THE MODEL
EXPLAINING AND PREDICTING LONELINESS LEVEL AND THE PROBLEMATIC INTERNET USE OF
TURKISH COMPUTER EDUCATION AND INSTRUCTIONAL TECHNOLOGIES (CEIT) STUDENTS
Aylin
TUTGUN ÜNAL
Maltepe
University, Faculty of Education
Computer
Education and Instructional Technologies Department
Istanbul,
Turkey
Abstract
The objective of present research is to develop a
model explaining and predicting relationship between loneliness level and
problematic internet use. The problematic internet use of Turkish Computer
Education and Instructional Technologies (CEIT) students has been described
with respect to the levels of problematic internet use and loneliness. The subjects
were CEIT students from 3 different universities in Istanbul, Turkey. In data
gathering, Personal Information Form, Generalized Problematic Internet Use
Scale 2 (GPIUS2) and UCLA Loneliness Scale have been used. In the research, it
has been found that problematic internet use of CEIT students is above medium
level. It has been concluded that CEIT
students' level of loneliness is medium and as their level of loneliness
increase, their problematic internet use also rises in the same manner. As for
the model, it has been found that loneliness level of the CEIT students is a
significant predictor of problematic internet use, thus it explains and
predicts 22 percent of the variance of problematic
internet use of CEIT students.
Keywords: Problematic Internet Use, Internet,
Technology, Students, Loneliness
1. Introduction
Parallel to the rapid rise in technology, the use of
information and communication technologies has also become widespread hence
computer and internet has become one of the indispensables of life. The use of
computer and internet technologies in education has become a great necessity as
the result of rapid developments in science and technology and this necessity
has made it a must for the instructor and students to make use of computer and
internet technologies. According to Deniz and Coşkun (2004), the internet as an
educational tool provides the way to affordable, global, interactive and
extensive computer communication and enables the student to raise his/her
learning experience. By virtue of all the means computer and internet use
provides, the necessity to learn and teach these technologies has also
surfaced. As a requirement to fulfill this demand computer and internet
technologies teaching departments have been established at universities. One of
these departments is “Computer
Education and Instructional Technologies Department” that constitutes the study group of present research.
Internet
has been used for various purposes particularly by young population. As stated
by Erikson (1998) the primary developmental task of university youth is to
establish close relations with the same or opposite sex. Today, young
population prefers to meet this need on the net social webs rather than face to
face communication in real world. In relevant literature there are several
researches supporting this deduction (Caplan, 2005; Ceyhan, Ceyhan and Gürcan,
2007; Deniz and Tutgun, 2010; Tutgun, Deniz and Moon, 2011; Tutgun, 2009).
Through affordable and interactive applications provided by cell phones in
particular, the students meet their social communication needs and they reserve
more time to online social interaction than real life social experiences.
On the
other hand, the teenagers who fail to develop their social skills in natural
social environment are likely to face problems in their familial and work
environments, particularly in family and work places, in future and driven from
this point it is observed that many of the studies related to problematic
internet use are particularly directed
to university students (Anderson, 2001; Caplan, 2010; Morahan-Martin and
Schumacher, 2000; Lavin, Marvin et al., 1999; Tsai and Lin, 2001; Niemz,
Griffiths et al., 2005; Young, 2006).
As put
forth by Caplan (2005) Problematic internet usage is a multi-dimensional
syndrome composed of cognitive and behavioral symptoms causing negative social,
academic/professional outcomes. Kandell (1998) defined internet addiction as a
psychological addiction which particularly affected teenagers and he emphasized
that excessive usage of internet was likely to introduce problems related to
health, social relations and time management. The researches covering young
population, university students in particular, have manifested rather critical
findings and revealed that university students formed the most risky group
(Ceyhan, Ceyhan and Gürcan, 2007; Kandell, 1998; Lavin, Morahan-Martin and
Schumacher, 2000; Tutgun and Deniz, 2010; Tutgun, Deniz and Moon, 2011; Young,
2006).
