Winter 2007 3
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 stu-
dents 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 per-
ceive more cheating by student peers. Alcohol consumption, seeing another stu-
dent 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 pun-
ishment 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 ofeconomics at the University of North Carolina at Wilmington, and KimMarie McGoldrick is an asso-ciate professor of economics at the University of Richmond. Copyright © 2007 Heldref Publications
In this section, the Journal of Economic Education publishes original theo-retical and empirical studies of economic education dealing with the analysisand evaluation of teaching methods, learning, attitudes and interests, materials,or processes.
PETER KENNEDY, Section Editor
Research in Economic Education
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 indi-
vidual 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, how-
ever, 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 mon-
itor behavior much more closely than can local enforcement officers—activities
that have been confirmed to reduce student cheating (Hollinger and Lanza-
Kaduce 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 class-
room 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 defini-
tion 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 knowl-
edge 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 unac-
ceptable 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 behav-
ior, 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 self-
reported cheating behavior by students. In an attempt to ensure truth in reporting,
Kerkvliet (1994) employed a randomized response technique to nearly 200 princi-
ples of economics students and found that 42 percent of students reported cheat-
ing, 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 stu-
dents answered the sensitive cheating questions. By providing a definition of
cheating to all surveyed students, our study sought to elicit more accurate cheat-
ing 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 uni-
versities 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 investi-
gating cheating behavior via student surveys may be forming erroneous conclu-
sions 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. univer-
sities, 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 5
integrated honor system in which students face frequent reminders of the impor-
tance of academic honesty whereas UNCW does not. After removing incomplete
surveys, a maximum of 300 (UR, 106; UNCW, 194) usable observations com-
posed 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. Fifty-
five 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 cheat-
ing 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 def-
inition 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 cheat-
ing 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 def-
inition 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 behav-
iors may not constitute cheating in all circumstances and that the policies of indi-
vidual 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 JOURNAL OF ECONOMIC EDUCATION
Winter 2007 7
TA
BL
E 1
. Var
iabl
e N
ames
and
Def
init
ions
Var
iable
Def
init
ion
Tota
lU
RU
NC
W
GPA
G.P
.A.
inte
rval
s “b
elow
1.7
5,”
“1.7
5 t
o 1
.99,”
“2.0
to 2
.249,”
“2.5
to 2
.749,”
“2.7
5 t
o
2.9
83.0
52.9
42.9
9,”
“3.0
0 t
o 3
.249,”
“3.2
5 t
o 3
.499,”
“3.5
to 3
.749,”
and “
7.7
5 t
o 4
.00”
are
coded
as
1.6
25,
1.8
75,
2.1
25,
2.3
75,
2.6
25,
2.8
75,
3.1
25,
3.3
75,
3.6
25,
and 3
.875,
resp
ecti
vely
.F
RA
TS
OR
(%
)1 �
frat
ernit
y/s
oro
rity
mem
ber
; 0 �
nonm
ember
28.6
720.1
044.3
4A
LC
OH
OL
(n)
num
ber
of
alco
holi
c dri
nks
consu
med
per
wee
k—
“11 o
r m
ore
”is
coded
as
11 (
not
%)
6.0
97.1
15.5
3A
TH
L (
%)
1 �
athle
te;
0 �
nonat
hle
te10.0
015.0
97.2
2H
ON
ES
T (
%)
1 �
studen
t co
nsi
der
s th
at h
e/sh
e is
hones
t;
0 �
oth
erw
ise
83.0
082.0
883.5
1S
NH
ON
ES
T (
%)
1 �
studen
t co
nsi
der
s th
at h
e/sh
e is
som
ewhat
hones
t or
not
hones
t;
0 �
oth
erw
ise
17.0
017.9
316.5
0S
EE
CH
EA
T (
%)
1 �
studen
t has
wit
nes
sed c
hea
ting;
0 �
oth
er71.3
362.2
676.2
9O
CC
AS
ION
(%
)1 �
chea
ting o
ccurs
only
on o
ccas
ion o
r nev
er;
0 �
oth
er54.6
762.2
650.5
2P
ER
IOD
IC (
%)
1 �
chea
ting i
s per
iodic
; 0 �
oth
er
32.6
730.1
934.0
2V
OF
TE
N (
%)
1 �
chea
ting o
ccurs
ver
y o
ften
; 0 �
oth
er12.6
77.5
515.4
6C
CA
UG
HT
(%
)1 �
studen
t has
wit
nes
sed a
