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Internet Addiction among Greek University Students: Demographic Associations with the Phenomenon, Using the Greek Version of Young's Internet Addiction Test

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Abstract

Internet addiction (IA) is a new disorder described in 1996 by the psychologist Kimberly Young. The aim of this paper is to estimate the percentage of IA among Greek university students. Results of a sample survey among 1876 Greek university students, 18-27 years old, are presented. The questionnaire consisted of eight questions from Young’s Diagnostic Test for Internet Addiction (YDTIA) as well as an inventory including demographic factors and questions about academic performance, computer and Internet use. YDTIA had a good reliability and diagnostic accuracy, tested with Cronbach’s alpha (0.71) and sensitivity analysis. Results show that the percentage of IA (5-8 YDTIA criteria) is 11.6%, while problematic Internet users were (3-8 YDTIA criteria) 34.7%. Men were more likely to be addicted to the Internet than women, and Internet addicted students were associated with poorer academic performance. Multiple logistic regression showed that significant predictors of IA included increased hours of daily Internet use, increased hours visiting chat rooms, sex pages and blogs, male gender, divorced status, poor grades, and accessing the Internet outside of the home. The results of this study will allow health officials to recognise students who are Internet addicted or on the verge of becoming addicted and stress risk factors indicating a need for intervention in order to prevent the appearance of IA.
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International Journal of Economic Sciences and Applied Research 3 (1): 49-74
Internet Addiction among Greek University Students:
Demographic Associations with the Phenomenon, using the Greek version
of Young’s Internet Addiction Test
Christos C. Frangos1, Constantinos C. Frangos2 and Apostolos P. Kiohos3
Abstract
Internet addiction (IA) is a new disorder described in 1996 by the psychologist
Kimberly Young. The aim of this paper is to estimate the percentage of IA among
Greek university students. Results of a sample survey among 1876 Greek university
students, 18-27 years old, are presented. The questionnaire consisted of eight questions
from Young’s Diagnostic Test for Internet Addiction (YDTIA) as well as an inventory
including demographic factors and questions about academic performance, computer
and Internet use. YDTIA had a good reliability and diagnostic accuracy, tested with
Cronbach’s alpha (0.71) and sensitivity analysis. Results show that the percentage of
IA (5-8 YDTIA criteria) is 11.6%, while problematic Internet users were (3-8 YDTIA
criteria) 34.7%. Men were more likely to be addicted to the Internet than women, and
Internet addicted students were associated with poorer academic performance. Multiple
logistic regression showed that signicant predictors of IA included increased hours
of daily Internet use, increased hours visiting chat rooms, sex pages and blogs, male
gender, divorced status, poor grades, and accessing the Internet outside of the home.
The results of this study will allow health ofcials to recognise students who are Internet
addicted or on the verge of becoming addicted and stress risk factors indicating a need
for intervention in order to prevent the appearance of IA.
Keywords: Greece, university students, Internet addiction, gender, academic performance,
sex pages
JEL classication: C83, I10, I21
1. Introduction
1.1 Denition: Internet Addiction
The Internet is a widely recognized channel for information exchange, academic
research, entertainment, communication and commerce (Moore, 1995; Widyanto and
Grifths, 2006; Douglas et al., 2008; Byun et al., 2009). Although the positive aspects of the
1. Department of Business Administration, Technological Educational Institute of Athens, Athens,
Greece - e-mail: cfragos@teiath.gr
2. Division of Medicine, University College London, London, UK
3. Department of Business Administration, Technological Educational Institute of Athens, Athens,
Greece
Christos C. Frangos, Constantinos C. Frangos and Apostolos P. Kiohos
50
Internet have been readily praised, there is a growing amount of literature on the negative
side of its excessive and pathological use (Chou and Hsiao, 2000; Caplan, 2003; Beard,
2005; Frangos and Frangos, 2009). Byun et al. (2009) estimate that 9 million Americans
could be labelled as pathological Internet users with unpleasant consequences for their
social life, their professional status and their psychological condition (Shapira et al., 2000;
Shapira et al., 2003; Young, 2004; Walker, 2006).
In the scientic literature, several terms have been proposed to describe pathological
Internet use: Internet addiction, cyberspace addiction, Internet addiction disorder, online
addiction, Net addiction, Internet addicted disorder, pathological Internet use, high
Internet dependency, problematic Internet use and others (Widyanto and Grifths, 2006;
Byun et al., 2009). To date, there is neither a conclusive nor a consistent denition for
this disorder, making it difcult to establish a coherent picture of this disorder throughout
the world. Nevertheless, efforts are being made to reach one uniform denition, which
might also be included in the DSM V, the authoritative guidebook for the diagnoses of
psychiatric disorders by the American Psychological Association (Block, 2008).
For purposes of this study, we chose the term Internet Addiction (IA) because it
was the rst term used to describe this phenomenon and for which the rst proposed
diagnostic criteria were based on an addictive disorder, that of pathological gambling
(Young, 1998; Widyanto and Grifths, 2006). Although the term addiction was combined
with technology in England before 1996 (Grifths, 1995), and even earlier the term
‘computer addiction’ had been used (Shotton, 1991), IA had been mentioned only as an
informal phrase by Ivan Goldberg, MD in 1995 (Federwisch, 1997; YouTube, 2008), in
order to describe excessive use of the Internet. However, it was not until 1996 when the
psychologist Kimberly Young gave a rst serious account of this disorder, proposing
diagnostic criteria and describing the collateral consequences of it on specic groups
(Young, 1996a; 1998). The major objections concerning this term were in the use of
the word “addiction”: although Young (1998) uses it to dene the compulsiveness
accompanying this disorder, Internet addiction is also accompanied with underlying
maladaptive cognitions, which would be better described psychologically if the term
‘problematic Internet use’ was used (Davis, 2001; Beard and Wolf, 2001). Moreover,
some researchers argue that a person’s overuse or abuse of the Internet is a behavioural
manifestation of other things that may be problematic in their lives (Thatcher et al.,
2008). Nevertheless, the term Internet addiction is frequently used in scholarly journals,
such as CyberPsychology & Behavior and Computers in Human Behavior. In a recent
attempt to meta-analyse quantitative data on IA, Byun et al. (2009, p. 204) note that the
matter of the denition of IA is the rst challenge to address and suggest developing “a
complete denition of Internet addiction that is not only conclusive but decisive, covering
all ages, gender, and educational levels”.
