STAFF AND STUDENT PERCEPTIONS OF ACADEMIC MISCONDUCT: a survey of academic staff and students in Scotland

Abstract

In a study of third-year accounting students from five Scottish universities and academic accounting staff in Scotland, perceptions of the seriousness and frequency of occurrence of 22 different kinds of academic misconduct were measured. Reasons for cheating and for not cheating were also examined. Data with respect to respondent, age, gender, institution, teaching specialism and teaching experience are discussed, along with the implications of these results for all accounting educators.

Full text

ACADEMIC MISCONDUCT DUFF
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STAFF AND STUDENT PERCEPTIONS OF ACADEMIC MISCONDUCT: a survey of
academic staff and students in Scotland
ANGUS DUFF, Accounting Forum, 21 (3-4), 1998, 283-305.
ABSTRACT
In a study of third-year accounting students from five Scottish universities and academic
accounting staff in Scotland, perceptions of the seriousness and frequency of occurrence of
22 different kinds of academic misconduct were measured. Reasons for cheating and for
not cheating were also examined. Data with respect to respondent, age, gender,
institution, teaching specialism and teaching experience are discussed, along with the
implications of these results for all accounting educators.

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INTRODUCTION
In recent years, considerable interest has been shown by accounting educators in the
ethical development of accounting students. Much of the discussion has been driven by
the now seemingly common disclosures of management fraud and malpractice in the
corporate world. Reported frauds in the City of London at the Bank of Credit and
Commerce International, Barings, Johnson Mathey, Lloyd’s and others are said to have
threatened London’s reputation as a financial centre (Bose and Gunn, 1989; Puxty et al.,
1994 and Sikka et al., 1992). Traditionally, it has been considered that businesses must
respond to the changing values and needs of consumers to maintain economic value and
maximise profitability. More recently, it has become evident that businesses must also
function within social, ethical and legally accepted principles in our society to create both
loyalty and a higher quality of life (Sheppard et al., 1992), although no strong empirical
evidence to date indicates acting in a socially responsible manner is related to the financial
performance of the business (McGuire et al., 1992).
Ethics research in accounting education has considered: how ethical reasoning can be
integrated into the accounting curriculum (Hiltebeitel and Jones, 1991; Karnes and Sterner,
1988; Loeb, 1988); the relationship between ethics education and the accounting
profession (Fleming, 1996 and Puxty et al., 1994) and alternative perspectives in accounting
ethics education (Mintz, 1996 and Reiter, 1996). Some researchers have provided examples
of accounting students ethical reasoning (Jeffrey, 1993; Ponemon and Glazer, 1990; St.
Pierre et al., 1990). However, only one reported study describes the ethical reasoning of
accounting students (in the US) as it relates to academic dishonesty (Ameen et al., 1996).
Understanding how accounting students feel about academic misconduct is significant, as
Sierles et al. (1990) found there was a progression from cheating in college to cheating in
high school to cheating in clerkships in patient care. As Ameen et al. (1996) observe, this
suggests accounting students who cheat at University may exhibit a propensity to engage in
unethical practices during a professional career.
Assessment has become a major topic of interest in education in the last few years, with
the development of specialist journals devoted to the subject (e.g. Assessment in Education:
Principles Policy and Practice), clearing houses dedicated to assessment and Internet
discussion groups carrying considerable correspondence and debate on the subject
(Sangster, 1996) 1. In turn, assessment is assuming greater importance amongst accounting
education researchers, with a recent edition (June 1996) of Accounting Education dedicated
to assessment and accounting education. The accounting professional bodies have shown a
similar interest in the roles different forms of assessment might play (for example, ICAEW,
1993 and Hoskin and Steele, 1991). Given an interest in the role assessment plays in
educating our students, it is perhaps surprising that behaviour that subverts this process
has attracted little interest from accounting education researchers, the accounting
professional bodies or other UK academics. Accounting education is generally believed to
promote a surface, rather than a deep approach to learning (Duff, 1996; French et al.,
1992), that is, the student memorises facts, techniques and procedures rather than
attempting to understand, interpret and critically analyse information 2. Tinker (1985, p. xx)
argues:

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Today’s student and tomorrow’s practitioners are saturated with a litany of rules
and procedures that are supported by little other than expedient reasoning, ad-
hoc explanations and piecemeal rationalizations. Professional accounting
education is certainly not a talkshop for exploring the meaning of social
existence: rather it resembles a rote learning process in which students are
inculcated with the profession’s party line by pedantic and legalistic methods.
This in turn, it has been argued, leads to the ossification or decline in moral development in
accountants, since accounting graduates will have not acquired the deep learning skills
needed for complex ethical reasoning (Fleming, 1996; Ponemon, 1990).
In the next part of this paper, the academic misconduct literature is reviewed. Second, the
research issues investigated are discussed. Third, the research method is described,
followed by the results of the study. In this section, information on accounting staff and
students’ perceptions of academic misconduct is provided. The relationship between this
information and the perceived frequency academic dishonesty occurs is examined. Finally,
the results are discussed in the context of accounting ethics education and the implications
of developing ethics education for accounting curricula considered. The results also provide
accounting educators with an additional insight into the ethical reasoning of UK accounting
students.
LITERATURE REVIEW
A considerable literature has developed in North America over the past 70 years examining
academic misconduct in high schools, colleges and universities. Only one published study
considers UK students (Franklyn-Stokes and Newstead, 1995) and only one samples
accounting students (Ameen et al., 1996). Many North American universities now publish
information for their staff and students (and the wider academic community) on the
Internet on: what constitutes academic misconduct, guidelines for professors on how to
cope with such behaviour, correspondence in campus newspapers concerning disciplinary
proceedings and information in student handbooks 3. Earlier work by Klein (1987), cited in
Fass (1990), found little reference to academic dishonesty in a content analysis of North
American student handbooks and college catalogues, so the present position should be
regarded as progress.
Table 1 reports the samples used, method and findings of ten recent studies of academic
misconduct. Typically, these investigate the frequency of cheating (e.g. Kerkvliet, 1994;
Drake, 1941), staff/student perceptions of cheating (e.g. Barnett and Dalton, 1981) and the
reasons for cheating (e.g. Haines et al., 1986; Hartshorne and May, 1928). Across academic
disciplines, 30% to 50% of university faculty members view cheating as a serious problem
and 50% to 70% have observed cheating in their classrooms (Stevens and Stevens, 1987;
Stern and Havlicek, 1986). Both Sierles et al. (1980) and Stern and Havlicek (1986) report
frequency rates of student academic misconduct in excess of 80%. Comparisons across
surveys is problematic as different surveys identify different types of cheating behaviour.
Notably, the North American literature pays particular attention to misconduct in
examinations, with high rates detected for in-class objective tests which are commonly

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used in the North American system (Barnett and Dalton, 1981; Houston, 1986). In UK
accounting courses, such tests are usually used for formative, rather than summative
assessment and therefore any perceived problem is likely to be lessened. Bence and Lucas
(1996) find that objective testing is used in 65% of UK accounting courses, but usually
account for no more than 20% of the overall mark. An important assessment method used
in UK business courses is the coursework project. These are typically completed by
students out of class hours and may include groupwork, essay writing and independent
study modules. Franklyn-Stokes and Newstead’s (1995) study found the reported
frequency of academic misconduct (cheating) in coursework assessments was significantly
higher than in examinations.
Franklyn-Stokes and Newstead (1995) considered second year psychology students and
staff at two UK universities and, in a second study, students from two (non-psychology)
science departments from the same university. The first study reported seriousness and
perceived frequencies behaviour occurred, the second directly asked students whether they
had actually cheated and their motives for cheating or not cheating. Some behaviours such
as copying each other’s work, plagiarism and altering and inventing data were admitted to
by more than 60% of the students.
Ameen et al. (1996) using a sample of 386 upper-level accounting students at four
universities in the US, considered students’ perceptions of 23 questionable academic
practices. Students were required to rate the severity of each of these practices and were
asked whether they had actually cheated in college. 56% of students admitted to having
cheated on an exam, project or written assignment.
A number of studies have considered staff estimates with student reports, but all have
limitations, indicating the findings should be treated with caution. Barnett and Dalton
(1981) identified a small number of behaviour types, used a dichotomous scale (honest or
dishonest) and reported low response rates (37% of staff and 53% of students). Stern and
Havlicek (1986) used different response categories and different items for staff and
students, making meaningful comparisons difficult.
Other results reported by Drake et al. (1992), and which are fairly typical of the North
American literature include:
1. gender differences, males are more likely to admit to cheating than females
2. differences in ability, more able students are less likely to cheat
3. differences in year of study, cheating decreases as students advance through higher
education
4. environmental differences, with academic misconduct thriving where there is a
pervading emphasis on grades, use of large poorly supervised multiple-choice
examinations in overcrowded conditions and an opinion amongst students that
everyone cheats
5. stress and pressure to succeed are the main reasons for academic misconduct.