In
diagnosing internet dependence, pathologic or problematic internet use, many
researchers regarded the time spent on internet as the major criteria (Young
and Rogers, 1998; Young, 1996a, 1996b). Time spent on the net is, though
significant, not a sufficient factor on its own in diagnosing problematic
internet use. At the universities, instructor/student
communication, assignment/project researches, free internet labs are obviously
facilitators of students’ internet use.
Furthermore
the students from technology related specific departments, compared to students
from other departments, use the computer and internet more frequently. Internet
is an environment open to all kinds of information and views. Internet may
become a huge risk factor particularly for students who fail to limit their
internet use in line with the needs. According to research findings amongst
teenagers, specifically university students, the students are greatly inclined
to problematic internet use and face hardship in setting control.
In
Tutgun (2009)’s research it has been found that compared to social sciences,
science and mathematics departments and fine arts departments students, the ones
in Computer
Education and Instructional Technologies Department are more inclined to problematic internet use. As the other departments
were analyzed within themselves, no difference was found. Based on this point,
it should be analyzed that if computer and internet use related departments may
be leading to problematic internet use. Therefore, the problematic internet use of
technology related departments should be analyzed distinctively and the leading
causes should be underlined; then more
specific solutions can be generated and the source of problem can be detected
more evidently. On accounts of all these reasons “problematic internet use at
Department of CEIT” constitutes the problem of current research.
The
objective of present research is to develop a model explaining and predicting relationship between loneliness level and problematic
internet use. In line with this objective, the study is trying to find answer
in following questions:
1. What is the level of problematic internet usage of CEIT students?
2. What is the relationship between problematic internet use and loneliness
levels of CEIT students?
3. What is the model explaining and
predicting
relationship between loneliness level
and problematic internet use of CEIT students?
2.
Method
Structural
equation modeling (SEM) is used in the study. SEM grows out of and serves
purposes similar to multiple regression, but in a more powerful way it takes
into account the modeling of interactions, nonlinearities, correlated
independents, measurement error, correlated error terms, multiple latent
independents each measured by multiple indicators. SEM may be used as a more
powerful alternative to multiple regression, path analysis, factor analysis and
analysis of covariance (Byrne, 2001).
Participants
The participants were 162 Turkish CEIT students from three universities
in Istanbul in Turkey (Marmara University, Maltepe University and Yıldız
Technical University). 53,1 % (n=86) of
the CEIT students are freshmen and 46,9% (n=76) are seniors. 38,9% (n=63) of
the participants are female and 61,1% (n=99) are male students.
Measurement
Personal Information Form, Problematic Internet Use
Scale and UCLA Loneliness Scale were used to collect data. ‘Personal
Information Form’ has been prepared by the researcher for the purpose of
discovering certain demographical features of prospective teachers. The data
gathered from ‘Personal Information Form’ are; the registered university, class
and gender.
‘Generalized
Problematic Internet Use Scale 2 (GPIUS2)’ developed by Caplan (2010) was used
to collect data about the problematic aspects of Internet use of prospective
teachers. GPIUS2 has five sub scales, preference for online social interaction
(POSI), mood regulation, cognitive preoccupation, compulsive internet use,
negative outcomes. As Caplan indicates (2010, p.1093) GPIUS2 scale can be used
in two different ways, as a set of separate sub-scales or as an overall
composite index of GPIUS. In the present study the use of composite index of
the scale was preferred. The scale’s internal consistency reliability was found
α= .91 by Caplan. In the present study internal consistency reliability was
found α= .89 which is as high as the original value. First, GPIUS2 was
translated into Turkish by the experts of language and the field who has
studies in computer/internet attitudes. After the translation, the scales were
applied to the bilingual (Turkish/English) university students for test
re-tests in three weeks intervals. High correlations and no differences were
found (r: .75, p<.001; [paired group] t: .34, df: 25, p>0.05 for the
Turkish sample). The results showed that the language equivalence and internal
consistency reliability of the scale was approved for Turkish version of
GPIUS2.
UCLA Loneliness Scale, which is developed by Russel, Peplau and Cutrona
(1980) to measure individuals’ general loneliness level, is a four-level Likert
Scale consisting of 20 items; 10 items worded in a negative direction and 10
items worded in a positive direction. In each item of the scale, a situation
which denotes a feeling or thought related to social relationships and the
person is expected to tell how often he experiences that situation. Getting a
high score from the scale indicates that the loneliness level is high. The
scale was adapted to Turkish by Demir (1989). During the adaptation studies,
cronbach α internal consistency coefficient of the scale was attained as .96.