We follow the denition of IA, according to Beard’s holistic approach wherein “an
individual is addicted when an individual’s psychological state, which includes both mental
and emotional states, as well as their scholastic, occupational and social interactions, is
impaired by the overuse of the medium” (Beard, 2005, pp. 8-9). We use the eight-item
questionnaire as an assessment tool, proposed by Young (1996a; 1998) in her rst papers.
Young’s Diagnostic Test for Internet Addiction (YDTIA) consists of eight yes or no
questions about the use of the Internet. Respondents who answered ‘yes’ to ve or more of
51
Internet Addiction among Greek University Students: Demographic Associations with the
Phenomenon, using the Greek version of Young’s Internet Addiction Test
the eight criteria were classied as Internet addicted, and the rest were classied as non-
addicted (Young, 1998).1
1.2 University Students and Internet Addiction
A common group for studying IA has been students. University students are
considered as a high risk group for IA (Kandell, 1998; Young and Rogers, 1998; Nalwa
and Anand, 2003; Niemz et al., 2005). Possible reasons for this are: (a) students have huge
blocks of unstructured time, (b) schools and universities provide free and unlimited access
to the Internet, (c) students from the ages of 18 – 22 years are for the rst time away from
parental control without anyone monitoring or censoring what they say or do online, (d)
young students experience new problems of adapting to university life and nding new
friends, and often end up seeking a companionship by using different applications of the
Internet, (e) students receive full encouragement from faculty and administrators in using
the different Internet applications, (f) adolescents are more trained to use the different
applications of technological inventions and especially the Internet, (g) students desire to
escape university sources of stress resulting from their obligations to pass exams, compose
essays and complete their degrees in the prescribed time with reasonable marks, and nally
(h) students feel that university life is alienated from social activities, and when they nish
their studies, the job market with all its uncertainties is a eld where they must participate
and succeed in nding employment (Young, 2004).
Internet addiction in university students has been recorded through academic research
in the USA (Mitchell, 2000; Fitzpatrick, 2008), South Africa (Thatcher and Goolam, 2005a,b),
South Korea (Hur, 2006; Kim et al., 2006; Ko et al., 2006), Taiwan (Chou and Hsiao, 2000;
Lin and Tsai, 2002; Tsai and Lin, 2001; 2003), Norway (Johansson and Götestam, 2004), Eng-
land (Grifths, 1995; 1996a,b; 1997; 2000; Grifths et al., 1999), Italy (Ferraro et al., 2007),
Switzerland, China (Byun et al., 2009), and Cyprus (Bayraktar and Gün, 2007). However, in
Greece, no study has examined IA among university students. Several studies have been car-
ried out among adolescents, and several other studies have examined Internet use among high
school students in Greece (Aslanidou and Menexes, 2008; Siomos et al., 2008; Tsitsika et al.,
2009). Thus, we conducted an extensive literature review and discovered the demographic
factors associated with IA among university students.
1.3 Demographic Risk Factors for Internet Addiction
Gender
Studies indicate that the use of computers and the Internet differs between men and
women. Weiser (2000) gave an extensive review and executed a study on gender differenc-
es in Internet use patterns and Internet application preferences in a sample of 1190 surveys.
He concluded that there were numerous gender differences in preferences for specic Inter-
net applications. Results had shown that men use the Internet mainly for purposes related
1. The questions of YDTIA in English are included in Table 2. The Greek version for these eight
questions have been validated in earlier publications of ours and others (Siomos et al., 2008; Frangos
and Frangos 2009; Frangos et al., 2009).
Christos C. Frangos, Constantinos C. Frangos and Apostolos P. Kiohos
52
to entertainment and leisure, whereas women use it primarily for interpersonal communica-
tion and educational assistance. However, additional analyses showed that several gender
differences were mediated by differences in age and Internet experience. His results were in
accordance with many previous results which had shown mainly that women were less fa-
miliar with the use of the Internet (Georgia Tech GVU WWW survey, 1994), suggesting at
some period that men comprised 95 % of Internet users and women just 5%. Explanations
for this gender gap have been given and rely on gender differences in self-efcacy and at-
titudes toward computers (Busch, 1995). Male students are generally considered more ex-
perienced in programming and computer games than females and report having had more
encouragement from parents and friends previously, in contrast to women who might have
been discouraged from using modern technologies (Busch, 1995, p. 147). However, this
gender gap is predicted to decrease over the years, due to the fact that technology spreads
widely towards all available channels (Morahan-Martin, 1998; Sherman et al., 2000; Shaw
and Gant, 2002).
In Greece, two studies of high school pupils similarly mention that boys use com-
puters more than girls (Papastergiou and Solomonidou, 2005; Aslanidou and Menexes,
2008). Interestingly, Papastergiou and Solomonidou (2005) mention that boys have more
opportunities to access the Internet and use the Internet for entertainment and Web page
creation than girls do, with no other differences in other activities. Specically, the percent-
ages of boys in their sample who used their computer and accessed the Internet from home
were 50% and 29.4% respectively compare to 31.8% and 11.8% among girls (p<0.001).
Boys accessed the Internet more frequently than girls did (44% vs. 5%, p<0.001), while,
a higher percentage of boys than girls used the Internet in places outside the home (73.5%
vs. 55.3%, p< 0.001) (Papastergiou and Solomonidou, 2005).
The same gender gap has been noticed with IA. Morahan-Martin and Schumacher
(2000) reported that males were more likely than females to be pathological users (12%
vs. 3%), whereas females were more likely than males to have no symptoms (28% vs.
26%) or have limited symptoms (69% vs. 61%) of behavioural pathology. Scherer (1997)
reported that dependent Internet users included a signicantly larger proportion of men to
women (71% men and 29% women, respectively) than non-dependent users (50% men and
women). Thus, these studies, and several more, demonstrate that at least male college stu-
dents are more prone to IA (Chou et al., 2005; Widyanto and Grifths, 2006). The reasons
for male predominance in IA have been proposed to be overuse of pornography sites and
online gaming addiction. Tsai et al. (2009, p. 298) give a satisfactory explanation support-
ing the view that pornographic sites leads to more frequent IA:
“A study on gender differences in sexual arousal found that men tend to be
more visual with respect to sexual fantasies while women are more process
or verbally oriented. As the cost of bandwidths decreased drastically in recent
years, the Internet has become more abundant with graphical information. The
increased availability of pornography in cyberspace may be one of the reasons
for the higher prevalence rate of Internet addiction in males”.
