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Investigating the effect of academic procrastination on the
frequency and variety of academic misconduct: a panel study
Justine Patrzek
a
*, Sebastian Sattler
b
, Floris van Veen
b
, Carola Grunschel
a
and
Stefan Fries
a
a
Department of Psychology, Bielefeld University, 33501 Bielefeld, Germany;
b
Faculty of
Sociology, Bielefeld University, 33501 Bielefeld, Germany
In prior studies, academic procrastination has been discussed as an influencing factor
of academic misconduct. However, empirical studies were conducted solely cross-
sectionally and investigated only a few forms of academic misconduct. This large
scale web-based study examined the responses of between 1359 and 2207
participants from different academic disciplines at four German universities to
address the effect of academic procrastination on seven different forms of
academic misconduct (using fraudulent excuses, plagiarism, copying from
someone else in exams, using forbidden means in exams, carrying forbidden
means into exams, copying parts of homework from others, and fabrication or
falsification of data) and its variety. In measuring academic procrastination six
months prior to academic misconduct, we found that academic procrastination
affected the frequency of all forms of academic misconduct and its variety. We
found the strongest effect of academic procrastination on using fraudulent
excuses. Implications for university counseling and theory are discussed.
Keywords: academic procrastination; fraudulent excuses; fabrication and
falsification of data; plagiarism; cheating in exams
Introduction
At present, investigations dealing with academic misconduct and, in particular, the
issue of plagiarism, are the subject of widespread attention in both the media and the
scientific community in Germany. This interest exists in part because academic miscon-
duct can lead to severe negative consequences for universities and students. Academic
misconduct violates scientific principles, as well as study and examination regulations.
In the worst cases, unwarranted academic titles are awarded, and the reputations of uni-
versities are at risk (Martinson, Anderson and de Vries 2005). Students who engage in
different forms of academic misconduct gain unfair advantages over their peers
(Keith-Spiegel and Whitley 2001; Sattler, Graeff and Willen 2013). Furthermore,
cheating students may impair their own learning processes (Sattler 2007).
Therefore, it is not surprising that many scientific investigations have dealt with the
issue of academic misconduct (McCabe and Treviño 1993; Lim and See 2001;
© 2014 Society for Research into Higher Education
*Corresponding author. Email: justine.patrzek@uni-bielefeld.de
Current address: Sebastian Sattler, Institute for Sociology and Social Psychology, University of
Cologne, Germany and the Cologne Graduate School for Management, Economics and Social
Sciences, Germany.
Studies in Higher Education, 2014
http://dx.doi.org/10.1080/03075079.2013.854765
Rettinger, Jordan and Peschiera 2004; Vowell and Chen 2004; Collins, Judge and
Rickman 2007; Teixeira and Rocha 2008). One major research topic within these inves-
tigations is the assessment of the prevalence of different forms of academic misconduct.
One study of undergraduate students at a US university found that 81% admitted to at
least one of the 17 investigated behaviors (O’Rourke et al. 2010; cf. Rettinger, Jordan
and Peschiera 2004; Vowell and Chen 2004). This prevalence is slightly higher than the
70% rate found in an often cited review by Whitley (1998). Whitley reported the fol-
lowing rates of different forms of academic misconduct: cheating on exams ranged
from 4% to 82% of students (mean = 43%), cheating on homework ranged from 3%
to 83% (mean = 41%), and plagiarism ranged from 3% to 98% (mean = 47%).
Another major research topic concerning academic misconduct is guided by the
question of why students behave dishonestly (Comas-Forgas and Sureda-Negre
2010; Teixeira and Rocha 2008; Kerkvliet and Sigmund 1999; Michaels and Miethe
1989; Vowell and Chen 2004; McCabe, Treviño and Butterfield 2002). In addition
to the finding that external factors, such as the existence of honor codes or conditions
during examinations, influence the tendency to behave dishonestly, there is evidence
that personality factors play an important role in the understanding of academic miscon-
duct as well (e.g. Teixeira and Rocha 2008; Whitley 1998). Academic procrastination
was initially discussed as a significant influencing personality factor behind academic
misconduct by Roig and DeTommaso (1995; cf. Ferrari and Beck 1998; Whitley 1998);
however, the investigation of this factor is less advanced.