In this study, cronbach α internal consistency coefficient of the scale was
attained as .87.
Procedures
Turkish
versions of the scales were applied simultaneously in spring semester in
2010-2011 academic years. The scales were administered between 8th
and 12nd weeks of the spring semester. The partipants were given 10 minutes to
answer the items in the scale. Before the application the attendants administering
the scales were briefed about the application order and rules.
Data Analysis
For the
first research question descriptive statistics, and for the second question
correlation was used.
For the third research question,
to test the model explaining and predicting the relation between lonelines and PIU
of CEIT student, SEM was used. Advantages of SEM compared to multiple
regressions include more flexible assumptions and use of confirmatory factor
analysis to reduce measurement error. Moreover, where regression is highly
susceptible to error of interpretation by misspecification, SEM strategy of
comparing alternative models to assess relative model fit makes it more
powerful (Arbuckle, 2006; Byrne, 2001; Hoyle, 1995; Kline, 1998).
Analysis of Moment Structures
(AMOS) implements the general approach to data analysis known as SEM. AMOS was
originally designed as a tool for teaching this powerful and fundamentally
simple method. AMOS integrates an-easy-to-use graphical interface with an
advanced computing engine for SEM. It also provides maximum likelihood,
unweighted least squares, and generalizes least squares (Arbuckle, 2006). For
these reasons AMOS was used to test the model in the study.
Structural equation modeling
(SEM) is used in the study and AMOS was used to test the model in the study.
The estimated model in the study is given in Figure 1.
In Figure 1 the arrows account
for the cause-effect relation. For instance, an arrow pointing negative
outcomes from loneliness level means that PIU depends partly on lonelines
level. The symbol ‘e’ stands for error difficult data (Arbuckle, 2006).
3.
Results
First of all in this research the level of problematic internet
usage of CEIT students has been analyzed. Means and standard deviations derived
from GPIUS2 scale are
given in Table 1 for CEIT students.
Table 1: Distributions of
Scores Derived from GPIUS2 Scale
by CEIT Students
GPIUS2 Scale
|
n
|
 |
sd
|
|
Problematic internet
usage
|
162
|
60.11
|
11.20
|
|
Table 1 shows that problematic
internet usage of participants is above medium level which means the internet
medium is rather problematic
Second
main research question is to investigate the relationship between problematic
internet use and loneliness levels of CEIT students.
Table 2: Correlation between problematic internet use and loneliness
levels of CEIT Students
Problematic
Internet Use & loneliness
|
n
|
r
|
p
|
Turkish
CEIT students
|
162
|
0.36
|
.00
|
Table 2 shows that there are
positive and medium correlations (p<0.01) between problematic Internet use
and loneliness levels of CEIT students. As a result, we can say that
problematic internet usage by CEIT students increases as loneliness level gets
higher.
As for the third main research
question, the model which was proposed above in the method part was tested. Having
defined the model, the first step in model testing, chi-square analysis was
done. The chi-square value is 59,76 (p=.00) and degree of freedom is 6. The
chi-square/ degree of freedom value is 9,96. Since this value is higher than 3,
chi-square test showed inadequate fit (Cesur and Fer, 2011).
As found in the estimated model,
the chi-square/degree of freedom value is over desired value and therefore
‘Model 2’ was generated. The variable ‘deficient self-regulation’ was omitted
from the estimated model. Then the model turned out to be the one in Figure 2. Then
the model turned out to be the one in Figure 2.
Having defined the model 2,
chi-square analysis was tested again. The chi-square value is 9,91 (p=.01) and
degree of freedom is 3. The chi-square/ degree of freedom value is 3,3. Since
this value is lower than 3,5, chi-square test showed good fit.
Following chi-square test, the
second step, good fit between data and the model was examined. In this stage,
first GFI (>.90)and AGFI (>.90)analyzed. The results were .97 and .901
respectively.
The next index is NFI and CFI.
The value for NFI is .63 while CFI value is .67.