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Author
Sample
Academic misconduct considered
Findings
Sierles et al. (1980)
Indulgence in 8 types of cheating behaviour in
college and at medical school
88% cheated in college
58% cheated in medical school
Barnett and Dalton (1981)
678 staff; 802 US
university students
4 attitudes towards cheating, related to
background and demographic variables
six factors why students cheat: stress; environment; personality;
knowledge of cheating; moral reasoning and commitment.
Haines et al. (1986)
380 US
undergraduate
students
49 behaviours, measuring cheating behaviour in
examinations, quizzes and homework assignments
identification of three primary factors underlying cheating:
student immaturity; lack of commitment to study; neutralisation
(denial of responsibility)
Stern & Havlicek (1986)
104 staff and 314
US undergraduate
health students
incidence of 36 behaviours occurring
staff and students differed significantly in their definitions of
cheating behaviours; 82% undergraduates admitted to some form
of cheating; few gender or year in class differences
Evans & Craig (1990)
1763 US
schoolchildren; 107
US schoolteachers
120 items grouped into three scales: is cheating a
problem?; what is cheating? and why does
cheating occur?
students perceive cheating to be a more serious problem than
teachers; teachers more knowledgeable about types of cheating
Davis et al. (1992)
6000 US high
school and college
students
frequency of cheating; reasons for cheating;
gender differences; student ability
cheating more common in high school than college; males admit
to more cheating than females; stress and pressure for good
grades are the main reasons for cheating; cheating is seldom
detected and action rarely taken when it is; less able students are
more likely to cheat
Anderson et al. (1994)
2000 US graduate
students
academic misconduct segmented into research
misconduct, employment misconduct and
personal misconduct
departmental climate and competition between students
strongest predictors of academic misconduct
Kerkvliet (1994)
443 US
undergraduate
economics students
whether ever cheated at college. Two survey
instruments used (direct question and randomised
response)
42% cheated (randomised response)
25% cheated (direct question)
Franklyn-Stokes &
Newstead (1995)
1 20 staff; 112 UK
psychology
students
2 128 UK science
students
1. Perceptions of seriousness and frequency of 22
types of academic dishonesty
2. Perceptions of seriousness and frequency of 22
types of academic dishonesty; reasons for/not
for cheating
Ameen et al. (1996)
386 US accounting
students
Perceptions of seriousness of 22 types of
academic dishonesty; cynicism; ways of reducing
cheating
inverse relationship between perceived seriousness of academic
misconduct and actually cheating; positive relationship between
cynicism and cheating; poor invigilation of examinations most
important means of reducing cheating
Table 1: Surveys of academic misconduct

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RESEARCH ISSUES
Most of the prior research conducted on academic misconduct in universities has
used samples of social science or liberal arts students in the US. As Ameen et al.
(1996) note, prior research indicates accounting students possess different attitudes
and perceptions that distinguish them from students majoring in social sciences or
liberal arts. Therefore, research on academic dishonesty should consider accounting
students.
The first area investigated was the relationship between the perceived severity and
frequency of academic misconduct. Prior research also finds that the more severe
an individual judges an act of cheating to be, the less likely the individual was to
commit the act (Tom and Borin, 1988). Similarly, Franklyn-Stokes and Newstead
(1995) found perceptions of the severity of academic misconduct inversely related
to the frequency this behaviour occurred.
A second area investigated was the contention that differences exist between staff
and students’ perceptions of the severity of academic misconduct. As assessment is
generally considered to drive student learning (Eraut, 1995; Jones, 1996) and
teaching staff determine and implement the assessment strategy, staff perceptions
are clearly important. Prior research indicates that staff rate the severity of
academic misconduct higher than students (Barnett and Dalton, 1980; Franklyn
Stokes and Newstead, 1995 and Stern and Havlicek, 1986). Staff and students were
required to rate their perceptions of the frequency this behaviour occurred, rather
than whether, in the case of students, they had indulged in this behaviour, or for
staff, how frequently they had observed such behaviour. This was necessary as an
objective of the study was to compare staff and student perceptions of the
seriousness and frequency academic misconduct occurs and this approach enabled a
valid comparison to be made.
A third issue examined was whether a relationship existed between perceptions of
academic misconduct and demographic variables such as, in the case of students,
age, gender and institution and in the case of staff, teaching specialism and number
of years teaching experience.
Finally, the most common reasons for cheating and for not cheating were
investigated. The rational for academic misconduct and behaving ethically has been
a motive for a number of recent studies (Anderson et al., 1994; Davis et al., 1992;
Evans and Craig, 1990 and Franklyn-Stokes and Newstead, 1995). This information
may help accounting educators determine how to stimulate an environment most
likely to encourage ethical behaviour.
METHOD
Questionnaire