Thus, we hypothesize that:
H1: Men are more likely to belong to the IA group than women.
53
Internet Addiction among Greek University Students: Demographic Associations with the
Phenomenon, using the Greek version of Young’s Internet Addiction Test
Academic performance
From the beginnings of research on IA, poor academic performance has been
associated with this disorder. Young (1998, p. 241) originally described this:
“Although the merits of the Internet make it an ideal research tool, students
experienced signicant academic problems when they surfed irrelevant web
sites, engaged in chatroom gossip, conversed with Internet pen pals, and played
interactive games at the cost of productive activity. Students had difculty
completing homework assignments, studying, or getting enough sleep to be
alert for class the next morning due to such Internet misuse. Oftentimes, they
were unable to control their Internet use, which eventually resulted in poor
grades, academic probation, and even expulsion from the university.”
This initial conclusion was consequently replicated in many studies with university stu-
dents. Grifths (2000) described a case of a Greek university student in the UK whose studies
had suffered considerably because he spent so much time on the Internet, which left him little
time to get on with his degree work. Morahan-Martin and Schumacher (2000) later measured
pathological Internet use, including now a new question on the extent to which academic
obligations suffered as a result of Internet usage; they found that 27.3% of students with
pathological Internet use had missed classes because of online activities. Kubey et al. (2001)
evaluated Internet dependency in a sample of 542 university students and found that 9% of
the participants classied themselves as being psychologically dependent on the Internet, and
also identied themselves as having trouble with schoolwork, missing class time, and having
a sense of guilt and lack of control over their Internet use. Internet dependent users seem to
be more likely to damage their academic careers due to excessive usage. The results support
greater use of the Internet by dependent users and increased probability for them to miss class
(Scherer, 1997).
Two very large studies from Asia demonstrated yet again the negative effect of ex-
cessive Internet use on academic performance. Chen and Peng (2008) conducted an online
survey on 49,609 students from 156 universities in Taiwan. They dened heavy Internet
users as those who used the Internet over 33.9 hours per week and those under this thresh-
old as non-heavy users. Differences in academic grades and learning satisfaction between
heavy and non-heavy Internet users were statistically signicant. Non-heavy users had bet-
ter grades and greater learning satisfaction than heavy users. Although the authors did not
study IA per se, the data suggested that students who spend a signicant amount of time
online, experience academic and learning difculties. A more recent study by Huang et al.
(2009) on a sample of 4,400 college students from China investigated IA, measured by
YDTIA, and examined whether poor academic achievement is a risk factor of IA. Multiple
logistic regression showed that poor academic achievement was a signicant risk factor of
IA (OR=1.54, p<0.001). The two factors of IA that cause poor academic attendance, are the
maladaptive cognitions related to Internet addiction (shyness, depression, low self-esteem)
(Davis, 2001; Yuen and Lavin, 2004), as well as the physical element of time loss. Internet
addicted users spend excessive amounts of time in front of their computers. Moreover, these
abnormal patterns of use cause lack of sleep because the user stays awake during late night
hours in order to surf different web pages. This lack of sleep causes a lack of concentration
and loss of interest in everyday lectures leading to reduced reading of course material and,
Christos C. Frangos, Constantinos C. Frangos and Apostolos P. Kiohos
54
consequently, poor marks during the exam period (Lavin et al., 1999; Yuen and Lavin, 2004).
Thus, we formed the following working hypothesis:
H2: Internet addicted students will present a poorer academic performance than
non-addicted Internet users. Additionally, this variable may be a risk factor of IA.
Family status
University students can be single, married or divorced. Although the possibility of mar-
riage among university students might seem low and even lower for divorce, we could argue
the opposite for Greek university students. In Greece circumstances could be different because
students are allowed to attend undergraduate courses on a free basis, giving them a greater op-
portunity to graduate or to complete their studies at a relatively older age. Moreover, many stu-
dents return to the university to complete another degree or because they didn’t access higher
education when they were younger. So, among Greek universities we could expect an existing
percentage of married or even divorced students. Unfortunately, there are not any studies on
percentages on this topic in Greece.
Married university students have always been regarded as a group with increased
stressors who might seek sources of social support much more than single or dating students
(Bayer, 1972). Until now, most results stem from research on graduate students. McRoy and
Fisher (1982) comment on the increasing number of married students attending universities
and note, “If appropriate support services are to be available for college students who are
married, it is important to understand the stresses on the marriages and on the students. Oth-
erwise, we can expect the dropout rate for students and the divorce rate for student marriages
to increase”. A recent review on marital satisfaction among graduate students suggested that
married students in graduate study experience marital strain that may affect their successes
in their marriage or graduate study (Gold, 2006).
The unique educational circumstances in Greece allow a certain degree of extrapola-
tion of these results to married undergraduate students. Taking into account as well that IA
is rather prevalent among university students, the combination of marriage and IA would
signicantly increase the stressors in a family. It has been reported that cybersex addiction
among couples, which is a variant of IA, has lead to serious interpersonal problems and
even to divorce (Hertlein and Piercy, 2006). Results from a survey on 94 subjects who had
experienced cybersex in their couple relationships, indicated that 22% of the respondents
had separated or divorced as a result of the compulsive cybersex (Schneider, 2000).
So, there could be a possible link between IA and family status, with worst family
status (e.g. divorced) being associated with IA. The question of IA and family status has
not yet been studied extensively in IA studies and among university students. Greek higher
education conditions afford the opportunity to explore this topic. Thus, we hypothesize:
H3: Divorced students are more likely to develop IA than married couples.
Location of Computer Usage and Internet Addiction
The presence of a computer with Internet access in a person’s environment is
necessary for the person to develop IA. Davis (2001) suggests that this is a necessary
contributory cause for the subject to develop pathological Internet use, which is similar to
55
Internet Addiction among Greek University Students: Demographic Associations with the
Phenomenon, using the Greek version of Young’s Internet Addiction Test
IA. This was part of his argument in the development of the cognitive behavioural model
of pathological Internet use (Davis, 2001)2. Research has shown that the environments of
Internet usage differ among each student. Some students prefer to access the Internet from
home, while others prefer to go outside of their home to places such as the school library or
an Internet café. Additionally, it has been proven that the location for accessing the Internet
has many times been associated with the development of IA (Young, 2004; Ceyhan, 2008).
