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ACADEMIC DISHONESTY AMONG UNIVERSITY STUDENTS:
A CATEGORY BASED COMPARATIVE STUDY
By
ABSTRACT
The aim of this study was to investigate the academic dishonesty among university students. For this the researcher has
formulated two types of objectives i.e., 1. To study the predictive power of gender and streams of education in relation to
academic dishonesty of university students, 2. To study the direct effect of gender, streams of education, and interaction
effect of both on academic dishonesty of university students. In this regard, descriptive survey method was used, and 120
university students were selected by using stratified purposive sampling. For data collection, academic dishonesty scale
was adopted which was developed by Bashir and Bala in 2018. Finally, the results of this study shown that gender and
stream of education are significant predictors of academic dishonesty and there is significant direct effect of gender,
streams of education and interaction of both on academic dishonesty of university students.
Keywords: Academic Dishonesty, Gender, University Students.
*,**** GM University, Sambalpur, Odisha, India.
**-*** Panchayat College Bargarh, Odisha, India.
***** CDOE Sambalpur University, Odisha, India.
RANJIT KUMAR BEHERA *
Date Received: 21/04/2023 Date Revised: 11/06/2023 Date Accepted: 09/10/2023
RUBI BAGARTI **
INTRODUCTION
In this cut-throat competitive world, ever ybody wants to
get success. This trend is reflected in the educational
sector also. Every student wants to get good marks in the
examination, be placed in good educational institutions
and get good employment. This competitive ecology
may lead to an increase the rate of anxiety among the
students. It has been reported that more than 20% in
Malaysia (Marthoenis et al., 2018), 64.3% in Egypt (Abdel
& Hassan, 2017), 47.1% in Turkish (Bayram & Bilgel, 2008),
and 57.98% in India (Kumar et al., 2009) university
students have experienced stress and anxiety. To cope or
adjust with these psychological problems and fulfil their
academic needs some students may adopt academic
dishonesty. Academic dishonesty is related with the
cheating on exams, plagiarising in project work, and
copying someone other's work (Jensen et al., 2002).
Colnerud and Rosander (2009) said that academic
dishonesty can be categorized into three categories such
as conscious deception, self-deception, and ignorant
deception. Conscious deception is related with
intentional dishonesty where a student presents someone
else's work as their own (Plagiarism). Self-deception
means when a student believes that he has produced a
work but this one was originally created by someone else.
This is also called unintentional plagiarism. Ignorant
deception means when the students do not follow the
rules and regulations in research work or project work.
Academic dishonesty is now becoming a global issue. It
has been reported that more than 35% students from the
USA were committing cheating on exams, 60% in report
writing, and 65% doing both (International Center for
Academic Integrity as cited by Ampuni et al., 2019). It has
MANISHA SAHU *** MANAS KUMAR SETH **** AKASH PADHAN *****
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limited resources.
As per the previous literature academic performance of
the students is one of the significant factors that is
negatively correlated with academic dishonesty (Hensley
et al., 2013; Olafson et al., 2013). It has been reported
that university students are more prone to academic
dishonesty than high school students (Munoz-Garcia &
Aviles-herrera, 2014). On the other hand, some other
studies reflect that younger students are more interested
in committing academic dishonesty than older students
(Olafson et al., 2013). Few other studies also indicate that
the students who engaged in academic dishonesty
during their school stage have continued to cheat
throughout the college life and later on also engaged in
the act of cheating in the workplace (Nonis & Swift, 2001).
Students' individual characteristics such as self-efficacy
and motives to study have significant relation with
academic dishonesty. High self-efficacy of the students in
relation to their confidence to perform well in examination
and ability to achieve their goals can be inversely
correlated with academic dishonesty (Jordan, 2001; Kelly
et al., 2005). Similarly, students who are academically
motivated to fulfil their needs, desires, expectations, and
interest are having less interest to commit academic
dishonesty (Jordan, 2001; Angell, 2006; Rettinger &
Kramer, 2009; Davy et al., 2007).
