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FACTORS INFLUENCING ENGINEERING
STUDENTS’ DECISIONS TO CHEAT BY TYPE
OF ASSESSMENT
Honor J. Passow,* Matthew J. Mayhew,** Cynthia J. Finelli,***
,
§
Trevor S. Harding,† and Donald D. Carpenter ‡
................................................................................................
................................................................................................
Academic dishonesty (cheating) has been prevalent on college campuses for
decades, and the percentage of students reporting cheating varies by college major.
This study, based on a survey of 643 undergraduate engineering majors at 11
institutions, used two parallel hierarchical multiple regression analyses to predict the
frequency of cheating on exams and the frequency of cheating on homework based
on eight blocks of independent variables: demographics, pre-college cheating
behavior, co-curricular participation, plus five blocks organized around Ajzen’s
Theory of Planned Behavior (moral obligation not to cheat, attitudes about cheating,
evaluation of the costs and benefits of cheating, perceived social pressures to cheat
or not to cheat, and perceived effectiveness of academic dishonesty policies). The
final models significantly predict 36% of the variance in ‘‘frequency of cheating on
exams’’ and 14% of the variance in ‘‘frequency of cheating on homework’’. Students
don’t see cheating as a single construct and their decisions to cheat or not to cheat
are influenced differently depending on the type of assessment. Secondary findings
are that a student’s conviction that cheating is wrong no matter what the
circumstances is a strong deterrent to cheating across types of assessment and
that a student who agrees that he/she would cheat in order to alleviate stressful
situations is more likely to cheat on both exams and homework.
................................................................................................
................................................................................................
KEY WORDS: cheating; examinations; homework; theory of planned behavior; engi-
neering; higher education.
*Center for the Study of Higher and Postsecondary Education, University of Michigan, Ann
Arbor, MI 48109, USA.
**Department of Student Life Assessment, University of North Carolina Wilmington, Wil-
mington, NC 28403, USA.
***College of Engineering and Center for Research on Learning and Teaching, University of
Michigan, Ann Arbor, MI 48109, USA.
Manufacturing Engineering, Kettering University, Flint, MI 48504, USA.
àCivil Engineering, Lawrence Technological University, Southfield, MI 48075, USA.
§Addresscorrespondence to: CynthiaJ. Finelli, 1071 Palmer Commons, 100 WashtenawAvenue,
Ann Arbor, MI 48109, USA. E-mail: cfinelli@umich.edu
Ó2006 Springer Science+Business Media, Inc.
Research in Higher Education (Ó2006)
DOI: 10.1007/s11162-006-9010-y
INTRODUCTION
Academic dishonesty, or cheating, is widespread on college campuses
throughout the United States (McCabe and Drinan, 1999). Reported
percentages vary widely, although the percentages remained consistent
over 30 years in the only known replication study. In 1993, McCabe
worked with Bowers to resurvey nine of the schools that Bowers had
surveyed in 1963. Although Bowers received responses from 5422 stu-
dents at 99 institutions, the subset of these at the nine schools that
McCabe resurveyed consisted of 452 responses (D.L. McCabe, personal
communication, April 1, 2002). This study, replicated over time, indi-
cates that the percentage of undergraduates self-reporting engagement in
various cheating behaviors during college has not changed substantially
from Bower’s 1963 survey (82% of 452 respondents) to McCabe and
Trevino’s 1993 survey (84% of 1793 respondents) (McCabe, 1997). The
steady percentage of self-reported cheating has been substantiated by a
meta-analysis (Brown and Emmett, 2001) and an additional study
(Spiller and Crown, 1995). However, the severity of the cheating has
increased substantially. McCabe (1997) offers examples:
For example, students admitting to copying from another student on an examina-
tion doubled from 26% to 52% between 1963 and 1993. Instances of helping some-
one else cheat on an examination and the use of crib notes each increased more than
50%. McCabe and Trevino also observed a four-fold increase (from 11% to 49%)
in the number of students who admitted they had collaborated on assignments when
the instructor had specifically asked for individual work. (p. 435)
Ten studies indicate that the percentage of undergraduates reporting
engagement in various cheating behaviors differs by college major
(Baird, 1980; Bowers, 1964; Brown, 1996; Harp and Taietz, 1966; Jack-
son, Levine, Furnham, and Burr, 2002; McCabe, 1997; Newstead,
Franklyn-Stokes, and Armstead, 1996; Rawwas and Isakson, 2000;
Roberts, Anderson, and Yanish, 1997; Shaughnessy, 1988). The findings
are consistent: percentages of undergraduates reporting cheating are
highest for those enrolled in ‘‘vocationally oriented majors such as busi-
ness and engineering’’ (McCabe, 1997, p. 444), where business majors
report the highest levels. McCabe collected survey data from 1,946
undergraduates at 16 highly selective institutions in 1995--1996, includ-
ing questions about engagement during college in five different cheating
behaviors on examinations, four different cheating behaviors on writing
assignments, plus collaboration with other students on assignments
when the instructor wanted individual work. Percentages of students
reporting any type of cheating on the survey differed significantly
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
(p<.05) by college major: business (91%), engineering (82%), social sci-
ences (73%), and natural sciences (71%).
The prevalence and increasing severity of cheating should be distress-
ing to educators because of their implications. First, most U.S. colleges
and universities have a mission that includes preparation for citizenship,
character development, moral leadership, and/or service to society; each
of these has a moral dimension (King and Mayhew, 2002; Whitley and
Keith-Spiegel, 2002). Prevalent undergraduate cheating undermines
efforts to accomplish such missions. Also, in professions such as engi-
neering, there is a growing, nationwide emphasis on graduating students
who understand professional and ethical responsibility (Stark and Lat-
tucca, 1997). Prevalent academic dishonesty indicates that many stu-
dents will approach learning experiences in professional ethics with
attitudes and habits that may interfere with their learning. Thus, inter-
ventions that effectively encourage a student not to cheat during college
could help institutions fulfill their missions.
Second, acts of academic dishonesty undermine the validity of mea-
sures of student learning. This, in turn, interferes with faculty’s ability
to correctly diagnose gaps in student learning for the purpose of both
re-teaching current students and re-designing instruction for future stu-
dents. Whitley and Keith-Spiegel (2002) make related claims that cheat-
ing undermines equity in grading and the mission to transfer knowledge.
Third, there are several costs to the entire educational enterprise that
result from high levels of cheating. Student and faculty morale, the rep-
utation of the institution, and public confidence in higher education are
all damaged by rampant cheating, especially when it is ignored by
faculty and administrators (Whitley and Keith-Spiegel, 2002). Any inter-
ventions that effectively encourage a student not to cheat during college
could increase the validity of measures of student learning and
also reduce damage to morale, institutional reputations, and public
confidence in higher education.
Fourth, research has shown that students who cheat in college are
more likely to cheat in graduate and professional schooling (Baldwin,
Daugherty, Rowley, and Schwartz, 1996), to engage in unethical work-
place behavior (Harding, Carpenter, Finelli, and Passow, 2003, 2004;
Hilbert, 1985; Nonis and Swift, 2001; Ogilby, 1995; Sims, 1993; Todd-
Mancillas, 1987), to shoplift (Beck and Ajzen, 1991), to cheat on income
taxes (Fass, 1990), and to abuse substances (Blankenship and Whitley,
2000; Kerkvliet, 1994). For college graduates whose workplace had a
strong corporate code of ethics, employees whose undergraduate school
had an honor code were less likely than graduates of non-code schools
to report engaging in unethical workplace behavior (McCabe, Trevino,
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
and Butterfield, 1996). Note that much lower rates of cheating are
reported by students at honor code schools (McCabe and Trevino,
1993). All of these correlations, though not known to be causal, raise
the possibility that interventions that effectively encourage a student not
to cheat during college could reduce the frequency of his or her deci-
sions to engage in other unethical behavior during college and beyond.
These four implications of the prevalence and severity of cheating
have inspired a substantial body of research on cheating among college
students. Eleven reviews (including three meta-analyses) of college
cheating behavior have been published since 1977 (Brown and Emmett,
2001; Bushway and Nash, 1977; Cizek, 1999; Cole and McCabe, 1996;
Crown and Spiller, 1998; Dowd, 1992; Kibler, 1993; McCabe, Trevino,
and Butterfield, 2001; Whitley, 1998; Whitley and Keith-Spiegel, 2002;
Whitley, Nelson, and Jones, 1999). There are three veins of published
studies, addressing three different overarching goals: (1) documenting
the prevalence of college student cheating to establish the importance of
the problem, (2) understanding the factors that influence students’ deci-
sions to cheat (or correlates of cheating), and (3) informing faculty and
institutional policy for preventing cheating and for handling cheating
incidents when they occur. As will be explained in the literature review,
most literature pertaining to policy separates the construct of cheating
into more specific behaviors on specific types of assessments, such as
plagiarism on term papers and copying answers from other students on
homework (Cizek, 1999; Whitley and Keith-Spiegel, 2002). However,
most studies aimed at documenting prevalence and understanding corre-
lates of cheating combine cheating behaviors on an assortment of
assessments into a single measure of cheating, presenting an unfortunate
obstacle to informing policy.