Places where Internet access is unlimited or free, where there is no guardian or parental
supervision increase the possibility for a subject to remain on the Internet. As mentioned
above, university students are most prone to this, because in their dorms or in the university,
free and unlimited access to the Internet is available with no parental supervision, enabling
them to use it without restriction. In two studies on Greek adolescents, regression analyses
showed that the primary location of Internet access was a signicant risk factor for
predicting IA (Siomos et al., 2008; Tsitsika et al., 2009). Their results replicated those of
previous studies on adolescents from Norway (Johansson and Götestam, 2004). Thus, we
hypothesize:
H4: The location of Internet access is a signicant predictor of Internet addiction
among Greek university students.
1.4 Aim
This is the rst study of IA among Greek university students. In this paper, we
analyse the properties of the questionnaire used, which is the rst Greek questionnaire for
IA in university students, and give sociodemographic correlates. Furthermore, we assess
the prevalence of IA among Greek university students and nd possible risk factors of IA.
2. Methods
2.1 Sample
For the purposes of our study, we selected by randomized stratied selection a sample
of 1,876 students, from 18 to 27 years old (mean age 19.52 ± 2.38), studying in 36 classes
among 9 university and technological educational institute (TEI) departments in Athens,
Greece (TEI of Athens, TEI of Piraeus, Athens University of Economics and Business,
National & Kapodistrian University of Athens, Agricultural University of Athens). Of the
studied sample, 878 (47%) were male and 997 were female (53%). The desirable accuracy
of the sample or the maximum sampling error E, derived from the formula
2. In brief, Davis (2001) proposed a model of the aetiology of pathological Internet use using the
cognitive behavioural approach. The main assumption of the model was that pathological Internet
use resulted from problematic cognitions coupled with behaviours that intensify or maintain mal-
adaptive response (Widyanto and Grifths, 2006). It emphasized the individual’s thoughts/cogni-
tions as the main source of abnormal behaviour. Davis specied that the cognitive symptoms of
pathological Internet use might often precede and cause the emotional and behavioural symptoms
rather than vice versa. Similar to the basic assumptions of cognitive theories of depression, it focused
on maladaptive cognitions associated with pathological Internet use. Davis next ascribed to specic
psychopathologies and conditions, concepts of necessary, sufcient, and contributory causes. For a
more extensive description of each cause, see Davis (2001) and Widyanto and Grifths (2006).
Christos C. Frangos, Constantinos C. Frangos and Apostolos P. Kiohos
56
(Tabachnick and Fidell, 2000), was E = 0.02, where n = 1876, zα/2 = 1.96 is the 97.5 quin-
tile of the Normal Distribution, and α = 0.05. Table 1 summarizes additional demographic
information.
Table 1: Demographic information of the sample
Frequencies Percentages
Gender
Female 997 53.1
Male 878 46.8
NA*1 0.1
Age
18 ≤ x < 20 751 40.0
20 ≤ x < 22 637 34.0
22 ≤ x < 24 305 16.1
24 ≤ x < 26 108 5.8
26 ≤ x < 28 74 4.0
NA 1 0.1
Personal family status
Married 63 3.4
Not married 1742 92.9
Divorced 66 3.5
NA 5 0.2
Highest title of studies obtained
Lykeion Diploma 1632 87.0
Public or Private IEK 46 2.5
Ptychion from Tech. Ed. Inst.(TEI) 148 7.9
B.Sc. from University 12 0.6
Diploma of Postgraduate Studies 8 0.4
Private College (Inst. of Liberal Studies) 10 0.5
NA 20 1.1
Average mark of studies during the last semester
x < 5 94 5.0
5 ≤ x < 6.5 576 30.7
6.5 ≤ x < 8 737 39.3
8 ≤ x ≤ 10 148 7.9
NA 321 17.1
Average Mark of entrance exams of 1st year of
studies
x < 10 38 2.0
57
Internet Addiction among Greek University Students: Demographic Associations with the
Phenomenon, using the Greek version of Young’s Internet Addiction Test
Frequencies Percentages
10 ≤ x < 12 42 2.2
12 ≤ x < 14 56 3.0
14 ≤ x < 16 184 9.8
16 ≤ x < 18 179 9.5
18 ≤ x ≤ 20 137 7.3
NA 1240 66.1
Staying with parents or not
No 717 38.2
Yes 1145 61.0
NA 14 0.7
Are you working full time?
No 1065 56.8
Yes 355 18.9
NA 456 24.3
Are you unemployed?
No 810 43.2
Yes 861 45.9
NA 205 10.9
* NA: not answered
2.2 Questionnaire
The questionnaire contained three parts: demographic information, computer or In-
ternet use information and the YDTIA. The demographic section collected information
about gender, age, employment status, and family status. The computer or Internet use
portion reported information on the Internet applications that are most frequently used, the
location of the computer and the frequency of time spent in certain Internet applications.
Young’s Diagnostic Test for Internet Addiction (YDTIA) was presented in the introduction.
It consists of eight yes or no questions regarding the use of the Internet. In this study, “at-
risk Internet users” (ATRIU) were categorised as those who answered 3 to 4 criteria of the
YDTIA positively. The category of users who answered yes in 3 to 8 questions were clas-
sied as “problematic Internet users” (PIU). This denition has been followed by Siomos
et al. (2008), Johannson and Götestam (2005) and Tsai et al. (2009). YDTIA was translated
into Greek and back into English by two independent translators. The two versions were
then compared, choosing nally the best versions for each question.
Characteristics of YDTIA
The eight items of YDTIA were subjected to principal component analysis
(PCA). Prior to performing PCA, the suitability of data for factor analysis was assessed.