This growing rate of academic dishonesty among the
students is a major issue for maintaining the academic
integrity. Though a significant number of studies have
been conducted on academic dishonesty of college or
university students all over the world, but it is very less
explicit in the Indian context especially in the Odisha
region. On the other hand, it is very much important to
study the academic dishonesty behaviour of university
students because they are the future milestone of our
social, political, and economical development of our
countr y. In this regard, the investigator has decided to
conduct a study on academic dishonesty among
university students.
2. Need and Significance of the Study
The aim of the study is to investigate academic dishonesty
been found from previous literatures that more than 65%
students from Russia (Lupton & Chaqman, 2002), 75%
students from Eastern European countries (Grimes, 2004),
84% students from Poland (Lupton et al., 2000) and 55%
students from Japan (Diekhoff et al., 1999) have cheated
in their college examination. Previous literature also
indicates that many students consider academic
dishonesty is an acceptable and justified educational
strategy because they have seen other students do it and
get away from it (Lupton & Chaqman, 2002; Denisova-
Schmidt et al., 2016). Academic motivation is one of the
significant predictors which decides whether a student will
commit academic dishonesty or not (Shmeleva, 2016;
Shmeleva & Semenova, 2018). Students who have
curiosity to learn new knowledge and skills are less likely to
cheat on examinations than the students who are
externally motivated such as grades and social
comparison (Jordan, 2001; Rettinger & Jordan, 2005;
David, 2015).
1. Related Literature
It has been reported that demographic variables such as
gender are signi ficantly related with acade mic
dishonesty. Findings from earlier research shows that
academic dishonesty is significantly higher among male
students than female students (Kuntz & Butler, 2014; Risser
& Eckert, 2016; Nazie et al., 2011; Marsden et al., 2005).
As women are taught to strictly follow the rules than men,
so it is reasonable to assume that women consider
academic dishonesty as an unacceptable behaviour
than men. Moreover, another study indicates that
academic dishonesty is more in women than men (Martin
et al., 2009). Some other studies also suggest that there is
a weak relationship between gender and academic
dishonesty (Jordan, 2001).
Along with gender as a predictor of academic dishonesty,
financial and academic resources have a negative
relationship with academic dishonesty. Miller et al. (2015)
conducted a study on 4538 schools within 35 nations and
found that lack of academic resources and financial
instability lead to academic dishonesty. This stance is also
supported by Carnero et al. (2017) where they said
academic dishonesty was more prevalent in areas with
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Undergraduate level were considered as participants for
this study. In the first stage, Panchayat College, Bargarh
affiliated under Sambalpur University was selected by
purposive sampling technique. In the second stage, one
hundred twenty (120) samples were selected by using
stratified sampling among which 60 male and 60 female,
40 students from each academic stream (Arts, Science,
and Commerce).
Tools: The investigator has adopted Academic Dishonesty
Scale (ADS) developed by Bashir and Bala in 2018 for data
collection from the participants. This scale consists of 23
items and six dimensions suc h as c heating in
examination, plagiarism, outside help, prior cheating,
falsification, and lying about academic assignments. The
reliability of the scale was 0.831.
Statistical Techniques Used: The investigator has used
simple percentage, mean, standard deviation, simple
linear regression, t-test, and ANOVA to derive the results
from the data via SPSS 27.
6. Analysis and Interpretation of Results
6.1 Predictor Analysis
Gender and stream of education are considered as
predictor variables and academic dishonesty is
considered outcome variable in this study. An attempt
was made to study whether academic dishonesty can be
predicted by gender and stream of education. For this
reason, the investigator has used simple linear regression
by converting the above cited categorical variables into
dummy variables via SPSS 27. The results of regression
analysis are presented below. Table 1 shows the sample
distribution
Simple linear regression (Table 2) was run to know how
much variation in academic dishonesty is occurred due
to gender and streams of education. The gender and
streams of education were considered as dummy
among university students in relation to their gender and
streams of education. Academic integrity is very much
important as it promotes sense of responsibility, ethical
pr a c t i ces, g enui n e n ess, m e ani n g f ul l e arn i n g
experiences, trust, honesty, and self-confidence among
the students. It reflects the actual and genuine work of the
students in their respective fields. But the prevalence of
academic dishonesty undermines the integrity, credibility,
trust, and degrade the quality of education. Academic
dishonesty like- cheating on exams, plagiarism, and
unethical behaviour has significant threat to the reliability
and trustworthiness of the educational system.