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The study used the questionnaire developed by Franklyn-Stokes and Newstead
(1995) to measure UK psychology students attitudes to academic cheating. The 22
types of behaviour are shown in Appendix 1.
Perceptions of seriousness (i.e. from an ethical o moral viewpoint) were measured
on a six-point Likert scale from 1 (not at all serious) to 6 (very serious). Frequency
was measured on a percentage scale from 0% (nobody does it) to 100% (everybody
does it at least once).
The final page of the questionnaire required students to rate the most important
reason for cheating from a list of nine alternatives and the most important reason
for not cheating, again, from nine possible alternatives. The reasons for cheating or
not cheating were taken from the Franklyn-Stokes and Newstead (1995) study of UK
psychology students, enabling a comparison to be made between accounting and
psychology students 4. Ideally, respondents should rate the reasons for cheating
and not cheating for each of the 22 behaviours. However, it was considered this
would create an unnecessarily long questionnaire, which would take respondents
perhaps up to half an hour to complete. Wolf (1988) suggests that questionnaires
that are administered to students should be short and require little time, preferably
15 to 20 minutes, in order to avoid respondent fatigue and ensure respondent co-
operation. A long questionnaire would also be unlikely to achieve a satisfactory
response from the academic staff mailed with the instrument, therefore staff and
students were simply required to identify the most important reason for cheating
and for not cheating.
Respondents
Respondents were accounting and finance teaching staff (n = 78) at 11 Scottish
universities and third-year students 5 (n = 243) from five accounting and finance
departments at those universities. The response rate from staff completing a
useable questionnaire was 48.15%. This response rate is better than that of Moser
and Kalton (1971) who indicated an expected response rate between 30% and 40%,
from questionnaires returned by mail. It also compares favourably with the rate
between 20% and 40% indicated by Nachmias and Nachmias (1976). There was only
one mailing and no follow-up, due to the non-identification of the respondents,
given the sensitive nature of the survey.
Procedure
Staff were sent the questionnaires by post and return anonymously in a pre-paid
envelope. They were asked to give their perceptions of the seriousness of each of
the 22 behaviours and their perception of the frequency each occurred in a typical
student cohort.

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Students were asked to complete the questionnaire in a lecture and were required
to rate their frequency responses on the basis of how they thought their immediate
peers behaved. The author was present at the administration of the instrument.
Analysis
Both univariate and multivariate methods are used to analyse the results. To reduce
the likelihood of Type 1 error occurring when making use of multiple univariate tests
the significance level is reduced to 0.01. The multivariate technique of discriminant
analysis is applied as an overall test of differentiation between the groups, to
further reduce the likelihood of making a Type 1 error (Pagano, 1981).
RESULTS
1. Seriousness ratings (all data)
Staff and student mean ratings for the seriousness of each cheating behaviour are
shown in Table 2. The seriousness ratings ranged from 5.74 (Item 8: impersonating
someone in an examination) to 2.70 (Item 10: writing after an examination has
ended). Independent-samples t-tests between staff and students revealed that
there was a statistically significant difference (p<0.01) on 15 of the behaviour types
(the exceptions were Items 2, 4, 8, 9, 11, 14 and 21). In every case, the ratings given
by staff were statistically significantly higher than those given by students.
Discriminant analysis confirmed the difference between the two groups (Wilks’
Lambda = 0.680; chi-square (22, n=290) =107.004, p<0.0001).
The reliability of the scores produced by the seriousness ratings scale is measured
using Cronbach’s alpha. An alpha coefficient of 0.92 (n=290) indicates high internal
consistency reliability.

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Behaviour
Seriousness
Rank
Frequency
Rank
1
Allow copying (coursework)
4.29
12
46.93
6
2
Cribs (examination)
5.45
2
15.74
18
3
Fabricating references
3.51
20
47.31
5
4
Lying (examination)
4.80
8
21.70
14
5
Copying coursework with knowledge
4.28
13
42.16
7
6
Lying (coursework)
4.35
11
31.50
8
7
Essay banks
4.93
7
17.47
16
8
Impersonation (examination)
5.74
1
3.40
22
9
Peer assessment
3.99
18
28.76
11
10
Writing after an examination has ended
2.70
22
65.73
1
11
Copying coursework without knowledge
5.35
3
13.29
20
12
Advance information (examination)
5.33
4
7.81
21
13
Inventing data
4.04
16
26.13
12
14
Not contributing (groupwork)
4.00
17
49.57
4
15
Library
4.59
10
29.41
10
16
Paraphrasing without references
3.04
21
56.08
2
17
Copying without references
3.94
19
52.07
3
18
Collusion (examination)
5.15
6
15.93
17
19
Copying (examination)
5.20
5
19.66
15
20
Altering data
4.21
15
29.70
9
21
Doing another’s coursework
4.69
9
15.73
19
22
Collusion (coursework)
4.27
14
23.56
13
Table 2: Staff and students’ mean ratings of 22 types of cheating behaviour for
seriousness and frequency
2. Frequency ratings (all data)
Staff and students’ mean ratings for the frequency they perceived each of the 22
behaviours occurred is shown in Table 2. The mean percentage estimates range
from a low of 3.40% (Item 8: impersonating someone in an examination) to 65.73%
(Item 10: writing after an examination has ended).
Independent-samples t-tests found the difference between staff and students was
statistically significant (p<0.01, two-tailed test) for 21 of the 22 types of behaviour.
The exception was Item 8 (p=0.026). In each instance, staff produced lower
frequency estimates than the students. Discriminant analysis confirmed the
difference between the two groups (Wilks’ Lambda = 0.699; chi-square (22, n=253)
=85.818, p<0.0001).
Pearson product-moment correlation coefficients between the seriousness and
frequency ratings yielded statistically significant (p>0.01, two-tailed test) negative
correlations on 21 of the 22 cheating behaviour types (the exception was Item 8).

