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International Journal of Assessment Tools in Education
2019, Vol. 6, No. 3, 396–414
https://dx.doi.org/10.21449/ijate.505863
Published at http://www.ijate.net http://dergipark.org.tr/ijate Research Article
396
Social Network Addiction Scale: The Validity and Reliability Study of
Adolescent and Adult Form
Ibrahim Gokdas 1,
*
,Yasar Kuzucu 2
1Department of Computer Education and Instructional Technology. Adnan Menderes University, Faculty of
Education, 09010 Aydin / Turkey
2Psychological Counseling and Guidance in Education, AdnanMenderes University, Faculty of Education, 09010
Aydin / Turkey
ARTICLE HISTORY
Received: 31 December 2018
Revised: 19 July 2019
Accepted: 29 July 2019
KEYWORDS
Social network addiction scale,
Social media addiction,
Social media usage
Abstract: In this study, it was aimed to develop a valid and reliable social
network addiction scale for adolescents and young adults. In the Exploratory
Factor Analysis of the scale, the application was conducted to 425 high
school students between 14-17 years of age and 310 young adults between
18-43 years of age. Confirmatory Factor Analysis was performed on a
different group and for this purpose, 322 high school students and 197
young adults were included in the analysis. As a result of the analyses
performed, the scale exhibited a-10-item and three-factor structure in both
groups. The total variance explained was 71.51% for adolescents and
70.96% for young adults. The total Cronbach Alpha reliability coefficient
of the scale was .87 for adolescents and .84 for young adults. With the 1st
and 2nd level Confirmatory Factor Analysis performed on a similar study
group, a good model was revealed for both adolescents and young adults.
The Social Network Addiction Scale developed within the scope of this
study is thought to have the adequate validity and reliability structure that
can be used to measure social network addiction levels of adolescents and
young adults.
1. INTRODUCTION
Depending on the widespread use of the internet and the developments in information
technologies, social networks are getting into our lives increasingly dayby day.Social networks
where texting and sharing (photos, documents, videos, etc.) are performed intensively affect
the lives of many people from different age groups with the opportunities they offer.Depending
on this development process, the use of social networkhas become an increasingly popular free
time activity in many countries (Kuss & Griffiths, 2011). Today, individualstend towardssocial
networks to participate in many different entertainment and social activities, including playing
games, socializing, spending time, communicating and sending pictures (Allen, Ryan, Gray,
Mclnerney, & Waters, 2014; Ryan, Chester, Reece, & Xenos, 2014). Such attractive
CONTACT: Ibrahim Gokdas igokdas@gmail.com Department of Computer Education and Instructional
Technology. Adnan Menderes University, Faculty of Education, 09010 Aydin / Turkey
ISSN-e: 2148-7456 /© IJATE 2019
Int. J. Asst. Tools in Educ., Vol. 6, No. 3, (2019) pp. 396–414
397
opportunities offered by social networks have an important role in the lives of many people
from different age groups and affect their lives.
Social Network Websites that are defined as virtual communities where users can create
individual and general profiles, interact with their friends and meet other people in line with
common purposes (Kuss & Griffths, 2011) have made significant changes in the way people
communicate with others (Vilca & Vallejos, 2015). Social networks, a new communication
technology paradigm (Kang, Shin, & Park, 2013; LaRose, Connolly, Lee, Li, & Hales, 2014),
have taken an important place in our lives with their popularity (LaRose, et al., 2014).
According to 2017 data, the population of the world is 7,476 billion people and 3,773 of it are
internet users. 2,789 billion people are actively using social networks. According to the usage
ratio of 2016, the number of internet users has increased by 354 million with a 10% rise and the
number of social media users has increased by 482 million with a 21% rise. Following the first
Global Digital Report for internet usage in January 2012, the number of global users has
increased more than 80% in five years (We are Social, 2017). When the annual change ratios
are taken into account, the increase in the number of social network users is particularly
noteworthy. A meta-analysis conducted found out that about 6% of the world's population has
internet addiction (Cecilia & Yee-lam, 2014). This ratio corresponds to about 226 thousand of
people when considered for 2017 data.