Academic procrastination and academic misconduct
Academic procrastination can be defined as the tendency to delay intended academic
tasks, even though this may result in negative consequences (e.g. Simpson and
Pychyl 2009; Steel 2007). Empirical studies illustrated that up to 70% of students pro-
crastinate on a regular basis (e.g. Schouwenburg 2004). Students reported procrastinat-
ing on different academic tasks, such as writing term papers, preparing for exams, and
completing weekly coursework (Solomon and Rothblum 1984). Instead, they watch tel-
evision, sleep, or socialize with friends or family members (Pychyl et al. 2000). In
recent years, surfing and communicating online appear to be popular alternative
tasks while procrastinating (Grund et al. 2012).
Many empirical studies have investigated the short- and long-term consequences of
academic procrastination, mostly relating to students’well-being (Sirios, Melia-Gordon
and Pychyl 2003; Tice and Baumeister 1997) and academic performance (Klassen,
Krawchuk and Rajani 2008; Tice and Baumeister 1997). These studies found that aca-
demic procrastination is likely to affect both domains negatively (e.g. causing stress and
decreasing performance). Considering the mechanisms by which procrastination leads
to the emergence of such negative consequences can aid in the understanding of how
academic procrastination might influence academic misconduct.
One of the main characteristics of academic procrastination is the postponing of the
initiation or completion of academic assignments (e.g. Ferrari 2004), even though
working on these assignments was intended (e.g. Simpson and Pychyl 2009). Indeed,
Schouwenburg and Groenewoud (2001) showed that procrastinating students began
to study later than non-procrastinating students. As a result, the timeframe for
working on assignments decreases, and the conditions for succeeding at the task
become unfavorable and difficult. This situation might result in a deadline effect: the
closer the deadline (i.e. the less time is available), the higher the pressure on the
2J. Patrzek et al.
student (cf. Weinstein and Dobkin 2002). Therefore, the occurrence of academic mis-
conduct is expected to be higher in situations with little time remaining. For example,
using fraudulent excuses could lead to an extension of a deadline and reduction of
time pressure (cf. Roig and Caso 2005). In line with this reasoning, ‘time pressure’
was found to be an important reason behind academic misconduct (Franklyn-Stokes
and Newstead 1995, 168). When procrastinating students feel that there is little time
left before a deadline and that delaying an academic assignment is likely to result in aca-
demic failure, they may pursue different forms of academic misconduct to account for
the time lost and to avoid negative effects. Behaviors such as copying and pasting
from the Internet are easier and faster than drafting an original term paper honestly.
Using crib notes or copying from others on exams could also be interpreted as coping
strategies, with the aim being to avoid or overcome the negative effects of academic pro-
crastination, such as decreased performance due to postponed studying or failure to
learn. Furthermore, following Ferrari and Beck (1998, 535), procrastinating students
may perceive a fraudulent excuse as an ‘adaptive response to the immediate needs of
the situation’. Other forms of misconduct can also be interpreted as such a response.
There is empirical evidence underpinning this argument. Two cross-sectional
studies (Ferrari and Beck 1998; Ferrari et al. 1998) showed that procrastinating
college students used fraudulent excuses more often than non-procrastinating college
students. The most frequently mentioned reason for using excuses was to expand the
timeframe in which they could accomplish an intended task, which is in line with
our reasoning. In addition, Roig and DeTommaso (1995) found academic procrastina-
tion and plagiarism, as well as cheating on exams, to be positively correlated.
Although this empirical evidence strengthens the view that academic procrastina-
tion influences academic misconduct, there are four drawbacks in these initial studies.
First, the conducted studies investigated only some forms of academic misconduct.
As previously mentioned, academic misconduct takes many forms, such as carrying
and using forbidden means on exams or falsifying or fabricating data. Taking into
account additional forms of academic misconduct allows for assessment of the predic-
tive power of academic procrastination and, additionally, comparisons of the impacts
of academic procrastination on a wider range of academic misbehaviors. Furthermore,
no study has yet investigated the influence of procrastination on the variety of aca-
demic misconduct. A variety measure (Sweeten 2012;Farrington1973)provides
information about the number of different forms of academic misconduct in which
the students engaged. For instance, when investigating 10 different forms of academic
misconduct, a variety scale technically ranges from zero to 10. In this way, it
describes whether students commit a diverse range of misconducts. The use of
such scales is recommended due to their validity and reliability (Sweeten 2012).