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The reliability of the scores produced by the frequency ratings scale is measured
using Cronbach’s alpha. An alpha coefficient of 0.90 (n=253) indicates high internal
consistency reliability.
3. Seriousness ratings (student data)
An analysis of the mean student ratings of seriousness and frequency of behaviour
reveals similar results to those obtained with all the data combined, which is to be
expected as students constituted 65.4% of the respondents.
Using the 22 behaviours as independent variables and age category as the
dependent variable, discriminant analysis revealed statistically significant
differences between the groups (Wilks’ Lambda = 0.670; chi-square (44, n=222) =
83.349, p<0.001). This was largely attributable to the mean ratings of 4.59 given by
students in the 25+ category compared to 4.28 for the 21-24 category and 4.23 for
the 18-20 category. One-way analysis of variance found statistically significant
differences (p<0.01) for Items 3, 7, 9, 12, 14 and 20. Mean student ratings of
seriousness as a function of age are shown in Table 3.
Age
Seriousness
(n =)
Frequency
(n =)
18-20
4.23
100
33.49
95
21-24
4.28
81
32.26
78
25 +
4.59
41
29.51
37
Table 3: Mean student ratings of seriousness and frequency by of age group
Using institution as the dependent variable, discriminant analysis found statistically
significant differences between the five institutions (Wilks’ Lambda = 0.470; chi-
square (88, n=223) = 157.632, p<0.0001). One-way analysis of variance found
statistically significant differences (p<0.01) for Items 1, 3, 4, 5 and 21. Mean ratings
ranged from 4.07 to 4.52 for the five institutions (see Table 4).
Institution
Seriousness
(n =)
Frequency
(n =)
1
4.52
45
34.39
45
2
4.49
50
38.48
49
3
4.28
44
32.61
44
4
4.07
38
40.06
33
5
4.19
46
30.69
39
Table 4: Mean student ratings of seriousness and frequency by institution
When gender differences were examined, discriminant analysis found no statistically
significant differences (Wilks’ Lambda = 0.854; chi-square (2, n=209) = 31.041, p =
0.073). Independent-samples t-tests found statistically significant differences
(p<0.01) for Items 6, 7, 13, 19. In each case female respondents rated each item as
more seriousness than males.

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4. Frequency ratings (student data)
The student frequency ratings were subjected to the same discriminant analysis and
independent-samples t-tests as the seriousness ratings. Examining differences
between the three age categories, statistically significant differences were found
(Wilks’ Lambda = 0.648; chi-square (44, n=210) = 85.163, p<0.001). One-way analysis
of variance on the 22 behaviours found statistically significant differences (p<0.01)
on Items 2, 10 and 13. This is mainly due to the lower ratings given by students in
the 25+ age category; they gave a mean rating of 29.51 compared to 33.49 for the
18-20 category and 32.26 for the 21-24 category (see Table 3).
Statistically significant differences were found between the five institutions (Wilks’
Lambda = 0.366; chi-square (88, n=210) = 196.428, p<0.0001). One-way analysis of
variance found statistically significant differences (p<0.01) on Items 1, 3, 4, 6, 13, 19
and 22. Mean ratings range from 30.69 to 40.06 (see Table 4).
Discriminant analysis found no statistically significant gender difference on ratings
for the 22 behaviours (Wilks’ Lambda = 0.849; chi-square (2, n=208) = 31.831, p =
0.0803). Independent-samples t-tests found only one statistically significant
difference (p<0.01), with male respondents rating Item 13 higher than females.
5. Reasons for cheating (all data)
The most commonly given reason for cheating by both staff (45.45% of respondents)
and students (34.53%) was ‘fear of failure’. However, students rated as the second
most common reason ‘to help a friend’ (21.97%) as opposed to only 6.06% of staff.
Both students and staff rated ‘everyone does it’ and ‘peer pressure’ as the least
most likely reasons for academic misconduct (see Table 5).
Reason
Students
Staff
n
Rank
n
Rank
To help a friend
49
2
4
5
Time pressure
27
4
5
3 =
Extenuating circumstances
13
6
7 =
Peer pressure
1
8
7 =
To increase the mark
38
3
21
2
Monetary reward
2
7
1
6
Fear of failure
77
1
30
1
Everyone does it
0
9
0
7 =
Laziness
16
5
5
3 =
Table 5: Frequency of rating of most important reason for cheating by respondent
6. Reasons for not cheating (all data)