Christos C. Frangos, Constantinos C. Frangos and Apostolos P. Kiohos
58
Inspection of the correlation matrix revealed the presence of many coefcients of
0.300 and above and Spearman’s correlations calculated between the eight items were
statistically signicant at the 0.001 level of signicance (p<0.001). The Kaiser-Meyer-
Oklin value was 0.81, exceeding the recommended value of 0.6 and Bartlett’s Test of
Sphericity reached statistical signicance, supporting the factorability of the correlation
matrix (Pallant, 2007). PCA revealed the presence of two components with eigenvalues
exceeding 1, explaining 34.3% and 13.9% of the variance respectively. An inspection
of the screeplot revealed a clear break after the second component. Using Catell’s scree
test, it was decided to retain two components for further investigation. This was further
supported by the results of parallel analysis, which showed only two components with
eigenvalues exceeding the corresponding criterion values for a randomly generated data
matrix of the same size (8 variables x 1876 respondents). The two-component solution
explained a total of 48.2% of the variance, with Component 1 contributing 34.3% and
Component 2 contributing 13.9%. These values are acceptable because other authors
have mentioned similar values of eigenvalues for YDTIA (Johansson and Götestam,
2005; Siomos et al., 2008).
The reliability of YDTIA was tested with Cronbach’s alpha (0.71) and Cronbach’s
alpha based on standardized items (0.72); also the Spearman-Brown coefcient was 0.72,
all values indicating satisfactory reliability. Thus, the YDTIA has a good reliability and
dimensionality.
Specicity, Sensitivity and Diagnostic Accuracy of the YDTIA for the Study Participants
The eight diagnostic criteria of YDTIA are considered in this section. The sensitivity of
a Diagnostic Criterion “A” refers to the probability of a positive answer in A by participants
who are addicted according to YDTIA. It measures how well A detects the addiction.
The specicity of a Diagnostic Criterion A refers to the probability of a negative
answer in A by participants who are not addicted according to YDTIA. It measures how
well the Diagnostic Criterion A excludes addiction. Diagnostic accuracy refers to the overall
probability of the detection or exclusion of the addiction due to the answer to Diagnostic
Criterion A of the test (American Psychiatric Association, 1994; Riffenburgh, 2005). The
positive prognostic value of Diagnostic Criterion A refers to the percentage of participants
who answered A positively and are addicted, from all the participants who answered
positively in Criterion A. Finally, the negative prognostic value refers to the percentage of
participants who answered negatively in A and are not addicted, from all the participants
who answered negatively in Criterion A. From Table 2 we nd that the fourth diagnostic
criterion of Young, “Do you feel restless, moody, depressed or irritable when attempting to
cut down or stop Internet use?” has the highest diagnostic accuracy (88.4%).
59
Internet Addiction among Greek University Students: Demographic Associations with the
Phenomenon, using the Greek version of Young’s Internet Addiction Test
Table 2: Specicity, Sensitivity and Diagnostic Accuracy of YDTIA
for the Study Participants
Answers
of
addictive
users
Answers
of non
addictive
users
Sensitivity
Specicity
Diagnostic
Accuracy
Positive
prognostic value
Negative
prognostic value
YES NO YES NO
(1) Do you feel
preoccupied with the
Internet (i.e., think
about previous online
activity or anticipate
next online session)?
201 34 478 1127 85.5% 70.4% 72.1% 29.6% 97.1%
(2) Do you feel
the need to use
the Internet with
increasing amounts
of time in order to
achieve satisfaction?
196 44 290 1324 81.7% 82.0% 81.9% 40.3% 96.8%
(3) Have you
repeatedly made
unsuccessful efforts
to control, cut back,
or stop Internet use?
141 95 113 1497 59.7% 92.9% 88.1% 55.5% 94.0%
(4) Do you feel
restless, moody,
depressed, or
irritable when
attempting to cut
down or top Internet
use?
157 79 133 1472 66.5% 91.7% 88.4% 54.1% 94.9%
(5) Do you stay
online longer than
originally intended?
208 28 909 690 88.1% 43.2% 48.9% 18.6% 96.1%
(6) Have jeopardized
or risked the loss
of a signicant
relationship, job,
educational, or
career opportunity?
143 98 135 1472 59.3% 91.6% 87.4% 51.4% 93.8%
Christos C. Frangos, Constantinos C. Frangos and Apostolos P. Kiohos
60
Answers
of
addictive
users
Answers
of non
addictive
users
Sensitivity
Specicity
Diagnostic
Accuracy
Positive
prognostic value
Negative
prognostic value
YES NO YES NO
(7) Have you lied to
family members, a
therapist, or others
to conceal the extent
of your involvement
with the Internet?
141 99 141 1465 58.8% 91.2% 87.0% 50.0% 93.7%
(8) Do you use the
Internet as a way
of escaping from
problems or of
relieving a distressed
mood (e.g., feelings
of helplessness,
guilt, anxiety,
depression)?
182 60 375 1227 75.2% 76.6% 76.4% 32.7% 76.6%
2.3 Statistical Analysis
We performed univariate analysis to examine the factors of our questionnaire
associated with Internet addiction. Chi-square values, degree of freedom and levels of
signicance are reported. Next, we performed multiple logistic regression with IA as the
dependent variable and independent variables including several demographic variables.
In all calculations, p values under 0.05 were considered signicant. All gures and graphs
were produced with SPSS 16.0, Stata 10.0 and SigmaPlot 10.0.
3. Results
3.1 Internet Use and Internet Addiction According to YDTIA
The patterns of computer and Internet use are shown in Table 3 and Table 7 in Ap-
pendix. It is evident that 93.2% had knowledge of computers and a similar percentage had
knowledge of computer applications (90.8%). Most of the students in the sample accessed
their computer from home, followed by Internet cafés (5.8%) and nally at the university at
which they studied (4.8%). The great majority of students did not pay for their own Internet
subscription (64.7%). Only one-third of our sample (31.3%) had the European Computer
Driving License (ECDL) diploma.
61
Internet Addiction among Greek University Students: Demographic Associations with the
Phenomenon, using the Greek version of Young’s Internet Addiction Test
Table 3: Computer and Internet Use
Frequencies Percentages
Computer knowledge
No 88 4.7
Yes 1748 93.2
NA*40 2.1
Computer applications knowledge
No 110 5.9
Yes 1704 90.8
NA*62 3.3
Computer access location
Home 1527 81.4
School 90 4.8
Internet Café 109 5.8
Friends’ house 70 3.7
Elsewhere 47 2.5
NA*33 1.8
Do you pay for your own Internet subscription?