Second, in line with Ferrari et al. (1998), it can be stated that the interpretation of
prior research is restricted to selective samples (e.g. only one college or only students
in one discipline were investigated). Moreover, different authors claim that investi-
gations concerning academic misconduct have been mostly conducted in North
America (e.g. Ashworth, Bannister and Thorne 1997). Thus, the generalizability of
the results is limited. Using a large random sample with university students from
different German universities and academic disciplines can address this limitation.
Third, the initial studies were cross-sectional; therefore, they were only able to
show whether or not associations between the two phenomena exist, while being
limited in their insights about causality (cf. Grasmick and Bursik 1990). Using a
panel design, in which academic procrastination is measured prior to academic
Studies in Higher Education 3
misconduct, avoids such causal order problems. Fourth, little has been said about the
mechanisms which connect academic procrastination to academic misconduct. Pro-
viding theoretical reasoning helps us to understand the underlying processes
between both phenomena, as well as informing and extending a model for researching
academic misconduct. Furthermore, understanding the mechanisms is fundamental in
developing appropriate intervention programs.
The present study aims to address the shortcomings of previous studies by inves-
tigating whether academic procrastination increases the frequency and variety of
seven different forms of academic misconduct. It uses a panel design based on
multi-campus data from a large-scale sample of students. By doing so, this investi-
gation could help to discover whether models explaining academic misconduct
should include academic procrastination as an influencing factor (cf. Whitley
1998). Moreover, the results could help lecturers and university counselors to form
a sound basis for informing students about the risks of academic procrastination. In
addition, the findings may emphasize the need for prevention and intervention pro-
grams that reduce academic procrastination, which could lower the prevalence and
incidence of academic misconduct.
Method
Participants
For our web-based panel study, students were randomly selected from four German
universities and different academic disciplines. Participation was voluntary, and anon-
ymity was guaranteed by our address handling methods. The partnering universities did
not provide the research team with students’contact information, and the universities
never had access to the students’responses. The survey was protected using secure
sockets layer (SSL) protocol encryption. The legal department and the official data pro-
tection officer of Bielefeld University approved and supervised the address handling.
Prior to the survey, the partnering universities sent out pre-notification letters. These
letters included information about the study, the incentives (which were issued at the
end of the questionnaire), and a data protection declaration. The intent was to increase
the awareness and legitimacy of the survey, as well as to motivate students to participate
(Porter and Whitcomb 2007; Stafford 1966). One week after sending out the letters, the
partnering universities contacted the sample members by email; the information from
the pre-notification letter was repeated in the email, and a personal link to the study
was provided. Students received up to two email reminders if they had not yet partici-
pated or completed the survey. After completion, five different incentives worth EUR 5
were offered to the respondents (e.g. Göritz 2006): money sent via mail or wired via
PayPal, a voucher that could be cashed at a popular online retailer, or donations to
two international charity organizations.
In wave 1 (t
1
), 69% of the 5048 contacted students entered the survey (response
rate), 93% of whom completed it (retention rate); 62% of the respondents were
female. Six months later, students who completed the survey in t
1
and did not graduate
or drop out of their universities were invited to a follow-up survey, using the same
contact procedure as in t
1
. Of these 3020 students, 81% participated in the second
wave (t
2
); the retention rate was 94%. Similar to t
1
, 61% of the participants were female.
The analytical subsamples consisted of 1359 to 2207 cases. The number of cases
depended on dropout, item nonresponses (see Table 1), and possible branching,
1
4J. Patrzek et al.
Table 1. Statistical properties of the Questionnaire for Academic Procrastination for students participating in both waves (measured in t1).
MSDr
it
N INR M
female
M
male
t
gender
r
[procras., semester]
1) Although I plan to work on a university assignment,
I don’t do it.
3.24 1.34 0.76 2461 5 3.17 3.34 3.13** 0.09***
2) If I intend to continue working on a university assignment,
I do it. (R).
2.75 1.07 0.77 2443 23 2.71 2.80 1.97* 0.08***
3) Even if I intend to finish a university assignment, I don’t do it. 2.59 1.25 0.75 2444 22 2.55 2.66 2.17** 0.10***
4) When I plan to start working on a university assignment,
I stick to this plan. (R)
3.01 1.20 0.73 2446 20 2.94 3.10 2.20* 0.08***
5) I don’t continue working on a university assignment, although
I intended to.
