistically higher proportion of students from UR reported that each of the aforementioned behaviors was cheating, except in the case of LOOKPAPER (where the proportion was higher for UR students but not significantly so). In other words, a higher proportion of students at UR judged these behaviors to be cheating. These differences are detailed in the last two columns of Table 1 and are quite large in most cases. We attributed these differences to a heightened awareness of the honor code at UR. As McCabe and Trevino (1997, 384) put it, 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.” Other institutions do have honor codes, but these codes are “tucked away in campus policy manuals that are rarely read (p. 384).” Consequently, we also expected that the students at UNCW, the institution without a formal honor code, would be less likely to report that they were cheaters and would report fewer incidences of cheating in the past 12 months. That is, we hypothesized that although the actual frequency of academic dishonesty at the two institutions may be similar, students at the university with a formal honor code would be more aware that they were actually cheating. The latter of these expectations was confirmed. Students at UR reported a significantly higher incidence of cheating (an average of 2.15) than students at UNCW (1.61) prior to the provision of the cheating definition. After the definition of cheating was provided, however, UNCW students reported significantly more incidences of cheating compared with UR students (3.50 to 2.99). In addition, 35.85 percent of the UR students reported at least one incidence of cheating prior to the provision of the definition compared with 40.21 percent of the UNCW students. After the definition was given, 42.45 percent of the UR students reported that they were cheaters whereas 58.76 percent of the UNCW students reported that they had cheated. Hence, UNCW students were not only less likely to report even grievous behaviors such as discussing a take-home exam as cheating, but were also more likely to underreport cheating incidences if a definition of cheating is not provided. MODEL To examine the factors that influence the probability of cheating both before and after a definition is provided, we estimated the following equation, using a bivariate probit specification: Yi 0 1(GPAi) 2(FRATSORi) 3(ATHLi) 4(HONESTi) 5(ALCOHOLi) 6(SEECHEATi) 7(CCAUGHTi) 8(SEVPUNISHi) 9(MODPUNISHi) 10(DKPUNISHi) (1) 11(PERIODICi) 12(VOFTENi) 13(75CAUGHTi) 14(50CAUGHTi) 15(25CAUGHTi) 16(DKCAUGHTi) 17(URi). Winter 2007

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We had two values of the dependent variable for each individual. CHEATER ( 1 if the student indicated cheating prior to the provision of a definition), and PCHEATER ( 1 if they indicated cheating after the provision of a definition) were used as dependent variables. Hence, in equation (1), Yi 0, if the student reported zero incidences of cheating in the past 12 months (pre- or postdefinition) and 1 otherwise. These two values will be different only if the provision of the definition caused students to realize they had been cheating when they previously thought otherwise. Obviously, the two values of the dependent variable may be correlated; hence, a standard probit model may result in biased coefficient estimates. The bivariate probit model was a simultaneous equations model that controlled for the correlation between the two related choices; thus, the bivariate probit model might provide more efficient estimates. Further, because the bivariate probit model estimated the pre- and postcheating responses simultaneously, it allowed for a meaningful statistical comparison of the parameter estimates between the two models.5 We could therefore gain insight into whether the influence of factors that affect cheating was different between the pre- and postdefinition models. HYPOTHESES The literature generally suggests that individual student characteristics affect cheating behavior. For example, students with higher GPAs will be less likely to cheat because they will have less to gain and more to lose from cheating.6 We anticipated that in both the pre- and postdefinition regressions the GPA variable would be negative and significant. On the basis of the results in Houston (1986), who showed that cheating usually occurs between acquaintances, we anticipated that students belonging to a fraternity or a sorority (FRATSOR) or who were involved in athletics (ATHL) were more likely to cheat than other students. These hypotheses were also supported by Baird (1980), Kerkvliet (1994), Genereux and McLeod (1995), McCabe and Bowers (1996); and McCabe and Trevino (1997). Following the results in Kerkvliet (1994), we expected a positive relationship between the amount of alcohol consumed by students (ALCOHOL) and cheating behavior. Students who considered themselves to be more honest (HONEST) were expected to be less likely to cheat than were students who considered themselves only somewhat honest or not honest. Environmental considerations may also affect cheating behavior. The literature suggests that student observations of cheating-related behaviors may have the greatest impact on student cheating behavior. Similar to the studies of Bunn, Caudill, and Gropper (1992), Mixon and Mixon (1996), and Mixon (1996), we expected that students who had witnessed cheating (SEECHEAT) would be more likely to cheat, whereas, if they witnessed a cheater being caught (CCAUGHT), they would be less likely to cheat. Likewise, cheating behavior was influenced by the degree to which students perceived that cheating occurs. PERIODIC and VOFTEN are a set of categorical indicator variables representing perceptions about how frequently cheating occurs, with cheating occurring on occasion or never as the omitted category. The parameter estimates for these variables were expected to be positive and increasing. 10