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The most commonly given reason for not cheating by both staff (28.13%) and
students (27.03%) was ‘it is immoral/dishonest’. However, 22.25% of students rated
‘it would devalue my achievement’ as the next most important reason for cheating
compared to only 6.25% of staff. 26.56% of staff rated ‘fear of
detection/punishment’ as being the most important reason for not cheating,
compared to only 18.02% of students (see Table 6).
Reason
Students
Staff
n
Rank
n
Rank
It would devalue my achievement
50
2
4
5
It is immoral/dishonest
60
1
18
1
Personal pride
30
4
9
4
It is unnecessary/pointless
4
8 =
3
6
Shame at being caught
19
5
11
3
I never thought of it
8
6
Fear of detection/punishment
40
3
17
2
I would not know how to go about it
4
8 =
It would be unfair to other students
7
7
2
7
Table 6: Frequency of rating of most important reason for not cheating by
respondent
7. Teaching experience and subject specialism
An open-ended question asked staff to give their main teaching specialism(s). The
responses were subjected to a content-analysis which yielded four main categories:
financial accounting, management accounting, finance, and information systems.
No statistically significant differences were found between the four subject
specialism groups on either the seriousness or frequency ratings.
Pearson product-moment correlation coefficients were calculated between the
seriousness ratings and number of years teaching experience. None of correlation
coefficients were statistically significant (p<0.01, two-tailed test). When Pearson
product-moment correlation coefficients were calculated between the frequency
ratings and number of years teaching experience, three of the coefficients were
found to be negatively statistically significantly correlated (p<0.01, two-tailed test)
for Items 3, 13 and 21.

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DISCUSSION AND CONCLUSIONS
Seriousness ratings
• staff rated the severity of all 22 behaviours higher than students
• items with lowest severity ratings related to coursework
Frequency ratings
• Students rated the frequency of occurrence of all 22 behaviours
higher than staff
• Inverse relationship between seriousness and frequency ratings
Reasons for cheating
• most common reason (staff and students): ‘fear of failure’
Reasons for not cheating
• most common response (staff and students): ‘it is
immoral/dishonest’
Gender differences
• no statistically significant gender differences
Age differences
• students aged 25 years or over rate severity of behaviours than
those under 25
• students aged 25 years or over rate the frequency of behaviours
lower than those under 25
Institutional differences
• Statistically significant differences between institutions for
seriousness and frequency ratings
Teaching experience
• no relationship between teaching experience and severity ratings
• statistically significant positive correlation between frequency
ratings for 3 behaviours and teaching experience
Table 7: Summary of results
Similar to Franklyn-Stokes and Newstead’s (1995) study of UK psychology students,
an inverse relationship was found between the seriousness of a behaviour and the
perceived frequency it occurred. The most serious behaviours in rank order, were as
follows:
Item 8. a student taking an examination for someone else or having someone else
take an examination for them
Item 2. taking unauthorised material into an examination
Item 11. copying another’s coursework without their knowledge
Item 12. illicitly gaining advance information about the contents of an examination
paper
Item 19. copying form a neighbour during an examination without them realising
Item 18. premeditated collusion between two or more students to communicate
answers to each other during an examination
Significantly, all but one of these activities relates to dishonesty involving formal
examinations, rather than coursework projects indicating the importance both staff
and students attach to this form of assessment.
The behaviours which were given low seriousness ratings (with the exception of
Item 10) were related to activities involving coursework (Items 16, 3, 17, 9, 14, 13,
20, 22, 5, 1 and 6 in ascending rank order). Similar to the findings of the items given
the least seriousness ratings related to items involving plagiarism of some kind
(Items 16 and 3). Interestingly, the participants in the present study regarded Items
13 (inventing data) and 20 (altering data) as being more serious than the psychology
students sampled by Franklyn-Stokes and Newstead (1995). Their study found these
students perceived these to have been carried out by 47% of students, compared to