2.68 1.22 0.79 2445 21 2.63 2.76 2.68** 0.08***
6) When I intend to work on a university assignment, I do it. (R) 2.73 1.10 0.83 2441 25 2.69 2.78 1.92
+
0.11***
7) I don’t start working on a university assignment, although
I intended to.
2.85 1.26 0.77 2444 22 2.79 2.95 3.19** 0.10***
8) If I intend to finish a university assignment, I do it. (R). 2.46 1.09 0.75 2443 23 2.42 2.52 2.22* 0.10***
Scale
Mean
2.78 0.99 2367 2.73 2.86 2.99** 0.11***
Notes: R=item is reverse coded for analysis; M=mean; SD = standard deviation; r
it
=corrected item-total correlation; N=number of respondents; INR = number of item
nonresponses; t=t-value of the t-test; r= correlation coefficient;
+
p< .10; *p< .05; **p< .01; ***p< .001.
Studies in Higher Education 5
which was used to increase response validity (e.g. respondents who had not taken an
exam during the last six months were not asked about carrying crib notes).
Instruments
Academic procrastination
At t
1
, the students completed the Questionnaire for Academic Procrastination (QAP).
This questionnaire was developed and validated in a research project on academic pro-
crastination (see Acknowledgements). The questionnaire consisted of eight items refer-
ring to the intention–action gap (cf. Lay and Schouwenburg 1993; Steel 2007) and
different stages of task processing (see Table 1). Responses were measured on a six-
point scale ranging from 1 (very seldom)to6(very often). Two pretests with
samples of more than 300 students each demonstrated that the questionnaire was
uni-dimensional, had good internal consistencies (α
study 1
= 0.93 and α
study 2
= 0.94),
and was highly correlated with the Tuckman Procrastination Scale (r= 0.77,
Tuckman 1991). In the present study, a principal component analysis conducted on
the data from all students participating in t
1
and t
2
revealed a uni-dimensional structure
(Kaiser-Meyer-Olkin: 0.93). Furthermore, good internal consistency (α= 0.93) and
high corrected item–total correlations (see Table 1) were found.
2
For our main analysis,
we used a regression factor score, with 0 being the score of someone with average pro-
crastination behavior and 1 being the standard deviation. This was done because some
items aiming to explain a certain construct are usually more important than others (have
greater reliability). The application of factor scores instead of unweighted sum scores
accounts for the different impacts of each variable (DiStefano, Zhu and Mindrila 2009).
Frequency of academic misconduct
Seven different forms of academic misconduct were measured at t
2
(see Table 2) via
self-report, which is the most frequently used method for this purpose due to the
Table 2. Description of academic misconduct within six months, measured in t
2
.
Done at least
once % M SD N INR
1) Using a false excuse or abusing a medical
certificate to postpone an exam or a deadline
14 0.21 0.65 2,207 22
2) Consciously using thoughts or quotes from other
authors without proper acknowledgement
(plagiarism)
12 0.21 0.79 1,911 25
3) Copying from someone’s paper in exams 36 0.72 1.44 2,097 17
4) Using forbidden means, such as crib notes, in
exams
14 0.28 0.93 2,091 26
5) Carrying crib notes or other forbidden means into
exams
28 0.64 1.45 2,086 28
6) Copying parts of protocols/homework from
others
27 0.68 1.68 2,202 27
7) Fabrication or falsification of data/results/facts 19 0.44 1.21 1,624 25
8) Variety of academic misconduct 75 1.44 1.54 1,359 /
Notes: M=mean; SD = standard deviation; N=number of respondents; INR = number of item
nonresponses.
6J. Patrzek et al.
ease of application in surveys and statistical analyses (cf. Kerkvliet 1994; Teixeira and
Rocha 2008). Students were asked to indicate how often they had engaged in each be-
havior during the last six months, using a scale ranging from 0 (never)to11(more than
10 times).
Variety of academic misconduct
We also employed a variety measure (Sweeten 2012) concerning academic misconduct.
This measure provided information about the number of different forms of academic
misconduct the students engaged in (e.g. when a student was involved at least once
in plagiarism and using forbidden means in exams but not in any other behavior, the
variety measure indicated a value of 2). As this measure was computed on the basis
of the seven items of self-reported behavior, it ranges from 0 to 7.