Another good fit index RFI equals
to .26. The last index RMSEA is .12 (<.09) which is a little over the
desired limits. All the indices pointed to good fit between data and the model
except RFI.
Then the third step analyzing
independent variables regression weights follows. Table 4 gives the values for
regression weights.
Table
4: Independent
variables regression weights
Estimates
|
Standard
Error
|
Critical
Ratio
|
p
|
|||
POSI
|
<----
|
Lonelines Level
|
.392
|
.17
|
2.20
|
.02
|
Negative outcomes
|
<----
|
Lonelines Level
|
.438
|
.14
|
3.04
|
.00
|
Mood regulation
|
<----
|
Lonelines Level
|
.412
|
.23
|
1.78
|
.07
|
As seen in the Table 4 all the
variables are significant except mood regulation . The variable ‘mood
regulation’ is significant at .08 level.
In Table 4 it can be seen that
regression weights between Loneliness level and POSI (p=.02); Loneliness level
and negative outcomes (p=.00); Loneliness level and Mood regulation (p=.07) are significant. The next step was to check
covariances in ‘Model 2’ ,
which would reveal the relation between dependent and independent variables.
The variances showing
significance of independent variables in ‘Model 2’ are analyzed, too. In Table 5,
variances for independent variables are presented.
Table
5: Variances
in ‘Model 2’
Estimates
|
Standard
Error
|
Critical
Ratio
|
p
|
|
Lonelines Level
|
1.15
|
.13
|
8.97
|
.00
|
POSI
|
.77
|
.09
|
8.97
|
.00
|
Negative outcomes
|
1.91
|
.21
|
8.97
|
.00
|
Mood regulation
|
.22
|
.02
|
8.97
|
.00
|
In Table 5, variance values are
significant and positive. It is found that loneliness level estimate is 1.15
(p<.01);POSI estimate is .77 (p<.01); negative
outcomes estimate is 1.91 (p<.01) and
mood regulation estimate is .22 (p<.01).
As a result, fit indices examined
for ‘Model 2’
supported the good fit for the model. In addition all values including
regression weights and variances in the model are significant. For this reason,
‘Model 2’
proposed in this study is statistically proved to be a valid model. In other
words, to explain relationship between
loneliness level and problematic internet use. In Figure 3, the valid model, ‘Model 2’ is shown.
As can be seen in
Figure 3, the arrows in one direction represent the regression weights. It was
found that loneliness regression weight to explain POSI is .39 (p<.01); loneliness
regression weight to explain Negative outcomes is .44 (p<.01) and loneliness
regression weight to explain Mood regulation is .41 (p<.01).
In addition, variance
value explaining the model is .22. This means that ‘Model 2’ explains 22 percent of the
variance of problematic internet use of
CEIT students. In other words, Loneliness
level predicts about one forth (1/4) of the variance explaining Turkish CEITstudents’
problematic internet use.
Caplan (2010) also found significat correlations between
POSI, Negative outcomes and mood regulation in the model.
4. Discussion
In the research, initially Turkish CEIT students’
problematic internet use levels have been detected and the level has been found
to be above medium. The value obtained in this group demonstrates that students
are more inclined to problematic internet use similar to some research findings
covering university students in general (Tutgun, 2009; Tutgun and Deniz, 2010;
Tutgun, Deniz and Moon, 2011).
Second, the relation between CEIT students problematic
internet use and their level of loneliness has been examined and significant
correlation has been found between CEIT students problematic internet use and
their level of loneliness (r=0,358;
p<0,01). This finding puts forth that as the level of loneliness
rises so does the inclination towards problematic internet use. In fact, this
result supports several researches in relevant literature on the relationship
between problematic internet use and loneliness (Kraut et. al, 2002; Kubey,
Lavin and Barrows, 2001; Caplan, 2002, 2003; Deniz and Tutgun, 2010; Odacı and
Kalkan, 2010).
The results of SEM analysis
showed that Loneliness can explain Problematic Internet Use. It can predict 22
percent of the variance in PIU. This is nearly equal to 1/4 of the total
variance explaining and predicting PIU. In other words, it was found that
Loneliness is the variable explaining and predicting Problematic Internet Use
in terms of variables; Preference for Online Social Interaction (POSI) , Mood
Regulation and Negative Outcomes.