ACADEMIC MISCONDUCT DUFF
14
30% of students in the present study, perhaps reflecting differences in learning
activities and assessment methods between the two disciplines.
Considerable differences exist between staff and students. All of the items were
rated as being more serious by the staff and the differences in the frequency ratings
were even more significant. Again, students rated every type of behaviour as
occurring more frequently than the staff and this difference was statistically
significant (p<0.01) for 21 of the 22 items. The most significant differences for both
the seriousness and frequency ratings were items 3 (fabricating references); 5
(copying coursework without permission); 13 (inventing data); 17 (copying without
references) and 20 (altering data). Taken with the results that students also rated
the items involving plagiarism and fabricating data as being of low seriousness and
occurring frequently, suggests staff may not be communicating the unacceptability
of this behaviour to their students.
No statistically significant gender difference was evident in either the seriousness or
frequency ratings, similar to Franklyn-Stokes and Newstead’s (1995) and Ameen et
al.’s (1996) findings but contrary to the extensive North American literature, where
males admit to more cheating than females (Dalton and Barnett, 1981; Davis et al.,
1992).
Statistically significant age differences were noted with students aged 25 years or
over more likely to rate the behaviours as more serious than those under 25 years
and as perceiving they occurred less frequently, which again concurs with the
Franklyn-Stokes and Newstead (1995) study. Curiously, age is a variable neglected
by North American researchers, perhaps because of an absence of significant
numbers of mature students in higher education. Davis et al. (1992) observe that
cheating is likely to decrease as students progress through higher education, and the
finding of the present study suggest that the greater experience of older students is
likely to affect their perceptions of the seriousness and perceptions of the frequency
of this behaviour. Previous research, conducted in the US, indicates the ethical
development of senior and alumni is greater than freshman, i.e. first-year students
(Ponemon and Glazer, 1990; Ruegger and King, 1992). Other reasons might include
the role older students’ experiences of life outside school and higher education or
differing motives for studying in higher education (intrinsic, rather than extrinsic
motivation, created by the pressure to succeed).
The statistically significant differences between institutions found are perhaps the
least surprising result of the study, especially when the findings in the North
American literature are considered. Differing assessment procedures, student
demographics and departmental/institutional socialisation processes are all likely to
affect attitudes towards academic misconduct and a quantitative study of this
nature is unlikely to elicit the real reasons for differences at this level. The relevance
of this finding is that these differences do exist, and that students are significantly
influenced by their environment, implying that attitudes might be changed by a
suitable intervention programme.

ACADEMIC MISCONDUCT DUFF
15
The finding that ‘fear of failure’ was the most often cited reason for academic
misconduct by staff and students concurs with the findings in North America (Davis
et al., 1992). However, Franklyn-Stokes and Newstead’s (1995) study of UK
psychology students found time pressure and desire to increase the mark as the
commonest reasons. This may suggest accounting students feel under greater
stress, perhaps due to a greater (acknowledged by staff), if only perceived,
workload. The second most commonly cited reason for cheating by the students in
the present study (but not the staff) was ‘to help a friend’ either suggesting a mis-
understanding of the nature of co-operative learning or that this behaviour was a
coping mechanism in response to high levels of stress encountered.
The most commonly cited reason for not cheating by staff and students was ‘it is
immoral/dishonest’, compared to Franklyn-Stokes and Newstead’s (1995) findings
citing it was unnecessary as the main reason - the present study found ‘it was
unnecessary’ cited as the least most important reason. The North American
literature pays little attention to the reasons for not cheating, and generally
indicates that the rate of reported cheating is negatively related to the severity of
academic policy of the institution regarding cheating (Astin, 1968; Barnett and
Dalton, 1981). The findings of the present study indicate, as Franklyn-Stokes and
Newstead (1995) suggest, that informing students on what behaviour is deemed to
be acceptable is likely to be the best preventive measure, in the first instance, rather
than imposing draconian sanctions.
No reference can be found in the North American literature as to the relationship
between teaching experience and perceptions of academic misconduct. A positive
relationship is found between experience and perceptions of frequency of
occurrence in three of the 22 behaviours (p<0.01). This indicates academic staff
may become oblivious or apathetic to monitoring such misconduct or that the
incidence of such behaviour may have increased over time. As more experienced
staff are likely to hold more senior faculty positions and, consequently, be
responsible for designing quality procedures to prevent such behaviour, and more
likely to be responsible for the disciplining of offenders, this is an important finding.
CONCLUSION
In summary, academic misconduct may occur amongst UK accounting students more
frequently than staff seem to be aware of, and is viewed less seriously by students
rather than staff. Differences in the socialisation experiences of teaching staff and
students are likely causes of the some of the differences in their perception of
academic misconduct. Staff may be unaware of the prevalence of this behaviour
and should explore what quality controls exist within their assessment procedures
to prevent this behaviour occurring. Notably, students seem to be unaware of the
seriousness of behaviours linked to the submission of coursework. In particular,
behaviours involving some form of plagiarism indicate students are ill-informed
about the correct practice in these matters. Also, as staff and students recognise
stress constitutes a major factor in why students cheat, staff may wish to ask