The purpose of our survey study was to understand the factors that
explain the frequency of cheating by undergraduate engineering students
on two types of assessments: exams and homework. To this end, we
identified two dependent variables for use in this study: frequency of
cheating on exams and frequency of cheating on homework. The blocks
of independent variables used in the two analyses were demographics,
pre-college cheating behavior, co-curricular participation, plus five
blocks organized around the theory of planned behavior (Ajzen, 1991;
Beck and Ajzen, 1991): moral obligation not to cheat; attitudes about
cheating; evaluation of the costs and benefits of cheating; perceived
social pressures to cheat or not to cheat; and perceived effectiveness of
academic dishonesty policies. The sample selection controlled for the
students’ major. A secondary purpose of our study was to test Ajzen’s
theory of planned behavior (TPB) for predicting cheating behavior.
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
Hierarchical multiple regression analyses were performed to determine
how blocks of variables organized around the TPB work together to
predict the two dependent variables.
LITERATURE REVIEW
In this section, we explain how we selected the TPB for organizing
our independent variable in our respective models, and we describe the
constructs in the theory and the construct we use to modify the basic
theory. Next, we explain why we selected dependent variables based on
the type of assessment by showing how the TPB, previous empirical
work on cheating, and policy discussions pertaining to cheating all indi-
cate that a decision to cheat is highly affected by the type of assessment.
Then, we describe how we selected independent variables guided by the
TPB and previous research on cheating. Finally, we share our rationale
for selecting a sample composed entirely of engineering undergraduates.
The Theory of Planned Behavior
Two recent reviews of cheating among college students (Crown and
Spiller, 1998; Whitley, 1998) each cite over 100 relevant studies pub-
lished from 1970 to 1997. Only a few of the studies have used a theoret-
ical framework to explain or predict cheating among college students.
Theoretical frameworks used include models of deviance (used by Gene-
reux and McLeod, 1995; Liska, 1978; Michaels and Miethe, 1989),
deterrence theory (used by Buckley, Wiese, and Harvey, 1998; Cochran,
Chamlin, Wood, and Sellers, 1999), cognitive consistency theory (used
by Tang and Zuo, 1997), moral development models (used by Lanza-
Kaduce and Klug, 1986; Whitley and Kost, 1999), rational choice the-
ory (used by Buckley et al., 1998; Cochran et al., 1999; Tibbetts, 1997),
anomie (used by Caruana, Ramaseshan, and Ewing, 2000), and the the-
ory of planned behavior (used by Beck and Ajzen, 1991; Genereux and
McLeod, 1995; Nonis and Swift, 2001; Whitley, 1998) or its earlier ver-
sion, the theory of reasoned action (used by Pratt and McLaughlin,
1989). Because a number of researchers have demonstrated its applica-
bility to academic cheating, we used the theory of planned behavior
(Ajzen, 1991; Beck and Ajzen, 1991) as the theoretical framework for
organizing our independent variables in our models.
1
Ajzen’s
2
theory of planned behavior (TPB) postulates that human
behavior is guided by rational decisions that are influenced by both the
intention to perform the behavior and also a perception of control over
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
the behavior (Ajzen, 1991). Intention is determined by three compo-
nents: (1) attitude toward a behavior (Attitude), (2) perceived social
pressures to engage in or not engage in the behavior (Subjective
Norms), and (3) the perceived ease of performing the behavior
(Perceived Behavioral Control). Note that beliefs are the antecedents of
attitude, subjective norms, and perceived behavioral control. ‘‘Beliefs
about the likely [positive and negative] consequences or other attributes
of the behavior (behavioral beliefs)’’ (Ajzen, 2002, p. 665) produce the
attitude toward the behavior. ‘‘Beliefs about the normative expectations
of other people (normative beliefs)’’ (p. 665) lead to subjective norms,
and ‘‘beliefs about the presence of factors that may further or hinder
performance of the behavior (control beliefs)’’ (p. 665) result in per-
ceived behavioral control. Further, perceived behavioral control is theo-
rized to have a direct influence on both actual behavior and intention.
The direct influence of perceived behavioral control on actual behavior
allows for the study of behaviors that are not under the complete voli-
tional control of the individual (Ajzen, 2002). Despite substantial sup-
port for the TPB as a means of predicting actual behavior (Armitage
and Conner, 2001), research continues to examine variables that might
enhance the predictive capabilities of the theory (Conner and Armitage,
1998). For example, Beck and Ajzen concede that ‘‘understanding the
determinants of dishonest behaviors can be more problematic than
understanding performance of socially acceptable behaviors’’ (1991,
p. 300). They propose that factors in addition to those encompassed by
the TPB, such as moral obligation, may be critical in understanding
cheating and other dishonest behaviors. We include moral obligation as
a modifying construct in the TPB for the purpose of organizing our
independent variables.
Rationale for the Selection of Dependent Variables
The TPB implies that the precursors of intention to act will vary by
situation, and consideration of each construct (attitude, subjective
norms, and perceived behavioral control) for different assessment situ-
ations, such as exams and homework assignments, reveals that type of
assessment should greatly affect each construct in the TPB resulting in
different behaviors. This notion that the type of assessment will greatly
affect behavior has been verified by multiple veins of literature as
described below and is the basis for the selection of our dependent
variables: frequency of exam cheating and frequency of homework
cheating.
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
Empirical Evidence that Prevalence of Cheating is Affected by the Type
of Assessment
Several studies have reported on prevalence of cheating separately by
type of assessment, finding differences in rates of engagement by assess-
ment type (e.g., Baird, 1980; Bowers, 1964; Brown, 1996; Diekhoff et al.,
1996; Hanson, 1990; Jensen, Arnett, Feldman, and Cauffman, 2002;
McCabe, 1997; Michaels and Miethe, 1989; Stearns, 2001; Storch and
Storch, 2002; data from McCabeÕs 1993 study reported in Whitley and
Keith-Spiegel, 2002). Also, research has shown that two components of
the TPB as applied to cheating differ by type of assessment: attitudes and
perceived behavioral control. In the realm of attitude toward cheating
behavior, two types of attitudes have been shown to differ by type of
assessment, specifically, general attitudes (Jordan, 2001; Lipson and
McGavern, 1993; Michaels and Miethe, 1989; Newstead et al., 1996;
Nuss, 1984; Thorpe, Pittenger, and Reed, 1999) and evaluation of costs,
benefits, and risks (Jensen et al., 2002; Lipson and McGavern, 1993;
Michaels and Miethe, 1989). In the realm of perceived behavioral control,
the ease or difficulty of performing the behavior has been shown to differ
by type of assessment (Lipson and McGavern, 1993). Although several
studies have addressed perceived social pressures (subjective norms in the
TPB) (e.g., Jordan, 2001; Newstead et al., 1996; Whitley and Kost, 1999),
none were found that report pressures by type of assessment.
Further evidence that prevalence of cheating is affected by the type of
assessment is provided by 30-year trends. In a 1993 study, McCabe,
et al. (2001) replicated a 1963 survey (Bower, 1964) of nine state univer-
sities. McCabe et al. found that while the number of students reporting
that they had copied on a test or exam doubled from 26% to 52%, the
number who admitted to plagiarism declined slightly from 30% to 26%.
Over the same period, the number of students who said that they had
done un-permitted collaboration on assignments more than quadrupled
from 11% to 49%. If the percentages had all risen or fallen in tandem,
even if their values differed in magnitude, the data might have indicated
that these different behaviors could and should be investigated as a
single phenomenon. However, some fell as others rose and the changes
occurred at different rates, which indicates that these behaviors are
controlled by different mechanisms and should be studied separately.
Treating ‘‘Cheating’’ as a Unitary Construct: A Flaw in Previous Research
Thus, the TPB and empirical evidence both indicate that a decision to
cheat is highly affected by the type of assessment. As we explain in this
section, research on academic dishonesty, or cheating, has often suffered
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
from the indiscriminant combination of widely varying behaviors that are
fundamentally different. In this statement, we make two claims: (1)
indiscriminant combination of behaviors is common in the literature, and
(2) indiscriminant combination of behaviors is a problem because it treats
fundamentally different behaviors as unitary. Woven into our support for
these claims, we supply evidence for the indiscriminant combination of
behaviors in two of the three main veins of cheating research: prevalence
and correlates of cheating. We also discuss the third vein—policy.