To ensure the practicability of the survey procedure and the instruments used, we
conducted quantitative and cognitive pretests.
Data analysis
The frequency and variety of academic misconduct are classical count variables, with
small values being more frequent than larger ones. Due to the skewness of all dependent
variables and the evidence for overdispersion (Hilbe 2011), which is a variance greater
than the mean, models such as ordinary least squares regression could result in ineffi-
cient, inconsistent, and biased estimates (Long and Freese 2006). To account for the
statistical distribution of the reported forms of misconduct, we used negative binomial
regression models (Hilbe 2011; MacDonald and Lattimore 2010). Negative binomial
regression models take unobserved heterogeneity among observations into account
and do not have downward-biased standard errors, compared to Poisson models.
In our models, we controlled for the students’genders and the number of semesters
they had completed, as different studies have shown that these variables can influence
the tendency to procrastinate and cheat at the university level (e.g. Steel 2007; Vowell
and Chen 2004; Underwood and Szabo 2003). We also tested whether procrastination
has different effects on academic misconduct in men and women, or in students from
different semesters. As no effects were found, we have not shown the results from this
interaction analysis.
Results
Descriptives
To provide insights into the prevalence of academic procrastination, we display raw
mean values for each item of the QAP, as well as the scale means (see Table 1). The
men showed a slightly higher mean value than the women ( p= 0.003). The correlation
with the number of semesters was small (p< 0.001), indicating slightly higher procras-
tination scores among students in later semesters compared to students in earlier
semesters.
Concerning academic misconduct, 75% of the students admitted that they had con-
ducted at least one of the seven investigated behaviors within the past six months (see
Table 2). They engaged in, on average, 1.44 different behaviors (see variety in Table
2). The most frequently reported behavior was copying from other students’papers in
Studies in Higher Education 7
exams; more than one-third of the students (36%) reported this behavior during the inves-
tigated time period. The frequency was 0.72 times. Plagiarism was the least prevalent be-
havior, reported by only 12% of the students; its frequency was 0.21. Generally, the item
nonresponse rate was low. It was highest for the fabrication and falsification of data,
results, or facts (2%) and was lowest for copying from someone’s paper in exams (1%).
Multivariate analysis
Tables 3 and 4display the effects of academic procrastination, gender, and semester
(measured in t
1
) on the occurrence and frequency of academic misconduct during a
six-month time period, as well as its variety (measured in t
2
); the results are based
on multivariate negative binomial regression models. To estimate the influence of
the predictors, we interpreted the incident rate ratios (IRR). An IRR greater than 1 indi-
cates that the predicted mean number of a certain type of academic misconduct
increases when the value of an independent variable (academic procrastination)
increases. An IRR smaller than 1 points to negative effects, while an IRR equal to 1
indicates no effect.
The IRRs for academic procrastination reveal an increasing mean frequency for all
forms of academic misconduct within the last six months. Figure 1 displays the increase
of the investigated forms of academic misconduct as a function of the increase in aca-
demic procrastination. For students whose tendencies to procrastinate are low, the fre-
quencies of the different forms of academic misconduct are comparatively low, as well.
The differing slopes of the lines indicate that academic procrastination has differential
effects on the different forms of academic misconduct.
The highest IRR was found for the use of false excuses or the abuse of a medical
certificate to postpone an exam or to extend a deadline (see Model 1). The correspond-
ing frequency increased by a factor of 1.68, or 68% (p< 0.001), when the factor score
of academic procrastination increased by 1. The smallest coefficient was found for
copying from someone’s paper in exams (see Model 3). The corresponding frequency
increased by a factor of 1.21, or 21% (p< 0.001), when the factor score of academic
procrastination increased by 1. The changes in the other forms of academic misconduct
resulting from increasing academic procrastination fall between those values (see
Tables 3 and 4): 1.23 (p= 0.014) for plagiarism, 1.29 (p< 0.001) for using forbidden
means on exams, 1.23 (p< 0.001) for carrying forbidden means into exams, 1.29 (p<
0.001) for copying parts of protocols/homework from others, and 1.27 (p= 0.001) for
the fabrication or falsification of data.