To compare current results with previous research that examined the degree to which the certainty and severity of punishment affected student cheating, we constructed a set of categorical dummies representing student perceptions of the severity of punishment. SEVPUNISH, MODPUNISH, and DKPUNISH are equal to one if the student perceived the punishment for cheating as severe, moderate, or unknown and equal to zero otherwise. MILDPUNISH is the omitted category. SEVPUNISH and MODPUNISH were expected to be negative in both the preand postdefinition regressions. DKPUNISH may, however, produce a positive or negative parameter estimate as a student who was uncertain of the punishment for cheating may be expected to be more likely to cheat than students who thought punishment was severe but less likely to cheat than a student who thought punishment was mild. To capture the effect of student perceptions of the certainty of punishment, we also included a set of variables representing the percentage of cheaters that the student respondent believed would be caught. The variables 75CAUGHT, 50CAUGHT, and 25CAUGHT indicate that more than 75 percent, between 50 percent and 75 percent, between 25 percent and 50 percent of cheaters would be caught, respectively, whereas DKCAUGHT indicates that the respondent was unsure of the certainty of punishment. 0CAUGHT, a variable that indicates that between zero and 25 percent of cheaters would be caught, is the omitted category. The regression coefficients for these indicator variables were expected to be negative and to decrease in absolute value moving from higher to lower percentages. Again, we had no clear expectation regarding the sign of DKCAUGHT. Finally, to control for differences across the two universities, we included a dummy variable for UR. Because this school has a formal honor code, we anticipated a negative sign for this variable in both the pre- and postdefinition regressions; however, this variable may simply capture other differences between the two universities. Specifying the model using pre- and postdefinition dependent variables allowed for interesting comparisons. For example, we anticipated that the coefficient on the UR dummy variable would increase in absolute value between the pre- and postdefinition specifications because the change between pre- and postdefinition cheating incidences was higher for UNCW than for UR. It was difficult to predict how the other coefficient signs would change from the predefinition specifications to the postdefinition specifications. Obviously, a coefficient estimate that was larger and more significant in the postdefinition regressions represented a variable that had more influence on cheating behavior than was indicated by the predefinition estimate. These changes might result from students being better informed about the behaviors that constituted cheating postdefinition. RESULTS The results of the bivariate probit model are reported in Table 2. Because the probit coefficients had no direct interpretation, we also report marginal effects.7 Most of the signs were in the direction predicted. The highly significant rho variable showed that the unobservable characteristics that influenced whether a student admited to cheating in the predefinition model also affected the cheating Winter 2007

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0.5925 –0.3877 0.4447 0.0368 1.1130 –0.8913 1.0532 0.2983 0.6393 –0.2863 0.0377 –0.6894 –0.3375 –0.4885 0.0369 –0.0837 –0.4723 –0.3396 0.7966

R 0.879 –2.046** 1.981** 1.583 2.876* –3.876* 3.385* 1.400 2.376** –1.283 0.056 –0.869 –1.091 –2.323** 0.105 –0.229 –1.177 –1.591 11.287*

Predefinition t –0.0668 0.1047 0.0070 0.3477 –0.2778 0.3488 0.0405 0.2595 –0.1646 0.0007 –0.2819 –0.1464 –0.1524 0.2176 0.2275 0.0802 –0.0025 –276.2184 –347.9074 143.378*

Marginal effect 2.0842 –0.4932 0.4638 0.0445 0.5432 –0.5672 0.6955 0.4277 0.1591 0.0668 0.0703 –0.2502 –0.1074 –0.3692 –0.7401 –0.9566 –1.0819 –0.6220

R 2.659* –2.790* 2.143** 2.000** 1.641*** –2.065** 3.235* 2.034** 0.475 0.293 0.147 –0.457 –0.327 –1.753*** –1.628*** –2.063** –2.187** –2.804*