A direct illustration of combining multiple behaviors into a single
measure of cheating is Brown and Emmett’s (2001) review of empirical
studies of the prevalence of cheating among college students. They iden-
tified 22 studies, published over 33 years, which simply summed
responses for separate behaviors (2 to 36 behaviors, mean=11.5) to cre-
ate a single measure: ‘‘overall level of cheating’’ (p. 531). In the study
that included 36 different behaviors (Stern and Havlicek, 1986), three of
the specific behaviors are ‘‘copying from another student during a quiz
or examination’’ (p. 133), ‘‘working in a group on a homework assign-
ment that was assigned as individual work’’ (p. 134), and ‘‘‘making up’
sources for bibliographic citation’’ (p. 134). Respondents were asked
about attitudes toward each behavior (i.e., whether or not the behavior
is ‘‘academic misconduct’’ (p. 131)) and also about engagement in the
behavior (i.e., whether or not the respondent had ‘‘done this at least
once while in college’’ (p. 131)). Students classified the behaviors differ-
ently: for one of the 36 behaviors, 7% classified it as misconduct while
for another behavior 96% classified it as misconduct. Despite the wide
range in perceptions about the behaviors, all 36 were combined into a
single measure of ‘‘frequency of misconduct’’ (p. 138).
A second illustration of combining multiple behaviors into a single
measure of cheating is Whitley’s (1998) meta-analysis of empirical stud-
ies of correlates of cheating among college students. For the 107 studies
reviewed, Whitley created a single dependent measure of prevalence of
‘‘cheating’’ by combining 19 estimates of total cheating, 36 estimates of
examination cheating, 12 estimates of homework cheating, and 9
estimates of plagiarism.
Such decisions to combine behaviors on all types of assessments into
a single prevalence measure is typical of correlates research on cheating
(e.g., Baird, 1980; Deikfhoff et al., 1996; Jordan, 2001; McCabe and
Trevino, 1997; Tang and Zuo, 1997). Typically, researchers choose to
create a single prevalence measure as the dependent variable by combin-
ing all cheating behaviors, regardless of the type of the assessment.
There is a notable exception to this trend of combining all behaviors
into a single dependent variable. In a correlates study of cheating
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
among college students, Pratt and McLaughlin (1989) used factor analy-
sis on 26 behaviors relating to assessments such as examinations, home-
work, and writing term papers to create four separate dependent
variables relating to ‘‘obtaining help in an examination situation’’
(p. 203), ‘‘obtaining help outside of a test situation’’ (p. 203), ‘‘obtaining
unfair credit...in nontest situations’’ (p. 203--204), and directly substitut-
ing for an assessment, such as one person taking an examination for
another or submitting a paper that someone else wrote. They found that
‘‘different path models fit different types of behaviors’’ (p. 214) for the
323 undergraduates in this multi-institutional study, substantiating our
claim that prevalence of cheating is affected by type of assignment.
Summary: Why Research Should Distinguish Between Types
of Assessments
The TPB and empirical evidence both indicate that a decision to cheat is
highly affected by the type of assessment. Yet in two of the primary veins
of cheating research, prevalence and correlates of cheating, cheating behav-
iors have almost always been combined indiscriminately. Recently concerns
have been raised about this common practice by Crown and Spiller (1998),
Whitley (1998), and Thorpe, et al. (1999) ‘‘treating all cheating behaviors as
a whole may ignore important interactions among variables’’ (1999, p. 57).
In the third primary vein of cheating literature, policy pertaining to
cheating, classifications by type of assessment dominate discussions in
areas such as prevention and detection, policy, working definitions, and
strategies for teachers who must deal with academic dishonesty (Cizek,
1999; Lipson and McGavern, 1993; Whitley and Keith-Spiegel, 2002).
This dominance of categorization by type of assessment is echoed in
two schemes for categorizing cheating behaviors. Pavela’s (1978) scheme
distinguishes between two broad classes of assessments—‘‘cheating’’ and
‘‘plagiarism’’—in addition to two types of behavior—‘‘facilitation’’ and
‘‘fabrication’’. Whitley and Keith-Spiegel (2002) extend Pavela’s catego-
ries by specifying type of assessment, such as cheating on examinations
and cheating on assignments. Collectively, studies in all three veins of
cheating research demonstrate the need to use distinct dependent vari-
ables for each type of assessment in any research on cheating behavior.
Thus, to evaluate whether the prevalence of cheating is affected by
type of assessment, we separated our analyses by type of assessment. Of
the many available types of assessments, we chose two dependent
variables: frequency of cheating on exams (an index of nine exam cheat-
ing behaviors from our survey) and frequency of cheating on homework
(an index of four homework cheating behaviors) (Table 1). We selected
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
TABLE 1. Dependent Variables for the Regressions (with Student’s Categorizations of Behaviors)
Dependent Variables
Definition of cheating
‘‘Cheating’’ ‘‘Unethical but not cheating’’ ‘‘Neither’’
Frequency of cheating on exams—Index of each student’s self-reported
cheating on exams as a college student
Copying from another student during a test or quiz 96.0% 2.9% 1.2%
Permitting another student to look at your answer
during a quiz or exam
72.7% 23.7% 3.6%
Asking another student about questions on an exam
that you have not yet taken
27.0% 45.5% 27.4%
Copying from an unapproved reference sheet during
a closed book test/ quiz
91.6% 6.1% 2.3%
Taking an exam for another student 92.0% 6.0% 2.0%
Witnessing a case of cheating in a class and not reporting it to the
instructor
9.4% 59.4% 31.1%
Storing answers to a test in a calculator or Personal
Digital Assistant (PDA)
73.6% 16.1% 10.2%
Working in groups on web-based quizzes 41.2% 28.1% 30.0%
Working in groups on take-home exams 39.5% 28.4% 32.1%
Frequency of cheating on homework—Index of each student’s
self-reported cheating on homework as a college student
Copying an old term paper or lab-report from a previous year 60.8% 26.4% 12.8%
Copying another student’s homework when it is not permitted by
the instructor
72.5% 22.9% 4.6%
Copying a passage out of the textbook for homework assignments 19.5% 36.9% 43.7%
Submitting or copying homework assignments from previous terms 52.4% 30.5% 17.1%
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
exams and homework because they are the backbone of assessment in
many mathematics, science, and engineering courses. Surprisingly, home-
work cheating behaviors have almost never been distinctly included in
cheating surveys. We selected only behaviors that at least 50% of the
respondents defined as either ‘‘cheating’’ or ‘‘unethical but not cheating’’
because previous research has shown that cheating is difficult to define
(e.g., Kibler, Nuss, Paterson, and Pavela, 1988; Ratner, 1996) and that
students often do not define a behavior as cheating even when faculty do
(e.g., Stern and Havlicek, 1986; Whitley and Keith-Spiegel, 2002).
Rationale for the Selection of Independent Variables
Our 139-item survey was designed based on a review of literature on
academic dishonesty (Carpenter, Harding, Montgomery, and Steneck,
2002; Harding, Carpenter, Montgomery, and Steneck, 2001). For our
analysis, we selected 37 items (Table 2) for our independent variables.
Thirty-three individual items refer to cheating in general with no possible
reference to any particular type of assessment. Another four items used as
independent variables are a matched set: two refer unambiguously to
exam cheating and two have parallel wording but refer to homework.
Only the two exam items were used as independent variables in the exam
cheating model, and only the two homework items were used as indepen-
dent variables in the homework cheating model. The selected independent
variables were organized into eight blocks according to demographics,
pre-college cheating behavior, co-curricular participation, and five blocks
organized around the TPB. As noted below, variables were checked for ef-
fect size (small, medium, or large) and statistical significance in Whitley’s
(1998) meta-analysis, which was also based on the TPB. All correlations
listed below are from Whitley (1998) unless otherwise noted.
The demographics block is composed of age (negative correlation,
medium effect), gender (males more likely, small effect), socioeconomic
status (parental education—positive correlation, small effect in a single
study), year in college (no correlation), and grade point average (nega-
tive correlation, small effect). Our pre-college cheating behavior block is
a single variable, frequency of high school cheating (related to Whitley’s
‘‘have cheated in the past’’ (p. 257), positive correlation, large effect).
Variables in the co-curricular participation block are membership in a
fraternity or sorority (positive correlation, small effect) and involvement
in clubs, teams, professional societies, or community service organiza-
tions (positive correlation, small effect).
There are five blocks of independent variables organized around our
theoretical framework: the TPB. Our purpose was to organize our study
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
TABLE 2. Independent Variables Used in Regression to Predict Frequency of Cheating
Theoretical Construct Independent Variables Values
Demographics Age Continuous
self-report
Gender Male, female
Socioeconomic status
(Highest parental education level)
6 education levels
Year in college First year, second year,
third year, fourth year,
fifth year or more
Grade point average Continuous self-report
Pre-college cheating behavior How often did you cheat in high school? 4-point frequency scale
a
Co-curricular participation Do you belong to a fraternity
or sorority?
Yes, no
Do you participate in any clubs,
student teams, professional
societies, or community service
organizations?
Yes, no
Moral obligation not to cheat See Table 4 for description of factor,
moral obligation not to cheat
Factor made up of
9 items on 5-point
agreement scale
b
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
Attitudes about cheating
(Corresponds with attitude in the
theory of planned behavior (TPB))
See Table 4 for description of factor,
diffusion of responsibility
for cheating to external sources
Factor made up
of 2 items on 5-
point agreement scale
b
See Table 4 for description of factor,
personal responsibility for cheating
Factor made up of
3 items on 5-point
agreement scale
b
Evaluation of the costs and benefits
of cheating (TPB: attitude)
How would you rate your course
load in an average term?