This study is important because it provides valuable insight
to understand and address this emerging issue. The
finding of this study helps to promote honesty in
educational settings, fairly evaluate the student's
performance, foster moral & ethical values, and inform
the educational institutions, curriculum framer & policy
maker to make proper rules and regulation for academic
integrity.
3. Objectives of the Study
To study the predictive role of gender and streams of
education in academic dishonesty among university
students.
To study the effect of gender, streams of education
and interaction of both on academic dishonesty
among university students.
4. Hypothesises of the Study
H01: There is no significant predictive role of gender and
streams of education in academic dishonesty among
university students.
H02: There is no significant effect of gender, streams of
education, and interaction of both on Academic
dishonesty of university students.
5. Methodology
Method: This study examines the academic dishonesty
among university students in terms of their gender and
stream of study. For this purpose, descriptive survey
method was used.
Par t icipa nts: Th e stu den ts who ar e study ing i n
·
·
Table 1. Sample Distribution
Variable Category
Gender
Stream of Education
Male
Female
Arts
Science
Commerce
Frequency Percentage
60
60
40
40
40
50%
50%
33.33%
33.33%
33.33%
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7. Analysis of Direct and Interaction Effect
7.1 Effect of Gender
One of the objectives of present students was to study the
effect of gender on academic dishonesty of university
students for which the investigator has used t-test via SPSS
27.
From Table 3 it has been found that the mean and
standard deviation of the female group of students is
42.73 and 6.087 respectively. Similarly, the mean and
standard deviation of male group of students is 46.12 and
6.274 respectively. The t-score is 2.998 for the degree of
freedom (df) 118. The t-score is significant at 0.01 level of
significance. So, the null hypothesis i.e., there is no
significant effect of gender on academic dishonesty of
university students is rejected and alternative hypothesis
i.e., there is significant effect of gender on academic
dishonesty of university students is accepted.
7.2 Effect of Streams of Education
Another objective of this study was to study whether the
variation in academic dishonesty is differed due to the
stream of education. As the stream of education has
three levels i.e., Arts, Science, and Commerce for which
the investigator has used one way ANOVA via SPSS 27. The
results of one-way ANOVA are given below. Table 4 shows
the results of one-way Anova.
variables. In first model female was considered as
reference group and in second model arts was
considered a reference group. The result of the first model
shows that gender was a significant predictor (F (117,2)-
8.989, P-.003) and describe 7.1% variance in academic
dishonesty (R2-.071). The Beta coefficient between male
and female students was significant (t (118)-2.998, P-.003)
which indicates male students are more likely to commit
academic dishonesty as compared to female students
(b-3.383).
The result of second model explains that streams of
education were significant predictors of academic
dishonesty (F (117,2)- 3.644, P-.029) and describe 5.9%
variation in academic dishonesty (R2-.059). The beta
coefficient between arts and science group of students
was not significant (t (78)-.125, P-.901) which indicates
there was no significant difference between arts and
science students in academic dishonesty (b- -.175). It
means the students from science background were
predicted to have -.175 lower score as compared to arts
students in academic dishonesty. The beta coefficient
between arts and commerce students were significant (t
(78)-2.273, P-.025) which indicates commerce students
were more likely to commit academic dishonesty as
compared to arts students (b-3.175).
Table 2. Results of Simple Linear Regression Analysis
Model Regression Weight
1 Gender
Science
Commerce
Beta coefficient 2
R
3.383
-0.175
3.175
.071
FP Value
8.989** .003
t Value P Value
2.998
0.125
2.273
.003
.901
.025
.059 3.644* .029
2
** Significant at 0.01
* Significant at 0.05
Table 3. Results of t-test
Groups N
Female
Male
60
60
Mean SD
42.73
46.12
6.087
6.274
df t Value
118 2.998
Sig. Remark
.003 P<0.003
Table 4. Results of one-way ANOVA
Source of Variation
Between group
Within group
Total
Sum of Squares df
284.450
4566.875
4851.325
2
117
119
Mean Square F Value
142.225
39.033 3.664
P Value Remark
.029 P<0.05
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science students did not differ significantly whereas the
mean difference between commerce vs science
students differs significantly.