The increase in the ratio of users caused social networks to be considered as a normal modern
phenomenon within the society (Boyd & Ellison, 2007). However, the increase in the time that
people spend online on social networks (Kuss & Griffiths, 2011) has brought together the
concerns about addiction and social network use (Andreassen, 2015; Griffiths, Kuss &
Demetrovics, 2014). There is also increasing evidence that social network addiction is a mental
problem that occurs in adolescents (Pantic, 2014; Ryan et al., 2014).
It is believed that the developments in the features of information technologies (laptop
computers, tablet PCs, smartphones, etc.) have a significant role in the widespread use of social
networking and the increase of addiction because new technologies support easy and fast access
to social networking sites and it is known that excessive use of such new technologies can be
addictive especially for adolescents (Echeburúa & de Corral, 2010). When the user profile of
social networks is investigated, it is seen that especially adolescents are the most intensive user
group (Van den Eijnden, Lemmens, & Valkenburg, 2016; Vilca & Vallejos, 2015). For
example, when the data of 2017 is analyzed, it is noted that about 73% of Facebook users are
the individuals between the age of 18-34, 9% of the users are between the age of 13-17and 10%
of the users are between the age of 35-44 (We are Social, 2017). This intensity is particularly
worrying in that the risk of social network addiction in especially adolescents and young adults
has increasedand it has caused adolescents to move away from the necessary activities for their
improvement. (Park, Kim, & Cho, 2008).
The egocentric nature of social networks push people towards problematic use by contributing
to the development of addiction behaviors. Similarly, social networks lead individuals to exhibit
themselves different from what they really are and live delightful experiences (Kuss & Griffths,
2011). Furthermore, the opportunities offered by social networks make users happy (Choi &
Lim, 2016; Yang, Liu, & Wei, 2016) and create excitement by filling a psychological gap in
the lives of individuals (Echeburúa & De, 2010; Yang et al., 2016). Together with the popularity
achieved by social networks and many benefits they provide the users (Kuss & Griffiths, 2012),
spending too much time in a social network (Can & Kaya, 2016) is considered a sign of social
network addiction (Gao, Liu, & Li, 2017; Turel & Serenko, 2012) and may cause psychological
disorders (Salehan & Negahban, 2013).
Despite the fact that the latest edition of the Diagnostic and Statistical Manual of Mental
Disorders (DSM-5) recognize Internet addiction as a temporary disorder in the appendix of this
Gokdas & Kuzucu
398
guide (APA, 2013), social network addiction does not still hold a status in DSM-5. The fact
that social network addiction is not included in DSM-5 creates the impression that social
network addiction is not a psychological problem (Van den Eijnden et al., 2016). However,
there are also studies that do not support this situation (Pantic, 2014; Ryan, Chester, Reece, &
Xenos, 2014). The fact that there are no explicit definitions and precautions for social network
addiction affects doing researches about these widespread behaviors negatively (Van den
Eijnden et al., 2016).
Different research suggests that excessive use of social networks is associated with anxiety,
frustration, intolerance, anger, low self-esteem, impoverishment of social relationships,
decrease in academic performance, verbal or physical aggression, and depression tendency
(Cheung & Wong, 2011; Huang & Liang, 2009; Satici & Uysal, 2015). Besides, it was found
that excessive use of social networks might lead to such negative conclusions as sleeping
disorders (Dewald, Meijer, Oort, Kerkhof, & Bögel, 2010) and procrastination of sleeping time
(Brunborg, Mentzoni, Molde, Myrseth, Skouverøe, Bjorvatn et al., 2011; Suganuma, Kikuchi,
Yanagi, Yamamura, Morishima, Adachi et al., 2007).
Considering the psychological, social, economic, cultural and educational losses caused by
social network addiction, it is significant to determine the level of social network addiction.
However, the number of studies regarding social network addiction are insufficient (Kuss &
Griffths, 2011; Andreassen, 2015). When the researches conducted are analyzed, it is possible
to get the impression that Facebook addiction has the same meaning as social network addiction
(Ryan et al., 2014; Griffiths, Kuss, & Demetrovics, 2014; Van den Eijnden, et al., 2016).