No 1213 64.7
Yes 472 25.2
NA*191 10.2
Have you obtained an ECDL diploma?
No 1102 58.7
Yes 587 31.3
NA*187 10.0
Internet experience
1 year 323 17.2
2 years 298 15.9
3 years 334 17.8
4 years 230 12.3
5 years 207 11.0
more than 5 years 447 23.8
NA*37 2.0
Hours of Internet use daily (hs)
x < 0.5 318 17.0
0.5 ≤ x < 1 388 20.7
1 ≤ x < 2 282 15.0
2 ≤ x < 3 221 11.8
3 ≤ x < 4 191 10.2
4 ≤ x < 5 140 7.5
Christos C. Frangos, Constantinos C. Frangos and Apostolos P. Kiohos
62
Frequencies Percentages
5 ≤ x < 6 98 5.2
6 ≤ x < 7 58 3.1
7 ≤ x < 8 41 2.2
8 ≤ x < 9 17 0.9
9 ≤ x < 10 15 0.8
more than 10 hs 76 4.1
NA*31 1.7
* NA: not answered
The percentage of Internet addicted students was 11.6% and the percentage of at-risk
Internet users was 23.1%. The percentage of problematic Internet users (who present 3 to
8 criteria of YDTIA) was 34.7%.
We were also interested in determining the Internet use time patterns according to
the criteria satised in YDTIA. We designated very frequent (VFIU) and frequent (FIU)
Internet users, the ones who used the Internet for more than 28 hours per week and for 8 to
27 hours per week respectively. Table 4 shows the following: a) The percentage of VFIU
was 24.1% b) The percentage of FIU was 37.6%. c) It is evident that students who satisfy
5-8 criteria of YDTIA (which signies that they are addicted Internet users), are in a much
greater percentage (45.3%) VFIU than those who satisfy fewer criteria.
Table 4: Percentages of users classied according to positive YDTIA criteria among
categories of Internet usage per week
YDTIA
0
Criteria
N (%)
1-2
Criteria
N (%)
3-4
Criteria
N (%)
5-8
Criteria
N (%)
Total
N (%)
Non frequent Internet
users (0-7 h / week) 225 (56.8) 295 (38.6) 122 (28.1) 61 (25.1) 703 (38.2)
Frequent Internet users
(8-27 h / week) 127 (32.1) 326 (42.6) 167 (38.5) 72 (29.6) 692 (37.6)
Very frequent Internet
users (> 27 h / week) 44 (11.1) 144 (18.8) 145 (33.4) 110 (45.3) 443 (24.1)
3.2 Sociodemographic and Academic Performance Associations with IA
In Table 5 the associations of IA with regard to gender, family condition, academic
performance and location of computer of the study participants are displayed. The statistical
signicance of differences in percentages was done using chi-square statistics.
63
Internet Addiction among Greek University Students: Demographic Associations with the
Phenomenon, using the Greek version of Young’s Internet Addiction Test
Table 5: Sociodemographic and academic characteristics
of the sample of university students
Positive Diagnostic Criteria (yes) in YDTIA
At risk
Internet users
Addicted Users
0 Positive
criteria
N (%)
1-2
Positive
criteria
N (%)
3-4 Positive
criteria
N (%)
5-8 Positive
criteria
N (%)
Total
N (%)
Section 1
Gender
differences
χ²=45.38, df=3, p<0.001
Male 160
(40.4%) 329 (43%) 220 (50.7%) 156 (64.2%) 877 (47%)
Female 236
(59.6%) 436 (57%) 214 (49.3%) 87 (35.8%) 990 (53%)
Section 2
Location of
computer
differences
χ²=37.34, df=15, p<0.001
Home 311
(77.9%)
651
(85.1%) 374 (86.0%) 187 (78.6%) 1523 (83%)
School 23 (5.8%) 35 (4.6%) 20 (4.6%) 11 (4.6%) 89 (5%)
Internet Café 26 (6.5%) 40 (5.2%) 23 (5.3%) 20 (8.4%) 109 (5.9%)
Friend’s
house 30 (7.5%) 23 (3.0%) 10 (2.3%) 7 (2.9%) 70 (3.8%)
Elsewhere 9 (2.3%) 16 (2.1%) 8 (1.8%) 13 (5.5%) 46 (2.5%)
Christos C. Frangos, Constantinos C. Frangos and Apostolos P. Kiohos
64
Positive Diagnostic Criteria (yes) in YDTIA
At risk
Internet users
Addicted Users
0 Positive
criteria
N (%)
1-2
Positive
criteria
N (%)
3-4 Positive
criteria
N (%)
5-8 Positive
criteria
N (%)
Total
N (%)
Section 3
Academic
performance
differences
χ²=31.45, df=9, p<0.0001
AVEMARK=Average mark in the last semester
AVEMARK
< 5 16 (4.9%) 26 (4.1%) 28 (7.4%) 23 (11.1%) 93 (6%)
5 ≤
AVEMARK
< 6.5
116
(35.5%)
234
(36.9%) 137 (36.1%) 87 (41.8%) 574 (37.1%)
6.5 ≤
AVEMARK
< 8
159
(48.6%)
325
(51.3%) 178 (46.8%) 71 (34.1%) 733 (47.3%)
8 ≤
AVEMARK
≤ 10
36
(11.0%) 49 (7.7%) 37 (9.7%) 27 (13.0%) 149 (9.6%)
Section 4
Family
condition
differences
χ²=31.2, df=12, p<0.001
Married 15 (3.7%) 25 (3.2%) 16 (3.7%) 8 (3.3%) 64 (3.4%)
Single 380
(92.7%)
732
(94.6%) 408 (93.6%) 213 (87.7%) 1733
(93.1%)
Divorced 15 (3.7%) 17 (2.2%) 12 (2.8%) 22 (9.1%) 66 (3.5%)
There is a statistically signicant difference in gender for Internet addicted users (5
to 8 positive criteria in YDTIA). Males were dependent at a higher percentage than females
(64.2% vs. 35.8% among Internet addicted users, p<0.0001). Concerning family condition,
there was a signicant association with Internet addiction. Moreover, we can see that the
percentage of divorced students who are addicted to the Internet (9.1%) is greater than that
of married students who are Internet addicted (3.3%). In the other categories of positive
65
Internet Addiction among Greek University Students: Demographic Associations with the
Phenomenon, using the Greek version of Young’s Internet Addiction Test
diagnostic criteria, the percentage of married and divorced is generally similar; however,
the small percentages do not allow for a causal deduction of conclusions.