7.3 Interaction Effect of Stream of Education and Gender
Another objective of this study was to investigate the
interaction effects of stream of education and gender on
academic dishonesty of university students. In this regard,
the investigator has used two-way ANOVA because here
we have two factors i.e., gender (male and female) and
stream of education (arts, science, and commerce).
Table 7 presents the results.
From Table 8 it has been found that the f-value of
interaction between stream of education and gender is
3.966 which is significant at 0.05 level of significance
(P<0.05). So, it can be said that there exists a significant
difference in the mean score of arts, science, and
commerce students in relation to their gender. Therefore,
the null hypothesis i.e., there is no significant effect of
interaction between stream of education and gender on
academic dishonesty is rejected.
8. Discussions
Academic dishonesty is a significant issue throughout the
world where the students are involved in unethical
activities either to increase their grade or complete
project works. To understand this issue in our country the
investigator has conducted research. In this study, the
researcher has found that both gender and stream of
education are significant predictors for committing
academic dishonesty among university students. 7.1%
and 5.9% variation in academic dishonesty is due to
gender and stream of education respectively. Apart
from that, the researcher has employed t-test to know the
difference in academic dishonesty between male and
It is found from Table 4 that the F-value of stream of
education is 3.664 for the df 119 which is significant at
0.05 level of significance. So, the null hypothesis i.e., there
is no significant effect of streams of education on
academic dishonesty among arts, science, and
commerce groups of students is rejected. It means
academic streams have significant effect on academic
dishonesty of university students.
As the results of one-way ANOVA is significant for which the
researcher has used post-hoc (Turkey method) test to
know whether each group has significant effect or not.
The results of post-hoc test is given below. Table 5 shows
the Mean, SD, and N of Stream of Education.
The results of the post-hoc test give a clear picture of
multiple comparisons on the academic dishonesty
among university students in relation to their stream of
education. As there were three levels of stream of
education, multiple comparisons were run three times
i.e., commerce vs science, commerce vs arts, and arts vs
science. From Table 6 it has been found that the mean
difference between commerce and science stream of
students is 3.350 which is significant at 0.05 level of
significance. The mean difference between commerce
and arts is 3.175 which is not significant at 0.05 level of
significance. Similarly, the mean score between arts and
science groups of students is 0.175 which is not significant
at 0.05 level of significance. So, it can be concluded that
the mean score between commerce vs arts and arts vs
Table 5. Mean, SD, and N of Stream of Education
Streams Mean
Commerce
Science
Arts
Total
46.60
43.25
43.43
44.43
SD N
5.286
8.478
4.156
6.385
40
40
40
120
Table 6. Results of Post-hoc Test
* The mean difference is significant at 0.05.
(I) Stream
Commerce
Science
Arts
(J) Stream Mean Difference (I-J)
Science
Arts
Commerce
Arts
Commerce
Science
3.350*
3.175
-3.350*
0.175
-3.175
.175
Std. Error Sig.
1.397
1.397
1.397
1.397
1.397
1.397
.047
.064
.047
.991
.064
.991
Lower Bound Upper Bound
.03
-.14
-6.67
-3.49
-6.49
-3.14
6.67
6.49
-.03
3.14
.14
3.49
Multiple Comparisons 95% Confidence Interval
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there is a significant difference between commerce and
science group of students. Finally, the results of the
interaction effect of streams of education and gender
shows that there is a significant interaction effect of the
streams of education and gender on academic
dishonesty of university students.
9. Recommendations
One of the growing educational issues is academic
dishonesty among the students. The effect of academic
dishonesty not only damage the academic integrity and
ethical activity of the students but also degrade the
quality and standard of education. In this regard, various
initiatives should be taken by educational institutions,
policy makers, and governments. The initiatives like
Educational institutions should establish the support
service, resource centre and organise ethical
programmes to promote academic integrity among
the students.
Governments, Policy makers, and educational
institutions should make proper rules and regulations
for research works and project works which will help to
prevent the plagiarism.
Educational institutions should organise seminar,
webinar, workshop on research ethics to promote
knowledge of research and proper citation.