Light, average, heavy
Do you think that you have heavy
family responsibilities?
Yes, no
How many hours/week do you work
at a non co-op job during a school term?
Continuous self-report
What is your primary method of
paying for your education?
Paying own way,
scholarship, parents paying
See Table 4 for description of factor,
situational cheating—the predicted
decision to cheat in situations when the
benefits outweigh the costs
Factor made up of
4 items on 5-point
agreement scale
b
Perceived social pressures to cheat
or not to cheat (TPB: social norms)
Prediction of consequence-embarrassment:
Most of the people whose opinion
I value would lose respect for me if
they found out I had benefited from
looking at my neighbor’s exam.*
3-point agreement scale
c
Deterrent effect-embarrassment:
This potential loss of respect would
prevent me from looking
at my neighbor’s exam.*
3-point agreement scale
c
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
TABLE 2. (Continued)
Theoretical Construct Independent Variables Values
Perceived effectiveness of academic dishonesty
policies (TPB: perceived behavioral control)
Do students and faculty understand
academic policies of institution?
3-point likelihood scale
d
Do faculty support academic dishonesty
policies of institution?
3-point likelihood scale
d
Do academic dishonesty policies at
institution deter cheating?
3-point likelihood scale
d
a
Frequency scale (1=never, 2=once, 3=a few times, 4=frequently).
b
Agreement scale (1=disagree strongly, 2=disagree somewhat, 3=neutral, 4=agree somewhat, 4=agree strongly).
c
Agreement scale (1=disagree, 2=not sure, 3=agree).
d
Likelihood scale (1=not at all, 2=somewhat, 3=a lot).
*Wording for this item varies depending on the dependent variable (either exam or homework) but the two wordings of the parallel items are
identical other than the type of assessment.
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
around a theoretical framework that previous research has shown is
useful in describing cheating behavior. We separated the block that we
named moral obligation not to cheat (negative correlation, medium
effect) from attitudes about cheating per Beck and Ajzen’s (1991)
adjustment to the TPB when applied to dishonest behaviors. This block
was a single factor composed of nine items.
We split the TPB construct of attitude into two blocks. One block,
general attitudes about cheating, is composed of two factors on attitudes
about responsibility for cheating. Although Whitley’s meta-analysis
includes a number of attitudes about cheating, some of which have large
effects, our survey items did not match the essence of his constructs,
and so cannot be compared directly. In another block, evaluation of the
costs and benefits of cheating, we include pressures that students typi-
cally experience: course load (positive correlation, medium effect); fam-
ily responsibilities (this apparently pertinent pressure was not included
in Whitley’s meta-analysis); employment responsibilities (Whitley
included an odd dichotomous variable from fewer than five effect sizes.
His finding, a small effect, was that students employed less than full
time were more likely to cheat.); and means for financing education
(students ‘‘supported by their parents’’ (p. 257) were more likely to
cheat than an undefined reference case, small effect). Also included in
this block is a factor of four items that propose a situation in which the
respondent would be under pressure and ask for a prediction of a deci-
sion to cheat or not. These items embody several effects in Whitley’s
meta-analysis (p. 257--258): ‘‘feel pressure to get high grades’’ (positive
correlation, medium effect), are ‘‘faced with important outcomes’’ (posi-
tive correlation, medium effect), ‘‘perceive a higher benefit-to-risk ratio’’
(positive correlation, medium effect), and ‘‘perceiving higher competition
for grades’’ (positive correlation, medium effect).
The block corresponding to the TPB’s subjective norms is perceived social
pressures to cheat or not to cheat. In this block, we include predicted feelings
of embarrassment after a decision to cheat and the deterrent effect of those
predicted feelings (oppositely related to Whitley’s ‘‘perceive that norms
allow cheating’’ (p. 257) which had a positive correlation, large effect).
Our survey’s only reference to the TPB construct of perceived behav-
ioral control was three items referring to perceived effectiveness of
academic dishonesty policies. In this block, we include three items about
student and faculty understanding of academic dishonesty policies, fac-
ulty support for those policies, and the deterrent effect of those policies.
Related items in Whitley’s meta-analysis are: subjection to honor codes
(negative correlation, medium effect) and ‘‘expect less punishment if
caught’’ (p. 258) (positive correlation, small effect).
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
Rationale for the Selection of the Sample
Our sample, comprised entirely of engineering undergraduates at
eleven institutions, is appropriate for our analysis for three reasons.
First, because students in different majors engage in cheating at different
rates, using a sample of students exclusively from one area of study
controls for students’ major. Second, engineering students self-report
higher frequencies of cheating than all other majors except for business
majors, yet, other than our own research (Carpenter et al., 2002;
Carpenter, Harding, Montgomery, Steneck, and Dey, 2002; Finelli,
Harding, Carpenter, and Passow, 2003; Harding, 2000, 2001; Harding,
et al., 2001; Harding, Carpenter, Montgomery, and Steneck, 2002;
Harding et al., 2003, 2004), we know of only nine studies of cheating
have specifically distinguished engineering students from students in
other majors (Bowers, 1964; Brown, 1994, 1996; Harp and Taietz, 1966;
McCabe, 1997; Newstead et al., 1996; Shaughnessy, 1988; Singhal, 1982;
Sisson and Todd-Mancillas, 1984). Of these, only Bowers (1964) and
McCabe (1997) conducted multi-institutional studies. Third, the impor-
tance of studying cheating among engineering undergraduates (100% of
our sample) is heightened by nationwide emphases among engineering
faculty on assessing student learning outcomes and explicitly teaching
professional ethics. Both of these emphases were codified in changes
to the nationwide accreditation requirements for engineering pro-
grams (Moore, 1996) and are still in effect (Engineering Accreditation
Commission, 2004).
Rationale for Using Blocked-Hierarchical Analysis
We had two goals for our analysis: (1) to allow comparison of the
patterns in the relationships between the independent variables and the
two dependent variables and (2) to test Ajzen’s TPB for predicting
cheating behavior. By entering variables into the models in hierarchical
blocks, we achieved both goals.
RESEARCH QUESTIONS
Altogether, the TPB includes the three elemental constructs of atti-
tude, subjective norms, and perceived behavioral control. For dishonest
behaviors such as cheating, moral obligation is an additional construct
in the theory. We used the TPB, which has proven effective in describ-
ing cheating behavior, as a theoretical framework for organizing our
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
independent variables in our models. Based on the items in our survey,
we represented TPB constructs with five blocks of variables: moral
obligation not to cheat; attitudes toward cheating; evaluation of the
costs and benefits of cheating; perceived social pressures to cheat or not
to cheat; and perceived effectiveness of academic dishonesty policies.
Our research addressed three questions:
1. Which of the constructs represented by these five blocks of variables
predict the frequency of cheating on exams among engineering
students?
2. Which of the constructs represented by these five blocks of variables
predict the frequency of cheating on homework among engineering
students?
3. Among engineering students, what are the differences in the predictive
power of these constructs for cheating on two different types of
assessments: exams and homework?
METHODS
Data Collection
Survey Instrument, Distribution, and Collection
Our study is based on data collected during the 2001 calendar year
using a direct-question survey. After a review of studies of college cheat-
ing (Carpenter et al., 2002), the survey was designed to identify percep-
tions and attitudes about cheating on the types of assessments typical in
engineering curricula, including exams, homework, and calculator usage.
Questions were strongly influenced by Cochran, et al. (1999), McCabe
and Trevino (1993), and McCabe, Trevino, and Butterfield (1999). The
survey was designed to incorporate published empirical findings and was
not based on theory. The items we selected for this study fitted the TPB.
The seven-page survey contains 139 questions, subdivided into seven
parts. Part 1 addresses students’ definitions of cheating and the
frequency with which they have engaged in twenty distinct cheating
behaviors. Parts 2 through 5 investigate attitudes, beliefs, and situa-
tional factors that might affect a student’s decision to cheat or not. Part
6 addresses deterrents to cheating and students’ perceptions of their
effectiveness, and Part 7 covers student demographics. We reduced the
possibility of underreporting due to desirability by posing questions in
a manner that assumed the behavior had occurred (Sudman and
Bradburn, 1982).
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
Sample: Institutions
The survey was completed by 695 students (643 undergraduates)
in engineering and pre-engineering courses at eleven institutions in
the United States and abroad, including large public universities, small
private universities, and community colleges (Table 3). Student partici-
pation in the study was voluntary and unmonitored, and the students
and institutions were informed that results would remain anonymous to
protect each participant. Institutions were selected based on the willing-
ness of a faculty member to distribute the surveys in a course. Thus,
our sample of convenience is not necessarily representative of the
engineering students on any single campus or of the types of institutions
involved.
Response Rate
Because of the informal method of selecting volunteer faculty to dis-
tribute surveys for this study, records that would enable the calculation
of response rates were not kept. However, in each class in which the sur-
vey was distributed, nearly all students completed the survey—yielding
an estimated response rate above 90%. Possibly because of the length of
the survey, several students did not respond to all questions and the re-
sponse rate declined near the end of the survey. For statistical analysis,
list-wise deletion was used to ensure that our study included only respon-
dents who answered all the items we selected for our analysis.