Addiction scales developed in this respect are focused on Facebook addiction or problematic
Facebook usage (Kuşay, 2013; Andreassen, 2015) and have become intense after 2011 (Ryan
et al., 2014). Social networks exhibit different characteristics in terms of functionality and
expediency. For example, social networks such as Blogger etc. for publishing content,
YouTube, Slideshare etc. for sharing, Messenger, Skype etc. for chatting, Facebook, LinkedIn
etc. for getting to know other people, Twitter, Twitpic etc. for expressing short ideas,
Friendfeed, Foursquare etc. for sharing life are widely used (Kuşay, 2013). On the other hand,
the ratio of people sharing on Instagram, Pinterest or Twitter instead of Facebook is increasing
rapidly. Besides, the ratio of WhatsApp users from different age groups is also increasing
rapidly. YouTube is a tool where especially adolescents watch and share videos. Today, the
number of present social network websites is over 100 (Pantic, 2014). Lots of people from
different age groups actively and intensely use more than one of these, not just one. Therefore,
social networks have a strong social impact on the lives of their users.
In spite of the popularity of social network use among people, empirical studies analyzing the
addiction to these networks are insufficient (Ryan et al., 2014). Considering this fact, it is
important to have psychological tools to be able to identify early possible social network
addiction (Vilca & Vallejos, 2015).However, the diversity in social networks makes the studies
regarding social network addiction problematic. The first reason for this is the rapid change in
the social network environment and the expansion of its interactive functions. This will cause
the measurement tools targeting specific social networks to lose their up-to-datedness easily.
The second reason is that the criterions that may cause social network addiction vary. These
reasons will cause problems in the process in comparing the related researches carried out (Van
den Eijnden et al., 2016). The distinctive nature of each social network environment and the
differences in the opportunities it offers reveals that social network addiction should be
considered as different from internet or Facebook addiction alone. Therefore, the development
of studies on social network addiction requires the development and validation of a general
social network addiction scale (Van den Eijnden et al., 2016).
Int. J. Asst. Tools in Educ., Vol. 6, No. 3, (2019) pp. 396–414
399
When the literature was examined examined (Esgi, 2016; Fırat & Barut, 2018; Şahin, 2018;
Tutgun-Ünal & Deniz 2015; Ülke, Noyan, & Dilbaz, 2017; Van den Eijnden et al., 2016), it
was found that various scales had been developed regarding social network addiction in recent
years. Insufficient number of researches in the field and the need for scale development in this
regard have been the main problem. However, it can be seen that some of the measurement
tools developed in Turkish language are directed to young adults and adults (Esgi, 2016;
Tutgun-Ünal, & Deniz, 2015; Ülke, Noyan, & Dilbaz, 2017) while some others are directed to
adolescents and young adults (Fırat & Barut, 2018;Şahin, 2018). Besides, the study of Taş
(2017), who conducted the Turkish adaptation study of the short form of social media addiction
scale developed by Van den Eijnden et al. (2016) for adolescents and young adults, also
involves adolescents and young group. In the international field literature, no social network
addiction scale involving a large target group was found. In this regard, Social Media Disorder
Scale developed by Van den Eijnden et al. (2016) involves the group of 10-17 years of age.
Bergen Social Media Addiction Scale (BSMAS) is a modified version of the previously
approved Bergen Facebook Addiction Scale (BFAS) (Andreassen et al., 2012). In the scales,
the word “Facebook” was replaced by the word “social media” and social media was defined
as “Twitter, Instagram and etc.”. Bergen Facebook Addiction Scale (Andreassen, Torsheim,
Brunborg, & Pallesen, 2012) was later adapted as Bergen Social Media Scale (Andreassen,
Pallesen, & Griffiths, 2017). The original scale involved university students.
When evaluated in general, it can be revealed that the scales in the literature for social network
addiction differ from each other in terms of the target group. Furthermore, as the factor
structures of the scales also differ from each (Esgi, 2016; Fırat & Barut, 2018; Şahin, 2018;
Taş, 2017; Turgut-Ünal & Deniz 2015; Ülke, Noyan, & Dilbaz, 2017; Van den Eijnden et al.,
2016), it is considered that they will be insufficient for the studies to be conducted and in
providing comparability among different age groups. Therefore, there is a need for an easily
applicable scale that involves a target population of wider age range.