We found mixed effects of gender on academic misconduct across the different
forms of academic misconduct (see Tables 3 and 4). We found evidence that women
more often copied from someone’s paper in exams (approximately 22% more often
than men, p= 0.017); on the other hand, women copied parts of protocols/homework
from others 31% less often than men (p< 0.001). Other forms of academic misconduct
were not significantly affected by gender. We also found effects of semester (see Tables
3and 4), indicating that some of the behaviors, such as the fabrication or falsification of
data, results, or facts, occurred less often in later semesters (p< 0.001). The frequency
of this behavior decreases by 12% per semester.
The variety of academic misconduct (see Model 8 in Table 4) is higher among
highly procrastinating students (p< 0.001). Variety was not affected by gender (p=
0.288), but the responses of students in later semesters indicated that they engaged
in fewer different forms of academic misconduct (p< 0.001).
8J. Patrzek et al.
Table 3. Frequency of academic misconduct over six months (measured in t
2
), predicted by academic procrastination, gender, and semester (measured in
t
1
) and based on multivariate negative binomial regression models.
Model 1 Model 2 Model 3 Model 4
Using a false excuse or
abusing a medical
certificate to postpone an
exam or a deadline
Consciously using
thoughts or quotes from
other authors without
proper
acknowledgement
(plagiarism)
Copying from someone’s
paper in exams
Using forbidden means,
such as crib notes, in
exams
IRR [95% CI]IRR [95% CI]IRR [95% CI]IRR [95% CI]
Academic procrastination 1.68*** [1.48 | 1.90] 1.23* [1.04 | 1.46] 1.21*** [1.12 | 1.31] 1.29*** [1.22 | 1.47]
Female 1.16 [0.90 | 1.50] 0.99 [0.71 | 1.40] 1.22* [1.04 | 1.44] 1.01 [0.75 | 1.35]
Semester 1.03 [0.99 | 1.07] 1.01 [0.97 | 1.06] 0.92*** [0.90 | 0.95] 0.97 [0.93 | 1.01]
N2207 1911 2097 2091
LR Chi
2
72.05*** 6.76
+
61.03*** 14.97**
Notes: N= number of respondents; IRR = incidence rate ratios; CI = confidence interval; LR = likelihood ratio
+
p< 0.10; *p< .05; **p< .01; ***p< .001.
Studies in Higher Education 9
Table 4. Frequency and variety of academic misconduct over six months (measured in t
2
) by academic procrastination, gender, and semester (measured in
t
1
), based on multivariate negative binomial regression models.
Model 5 Model 6 Model 7 Model 8
Carrying crib notes or
other forbidden means
into exams
Copying parts of
protocols/homework from
others
Fabrication or falsification
of data/results/facts
Variety of academic
misconduct
IRR [95% CI]IRR [95% CI]IRR [95% CI]IRR [95% CI]
Academic procrastination 1.23*** [1.12 | 1.36] 1.29*** [1.17 | 1.43] 1.27** [1.10 | 1.46] 1.21*** [1.14 | 1.28]
Female 0.99 [0.81 | 1.21] 0.69*** [0.56 | 0.84] 0.81 [0.61 | 1.07] 1.07 [0.95 | 1.20]
Semester 0.96* [0.94 | 0.99] 0.89*** [0.86 | 0.92] 0.88*** [0.84 | 0.92] 0.95*** [0.93 | 0.97]
N2086 2202 1624 1359
LR Chi
2
22.80*** 85.66*** 42.23*** 68.49***
Notes: N= number of respondents; IRR = incidence rate ratios; CI = confidence intervals; LR = likelihood ratio
+
p< .10; *p< .05; **p< 0.01; ***p< 0.001.
10 J. Patrzek et al.
Discussion
In this panel study, we analyzed whether academic procrastination increases the fre-
quency of seven different forms of academic misconduct, as well as the variety of aca-
demic misconduct. Our study found that 75% of the students reported engaging in at
least one of the seven investigated behaviors during the last six months. This is
similar to the 70% reported by US and Canadian studies (Whitley 1998). In a more
recent survey by O’Rourke et al. (2010), 81% of all respondents reported having
been involved in some form of cheating during the last semester. On average, the stu-
dents in our study admitted to 1.44 of the investigated behaviors. The most frequent
behavior was copying from other students’papers in exams. The least frequent behav-
ior was plagiarism.