Postdefinition t

–0.0600 0.0406 0.0051 –0.0336 –0.0021 –0.0030 0.0541 –0.0692 0.0556 0.0110 0.0458 0.0273 –0.0118 –0.2110 –0.3057 –0.2852 –0.1124

Marginal effect

Note. CHEATER 1 if indicated cheating prior to the provision of a definition; PCHEATER 1 if indicated cheating after the provision of a definition. *Indicates significant at .01 Type I error level, **indicates significant at .05 Type I error level, ***indicates significance at .010 Type I error level.

INTERCEPT GPA FRATSOR ALCOHOL ATHL HONEST SEECHEAT PERIODIC VOFTEN CCAUGHT CAUGHT75 CAUGHT50 CAUGHT25 DKCAUGHT SEVPUNISH MODPUNISH DKPUNISH UR LL Restricted LL

Variable

TABLE 2. Bivariate Probit Model Results with CHEATER and PCHEATER as Dependent Variables.

response after a definition was given. In other words, we rejected the null hypothesis that the two equations were uncorrelated; the model did not consist of independent probit equations that could be estimated separately. Ignoring for the moment the impact of perceptions of the certainty and severity of punishment for cheating, there are several interesting and consistent demographic results across the bivariate probit pre- and postdefinition equations.8 Students with higher GPAs and students who believe that they are honest are less likely to be cheaters (Table 2). Students who participate in Greek organizations and university athletics and who have witnessed other students cheating are more likely to be cheaters. These results are consistent with previous research.9 Student characteristics that were significant in the postdefinition equation but not in the predefinition equation include alcohol consumption and whether a student considers cheating to be periodic rather than only on occasion. This suggests that these variables significantly affect whether a student cheats only if students are enlightened when providing self-reported cheating data.10 The belief that cheating occurs very often only significantly affects whether a student is a cheater in the predefinition equation. Witnessing a student being caught for cheating was the only demographic characteristic that did not significantly affect whether a student is a cheater in either the pre- or postdefinition equations. This is consistent with Mixon and Mixon (1996) and Bunn, Caudill, and Gropper (1992) although, in our results, the signs for these parameters were in the direction predicted.11 We now consider variables representing perceptions about the certainty and severity of punishment. Across both equations, there is no indication that students who believed that the probability of being caught cheating was higher than 25 percent were less likely to cheat than students who believed that the probability of detection was lower than 25 percent. On the other hand, uncertainty about the likelihood of detection significantly reduced the probability that a student was a cheater in both the pre- and postdefinition equations; this may indicate risk aversion by students who did not know the likelihood of capture for cheating. This result is consistent with the fact that students in our sample generally believed that the probability of being caught was low. That is, because most students perceived a low probability of getting caught cheating, those who were uncertain may view the probability as higher, even though they could not give an actual estimate of the probability of being caught. Our most interesting finding was that the severity of punishment variables were significant (with the correct signs) only after a definition of cheating was given. This indicated that students who believed that the severity of punishment for cheating was severe or moderate or students who were uncertain about the severity of punishment were less likely to be cheaters than students who believed that punishment for cheating was mild if students reported informed cheating responses.12 These results are important and are generated by the apparent fact that some of the students who answered that the severity of punishment was mild reported that they did not cheat before the definition of cheating was given but answered that they did cheat after the definition was provided. In two of the four models estimated by Mixon (1996), he found the severity of punishment to affect significantly the likelihood that students were cheaters, although other Winter 2007