TABLE 3. Demographic Information for Institutions in the Data Set
Carnegie Classification (in 2000)
Number of
respondents
Percent of
respondents
Number of
institutions
Doctoral/Research Universities—Extensive 205 29.5 3
Doctoral/Research Universities—Intensive 42 6.0 1
Master’s Colleges and Universities I 233 33.5 3
Associate’s Colleges 42 6.0 2
Specialized Institutions: Schools of
Engineering and Technology
138 19.9 1
International 30 4.3 1
Institutional Affiliation Unknown
for Respondent
5 0.7 --
Totals 695 100.0 11
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
Sample: Respondents
The mean age of students in the analytical sample (n=643) was
21.6 years with a range of 17 to 48 years of age. A total of 81.2% of
respondents were male and 18.8% female, which is close to U.S.
national figures. (In the most recent data published by the National
Science Board of the National Science Foundation (2004), 20.5% of all
engineering bachelor’s degrees granted in 2000 were granted to females).
Information on students’ ethnicity and race was not collected for
reasons of protecting student identities within small sample subsets.
There was a wide range of socioeconomic status with parents’ house-
hold incomes ranging from less than $20,000 (7.3% of respondents) to
more than $200,000 (6.6%) annually. Only 31.3% of respondents indi-
cated their parents were the primary method of paying for college, with
41.3% paying their own way and 27.5% on scholarship. Most respon-
dents (78.8%) were raised in the United States, including 59.0% who
were from the Midwest.
There is a variety of class level in this sample: 22.9% of respondents
reported they were in their first year, 13.7% were in their second year,
24.1% were in their third year, 21.3% were in their fourth year, and
18.0% were in their fifth year (or more) of their undergraduate engineer-
ing career. In addition, the discipline of engineering with which the par-
ticipants were affiliated represents a wide variety—surveys were
administered in first year engineering or pre-engineering programs and to
students in electrical, civil, chemical, and mechanical engineering courses.
The mean grade point average of students in the sample was approxi-
mately a 3.2±0.5 on a 4.0 scale, and a majority of students (59.7%) indi-
cated they typically carried a heavy course load. Some of the respondents
(12.9%) had at least one dependent, with 3.6% having three or more
dependents. For this sample, 18.9% of the students were members of a
fraternity or sorority. Further, 64.1% participated in some form of stu-
dent team, professional society, or community service organization.
Finally, 29.0% of respondents reported that they never cheated in high
school, while 60.6% admitted to cheating in high school more than once.
Variables
We investigated two dependent variables for this study. Both vari-
ables are summative indices of items from a 20-part question: one
reflecting self-reported frequency of cheating on exams and the other
reflecting self-reported frequency of cheating on homework. The ques-
tion read: ‘‘if you have ever engaged in any of these actions as a college
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
student please indicate how many times you have engaged in [it]’’. This
question was followed by a list of 20 specific ‘‘cheating’’ behaviors,
including the thirteen behaviors selected for this study (the behavior
items are listed in Table 1). The time period for these questions was de-
fined by the question, which asked how many times the respondent en-
gaged in the action ‘‘as a college student’’. The frequency of cheating on
exams dependent variable was constructed by summing nine items. Sim-
ilarly, the frequency of cheating on homework variable was created by
summing four items. Dependent variables were standardized for ease of
interpretation across models and both are normally distributed.
Independent variables were organized into eight blocks around a the-
oretical framework (Ajzen’s TPB): student demographics (i.e., age, gen-
der, socioeconomic status, year in college, and grade point average);
pre-college cheating behavior; co-curricular participation (i.e., fraternity
and sorority membership and club participation); moral obligation not
to cheat (a single factor composed of nine items); attitudes about cheat-
ing (a two-item factor and a three-item factor); evaluation of the costs
and benefits of cheating (one four-item factor and four separate items);
perceived social pressures to cheat or not cheat (two items); and per-
ceived effectiveness of academic dishonesty policies (three items).
Table 2 presents an overview of independent variables including a
description of the scale for each item.
Analysis
Descriptive and exploratory analyses were performed on the 13 indi-
vidual items which, when summed and standardized, comprise the two
dependent variables for this study, frequency of cheating on exams and
frequency of cheating on homework. These analyses identify which
behaviors the respondents defined as cheating, as unethical but not
cheating, or as neither unethical nor cheating (Table 1). In order to
reduce the number of independent variables used in the regression mod-
el, exploratory factor analyses were conducted using principle axis fac-
toring and orthogonal rotation methods. Factor loadings that contained
a score of at least .69 or higher were used in the development of sub-
sequent summated scales. Internal validity for each of these scales was
high, with Cronbach’s alpha reliabilities ranging from .69 to .95.
Table 4 contains a complete description of the four factors used in the
final model for this study.
Hierarchical multiple regression analyses were performed to determine
how the eight blocks of independent variables work together to predict
the two dependent variables used for this study. Regression diagnostics
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
TABLE 4. Variable Names, Loadings and Reliability of Factors Created for this
Study
Scale and Individual Item Measures Loading Alpha
Moral obligation not to cheat .95
Indicate the extent to which you agree. 1=Strongly disagree,
2=Disagree, 3=Neutral, 4=Agree, 5=Strongly agree
It is wrong to cheat even if the course material was too hard .89
It is wrong to cheat even if other students’ scores are not affected .89
It is wrong to cheat even if I am in danger of failing the class .86
It is wrong to cheat even if the instructor assigned too much
material
.86
It is wrong to cheat even if the course material seemed useless .86
It is wrong for me to cheat even if the instructor does not grade
fairly
.86
It is wrong to cheat even if the instructor has done an inadequate
job of teaching the course
.85
It is wrong to cheat even if the instructor didn’t seem to care
if I learned the material
.84
It is wrong to cheat no matter what the circumstances .79
Situational cheating—Predicted decision to cheat in situations
when the benefits outweigh the costs
.87
Indicate the extent to which you agree. 1=Strongly disagree,
2=Disagree, 3=Neutral, 4=Agree, 5=Strongly agree
I would cheat if doing so helped me retain financial assistance .88
I would cheat to avoid letting my family down if I failed .87
I would cheat to avoid getting a poor or failing grade in class .85
I would cheat in a class if it seemed that everyone else was cheating .82
Diffusion of responsibility for cheating to external sources .80
Indicate the extent to which you agree. 1=Strongly disagree,
2=Disagree, 3=Neutral, 4=Agree, 5=Strongly agree
It is the institution’s responsibility to prevent cheating .87
It is the instructor’s responsibility to prevent cheating .86
Personal responsibility for cheating .69
Indicate the extent to which you agree. 1=Strongly disagree,
2=Disagree, 3=Neutral, 4=Agree, 5=Strongly agree
If I saw another student cheating, I would report
the student to the instructor
.80
If I saw another student cheating, I would confront the student .75
It is my responsibility to prevent cheating .65
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
suggested that the assumptions of normality, linearity, and homogeneity
were met. Two variables were recoded for use in the regression model:
year in college (dummy coded with first-year serving as the reference
group) and means for financing education (dummy coded with ‘‘paying
own way’’ serving as the reference group). In addition, due to the differ-
ent bases for the grade point averages at each institution, we trans-
formed the grade point average variable for each student using the
mean and standard deviation for that student’s institution and then
combined these transformations into a single variable for grade point
average.
A structured, blocking approach was used to add variables to the
respective models. This procedure yielded an eight-construct solution for
each model. Tables 5 and 6 contain a complete description of the stan-
dardized regression coefficients for each variable used in each model. In
addition, we present the parameter estimates for the final models for
both dependent variables in Table 7 for ease of comparison.
RESULTS
Model 1: Frequency of Cheating on Exams
The final model significantly predicts 36% of the variance in the
dependent variable frequency of cheating on exams,F(25, 585)=14.35,
p<.0001. Five of the eight blocks of variables (i.e., pre-college cheating
behavior, co-curricular participation, moral obligation not to cheat,
attitudes about cheating, and evaluation of the costs and benefits of
cheating) contributed significantly to this dependent variable.
Demographics
The first block of variables, demographics, explains 2% of the vari-
ance in the dependent variable, frequency of cheating on exams. The
only variable that reaches statistical significance is year in college: stu-
dents in their ‘‘fifth year (or more)’’ are more likely to report cheating
on exams than first-year students (b=.14, p<.01).
Pre-college Cheating Behavior
The second block, which contains a single-item indicator that mea-
sures frequency of cheating in high school, contributes a significant 10%
of the variance in the dependent variable beyond the variance explained
by demographics. Students who report cheating more often in high
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
school also are more likely to report cheating on exams in college
(b=.32, p<.001).
Effects for year in college remained significant after adding the second
block of variables. In addition to significant differences between stu-
dents in their fifth year (or more) and first-year students, fourth-year
students are also more likely to report cheating on exams than first-year
students (b=.10, p<.05) after adding pre-college cheating behavior to
the model.