chea
ter
get
cau
ght;
0 �
oth
er19.6
712.2
623.7
175C
AU
GH
T (
%)
1 �
over
75%
of
chea
ters
get
cau
ght;
0 �
oth
er3.6
73.7
73.6
150C
AU
GH
T (
%)
1 �
bet
wee
n 5
0 a
nd 7
5%
of
chea
ters
get
cau
ght;
0 �
oth
er2.3
30.9
43.0
925C
AU
GH
T (
%)
1 �
bet
wee
n 2
5 a
nd 5
0%
of
chea
ters
get
cau
ght;
0 �
oth
er9.3
39.4
39.2
80C
AU
GH
T (
%)
1 �
bet
wee
n 0
and 2
5%
of
chea
ters
get
cau
ght;
0 �
oth
er48.3
357.5
543.3
0D
KC
AU
GH
T (
%)
1 �
don’t
know
per
centa
ge
of
chea
ters
that
get
cau
ght;
0 �
oth
er36.3
328.3
040.7
2S
EV
PU
NIS
H (
%)
1 �
punis
hm
ent
for
chea
ting i
s se
ver
e; 0
�oth
er44.3
360.3
835.5
7M
OD
PU
NIS
H (
%)
1 �
punis
hm
ent
for
chea
ting i
s m
oder
ate;
0 �
oth
er29.6
724.5
332.4
7M
ILD
PU
NIS
H (
%)
1 �
punis
hm
ent
for
chea
ting i
s m
ild;
0 �
oth
er7.0
02.8
39.2
8D
KP
UN
ISH
(%
)1 �
punis
hm
ent
for
chea
ting i
s unknow
n;
0 �
oth
er19.0
012.2
622.6
8N
CH
EA
T (
n)
Num
ber
of
epis
odes
of
chea
ting e
pis
odes
rep
ort
ed p
rior
to t
he
pro
vis
ion o
f a
def
init
ion
1.8
02.1
51.6
1of
chea
ting
(Tab
le c
onti
nues
)
8 JOURNAL OF ECONOMIC EDUCATION
TA
BL
E 1
. Con
tinue
d
Var
iable
Def
init
ion
Tota
lU
RU
NC
W
PN
CH
EA
T (
n)
Num
ber
of
chea
ting e
pis
odes
rep
ort
ed p
ost
the
pro
vis
ion o
f a
def
init
ion
3.3
22.9
93.5
0of
chea
ting
CH
EA
TE
R (
%)
1 �
indic
ated
chea
ting b
efore
the
def
init
ion o
f ch
eati
ng w
as p
rovid
ed;
0 �
oth
er38.7
35.8
540.2
1P
CH
EA
TE
R (
%)
1 �
indic
ated
chea
ting a
fter
the
def
init
ion o
f ch
eati
ng w
as p
rovid
ed;
0 �
oth
er53.0
42.4
558.7
6L
OO
KPA
PE
R (
%)
1 �
gla
nci
ng a
t an
oth
er s
tuden
t’s
quiz
or
exam
is
chea
ting;
0 �
this
beh
avio
r is
not
93.0
095.2
891.7
5ch
eati
ng
TA
KE
HO
ME
(%
)1 �
askin
g a
cla
ssm
ate
a ques
tion a
bout
a ta
ke-
hom
e ex
am i
s ch
eati
ng;
0 �
this
42.3
374.5
324.7
4beh
avio
r is
not
chea
ting
OL
DT
ES
T (
%)
1 �
usi
ng a
tes
t or
quiz
fro
m a
pre
vio
us
sem
este
r to
stu
dy i
s ch
eati
ng;
0 �
this
beh
avio
r 19.3
331.1
312.8
9is
not
chea
ting
CO
MP
HW
(%
)1 �
com
par
ing h
om
ework
answ
ers
wit
h a
cla
ssm
ate’
s pri
or
to c
lass
is
chea
ting;
0 �
this
9.3
315.0
96.1
9beh
avio
r is
not
chea
ting
AS
KH
W (
%)
1 �
askin
g f
or
hel
p f
rom
a c
lass
mat
e on t
he
assi
gned
hom
ework
is
chea
ting;
0 �
this
2.3
34.7
21.0
3beh
avio
r is
not
chea
ting
UR
(%
)1 �
studen
t at
tends
the
Univ
ersi
ty o
f R
ichm
ond;
0 �
studen
t at
tends
the
Univ
ersi
ty o
f 35.3
3N
/AN
/AN
ort
h C
aroli
na
Wil
min
gto
n
Not
e.G
PA �
gra
de
poin
t av
erag
e; U
R �
Univ
ersi
ty o
f R
ichm
ond;
UN
CW
�U
niv
ersi
ty o
f N
ort
h C
aroli
na-
Wil
min
gto
n.
(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 per-
cent, 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 stu-
dents 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 dis-
tributed 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 with-
out 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 expec-
tations 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 9
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 inci-
dences 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 correla-
tion between the two related choices; thus, the bivariate probit model might pro-
vide 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 them-
selves 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 JOURNAL OF ECONOMIC EDUCATION
To compare current results with previous research that examined the degree to
which the certainty and severity of punishment affected student cheating, we con-
structed 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 pre-
and 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 punish-
ment was mild. To capture the effect of student perceptions of the certainty of pun-
ishment, 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 per-
cent 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 neg-
ative 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 antic-
ipated a negative sign for this variable in both the pre- and postdefinition regres-
sions; 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 post-
definition 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 predefin-
ition 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 vari-
able showed that the unobservable characteristics that influenced whether a stu-
dent admited to cheating in the predefinition model also affected the cheating
Winter 2007 11
12 JOURNAL OF ECONOMIC EDUCATION
TA
BL
E 2
. Biv
aria
te P
robi
t M
odel
Res
ults
wit
h C
HE
AT
ER
and
PC
HE
AT
ER
as
Dep
ende
nt V
aria
bles
.