When comparing certain misconduct variables with the results of the review of
other studies, it seems that the prevalence rates among students from German univer-
sities are slightly lower. This result may be caused by systematic differences in the
detection probability of academic misconduct, resulting from differing awareness
levels among academic lecturers or the internalization of social norms that discourage
dishonesty. Generally, the methods (e.g. definition of the behavior, time frame of inves-
tigation, scales, etc.) used to assess prevalence may also matter when comparing certain
forms of misconduct. Further research is needed to shed light on such cross-cultural
differences.
We found that academic procrastination had robust effects on the frequencies of
different forms of academic misconduct, as well as on the variety of academic miscon-
duct. Procrastinating students were more often involved in dishonest behavior and
showed more variety during the last six months. These results are in line with correla-
tive results of prior cross-sectional studies of three different behaviors: fraudulent
Figure 1. Effect of academic procrastination on the frequency and variety of academic miscon-
duct over six months (based on Models 1 through 8 in Tables 3 and 4).
Notes: Plotted for male students in the fifth semester. SD = standard deviation.
Studies in Higher Education 11
excuse-making (Ferrari and Beck 1998; Ferrari et al. 1998), plagiarism, and cheating in
exams (Roig and DeTommaso 1995). However, our study enhances our knowledge of
the effect of academic procrastination, showing that it also frequently leads students to
carry and use forbidden means in exams, copy protocols or homework from other stu-
dents, and fabricate or falsify data. We found the strongest association between aca-
demic procrastination and fraudulent excuse-making. False excuses differ from the
other investigated behaviors in the sense that students can extend their deadlines to
complete a task. This time gain might be a strong motivator for procrastinators, as
they can make up for the time lost while procrastinating. Interestingly, Franklyn-
Stokes and Newstead (1995, 168) found that ‘time pressure’was one of the most fre-
quently mentioned reasons for academic misconduct, and Ferarri and Beck (1998, 533)
showed that ‘gaining additional time’was one reason why students used fraudulent
excuses. Academic procrastination might be understood as one important reason for
such time pressure. In this study, the QAP proved its predictive validity for academic
misconduct.
Future studies should investigate mediators and moderators that could influence the
effect of academic procrastination on academic misconduct. Following our argument,
important factors could include the time remaining before a deadline, the anticipation of
failure on a specific academic assignment, and the availability of other coping strategies
(e.g. forming study groups to make up for deficits in preparation for an assignment).
With regard to our control variable, gender, our findings were mixed; this is also the
case for many other studies in this area (e.g. Blankenship and Whitley 2000; Diekhoff
et al. 1999; Rettinger, Jordan and Peschiera 2004; Storch and Storch 2003; Vowell and
Chen 2004). Future research should investigate the effect of gender on academic mis-
conduct in greater detail. Among others, reasons for gender differences could be found,
for example, in different levels of moral beliefs, risk preferences, and other socialization
variations (e.g. Tibbetts 1997).
Our study also showed that students in later semesters of their study engaged in four
out of seven forms of academic misconduct less often than students in earlier semesters,
including copying from someone’s paper during exams (cf. Underwood and Szabo
2003; Weinstein and Dobkin 2002), and these students were involved in fewer forms
of misconduct. It can be argued that students in later semesters are more likely to
refrain from cheating because they have invested more time, effort, and financial
resources in their studies and are therefore more risk averse. Their losses would most
likely be greater than younger students’, should their misconduct be discovered (Under-
wood and Szabo 2003). Weinstein and Dobkin (2002) also argued that younger stu-
dents (age and the number of semesters are correlated) might have less awareness of
what is considered cheating. Furthermore, especially in the earlier semesters, examin-
ation formats might differ from those in later semesters. For example, in Germany,
exams are more frequently used than term papers early on in university programs, so
younger students have more opportunities to cheat on exams.
The results of our study might be used to direct activities and interventions under-
taken by university counseling services. We found that academic procrastination affects
all investigated forms of academic misconduct, and these results can be used to inform
university managements, academic advisors, academic teachers, and students about the
risks of academic procrastination. Not only does academic procrastination have unfa-
vorable effects on students’well-being and academic successes (e.g., Sirios, Melia-
Gordon and Pychyl 2003; Tice and Baumeister 1997), but it enhances the risk for enga-
ging in academic misconduct. Such information might raise awareness of the
12 J. Patrzek et al.
problematic and damaging character of academic procrastination and strengthen the call
for intervention programs that address academic procrastination.