Co-curricular Participation
Controlling for demographics and pre-college cheating behavior, the
block of variables that included measures of the students’ co-curricular
participation significantly explained an additional 2% of the variance in
the dependent variable. Students who participated in fraternities and
sororities were more likely to report cheating on exams than unaffiliated
students (b=.11, p<.01).
Effects for year in college (comparing students in their ‘‘fifth year (or
more)’’ to first-year students and fourth-year students to first-year
students) and pre-college cheating behavior remained statistically signifi-
cant.
Moral Obligation Not to Cheat
Students’ moral obligation not to cheat significantly explained an
additional 16% of the variance in the dependent variable beyond the
variance explained by demographics, pre-college cheating behavior, and
co-curricular participation. On average, students who believed that
cheating was wrong were significantly less likely to report cheating on
exams (b=).42, p<.001). After adding this block, year in college differ-
ences, pre-college cheating behavior, and membership in a fraternity or
sorority remained statistically significant.
Attitudes About Cheating
Controlling for demographics, pre-college cheating behavior, co-cur-
ricular participation, and moral obligation not to cheat, variables com-
prising the ‘‘attitudes about cheating’’ block significantly explained an
additional 2% of the variance in the dependent variable. Specifically,
students who felt personally responsible for preventing cheating were
significantly less likely to cheat on exams (b=).13, p<.001). After add-
ing this block, year in college differences, pre-college cheating behavior,
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
TABLE 5. Regression Block Entry: Frequency of Cheating on Exams (n=586)
Block 1 Block 2 Block 3 Block 4 Block 5 Block 6 Block 7 Block 8
1. Demographics
Age ).07 ).04 ).04 ).03 ).04 .02 .03 .03
Gender (Male) ).00 ).03 ).04 ).05 ).05 ).05 ).05 ).05
Socioeconomic status ).02 ).01 ).02 ).03 ).03 ).01 ).01 ).01
Year in college
Second year (First year) .04 .03 .04 .05 .04 .02 .02 .02
Third year (First year) .02 .06 .06 .08 .08 .08 .09 .09
Fourth year (First year) .08 .10* .11* .13** .13** .12** .13** .13**
Fifth year or more (First year) .14** .17*** .17*** .19*** .18*** .18*** .18*** .18***
Grade point average ).06 ).04 ).04 ).01 ).00 ).00 ).00 ).00
2. Pre-college cheating behavior
Frequency of high school cheating .32*** .32*** .23*** .22*** .15*** .15*** .15***
3. Co-curricular participation
Fraternity/sorority membership (No) .11** .09** .09** .09** .07* .07*
Club participation (No) .05 .05 .06 .05 .05 .05
4. Moral obligation not to cheat
It is wrong...[Factor] ).42*** ).37*** ).23*** ).23*** ).22***
5. Attitudes about cheating
Diffusion of responsibility [Factor] ).01 ).00 ).00 .01
Personal responsibility [Factor] ).13*** ).10** ).08* ).08*
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
6. Evaluation of the costs and benefits of cheating
Personal pressures
Course load .05 .05 .05
Heavy family responsibility (No) .02 .03 .03
Hours/week spent working .00 ).01 ).01
Means for financing education
Scholarship (Pay own way) .13*** .13*** .12**
Parents (Pay own way) .04 .04 .04
Situational cheating [Factor] .31*** .29*** .29***
7. Perceived social pressures to cheat or not to cheat
Prediction of consequence-embarrassment ).08 ).08
Deterrent effect-embarrassment .02 .02
8. Perceived effectiveness of academic dishonesty policies
Students and faculty understand policies ).00
Faculty support of policies ).03
Academic dishonesty policies deter cheating ).00
Model statistics
Adjusted R
2
.01 .11 .12 .28 .30 .36 .36 .36
Change in R
2
.02 .10*** .02** .16*** .02** .07*** .00 .00
Parentheses indicate reference group for comparison. *p<.05, **p<.01, ***p<.001.
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
TABLE 6. Regression Block Entry: Frequency of Cheating on Homework (n=590)
Block 1 Block 2 Block 3 Block 4 Block 5 Block 6 Block 7 Block 8
1. Demographics
Age .07 .08 .08 .08 .08 .08 .08 .07
Gender (Male) .08 .07 .07 .06 .06 .05 .05 .05
Socioeconomic status ).06 ).06 ).06 ).07 ).07 ).05 ).05 ).05
Year in college
Second year (First year) .09 .09 .09 .10* .10* .08 .09 .09*
Third year (First year) .05 .06 .06 .07 .07 .07 .08 .09
Fourth year (First year) ).01 ).01 .00 .01 .01 .01 .02 .03
Fifth year or more (First year) ).02 ).02 ).02 ).00 ).00 ).01 ).00 ).00
Grade point average ).09* ).08* ).08* ).06 ).05 ).05 ).05 ).05
2. Pre-college cheating behavior
Frequency of high school cheating .07 .07 .00 ).00 ).03 ).04 ).05
3. Co-curricular participation
Fraternity/sorority membership (No) .05 .04 .04 .04 .03 .02
Club participation (No) .01 .02 .02 .02 .02 .02
4. Moral obligation not to cheat
It is wrong...[Factor] ).31*** ).30*** ).24*** ).23*** ).22***
5. Attitudes about cheating
Diffusion of responsibility [Factor] ).02 ).02 ).02 ).02
Personal responsibility [Factor] ).04 .01 .00 ).01
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
6. Evaluation of the costs and benefits of cheating
Personal pressures
Course load ).01 ).02 ).02
Heavy family responsibility (No) ).01 .00 ).00
Hours/week spent working .07 .06 .07
Means for financing education
Scholarship (Pay own way) .00 .02 .02
Parents (Pay own way) ).02 ).00 ).01
Situational cheating [Factor] .14** .13** .13**
7. Perceived social pressures to cheat or not to cheat
Prediction of consequence-embarrassment ).06 .07
Deterrent effect-embarrassment ).04 ).05
8. Perceived effectiveness of academic dishonesty policies
Students and faculty understand policies ).06
Faculty support of policies ).08
Academic dishonesty policies deter cheating .11**
Model statistics
Adjusted R
2
.02 .03 .03 .12 .12 .12 .13 .14
Change in R
2
.04** .01 .00 .09*** .00 .02 .01 .02*
Parentheses indicate reference group for comparison. *p<.05, **p<.01, ***p<.001.
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
membership in a fraternity or sorority, and moral obligation not to
cheat remained statistically significant.
Evaluation of the Costs and Benefits of Cheating
Controlling for demographics, pre-college cheating behavior, co-cur-
ricular participation, moral obligation not to cheat, and attitudes about
cheating, items comprising the ‘‘evaluation of the costs and benefits of
cheating’’ block significantly explained an additional 7% of the variance
in the dependent variable. Specifically, students on scholarship were
more likely to report cheating on exams than students who paid for col-
lege on their own (b=.13, p<.001). Similarly, student who agreed that
‘‘I would cheat...[to alleviate a stressful situation]’’ such as to maintain
financial assistance, to avoid failing, to avoid letting their family down,
and to go along with the crowd were significantly more likely to cheat
on exams (b=.31, p<.001).
All of the aforementioned variables making up year in college, pre-
college cheating behavior, moral obligation not to cheat, and attitudes
about cheating remained statistically significant.
Perceived Social Pressures and Perceived Effectiveness
of Academic Dishonesty Policies
Variables making up the remaining blocks, ‘‘perceived social pressures
to cheat or not to cheat’’ and ‘‘perceived effectiveness of academic
dishonesty policies’’ explained 0% of additional variance in the depen-
dent variable beyond the variance explained by the first six blocks of
variables in the model. Consistent with our other findings, effects of the
aforementioned variables making up year in college, pre-college cheating
behavior, moral obligation not to cheat, attitudes about cheating, and
evaluation of the costs and benefits of cheating remained statistically
significant.
Model 2: Frequency of Cheating on Homework
The final model significantly predicts 14% of the variance in the
dependent variable, frequency of cheating on homework,F(25,
589)=4.80, p<.0001. Three of the eight blocks of variables (i.e., demo-
graphics, moral obligation not to cheat, and perceived effectiveness of
academic dishonesty policies) contributed significantly to explaining the
variance in this dependent variable.
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
Demographics
The first block of variables measuring demographics explains a signifi-
cant 4% of the variance in the dependent variable, frequency of cheat-
ing on homework. Students with higher grade point averages are less
likely to report cheating on homework (b=).09, p<.05).
Pre-college Cheating Behavior
The second block containing a single-item indicator that measures
frequency of cheating in high school contributes only 1% of the vari-
ance in the dependent variable beyond the variance explained by demo-
graphics. Effects for self-reported grade point average remained
significant after adding the second block of variables.
Co-curricular Participation
Controlling for demographics and pre-college cheating behavior, the
block of variables that included measures of co-curricular participation
did not explain any additional variance in the dependent variable.
Effects for grade point average stayed the same.