Based on these basic justifications, the main purpose of the study was to develop a highly valid
and reliable social network addiction measurement tool for adolescents, young adults and
adults. The research is deemed important in terms of involving different target groups as
adolescents, young adults and adults with regard to their social network addiction levels.
2. METHOD
2.1. Study Group and Process
Social Network Addiction Scale (SNAS) was implemented to 461 students between the ages of
15 and 18 from five different high schools studying in Efeler, the central district of Aydın
province in the spring semester of 2016-2017 academic year. The participants were given the
information that the data would not be considered personally and that there would not be only
one correct answer for everyone. The application lasted about 15-20 minutes. However, because
of faulty and missing information, and after excluding extreme values, 425 forms were included
in the evaluation. In the first stage, Exploratory Factor Analysis (EFA) was performed on the
data collected from 425 students. In order to test the results of EFA with Confirmatory Factor
analysis (CFA), additional data was collected from 371 high school students of the same age
group. Participant students were determined by convenience sampling and it was noted from
each level of education that they were using at least one social network and were voluntarily
participating. SNAS was also applied to the adult group. For the adult group, 750 people who
had undergraduate education at Adnan Menderes University and who graduated from a higher
education institution and participated in pedagogical formation training were asked to fill in the
form via e-mail. The form of the scale was organized online. A total of 367 participants
completed the scale during the three-week period. However, a total of 310 forms were included
Gokdas & Kuzucu
400
in the evaluation after excluding extreme values and EFA was performed on this dataset. In
order to test the results of the EFA with CFA, 430 people were e-mailed and 201 of them
responded. Nonetheless, 197 data were included in the evaluation after the extreme values were
excluded. The distribution of the study groups is given in Table 1and Table 2.
Table 1. The distribution of the study group for the Exploratory Factor Analysis
Adolescent (14-17 years of age)
Adult (18-45 years of age)
Applied
Valid
Applied
Valid/Returned
Age
f
%
f
%
Education
f
%
f
%
14
98
21,3
91
21,4
Undergraduate
188
51.2
158
51
15
108
23,4
98
23,1
Graduate
179
48.8
152
49
16
153
33,2
140
32,9
Total
367
100
310
100
17
102
22,1
96
22,6
Age
Total
461
100,0
425
100,0
18-22
172
46,5
135
43.5
23-27
130
35,1
110
35.4
Gender
28-32
32
8,6
30
9.7
Male
125
27,1
114
26,8
33-37
23
8,6
23
7.5
Female
336
72,9
311
73,2
38 and above
12
3,8
12
3.9
Total
461
100,0
425
100,0
367
100
310
100
Gender
Female
226
61,6
193
62.2
Male
141
38,4
117
37.8
Total
367
100
310
100
Table 2. The distribution of the study group for the Confirmatory Factor Analysis
Adolescent (14-17 years of age)
Adult (18-45 years of age)
Applied
Valid
Applied
Valid/Returned
Age
f
%
f
%
Education
f
%
f
%
14
84
22.6
73
24,2
Undergraduate
100
30,3
78
39,6
15
96
25.9
90
30,2
Graduate
330
69,7
119
60,4
16
150
40.4
126
31
Total
330
100
197
100
17
41
11
33
14,6
Age
Total
371
100
322
100
18-22
19
9.6
19
9,6
23-27
77
38.4
79
40,1
Gender
28-32
45
22.4
46
23,4
Male
148
39,9
117
36,3
33-37
23
11.6
23
11,7
Female
223
60,1
205
63,7
38 and above
30
18
30
15,2
Total
371
100,0
322
100,0
Total
201
100
197
100
Gender
Female
113
56.2
110
55.8
Male
88
43.8
87
44.2
Total
201
100
197
100
2.1.1.The Development of Social Network Addiction Scale
In order to develop a measurement tool for social network addiction, literature was analyzed
and questions were prepared taking into account especially the studies of Young (1998),
Griffiths (2005), Block (2008), Tao (2010), and Van den Eijnden et al., (2016). By obtaining
the views of three experts who had PhD degrees in the field of psychology and who studied on
internet addiction, five inappropriate items with similar meanings were removed from the draft
form. The remaining 30 items were taken into the trial form of the scale. All of the items are
positive statements and they are in the 5-point- Likert form ranked as 1=Never, 2=Rarely,
3=Sometimes, 4=Often, 5 = Very Often. For the items in the trial form, EFA, CFA and item
analysis were performed.