ACADEMIC MISCONDUCT DUFF
16
themselves whether the workload they prescribe is appropriate, particularly as
many students have to take paid work to supplement their grant income and
balance domestic and university commitments.
UK universities are making increased use of experiential learning activities and
alternative forms of assessment, such as coursework and independent studies
projects, to improve student learning and their experience of higher education
(Entwistle et al., 1992). Often these projects are perceived as being less resource
intensive means of teaching by academics. Funding pressures and increased student
numbers have also led to institutions looking for cost-effective ways to assess
students 7, with the use of multiple choice objective testing increasing (Bence and
Lucas, 1996; Bull, 1993). The results of this study indicate considerable collusion and
plagiarism is associated with coursework and that increased use of these assessment
means may encourage student cheating.
The relationship between education, ethics and accountancy practices (Fleming,
1996; Puxty et al., 1994) and the continuum of cheating in college to engaging in
unethical practices in professional life (Ameen et al., 1996; Sierles et al., 1980),
warrants educators paying greater attention to issues surrounding academic
misconduct. However, before this can be achieved, teachers need to understand
the ethical perspectives of those they teach. Ethical accounting education starts in
the classroom: educators should ensure their students understand what behaviour
is acceptable and that academic misconduct is inadmissible. Similarly, teachers
should note that ‘fear of failure’ was the commonest reason given by students for
indulging in academic misconduct and therefore, that academic stress contributes
significantly towards this behaviour.
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Wolf, R.M. (1988) Questionnaire in Keeves, J P (Ed) Educational Research.
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Pergamon Press.
APPENDICES
Academic misconduct behaviours used in the questionnaire
1. Allowing own coursework to be copied by another student
2. Taking unauthorised material into an examination
3. Fabricating references or a bibliography
4. Lying about other medical or other circumstances to get special consideration
by examiners (e.g. the Examination Board take a more lenient view of results;
extra time to complete the examination)
5. Copying another student’s coursework with their knowledge
6. Lying about medical or other circumstances to get an extended deadline or
exemption form a piece of work
7. Submitting coursework from an outside source (e.g. a former student offers to
sell pre-prepared essays; ‘essay banks’)
8. A student taking an examination for someone else or having someone else
take an examination for them
9. In a situation where students mark each other’s work, coming to an
agreement with another student or students to mark each other’s work more
generously than it merits
10. Continuing to write after the invigilator has asked candidates to stop writing
11. Copying another student’s coursework without their knowledge
12. Illicitly gaining advance information about the contents of an examination
paper
13. Inventing data (i.e. entering non-existent results into he data base)
14. Not contributing a fair share to group-work
15. Ensuring the availability of books or journal articles in the library by
deliberately mis-shelving them so other students cannot find them, or by
cutting out the relevant article or chapter
16. Paraphrasing material from another source without acknowledging the
original author
17. Copying material for coursework from a book or other publication without
acknowledging the source
18. Premeditated collusion between two or more students to communicate
answers to each other during an examination
19. Copying from a neighbour during an examination without them realising
20. Altering data (e.g. adjusting data to obtain a significant result)
21. Doing another student’s coursework for them
22. Submitting a piece of coursework as an individual piece of work when it has
actually been written jointly with another student

ACADEMIC MISCONDUCT DUFF
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NOTES
1. See for instance, the Educational Research Center’s (ERIC) Clearing House on
Assessment and Evaluation (Internet address:
http://www.cua.edu/www/eric_ae/) or discussion groups such as ANET’s
(International Accounting Network) ateach (Email: ateach-l@scu.edu.au)
2. See Entwistle et al. (1992) for a discussion of the distinction between deep
and surface learning.
3. Interesting examples can be found at:
http://www.colorado.edu/sacs/ralphie/c/cheating.html
http://the-tech.mit.edu/V112/N15/heresch.15o.html
http://ww.cs.siu.edu/department/cheating.html
4. The Franklyn-Stokes and Newstead questionnaire was used as the academic
misconduct behaviours identified are common to assessment in UK
accounting courses.
5. Third-year students were sampled as to have progressed to third-year,
subjects have performed well in the classroom and appear ready to take up
positions in accounting firms, industry, commerce or the public sector.
6. Students are sampled from five (of 11) institutions in Scotland offering an
accounting degree, due to problems in gaining access to all 11 institutions.
All teaching staff were surveyed to improve the sample size, and
consequently generalisability, of the response.
7. See for example in the UK, the Teaching and Learning Technology
Programme (TLTP) Assessment of Learning through Technology for Efficiency
and Rigour (ALTER) at the Universities and Colleges Staff Development
Agency (Email: J.Bull@sheffield.ac.uk)