Regarding academic performance, in Internet addicted users (5-8 YDTIA) the
percentage of students who failed in the last semester (11.1%) is signicantly higher than
those who failed in the group of normal Internet users (0 and 1-2 YDTIA) (4.9% and 4.1%
respectively, p<0.05 in both differences). Moreover, in the group of addicted users, students
with grades under 6.5 (52.9%) are slightly higher than students grades over 6.5 (47.1%) (p
= 0.23). The percentage of addicted users who achieved marks in the scale “very good” or
“excellent” (6.5 to 10) is 47.1%, and it is lower than the corresponding percentage of the
normal Internet users with 0 positive criteria in YDTIA (59.6%) (p = 0.005).
The location of computer usage was also associated signicantly with Internet
addiction (p<0.001). It is worth noting that the group of students addicted to Internet is
more likely to visit Internet cafés than the other three groups (8.4% vs. 5.3%, 5.2%, 6.5%),
although these proportion differences are not signicantly different (p = 0.12, p = 0.07, p
= 0.37 respectively).
3.3 Predicting Factors of Internet Addiction
We performed multiple logistic regression with Internet addiction as the dependent vari-
able to assess the impact of a number of factors on the likelihood of developing IA. The model
contained nine independent categorical variables: gender, location of computer usage, family
status, staying with parents, Internet daily use (hs), average marks during last semester, viewing
sex pages, viewing chatrooms, and viewing blogs. The full model containing all predictors was
statistically signicant, χ2 = 192.09, df = 37, p<0.0001, indicating that the model was able to dis-
tinguish between Internet addicted and non-addicted students. The model as a whole explained
between 14.6% (Cox and Snell R square) and 26.6% (Nagelkerke R square) of the variance in
IA, and correctly classied 87.4% of cases. The odds ratios are presented in Table 6 and in Fig-
ure 1. All of the independent variables (in various categories) were signicant predictors of IA,
except for the variable “staying with parents” (Table 6). Although the odds ratio (OR) for gen-
der is not signicant, this result is only borderline (p = 0.067). So, male gender is most likely a
positive predictor of IA, but given this model, we cannot produce an effect. Moreover, students
who accessed the Internet from Internet cafés were more likely to develop IA than students who
accessed it through their homes (OR = 2.11, 95% CI 1.06-4.20). In regard to the family condi-
tion of students, divorced students were signicantly more likely to develop IA than married
students (OR = 4.33, 95% CI 1.23-15.29). With reference to academic performance, students
who had an average grade during the last semester between 5 and 8 out of 10 were about half
as likely to develop IA, compared to students who had grades under 5. Concerning general pat-
terns of Internet use, students who used the Internet for more hours during the day and visited
sex pages, chat rooms and blog sites, were more likely to become Internet addicted.
Christos C. Frangos, Constantinos C. Frangos and Apostolos P. Kiohos
66
Table 6: Multiple logistic regression with Internet addiction
as the dependent variable
Code Variables Odds Ratio p-value OR 95% CI
Gender*
Var1 Male 1.47 0.067 0.97 2.21
Computer access location
Var2 School 1.02 0.961 0.39 2.66
Var3 Internet Café 2.11 0.033 1.06 4.20
Var4 Friends’ house 0.79 0.714 0.22 2.86
Var5 Elsewhere 1.85 0.222 0.69 5.00
Personal family Status
Var6 Not married 2.31 0.134 0.77 6.91
Var7 Divorced 4.33 0.023 1.23 15.29
Staying with parents or not§
Var8 Yes 0.90 0.598 0.61 1.32
Internet daily use (hs)**
Var9 0.5 ≤ x < 1 1.19 0.677 0.53 2.68
Var10 1 ≤ x < 2 1.86 0.141 0.81 4.23
Var11 2 ≤ x < 3 1.40 0.441 0.59 3.33
Var12 3 ≤ x < 4 2.40 0.036 1.06 5.46
Var13 4 ≤ x < 5 2.87 0.013 1.25 6.60
Var14 5 ≤ x < 6 2.39 0.061 0.96 5.96
Var15 6 ≤ x < 7 2.03 0.189 0.71 5.87
Var16 7 ≤ x < 8 4.58 0.003 1.67 12.55
Var17 8 ≤ x < 9 2.45 0.285 0.47 12.69
Var18 9 ≤ x < 10 3.70 0.086 0.83 16.53
Var19 more than 10 hs 2.41 0.060 0.96 6.00
Average mark of studies during the last semester††
Var20 5 ≤ x < 6.5 0.50 0.037 0.26 0.96
67
Internet Addiction among Greek University Students: Demographic Associations with the
Phenomenon, using the Greek version of Young’s Internet Addiction Test
Code Variables Odds Ratio p-value OR 95% CI
Var21 6.5 ≤ x < 8 0.39 0.005 0.21 0.76
Var22 8 ≤ x ≤ 10 0.58 0.185 0.26 1.29
Viewing Sex pages‡‡
Var23 1 ≤ x < 3 1.13 0.671 0.64 1.99
Var24 3 ≤ x < 5 3.40 0.001 1.70 6.80
Var25 5 ≤ x < 7 3.19 0.015 1.25 8.15
Var26 7 ≤ x < 9 1.68 0.408 0.49 5.73
Var27 x ≥ 9 2.70 0.005 1.34 5.41
Viewing chat rooms§§
Var28 1 ≤ x < 3 1.01 0.956 0.60 1.72
Var29 3 ≤ x < 5 2.22 0.011 1.20 4.08
Var30 5 ≤ x < 7 2.51 0.012 1.22 5.17
Var31 7 ≤ x < 9 4.78 0.000 2.07 11.00
Var32 x ≥ 9 4.38 0.000 2.04 9.43
Viewing blogs***
Var33 1 ≤ x < 3 2.15 0.001 1.35 3.43
Var34 3 ≤ x < 5 2.49 0.001 1.43 4.33
Var35 5 ≤ x < 7 1.40 0.441 0.59 3.32
Var36 7 ≤ x < 9 0.45 0.202 0.13 1.54
Var37 x ≥ 9 0.87 0.785 0.32 2.37
Notes: i. CI: condence interval. ii. Reference values for each category: *female; home; married;
§No; **x < 0.5; ††AVEMARK < 5; ‡‡, §§,***0 ≤ x < 1
Christos C. Frangos, Constantinos C. Frangos and Apostolos P. Kiohos
68
Figure 1: A scatter plot of the odds ratios produced from multiple logistic regression.