Pre
def
init
ion
Post
def
init
ion
Var
iable
Rt
Mar
gin
al e
ffec
tR
tM
argin
al e
ffec
t
INT
ER
CE
PT
0.5
925
0.8
79
2.0
842
2.6
59*
GPA
–0.3
877
–2.0
46**
–0.0
668
–0.4
932
–2.7
90*
–0.0
600
FR
AT
SO
R0.4
447
1.9
81**
0.1
047
0.4
638
2.1
43**
0.0
406
AL
CO
HO
L0.0
368
1.5
83
0.0
070
0.0
445
2.0
00**
0.0
051
AT
HL
1.1
130
2.8
76*
0.3
477
0.5
432
1.6
41***
–0.0
336
HO
NE
ST
–0.8
913
–3.8
76*
–0.2
778
–0.5
672
–2.0
65**
–0.0
021
SE
EC
HE
AT
1.0
532
3.3
85*
0.3
488
0.6
955
3.2
35*
–0.0
030
PE
RIO
DIC
0.2
983
1.4
00
0.0
405
0.4
277
2.0
34**
0.0
541
VO
FT
EN
0.6
393
2.3
76**
0.2
595
0.1
591
0.4
75
–0.0
692
CC
AU
GH
T–0.2
863
–1.2
83
–0.1
646
0.0
668
0.2
93
0.0
556
CA
UG
HT
75
0.0
377
0.0
56
0.0
007
0.0
703
0.1
47
0.0
110
CA
UG
HT
50
–0.6
894
–0.8
69
–0.2
819
–0.2
502
–0.4
57
0.0
458
CA
UG
HT
25
–0.3
375
–1.0
91
–0.1
464
–0.1
074
–0.3
27
0.0
273
DK
CA
UG
HT
–0.4
885
–2.3
23**
–0.1
524
–0.3
692
–1.7
53***
–0.0
118
SE
VP
UN
ISH
0.0
369
0.1
05
0.2
176
–0.7
401
–1.6
28***
–0.2
110
MO
DP
UN
ISH
–0.0
837
–0.2
29
0.2
275
–0.9
566
–2.0
63**
–0.3
057
DK
PU
NIS
H–0.4
723
–1.1
77
0.0
802
–1.0
819
–2.1
87**
–0.2
852
UR
–0.3
396
–1.5
91
–0.0
025
–0.6
220
–2.8
04*
–0.1
124
�0.7
966
11.2
87*
LL
–276.2
184
Res
tric
ted L
L–347.9
074
�143.3
78*
Not
e.C
HE
AT
ER
�1 i
f in
dic
ated
chea
ting p
rior
to t
he
pro
vis
ion o
f a
def
init
ion;
PC
HE
AT
ER
�1 i
f in
dic
ated
chea
ting a
fter
the
pro
vis
ion o
f a
def
init
ion.
*In
dic
ates
sig
nif
ican
t at
.01 T
ype
I er
ror
level
,**in
dic
ates
sig
nif
ican
t at
.05 T
ype
I er
ror
level
,***in
dic
ates
sig
nif
ican
ce a
t .0
10 T
ype
I er
ror
level
.
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 sever-
ity of punishment for cheating, there are several interesting and consistent demo-
graphic 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 stu-
dent 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 stu-
dent 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 aver-
sion 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 per-
ceived 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 esti-
mate 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 sever-
ity 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 13
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 postdefin-
ition equation, researchers that do not provide a definition of cheating may under-
estimate 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 stu-
dents 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, regard-
ing 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 (with-
out 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 unac-
ceptable. 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 JOURNAL OF ECONOMIC EDUCATION
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 percep-
tions 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 fac-
ulty-student and student-student definitions of cheating. There may be a set of
student characteristics that contribute to misinformation about acceptable aca-
demic 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 andTrevino (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 predefini-tion cheating response, and y2 is the postdefinition response and marginal effects for the dummyvariables are given by E[y1|y2 � 1, d � 1] � E[y1|y2 � 1, d � 0] where d is the dummy vari-able. For the postdefinition equation, marginal effects for GPA and ALCOHOL are the corre-sponding partial derivatives of E[y2|y1 � 1] and marginal effects for the dummy variables areE[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 aswas 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 estimatesfor these variables are equal in the pre- and postdefinition equations, except in the case of uni-versity athletes.
10. Again, we did not reject the null hypothesis that the magnitudes of the individual parameter esti-mates for these variables are equal in the pre- and postdefinition equations.
11. The coefficient estimates for witnessing a cheater being captured were positive but insignificantin 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 whobelieved that the punishment for cheating was severe or moderate and students who did not knowthe severity of punishment were 21 percent, 30 percent, and 28 percent less likely to cheat, respec-tively, 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-lettergrade reduction and students who believed that punishment was a reprimand only were more
Winter 2007 15
likely to cheat than were students who believed that the punishment for cheating was suspensionfrom the university. These results were obtained in a model that did not include perceptions thatother students cheat and in a model that did not include both perceptions that other students cheatand GPA, respectively.
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