Int. J. Asst. Tools in Educ., Vol. 6, No. 3, (2019) pp. 396–414
401
2.2. Data Collection Tools
In order to determine the criterion validity of the SNAS Adolescent Application, the
relationship of the Problematic Mobile Phone Use Scale with the SNAS was analyzed. For the
criterion validity of SNAS Adult Application, the relationship of it with the Internet Addiction
Scale was analyzed.
2.2.1. Problematic Mobile Phone Use Scale
In order to determine the criterion validity of the SNAS, the Problematic Mobile Phone Use
Scale developed by Augner &Hacker (2012) and adapted by Tekin, Güleş, &Çolak (2014) was
utilized. Problematic Mobile Phone Use Scale is composed of three sub-dimensions in total.
The first sub-dimension is defined as “addiction” (9 questions), the second sub-dimension is
defined as “social relations” (7 questions), and the third sub-dimension is defined as “results”
(10 questions). In the adaptation study, the three-factor scale explains 45% of the total variance.
The Cronbach Alpha value of the scale was found 0.85. Besides, the Cronbach Alpha value of
the first sub-dimension (addiction) was found 0.73, that of the second sub-dimension (social
relations) was found 0.60, and that of the third sub-dimension(results) was found 0.85 (Tekin
et al., 2014).
2.2.2.Internet Addiction Scale
The Internet Addiction Scale developed by Young (1998) and adapted to Turkish by Cakir and
Horzum (2008) was used to determine the criterion validity of the scale. The Turkish adaptation
of the scale is composed of three sub-dimensions in total. The first sub-dimension is defined as
“preferring being online to daily life” (8 items), the second sub-dimension is defined as “having
desire to increase the duration of being online” (7 items), and the third sub-dimension is defined
as “the problems arising from being online” (4 items)". The total variance explained was
52.83% and the total Cronbach Alpha internal consistency coefficient was found .90 (Çakır &
Horzum, 2008).
2.3. Data Analysis
SPSS 22.0 (SPSS Inc.) and LISREL 8.80 (Joreskog & Sorbom, 1993) statistical package
programs were used in the analysis process. The data of the scale applied separately to
adolescents and adults were analyzed using EFA and CFA techniques for the construct validity.
By examining the measurement invariance in adolescent and adult samples, it was tested
whether the measurement tool was appropriate for the comparisons between groups.
Furthermore, item-test score correlations, test-retest scores correlation, internal consistency
McDonald Omega coefficient (McDonald, 1999) were calculated. T-test was performed to test
whether the items of the scale distinguished between the lower and upper 27% groups. Item
Response Theory (IRT) was used to check the reliability results obtained.
3. FINDINGS
3.1.Adolescent Application
3.1.1. Pre-analyses
In order to determine whether the data showed normal distribution or not, Skewness and
kurtosis values were examined. Skewness was found .61 and Kurtosis was found -.03. The fact
that both values are between the range of -1, +1 implies that they show normal distribution. In
addition to Skewness and Kurtosisanalyses, Kolmogorov-Smirnov test results (p> .05) support
normal distribution.
Kaiser Meyer Olkin (KMO) coefficient was used to determine whether the data structure was
appropriate for factor analysis in terms of the sample size of the SNAS adolescent application.
As a result of the analysis, KMO value was determined as 0.87. The fact that KMO value is
Gokdas & Kuzucu
402
high means that each variable in the scale can be estimated well by the other variables (Çokluk,
Şekercioğlu, & Büyüköztürk, 2012). Another indicator for the appropriateness of the data for
factor analysis is the Anti-image Correlation Matrix. These values need to be above 0.5 and the
values below this must be excluded from the analysis (Field, 2009). The diagonal values for
each variable in the anti-image matrix vary between .80 and .91. The fact that all the values at
the intersection point are above 0.5 indicates that it is accurate to include all the items in the
scale.