Such intervention programs should deploy strategies that comprehensively broach
the issue of the time factor in academic study. Important components of these programs
could therefore include teaching strategies for time-management and goal setting to
promote self-regulated learning (cf. Zimmerman 2002) at the university level. Interven-
tions should help students to learn how to assess typical task durations for various
assignments, such as writing essays or studying for exams. Furthermore, students
should learn to account for how many hours per week they spend in lectures, at
work, or with their friends. Additionally, it could be helpful to teach students to
break their assignments down into small goals and deliberately allocate their resources
to these goals (cf. van Essen, van den Heuvel and Ossebaard 2004; van Horebeck et al.
2004).
Time-management and goal setting strategies should be conveyed not only through
intervention programs run by university counseling services but also in lectures during
the semester, so they can be applied directly on different university assignments. In par-
ticular, at the beginning of a course, the academic teacher could help the students to
develop a sense of the time requirements needed to accomplish certain assignments.
They could also inform their students of appropriate dates to start various assignments.
Furthermore, academic teachers can serve as role models for students by explaining
their strategies for working on different assignments, such as writing empirical articles
or preparing oral presentations.
Our study is not without limitations, which should be taken into account in future
studies. First, although our study yielded reasonable response and retention rates
(Babbie 2007; Groves 2006), several students did not respond to the survey, either
once or twice. However, the proportion of females is similar in both waves, and we
found no increase in dropout rates at the different levels of procrastination.
3
Second,
when interpreting our prevalence rates of academic misconduct and academic procras-
tination, one must consider that definitions and methodologies (such as operationaliza-
tions and anonymity) may have an impact on the results and their comparability to other
studies (see McCabe, Butterfield and Treviño 2012; Sattler 2007). Certain forms or
facets of specific types of misconduct may not be captured in our study, such as
having another individual take an exam vicariously or submitting a paper from a
paper mill. To ensure that students understand our items and respond according to
our definitions, we conducted extensive pretesting (e.g. with experts, using quantitative
pretesting and cognitive pretesting). However, because we used self-report measures,
we may observe some downward bias in the prevalence of these measures.
However, in our study, we paid strong attention to utilizing a fully anonymous pro-
cedure that protected all respondents’answers, which should reduce these potential
biases. We found no effects from the anonymity perception (concerning our survey)
on either the frequency and variety of academic misconduct or on academic procrasti-
nation (results available upon request). Third, we only controlled for gender and the
number of semesters when assessing the effect of academic procrastination on aca-
demic misconduct. In his review, Whitley (1998) discussed that cheating among
college students is influenced not only by the students’personality factors but also
by situational factors, such as honor codes, the numbers of students in classes, and cir-
cumstances during tests. Future research should analyze the interplay between aca-
demic procrastination, situational factors at universities, and academic misconduct.
Studies in Higher Education 13
We have already tested the interplay between procrastination, gender, and the number
of semesters, but no effects were found (results are available upon request).
In sum, our study implies that models explaining academic misconduct should
include academic procrastination as an influencing factor (cf. Whitley 1998).
However, the relevance of academic procrastination in the understanding of academic
misconduct should be continuously observed and confirmed in future studies due to the
need to evaluate the effect of counter-measures and changing study environments.
Acknowledgements
This research was supported by the German Federal Ministry of Education and Research
(BMBF, grant number 01PH08024, headed by Sebastian Sattler and Martin Diewald, and
grant number 01PH08005A, headed by Stefan Fries) and a Rectorate Fellowship of the Bielefeld
University for Sebastian Sattler (grant number 3521.01). The funders had no role in the study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Notes
1. Due to the reduction in sample size, we refrained from using a homogeneous analytical
sample consisting of respondents who received all questions on academic cheating (n=
1359) and took into account that our models were not based on the same population.
However, highly similar patterns were found when restricting the sample to n= 1359
(results available upon request).
2. Conducting the principal component analysis on the sample consisting of respondents who
received all questions on academic cheating (n= 1359) led to highly similar patterns (results
available upon request).
3. This was tested using academic procrastination, as measured in t
1
, as a predictor of partici-
pation in t
2
, based on a logit regression model (results are available upon request).
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