The variables Var1-Var37 are explained in Table 6
4. Discussion
We performed the rst cross-sectional study of university students in Greece
to estimate the percentage of Internet addiction. The diagnostic tool used was Young’s
Diagnostic Test for Internet Addiction (Young, 1996a; 1998). We tested the reliability and
dimensionality of YDTIA and it was in satisfactory accordance with the results of other
studies (Johansson and Götestam, 2004; Siomos et al., 2008). The most signicant result
was that the percentage of IA was 11.6% among our sample, while that of at-risk Internet
users was 23.1%. We further dened problematic Internet users as the ones who full 3 to
8 criteria of YDTIA, and found a percentage of 34.7% students met the criteria. Siomos et
al. (2008) examined Internet addiction among Greek adolescents (12-18 years of age) and
found that 8.2% were addicted to the Internet (6.2% for males and 2% among females), a
percentage relatively close to that of this study. This classication allows health ofcials
to recognise students who are on the verge of becoming addicted and signies a point of
intervention in order to prevent the appearance of IA.
Additionally, we found signicant associations of IA with gender, location of
computer usage, family status, and academic performance. The prole of the user addicted
to the Internet is a male person, who accesses the most likely from Internet public spots,
has poor academic achievement and might be divorced. We performed multiple logistic
regression with Internet addiction as the dependent variable, and results showed that in-
creased hours of daily Internet use, increased hours visiting chat rooms, sex pages and
blogs, male gender, being divorced, poor grades, and accessing the Internet outside of the
home were signicant predictors of IA. We set four hypotheses in the Introduction and we
tested them to examine their validity.
69
Internet Addiction among Greek University Students: Demographic Associations with the
Phenomenon, using the Greek version of Young’s Internet Addiction Test
Concerning the rst hypothesis, we truly are in accordance with other researchers,
because we found that male students were more likely to be addicted to the Internet and
male gender predicted marginally IA on multiple logistic regression. This gender difference
is explained by the preference of men to use the Internet for sexual satisfaction (e.g. view-
ing sex pages) more than women do as well as the increased frequency of online gaming
compared to that among females (Young, 1998; 2004; Kraut et al., 2002; Ybarra and Mitch-
ell, 2005; Tsai et al., 2009;). Accordingly, we found that viewing sex pages predicted IA,
but our results do not support online gaming as a risk factor of IA. An explanation for the
lower percentage of IA among females, involves the fact that female college students often
receive more family supervision than males, which may prevent females from spending as
much time on the Internet (Tsai et al., 2009).
Concerning our second hypothesis, we found that academic performance was sig-
nicantly associated with IA, and poorer grades were a predictor. This result is in accord-
ance with other studies put forward in the introduction. Usually, IA causes this outcome
because the student loses his capacity to concentrate, most possibly because of late-night
Internet sessions. Our third and fourth hypotheses involved the association of IA with
family status and the point of accessing the Internet. Being divorced was associated with
IA and predicted the phenomenon, and the location of using the computer was also as-
sociated with IA. Students who accessed the Internet from Internet cafés were more likely
to develop IA than those who accessed it from home. Impaired family status leading to
IA could be explained by the cognitive behavioural model of Davis (2001). This model
suggests that the presence of maladaptive cognitions, as a result of personal or social dis-
appointments, is a necessary cause to create IA. A divorced person possibly experiences
negative feelings resulting from his divorce, such as “I have failed my marriage”, “I might
not get married again”, “It is my fault we divorced”, “I feel lonely”. This low self-esteem
and self-accusatory attitude may nd sympathy from others in Internet forums or chat
rooms. Hence, they will experience positive emotions from this use, such as feeling more
qualied, more social and more comfortable, and these positive feelings play a reinforcing
role in the continued use of the Internet.6 However, it could be that IA addiction leads to
divorce in married couples. This has been readily described in studies of online indel-
ity and cybersex experiences (Schneider, 2000). The cross-sectional nature of this study
does not allow us to support whether divorce among students with IA was a result of this
behavioural addiction or a cause of it. A prospective design of following up with a group
of students with IA could distinguish this.
Overall, IA is a serious behavioural addiction. There is a need for a campaign to in-
form parents, teachers and state ofcials about the dangers of the Internet, which are apart
from IA, online gambling, trafcking of pornographic material, cybersex and cyberbully-
ing (Young, 2004; Patchin and Hinduja, 2006).
6. This situation is indicative of the conict described in many addictions. On the one hand, the per-
son is harmed by the behaviour he is addicted to, and on the other hand he experiences the enhancing
emotional changes that lead to the recurrence of the addictive behaviour.
Christos C. Frangos, Constantinos C. Frangos and Apostolos P. Kiohos
70
Acknowledgements
We thank the useful comments of two anonymous reviewers, which signicantly
improved the standards of this paper.
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Appendix
Table 7: Time use of certain Internet applications
* NA: not answered
F: Frequency, P: percentage
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Statistics in Medicine, Second Edition, makes medical statistics easy to understand and applicable to students, practicing physicians, and researchers. The book begins with the presentation of databases from clinical medicine and uses such data throughout to give multiple worked-out illustrations of every method. The text uses a unique approach, organizing the material into two Parts. Part I is an introductory, core-concepts guide for students in medicine, dentistry, nursing, pharmacy, and other health care fields. Part II then provides a reference manual to support practicing clinicians in reading medical literature and conducting research studies. Book jacket.
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Statistics in Medicine, Second Edition, makes medical statistics easy to understand and applicable to students, practicing physicians, and researchers. The book begins with the presentation of databases from clinical medicine and uses such data throughout to give multiple worked-out illustrations of every method. The text uses a unique approach, organizing the material into two Parts. Part I is an introductory, core-concepts guide for students in medicine, dentistry, nursing, pharmacy, and other health care fields. Part II then provides a reference manual to support practicing clinicians in reading medical literature and conducting research studies. Book jacket.