3.1.2.The Validity of Social Network Addiction Scale Adolescent Application
After determining that the sample size is appropriate for factor analysis, the factor structure for
the construct validity of the scale was determined by performing EFA. The purpose of
performing EFA is to gather the variables that are related to each other and that measure the
same quality together, and to reduce the number of items forming the scale (Aksu, Eser, &
Güzeller, 2017). CFA was performed to test whether the restricted structure defined by EFA
was verified as a model (Çokluk, Şekercioğlu, & Büyüköztürk, 2012).
After the first factor analysis with a total of 30 items, the items were collected in 5 sub-
dimensions, with eigen values greater than 1. However, the items numbered 1, 3, 4, 7, 8, 10,
11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 24, 25, 26, 27 with factor loadings below 0.30 and that
were overlapping were gradually removed from the scale. Factor analysis was made again by
removing one item at each step. As a result, 20 items were removed from the scale and the
remaining 10 items were collected in 3 sub-dimensions. The items of each sub-dimension were
examined and it was determined that they were grouped under the factor to which they were
related. To clarify the relationship among factors, direct oblimin rotation (the oblique rotation
technique of Principal Component Analysis) was used. As a result of the EFA performed by
using the oblimin rotation method, it was found that the eigenvalue of the first factor was 4.6
and the variance it explained was 26.27, the eigenvalue of the second factor was 1.49 and the
variance it explained was 25.61, the eigenvalue of the third factor was 1.05 and the variance it
explained was 19.63. The total variance explained by the scale was found 71.51%. When the
eigenvalues and cumulative variance percentages of the three factors were taken into
consideration, it was determined that the scale had three factors. The findings obtained as a
result of the EFA performed for SNAS Adolescent Application revealed that the construct
validity of the scale was sufficient. The factors formed after EFA and the items collected under
each factor are given in Table 3.
When Table 3 is examined, it can be seen that the first factor is composed of 4 items (2, 5, 6,
9), the second factor is composed of 3 items (28, 29, 30) and the third factor is composed of 3
items (16, 17, 23). The results reveal that each item is clustered under a factor that is related
with a value that is more than twice as much as the factor loading value that they have in other
factors. This finding, which shows that the items differentiate in terms of factors, support the
construct validity of the scale.
When the scree plot conducted to reveal the factor structure of the scale is analyzed, it can be
seen that the graph curve shows a sharp decrease till the third factor and that the curve proceeds
horizontally after the third factor (Figure 1). This finding supports the three-factor structure of
the scale.
Int. J. Asst. Tools in Educ., Vol. 6, No. 3, (2019) pp. 396–414
403
Table 3. Factor Loadings of SNAS Adolescent Application
Factors
Items
Factor Loadings
1
2
3
Control
Difficulty
Q1
I find myself surfing the social networks in most of my
daily life. (5)
.890
.030
.115
Q2
I spend most of my time in social networks. (9)
.807
.034
.009
Q3
I do not give up using social networks even if they affect
my daily life.(6)
.765
.036
.148
Q4
I make an effort to use social networks every day. (2)
.716
.128
.126
Negativeness in
Social Relations
Q8
I feel happy to share my ideas on social networks. (23)
.111
.887
.017
Q9
I prefer to share my daily activities on social networks.
(16)
.086
.768
.082
Q10
I express myself better on social networks. (17)
.082
.685
.097
Decrease in
Functions
Q5
The time I allocate for my work/lessons have decreased
since I began to use social networks. (29)
.013
.009
.911
Q6
My performance at work/school have decreased since I
began to use social networks. (28)
.013
.026
.909
Q7
I have begun to have problems focusing on my
work/school since I began to use social networks. (30)
.037
.016
.875
Notes: N =425 * p<.05 **p<.01
Figure 1. SNAS Adolescent Application Scree Plot Graph
Following this phase, the items in each sub-dimension were examined as a whole and a factor
structure consistent with the theoretical framework was observed. Within this context, in
relation to the literature on addiction, the first sub-dimension of the scale was named as
“Decrease in Functions”, the second sub-dimension was named as “Control Difficulty” and the
third sub-dimension was named as “Negativeness in Social Relations”. In order to determine
whether there were significant correlations among the factors forming SNAS Adolescent
Application, Pearson Product-Moment Correlation Analysis was performed. It was revealed
that the relationship of “Control Difficulty” factor with “Decrease in Functions” and
“Negativeness in Social Relations” factors was found as .52 and .41, respectively; and the
relationship between “Decrease in Functions” and “Negativeness in Social Relations” was
determined as .30. The results obtained, consistent with the literature (Şahin, 2018), show that
there was a positive significant relationship among all the sub-dimensions of the scale p≤.001.