The reason for the model
explaning only 22 percent of the variance in PIU can be other variables
constituting Loneliness. There may be other variables in Loneliness. What is
loneliness? How do people describe their loneliness? When do they feel
loneliness? These questions haven’t been
answered in this study. Loneliness was accepted a variable describing
loneliness. The factors creating loneliness can be the limitation of the study.
In ‘Model 2’ it was found that
Loneliness is a significant predictor for the variable POSI. Students who feel lonely are to prefer online
social interaction. It can also be said that they would feel more comfortable
with online social interaction than face-to-face interaction. There are other
researches with similar results in literature. According to Young (1996a), problematic internet users who
allocate little time for real people prefer to spend their time alone using a
computer. The reason is that, as Caplan (2005) mentioned in his
research, people who have poor social interaction skills in real life prefer
online social interaction to face-to-face communication and they tend to show
themselves off getting into social interaction on the internet.
Another finding according to ‘Model
2’ was that Loneliness significantly predicts the variable Mood Regulation.
Students who are lonely will probably use internet to talk with others when
they are feeling isolated. In the same way, in a study by Kraut et al. (2002),
isolation and loneliness lead individuals to prefer social interaction on the
internet.
Still another finding according
to ‘Model 2’ was that Loneliness significantly predicts the variable Negative
Outcomes. Lonely students are most probably using internet and this habit makes
their lives difficult. It is probable that they usually miss their social
engagements and some planned activities. Sometimes they face with problems in
their life because their internet use. Similarly,
in a study conducted by Kubey, Lavin and Barrows (2001), a group of participant students were
identified to be addicted to the internet and according to the results of the
study, it was concluded that these students are academically
disadvantaged because of internet usage and they are “lonelier” compared to the
other group. Students who are addicted to the internet and mention that they
are academically disadvantaged prefer real time applications (MUDs and IRC/chat
programs) on the internet. According to the researchers, these interactive
applications form an important escape way for lonely people.
5.
Conclusion
Internet
addiction is a comprehensive term including various behavioral disorders as
well as stimulus-control disorders. From this perspective, internet use may
harass a person’s psychological wellness. It is also important to understand
what factors trigger problematic internet use among CEIT students. In the
present study, the ‘Model 2’ aiming to explain and predict PIU gives
significant clues for this problem. In other words, the model developed shows
that ‘Loneliness’ is the significnt predictor and cause of PIU among CEIT
students. It would not be surprising to express that loneliness level of
students in general would cause PIU.
In addition, according to the
present study it is obvious that the above-medium problematic internet use
inclination of CEIT students heralds the potential problems in future. To sum
up, attempts underlying the fact that immediate precautions must be taken for
CEIT students and technology related departments should be started without
delay. In this respect, certain
suggestions have been given according to findings obtained from this research.
It is understood that, main
reason to use internet problematicly is loneliness. Therefore in order not to
spend excessive time on the net by freshmen teachersand parents should help
students make new friends and adapt easily to new social environments. For
this, the instructors may organize group projects and employ cooperative
working methods in class to support particularly 1st year students.
6. Recommendations
Some attempts should be regularly and frequently
made to detect internet use levels of CEIT students and to control their uses.
Besides,
to analyze problematic internet use’s relation with personal and psychological
traits, qualitative and in-depth analyses can be conducted particularly in
technology-related departments.
It is also advisable for
instructors organize activities and talks to inform the students about
problematic internet use, achieving time control and computer/internet ethics.
This would help them prevent the lack of control in CEIT students’ internet use.
In order to detect if there is a difference amongst the problematic internet
use of CEIT students from a variety of universities, more practices can be
organized in a larger scope of universities and context of research can be
extended.
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Tam Metin:
http://ijsse.com/sites/default/files/issues/2013/v3i3/Paper-20.pdf
Cite:
Tutgun Ünal, A. (2013). The Model Explaining and Predicting
Loneliness Level and the Problematic Internet Use of Turkish Computer
Education and Instructional Technologies (CEIT) Students, IJSSE-International Journal of Social Sciences and
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