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researchers that formally quantify severity of punishment found no significant impact (Bunn, Caudill, and Gropper 1992; Mixon and Mixon 1996).13 Perhaps these researchers did not find that severity of punishment affects cheating because students who believed that the severity of punishment was mild did not believe that certain behaviors were, in fact, cheating. Researchers in prior studies may also have understated the magnitude with which severity of punishment affects cheating, even when severity of punishment was significant. Using likelihood ratio tests, we rejected the null hypothesis that the coefficients, before and after a definition of cheating was given, for SEVPUNISH and MODPUNISH were equal. Because these coefficients were greater in absolute value in the postdefinition equation, researchers that do not provide a definition of cheating may underestimate the role of severity of punishment on cheating. Finally, the UR variable, which indicated the university with the publicized honor code, was negative and insignificant in the predefinition regression and negative and significant in the postdefinition regression. This indicated that students were less likely to admit to cheating at this institution (or more likely to cheat at UNCW), particularly if students were informed about the definition of cheating. However, keep in mind that students at both institutions reported a higher frequency of cheating after a definition was provided. An honor code may reduce the probability that a particular student cheats, but, for those who cheat nonetheless, the honor code may not reduce the frequency to which they cheat. CONCLUSIONS In addition to identifying factors that contribute to the probability of cheating, two general conclusions can be drawn from the results of this study. First, regarding students’ understanding of which behaviors are regarded as cheating, both direct results from the survey and comparison of pre- and postdefinition models suggest that student definitions of cheating are, at best, incomplete. Similar to the randomized response survey techniques of Kerkvliet (1994), the provision of a definition of cheating elicits an increase in self-reported cheating behavior (without reducing sample size). In fact, our results indicate that any survey of students regarding cheating behavior that does not provide a clear definition of cheating may still contain an inherent underreporting bias; the students in our survey were more likely to be cheaters over the past 12 months after a definition of cheating was provided. Because students attending both schools did not fully comprehend what cheating is, it seems that an obvious first step toward combating cheating would be to provide clear and consistent reminders of which behaviors are unacceptable. Well-publicized honor codes may achieve this objective. Second, our findings have implications for researchers seeking to understand the specific student factors that contribute to cheating. Researchers’ purpose in past studies was to identify student characteristics that significantly affect cheating behavior so that cheating could be combated more effectively. Results of our study which replicates a set of explanatory variables previously established in the literature, generally supports prior research. In addition, we also find that 14


well-publicized honor codes may reduce the number of cheaters if students report cheating after reading a clear definition of cheating. Our primary contribution may be associated with student perceptions of the severity of punishment for cheating. We find that students in our sample who believe that the severity of punishment for cheating is relatively high are less likely to cheat if cheating is defined when students report cheating behavior. We also find that the magnitude with which cheating is reduced by increased perceptions of the severity of punishment is increased when students provide cheating data after a definition of cheating is given. Our results imply an area that is rich for further study. The analysis used here should be extended to further investigate causes of the discrepancy between faculty-student and student-student definitions of cheating. There may be a set of student characteristics that contribute to misinformation about acceptable academic behavior which are identifiable. Perhaps more important, if publicizing formal definitions of acceptable and unacceptable academic behaviors lead to reduced cheating incidences across all types of students, educators are faced with the question of how to best accomplish that task. NOTES 1. See Baird (1980); Gardner, Roper, and Gonzalez (1988); Bunn, Caudill, and Gropper (1992); Kerkvliet (1994); Genereux and McLeod (1995); McCabe and Bowers (1996); McCabe and Trevino (1997); and Kerkvliet and Sigmund (1999). 2. See Bunn, Caudill, and Gropper (1992); Genereux and McLeod (1995); McCabe and Bowers (1996); Mixon and Mixon (1996); Mixon (1996); McCabe and Trevino (1997); Whitley (1998); and Magnus et al. (2002). 3. A copy of the survey is available from the authors upon request. 4. We removed four completed surveys because responses to frequency of cheating questions were so large as to be obviously fictitious. 5. For more information on bivariate probit estimation, see Greene (2003). 6. Notable exceptions included studies by Kerkvliet (1994) and Nowell and Laufer (1997), the results of which suggested that GPA did not significantly affect cheating. 7. For the bivariate probit model, marginal effects for the continuous variables in the predefinition equation are equal to the relevant partial derivatives of E[y1|y2 1] where y1 is the predefinition cheating response, and y2 is the postdefinition response and marginal effects for the dummy variables are given by E[y1|y2 1, d 1] E[y1|y2 1, d 0] where d is the dummy variable. For the postdefinition equation, marginal effects for GPA and ALCOHOL are the corresponding partial derivatives of E[y2|y1 1] and marginal effects for the dummy variables are E[y2|y1 1, d 1] E[y2|y1 1, d 0]. 8. Using the likelihood ratio test, the null hypothesis that all the parameters are zero was rejected as was the null hypothesis that all the parameter values are equivalent. 9. We did not reject the null hypothesis that the magnitudes of the individual parameter estimates for these variables are equal in the pre- and postdefinition equations, except in the case of university athletes. 10. Again, we did not reject the null hypothesis that the magnitudes of the individual parameter estimates for these variables are equal in the pre- and postdefinition equations. 11. The coefficient estimates for witnessing a cheater being captured were positive but insignificant in Mixon and Mixon (1996) and Bunn, Caudill, and Gropper (1992). Mixon (1996), however, found that observing a capture significantly increases the probability that a student is a cheater. 12. The importance of these results is reinforced by the marginal effects that show that students who believed that the punishment for cheating was severe or moderate and students who did not know the severity of punishment were 21 percent, 30 percent, and 28 percent less likely to cheat, respectively, than students who thought that the severity of punishment for cheating was mild. 13. Mixon (1996) found that students who believed that the punishment for cheating was a one-letter grade reduction and students who believed that punishment was a reprimand only were more