Gokdas & Kuzucu
404
First level and second level Confirmatory Factor Analysis (CFA) was performed to evaluate
the applicability of the three sub-dimensions of SNAS Adolescent Application to the data
obtained from the study group. The models obtained from these analyses are given in Figure 2
and Figure 3.
Figure 2. SNAS Adolescent Application 1st
Level CFA
Figure 3. SNAS Adolescent Application 2nd Level
CFA
First and second level CFA was performed for the 10-item structure that was collected under
three factors as a result of EFA performed for SNAS adolescent application. When the findings
revealed as a result of CFA were evaluated, χ2/sd ratio for the first and second level was
determined as 2.08 (χ2/sd=66.65/32). The fact that χ2/sd ratio obtained as a result of first and
second level CFA is between 2.0≤3.0 correspond to an acceptable fit. RMSEA fit index value
was determined as 0.053 as a result of first and second level CFA. The fact that RMSEA fit
index value is below 0.08 can be interpreted as acceptable fit (Kline, 2015). It was determined
that, among the fit index values related to the model as a result of the first and second level
CFA, AGFI was 0.94, GFI was 0.96, standardized RMR fit index value was 0.041, NFI fit index
value was 0.97, and CFI fit index value was 0.99. When all the values related to data fit of the
model are taken into consideration, it can be seen that the model formed shows adequate level
of fit with the data.
An additional CFA was performed to support the multifactorial structure of SNAS Adolescent
Application; the results of first and second level factor analysis were compared with the 1-factor
analysis of the scale. Scale was assumed one dimensional and it produced following statistics:
χ2/sd ratio of the fit values used in the model comparisons was calculated as 16.5
(χ2/sd=580.1/35, NFI=0.77, GFI=0.73, CFI=0.78 RMSEA=0.22). The results obtained showed
that the 1-factor structure had poorer fit values than the 3-factor structure. In order to determine
the criterion validity of SNAS Adolescent Application, the relationship between Problematic
Mobile Phone Use Scale (PMPUS) and SNAS Adolescent Application was examined with
Pearson Product-Moment Correlation Analysis and it was found that there was a positive
(r=.55) and statistically significant (p≤.001) relationship between the two variables.
Int. J. Asst. Tools in Educ., Vol. 6, No. 3, (2019) pp. 396–414
405
3.1.3.The Reliability of SNAS Adolescent Application
Item analysis was conducted to determine the contribution of the items in the scale to the
implicit structure they belong to, and to measure the level of discrimination between the items
with and without relevant characteristics in the structure they belong to (Erkuş, 2012). It was
revealed that item total correlation coefficients varied between .41 and .73. On the condition
that the items are congeneric measurements, McDonald Omega coefficient is used (McDonald,
1999). McDonald Omega coefficient of the overall scale was calculated as .87. The reliability
analysis for each factor of the scale was also conducted. As a result of the analysis performed
for this purpose, it was found for the first factor that McDonald Omega coefficient was .76 and
item total correlation coefficients varied between .61 and .72. For the second factor, McDonald
Omega coefficient was found .81 and item total correlation coefficients varied between.51 and
.56. For the third factor, McDonald Omega coefficient was found .72 and item total correlation
coefficients varied between .77 and .83. It can be seen that the reliability values of the overall
and sub-dimensions of the SNAS Adolescent Application are generally acceptable values for
social sciences.
It was also analyzed whether there was a significant difference between the individuals with
low scores and high scores. As a result of the t test conducted to determine the difference
between the responses of the individuals in the lower 27% group and the responses of the
individuals in the upper 27% group to all the items in the scale, the items’ tvalues varied
between 4.14 (p<.001) and 10.67 (p<.001) and a significant difference was found. In the
analysis performed, it was found that the variances were heterogeneous.