Moral Obligation Not to Cheat
Students’ moral obligation not to cheat significantly explained an
additional 9% of the variance in the dependent variable beyond the
variance explained by demographics, pre-college cheating behavior, and
co-curricular participation. On average, students who reported that
cheating was ‘‘wrong’’ were significantly less likely to report cheating on
homework (b=).31, p<.001). Effects for year in college (second-year
students compared to first-year students) became statistically significant
after adding this block (b=.10, p<.05), meaning that when compared
with first-year students, second-year students are significantly more like-
ly to report cheating on homework. However, grade point average was
driven out of statistical significance.
Attitudes About Cheating
Controlling for demographics, pre-college cheating behavior, co-cur-
ricular participation, and moral obligation not to cheat, variables com-
prising attitudes about cheating significantly explained an additional 0%
of the variance in the dependent variable. Effects for both year in col-
lege (second-year students compared to first-year students) and moral
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
obligation not to cheat remained statistically significant predictors of the
dependent variable, even after adding this new block of variables.
Evaluation of the Costs and Benefits of Cheating
Controlling for demographics, pre-college cheating behavior, co-cur-
ricular participation, moral obligation not to cheat, and attitudes about
cheating, items comprising the ‘‘evaluation of the costs and benefits of
cheating’’ block explained an additional 2% of the variance in the
dependent variable. Specifically, students who agreed that ‘‘I would
cheat...[to alleviate a stressful situation]’’ (i.e., in situations when the
respondent deemed the benefits of cheating outweighed the costs) were
more likely to report cheating on homework (b=.14, p<.01).
After adding this additional set of variables into the model, the effects
of students’ moral obligation not to cheat remained statistically signifi-
cant. However, the difference in cheating on homework between second-
year students and first-year students fell out of significance.
Perceived Social Pressures to Cheat or Not to Cheat
Variables making up the block ‘‘perceived social pressures to cheat
or not to cheat’’ explained an additional 1% of the variance in the
dependent variable beyond the variance explained by the first six blocks
of variables in the model. Consistent with our other findings, effects of
students’ moral obligation not to cheat and evaluation of the costs and
benefits of cheating remained statistically significant.
Perceived Effectiveness of Academic Dishonesty Policies
Controlling for all other variables in the model, the remaining block,
‘‘perceived effectiveness of academic integrity policies,’’ significantly
explained 2% of additional variance in homework cheating beyond the
variance explained by the first seven blocks of variables in the model.
Students who believed that the academic policies at the institution
deterred cheating were more likely to report cheating on homework
(b=.11, p<.01).
After adding this block of variables to the model, one effect of year in
college became statistically significant: second-year students are more
likely to report cheating than first-year students. Consistent with our
other findings, students’ moral obligation not to cheat and the afore-
mentioned significant variable from the student’s evaluation of the costs
and benefits of cheating remained statistically significant.
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
DISCUSSION
Correlates of Cheating Vary by Type of Assessment
The differences in the regression models for exam cheating and home-
work cheating (Table 7) clearly demonstrate that correlates of cheating
vary by type of assessment. Evidence that correlates of cheating vary by
type of assessment is the statistically significant differences in the six
independent variables that predict either frequency of cheating behavior
for exams and for homework but do not predict both (i.e., year in
college, pre-college cheating behavior, fraternity/sorority membership,
personal responsibility for cheating, means for financing college, and
academic dishonesty policies deter cheating). Further evidence is the
difference in the percentage of the variance explained by the parallel
models (36% for exam cheating and 14% for homework cheating). This
dramatic difference indicates that the factors selected for this model pre-
dict exam cheating well but that other factors not included in the model
must also contribute to predictions of homework cheating; in other
words, the difference in how well the model fits each variable demon-
strates that frequency of exam cheating is a different construct than
frequency of homework cheating.
Cheating patterns vary by year in college. First-year students reported
the least frequent cheating on both exams and homework. Although 4th
year and 5th year undergraduates cheat significantly more than first
year students on exams, second year undergraduates cheat significantly
more than first year students on homework. Perhaps cheaters are
dishonest on a type of assessment with a lower risk of detection (such as
homework) in their early years at college and progress to cheating on
higher-benefit, but higher-risk assessments (such as exams) in their later
years at college as they develop skill at cheating without detection. This
is consistent with the TPB (Ajzen, 1991) because in typical engineering
courses, exam scores make up the majority of the course grade while
homework is worth a small percentage of the course grade. However,
the wording of the survey item complicates this explanation because if
respondents carefully interpreted our survey question (‘‘if you have ever
engaged in any of these actions as a college student please indicate how
many times you have engaged in [it]’’) as a cumulative total of all their
cheating during college, a student who cheats at a steady annual rate
would report an increased number of engagements with each passing
year. Alternately, if many respondents misinterpreted this question as
pertaining to a shorter period, such as an academic year or a semester,
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
TABLE 7. Comparison of Unstandardized B-Weights between Dependent Variable
in the Two Models (for Block 8), Exam and Homework
Exam Homework
1. Demographics
Age .01 .02
Gender (Male) ).11 .12
Socioeconomic status ).01 ).04
Year in college
Second year (First year) .05 .27*
Third year (First year) .20* .20
Fourth year (First year) .31** .08
Fifth year or more (First year) .46*** ).00
Grade point average ).00 ).05
2. Pre-college cheating behavior
Frequency of high school cheating .16*** ).05
3. Co-curricular participation
Fraternity/sorority membership (No) .18* .06
Club participation (No) .10 .04
4. Moral obligation not to cheat
It is wrong...[Factor] ).22*** ).21***
5. Attitudes about cheating
Diffusion of responsibility [Factor] .01 ).02
Personal responsibility [Factor] ).14* ).01
6. Evaluation of the costs and benefits of cheating
Personal pressures
Course load .09 ).03
Heavy family responsibility (No) .06 ).00
Hours/week spent working ).00 .04
Means for financing education
Scholarship (Pay own way) .27** .05
Parents (Pay own way) .09 ).01
Situational cheating [Factor] .29*** .13**
7. Perceived social pressures to cheat or not to cheat
Prediction of consequence-embarrassment ).10 ).09
Deterrent effect—embarrassment .02 ).06
8. Perceived effectiveness of academic dishonesty policies
Students and faculty understand policies ).01 ).11
Faculty support of policies ).04 ).12
Academic dishonesty policies deter cheating ).00 .16**
Model statistics
Adjusted R
2
.36 .14
Parentheses indicate reference group for comparison. *p<.05, **p<.01, ***p<.001.
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
as suggested by McCabe (personal communication, April 1, 2002), the
results would strongly support our explanation.
The frequency of high school cheating strongly predicted exam cheat-
ing but not homework cheating. We propose that frequent high school
cheating changes a college student’s evaluation of the costs and benefits
of cheating by developing skill at cheating without detection (which
would both demonstrate the benefit of cheating and reduce the actual
risk of detection). Because the benefits of cheating on exams are typi-
cally greater than the benefits of cheating on homework in engineering
courses, an experienced cheater would be more likely to engage directly
in the type of cheating with the highest benefit, cheating on exams. This
is consistent with the TPB (Ajzen, 1991).
Similarly, fraternity/sorority membership predicted exam cheating but
not homework cheating. We propose that fraternity/sorority member-
ship might allow a group of students to pool their cheating experience
in a manner that allows inexperienced cheaters to observe the benefits of
cheating and to reduce the actual risk of detection, much like personal
cheating experience would, which is consistent with the TPB (Ajzen,
1991).
Students who reported feeling personal responsibility to report and
prevent cheating were significantly less likely to report cheating on
exams. This seems natural because students who assume more personal
responsibility to prevent cheating might well begin their efforts with
themselves and be less likely to cheat. By this reasoning we would
expect to see a similar relationship for cheating on homework, however,
no such relationship was found. We speculate that the wording of ques-
tions about personal responsibility focused students’ thoughts on the
public nature of exam performance versus the private nature of home-
work activity. For example, two of the questions were worded in the
form ‘‘If I saw another student cheating, I would ...’’. It would be
unlikely to ‘‘see’’ a cheater in action outside of an exam situation. Thus,
these questions may have evoked students’ definitions of exam cheating.
Multiple researchers have shown that students’ definitions of what
behaviors constitute cheating vary widely (e.g., Stern and Havlicek,
1986), and our survey respondents classified ‘‘cheating’’ behaviors dur-
ing exams much more crisply than ‘‘cheating’’ behaviors on homework
(Table 1). This may explain why students’ personal responsibility for
cheating did not have a relationship with homework cheating.
Scholarship students were more likely to cheat on exams than were
students who reported paying their own way, but this distinction was
not observed for homework cheating. We propose that scholarship stu-
dents are often under financial pressure to maintain a minimum grade
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
point average and that the benefit of achieving a higher grade on an
exam is much greater than the benefit of achieving a higher grade on a
homework assignment in typical engineering classes. Thus, scholarship
students would not be likely to see a benefit to cheating on homework
when they evaluate the costs and benefits of cheating, which is consis-
tent with the TPB (Ajzen, 1991).