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likely to cheat than were students who believed that the punishment for cheating was suspension from the university. These results were obtained in a model that did not include perceptions that other students cheat and in a model that did not include both perceptions that other students cheat and GPA, respectively. REFERENCES Baird, J. S., Jr. 1980. Current trends in college cheating. Psychology in the Schools 17 (4): 515–22. Barnett, D. C., and J. C. Dalton. 1981. Why college students cheat. Journal of College Student Personnel 22 (6): 545–51. Becker, G. 1968. Crime and punishment: An economic approach. Journal of Political Economy 76 (2): 168–217. 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. Gardner, W. M., T. Roper, and C. C. Gonzalez. 1988. Analysis of cheating on academic assignments. Psychological Record 38 (Fall): 543–55. Genereux, R. L., and B. A. McLeod. 1995. Circumstances surrounding cheating: A questionnaire study of college students. Research in Higher Education 36 (6): 687–704. 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. Greene, W. H. 2003. Econometric Analysis. 5th ed. New York: Macmillian. 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. Houston, J. P. 1986. Classroom answer copying: Roles of acquaintanceship and free versus assigned seating. Journal of Educational Psychology 78 (June): 230–32. Kerkvliet, J. 1994. Cheating by economics students: A comparison of survey results. Journal of Economic Education 27 (2): 121–33. Kerkvliet, J., and C. L. Sigmund. 1999. Can we control cheating in the classroom? Journal of Economic Education, 30 (4): 195–200. Magnus, J. R., V. M. Polterovich, D. L. Danilov, and A. V. Savvateev. 2002. Tolerance of cheating: An analysis across countries. Journal of Economic Education 33 (2): 125–35. Meade, J. 1992. Cheating: Is academic dishonesty par for the course? Prism 1 (7): 30–32. McCabe, D. L., and W. J. Bowers. 1996. The relationship between student cheating and college fraternity or sorority membership. NASPA Journal 33 (4): 280–91. McCabe, D. L., and L. K. Trevino. 1993. Academic dishonesty: Honor codes and other contextual influences. Journal of Higher Education 64 (5): 522–38. ———. 1997. Individual and contextual influences on academic dishonesty: A multicampus investigation. Research in Higher Education 38 (3): 379–96. Mixon, F. G. Jr. 1996. Crime in the classroom: An extension. Journal of Economic Education 27 (3): 195–200. Mixon, F. G., Jr., and D. C. Mixon. 1996. The economics of illegitimate activities: Further evidence. Journal of Socio-Economics 25 (3): 373–81. Nowell, C., and D. Laufer. 1997. Undergraduate student cheating in the fields of business and economics. Journal of Economic Education 28 (1): 3–11. Singhal, A. C. 1982. Factors in students’ dishonesty. Psychological Reports 51(December): 775–80. Stern, E., and L. Havlicek. 1986. Academic misconduct: Results of faculty and undergraduate student surveys. Journal of Allied Health 5 (2): 129–42. Stevens, G., and F. Stevens. 1987. Ethical inclinations of tomorrow’s managers revisited: How and why students cheat. Journal of Education for Business 63 (1): 24–29. Whitley, B. E. Jr. 1998. Factors associated with cheating among college students: A review. Research in Higher Education 39 (3): 235–74. Wright, J. C., and R. Kelly. 1974. Cheating: Student/faculty views and responsibilities. Improving College and University Teaching 22 (1): 31.

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