Plagiarism detection technology should be used to
prevent the plagiarism and maintain academic
integrity.
Educational institutions should promote a positive
learning environment where students can express
their academic problems, make collaborative work,
and interact with their mentors without any fear.
Conclusion
Academic dishonesty is a growing problem all over the
world. It significantly affects the educational system. There
are several causes (individual and academic), and its
effects are detrimental for the students' community.
Preventing this problem requires consistent effort from the
side of educational institutions, stakeholders, and policy
makers.
·
·
·
·
·
female. In this regard, the researcher has found that
there is significant effect of gender on academic
dishonesty of university students. As the mean score of
males (46.12) is significantly higher than the female
(42.73) groups of students, so, it can be said that males
are more prone to academic dishonesty as compared
to female university students. This result is consistent with
several studies (Kuntz & Butler, 2014; Risser & Eckert,
2016; Nazie et al., 2011; Marsden et al., 2005). But, at
the same time this result is also contradictory with some
studies (Martin et al., 2009).
Along with the effect of gender, the researcher has
employed one-way and two-way ANOVA to know the
difference among streams of education as well as the
interaction effect between streams of education and
gender on academic dishonesty. In this way, the
researcher has found that there is a significant effect of
streams of education on academic dishonesty. For
getting a clear picture of this study, post-hoc (Turkey) test
has been employed. The results of the post-hoc test
shown that there is no significant difference between arts
& science and commerce & arts groups of students but
Table 8. Results of two-way ANOVA (3 × 2)
a. R Squared = .190 (Adjusted R Squared = .155)
Source
Stream
Gender
Stream * Gender
Error
Total
Corrected Total
Sum of Squares df
259.685
346.077
273.398
3928.933
241681.000
4851.325
2
1
2
114
120
119
Mean Squares F Value
129.842
346.077
136.699
34.464
3.767
10.042
3.966
Sig.
.026
.002
.022
Table 7. Stream and Gender Wise Mean, SD, and N
Stream Gender
Commerce
Science
Arts
Total
Female
Male
Total
Female
Male
Total
Female
Male
Total
Female
Male
Total
Mean SD
42.80
50.40
46.60
43.07
43.36
43.25
42.48
45.00
43.43
42.73
46.12
44.43
2.167
4.717
5.286
10.740
7.035
8.478
4.464
3.117
4.156
6.087
6.274
6.385
N
20
20
40
15
25
40
15
25
40
60
60
120
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ABOUT THE AUTHORS
Ranjit Kumar Behera is a student at Gangadhar Meher University in Sambalpur, Odisha, India. He completed his M.A. in
Education from Gangadhar Meher University in 2022 and qualified for UGC-NET-JRF in Education. He has participated in two
national seminars and presented papers. Furthermore, he has published four articles in international journals. His research
interests include ICT in Education, Educational Psychology, and Educational Sociology.
Rubi Bagarti is a student at Panchayat College, Bargarh, Odisha, India. She is pursuing an M.A. in Education at Women's
College, Sambalpur. Her areas of interest include Educational Philosophy, Educational Psychology, and Educational Sociology.
Manisha Sahu is working as a Guest Faculty in Education at Panchayat College, Bargarh, Odisha, India. She has completed her
Master's and M. Phil degrees in Education from Gangadhar Meher University. She has also qualified for UGC-NET in Education.
Her areas of interest include Educational Philosophy, Educational Psychology, and Educational Sociology.
Manas Kumar Seth is a student at Gangadhar Meher University in Sambalpur, Odisha, India. He completed his M.A. in Education
from Gangadhar Meher University in 2022 and qualified for UGC-NET in Education. Additionally, he has published two research
articles in international journals and presented a paper in a national seminar. His research interest areas include Educational
Technology, Educational Sociology, and Educational Psychology.
Akash Padhan is working as an Assistant Professor in Education at the Centre for Distance and Online Education, Sambalpur
University, Odisha, India. He completed his Master's degree in Education from Gangadhar Meher University, Sambalpur. He has
qualified for UGC-NET in Education and has attended workshops and training programs related to his professional career. His
research interests include Psychology, Sociology, and Teacher Education.
RESEARCH PAPERS
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