The deterrent effect of academic dishonesty policies differentially pre-
dicted cheating on exams and homework. Counterintuitively, students
who agreed that ‘‘academic dishonesty policies at your institution deter
cheating’’ were more likely to report cheating on homework. We specu-
late that students feel that enforced academic dishonesty policies would
deter their cheating; however, in the absence of enforced policies, they
do cheat on types of assessments for which policies are least defined and
enforced, such as homework. Responses to a question on the survey
that was not included in our models indicate that students feel that aca-
demic dishonesty policies are not enforced at their institutions (In this
sample, when answering the question ‘‘Do faculty support the academic
dishonesty policies of your institution?’’, 48.8% answered either ‘‘not at
all’’ or ‘‘somewhat’’). Implicit policies on exam cheating, and their occa-
sional enforcement, may explain why this effect is seen for homework
cheating but not exam cheating.
Unilateral Deterrents to Cheating: Moral Obligation
and Situational Cheating
Two factors showed a strong deterrent effect to cheating in both types
of assessment: moral obligation not to cheat and situational cheating.
The moral obligation not to cheat had the most explanatory power of
any block of variables in the regression models, significantly explaining
16% of the variance in cheating on exams and 9% of the variance in
cheating on homework. (Note that these percentages are much larger
than the 3% of the variance in Beck and Ajzen’s (1991) regression mod-
el for cheating.) The percentages of the variance explained by moral
obligation in our models strongly support Beck and Ajzen’s proposal
that moral obligation plays an important role in the TPB for dishonest
acts. Specifically, a student’s agreement that ‘‘It is wrong to cheat even
if [difficult circumstance]...’’ is strongly negatively correlated with both
the ‘‘frequency of cheating on exams’’ and ‘‘the frequency of cheating
on homework’’. Looking at this result conversely, students who dis-
agreed with these statements ‘‘recognize and accept cheating as an unde-
sirable behavior; however, its occurrence can be excused in certain
instances’’ (Haines, Diekhoff, LaBeff, and Clark, 1986, p. 353). This
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
attitude, called neutralization, has been found to be an important influ-
ence on college students’ cheating behavior (e.g., Haines et al., 1986;
Liska, 1978). Our results also support this finding.
Student agreement with statements that ‘‘I would cheat...[if it helped
me alleviate a stressful situation]’’ is positively correlated with the fre-
quency of cheating on both types of assessment. This is a logical result
because stressful situations that might be alleviated by (undetected)
cheating could be alleviated by cheating on any type of assessment.
Summary
Our major finding is that correlates of cheating vary by type of
assessment. This finding is consistent with several aspects of previous
work, notably: (1) the TPB (Ajzen, 1991) which implies that each con-
struct that contributes to actual behavior will vary by situation; (2)
differences in prevalence of cheating by type of assessment (e.g., Baird,
1980; Bowers, 1964; Brown, 1996; Diekhoff et al., 1996; Hanson, 1990;
Jensen et al., 2002; McCabe, 1997; Michaels and Miethe, 1989;
Stearns, 2001; Storch and Storch, 2002; data from McCabe’s 1993
study reported in Whitley and Keith-Spiegel, 2002); (3) differences
identified in the relationships in four different path models for four
different cheating situations (Pratt and McLaughlin, 1989); (4) con-
cerns about the common practice in cheating research of combining
cheating behaviors for different types of assessments (Crown and Spil-
ler, 1998; Thorpe et al., 1999; Whitley, 1998); (5) published difficulties
in creating general definitions for cheating and academic dishonesty
without specifying situations and behaviors (e.g., Ratner, 1996), and
(6) published classifications of cheating behaviors by type of assess-
ment for practical applications of cheating research, such as prevention
and detection, policy, working definitions, and strategies for teachers
who must deal with academic dishonesty (Cizek, 1999; Lipson and
McGavern, 1993; Whitley and Keith-Spiegel, 2002). Future research on
cheating should carefully distinguish between behaviors on different
types of assessment.
Our secondary findings are that a student’s conviction that cheating is
wrong no matter what the circumstances is a strong deterrent to cheat-
ing across types of assessment and that a student who agrees that he or
she would cheat in order to alleviate stressful situations is more likely to
cheat on exams and on homework. Future research on cheating should
explore students’ moral obligation not to cheat and their moral develop-
ment.
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
LIMITATIONS
The sample of convenience is not necessarily representative of the
engineering students on any single campus or of the types of institutions
involved. The sample of convenience also created a situation in which
records that would enable the calculation of response rates were not
kept. If our survey had been designed based on the TPB, a fuller
complement of variables would have addressed the TPB constructs of
subjective norms and perceived behavioral control.
CONCLUSIONS AND IMPLICATIONS
Since the 1960’s, upwards of 80% of U.S. undergraduates report that
they have cheated during college, although rates vary by college major.
Yet the severity of the cheating is increasing: ‘‘for example, students
admitting to copying from another student on an examination doubled
from 26% to 52% between 1963 and 1993’’ (McCabe, 1997, p. 435).
The prevalence and increasing severity of cheating should be distressing
to educators because of their implications for: (1) undermining institu-
tional missions that include preparation for citizenship and service to
society, each of which has a moral dimension (King and Mayhew, 2002;
Whitley and Keith-Spiegel, 2002); (2) invalidating measures of student
learning and grading equity (Whitley and Keith-Spiegel, 2002); (3)
damaging student and faculty morale, the reputation of the institution,
and public confidence in higher education (Whitley and Keith-Spiegel,
2002); and (4) increasing the likelihood of engagement in dishonest acts
both outside the classroom and after graduation (e.g., Baldwin et al.,
1996; Beck and Ajzen, 1991; Nonis and Swift, 2001). These four impli-
cations of the prevalence and severity of cheating have inspired a sub-
stantial body of research on cheating among college students, including
eleven review articles published since 1977 (Brown and Emmett, 2001;
Bushway and Nash, 1977; Cizek, 1999; Cole and McCabe, 1996; Crown
and Spiller, 1998; Dowd, 1992; Kibler, 1993; McCabe et al., 2001;
Whitley, 1998; Whitley and Keith-Spiegel, 2002; Whitley et al., 1999).
Our study fills several gaps in the existing literature on student cheating.
Separate models for cheating behavior are made for two types of assess-
ment, exams and homework. Both the careful distinction between the
types of assessment and also the distinct study of homework are rare
contributions to research on cheating. Also, our sample of engineering
undergraduates is an important contribution because engineering students
self-report higher frequencies of cheating than all other majors except for
business majors (e.g., McCabe, 1997), yet only two multi-institutional
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
studies of cheating other than our own have specifically identified engi-
neering students (Bowers, 1964; McCabe, 1997). The importance of study-
ing cheating among engineering undergraduates is heightened by
nationwide emphases among engineering faculty on assessing student
learning outcomes and explicitly teaching professional ethics. Both of
these emphases were codified in changes to the nationwide accreditation
requirements for engineering programs (Moore, 1996).
In this study, we found that students don’t see cheating as a single
construct and their decisions to cheat or not to cheat are influenced differ-
ently depending on the type of assessment. Therefore, faculty and admin-
istrators should carefully define for students what does and does not
constitute cheating for each type of assessment, such as exams, home-
work, term papers, projects, laboratory reports, and oral presentations.
Explicit definitions of ‘‘cheating’’ seem especially appropriate because of
the recent emphasis on collaborative learning, which communicates to
students that working together is often encouraged by faculty.
In addition, we found that a student’s conviction that cheating is
wrong no matter what the circumstances is a deterrent to cheating across
types of assessment and that a student who agrees that they would cheat
in order to alleviate stressful situations is more likely to cheat on exams
and on homework. Thus, interventions that develop student understand-
ing that cheating is wrong could deter all forms of cheating, if clear
definitions of cheating are communicated to students.
Our findings have two implications for future research on cheating.
First, future research on cheating should carefully word each behavior
as specifically for one type of assessment. Second, future research
should explore students’ moral obligation not to cheat and their moral
development.
ACKNOWLEDGMENTS
We would like to thank Dr. Susan M. Montgomery and Dr. Nicholas
H. Steneck for their contributions to the design of the survey; Dr. Eric
L. Dey and Dr. Heidi E. Grunwald for their suggestions regarding
statistical analysis in exploratory versions of this study; the engineering
faculty who distributed the surveys in their classes; and the students
who responded. We also gratefully acknowledge the financial support of
the University of Michigan College of Engineering and the Educational
Research and Methods Division of the American Society for Engineer-
ing Education (ASEE).
CORRELATES OF CHEATING BY TYPE OF ASSESSMENT
END NOTES
1. We recognize that some cheating may not be planned. For example situations in which
cheating might not be planned (such as a student observing, during an exam, that a neigh-
bor’s paper is available) see Hetherington and Feldman (1964).
2. Note that ‘‘Ajzen’’ recently changed his name to ‘‘Aizen’’. Armitage and Conner (2001)
describe this in a footnoted personal communication dated November 8, 1999.
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Received February 22, 2005.
PASSOW, MAYHEW, FINELLI, HARDING, AND CARPENTER