Item Response Theory (IRT) was used to confirm the reliability results obtained. IRTPRO 4.2
software was used to analyze with IRT. For this purpose, the three-factor structure fit of the
scale was analyzed by using the two-parameter logistic model (2PL) to examine the items. The
item difficulty (a) and item discrimination power (b) that were considered important were
analyzed according to this formulation (Hambleton, Swaminathan & Rogers, 1991). Besides,
X2value, which is the measure of item-model fit, and the items that were insignificant (p<=
0,01) were also examined. The calculated “a” and “b” item parameter values and X2values are
given in Table 4.
Table 4. Parameter values in terms of SNAS Adolescent Application according to IRT
Item
a
s.e.
b1
s.e.
b2
s.e.
b3
s.e.
b4
s.e.
X2
df
p
1
1.39
0.17
-2.69
0.33
-0.90
0.15
0.79
0.13
2.45
0.27
57.51
57
0.457
2
1.62
0.20
-2.33
0.26
-0.37
0.11
1.25
0.14
3.00
0.33
33.92
49
0.950
3
1.41
0.17
-1.58
0.20
0.06
0.11
1.31
0.16
3.59
0.44
50.42
58
0.750
4
1.38
0.17
-1.41
0.19
0.04
0.11
0.90
0.13
1.97
0.22
60.59
67
0.697
5
2.82
0.39
-0.66
0.09
0.11
0.09
0.92
0.12
1.67
0.17
67.38
51
0.061
6
2.79
0.42
-0.46
0.08
0.45
0.10
1.22
0.15
2.01
0.21
102.04
49
0.058
7
2.36
0.30
-0.55
0.09
0.46
0.10
1.38
0.16
2.24
0.24
70.02
49
0.045
8
0.65
0.12
-1.65
0.64
2.11
0.76
4.84
1.62
8.13
2.74
90.14
79
0.183
9
0.47
0.12
-0.45
0.28
2.16
0.58
4.87
1.26
7.37
1.95
82.48
70
0.145
10
0.64
0.13
-0.52
0.22
1.22
0.28
3.39
0.65
5.51
1.10
76.31
64
0.139
The item discrimination parameter provides information about the quality of the item. While
items with A parameter value below 0.5 are regarded as weak in terms of discrimination (De
Beer, 2004), those above 1 are not deemed adequate (Gültaş, 2014). From Table 4, it can be
seen that all the values except for the value of item 9 are sufficient and the values of item 8 and
10 are at the borderline. Item discrimination serves to differentiate between the individuals with
Gokdas & Kuzucu
406
low and high social network addiction. Item difficulty indicates where the item is functional on
the social network addiction level of the item. High level of "b" value exhibits that the item is
functional or it measures among the individuals with high addiction levels, whereas low level
of "b" value indicates that the item is functional or it measures among the individuals with low
addiction levels. Item difficulty value varies between -2.69 and 8.14. It was noted that while
the first threshold value of the scale (Likert 1 and 2 interval) was about -2, the second threshold
value was 0, the third threshold value was 1, and the fourth threshold value varied between 2
and 8. This suggests that the scale is better discriminated in the individuals with high social
network addiction. It was found that from the X2values indicating item model fit, only item 7
was significant and did not meet the model fit. This item was not removed from the scale due
to the fact that it had one of the highest factor loadings with a factor loading of .84, and that its
item total correlation was high (.60) as a result of EFA.
3.2. SNAS Adult Application
3.2.1. Pre-analyses
KMO coefficient was used to determine whether the data structure was appropriate for factor
analysis in terms of the sample size of the SNAS adult application. As a result of the analysis,
KMO value was determined as 0.84. Besides, the Anti-Image Correlation Matrix intersection
values were also analyzed and it was found that these values varied between .78 and .91. Asthe
values at this intersection point were above 0.5, it was determined that it was accurate to include
all the items in the scale.
In order to determine whether the data showed normal distribution or not, Skewness and
kurtosis values were examined. Skewness was found .52and Kurtosis was found -06. The fact
that both values are between the range of -1, +1 implies that they show normal distribution.
Kolmogorov-Smirnov test results (p>.05) also support normal distribution.
3.2.2. The Validity of SNAS Adult Application
EFA and CFA were performed for the construct validity of the scale. After the first factor
analysis with a total of 30 items, the items were collected in 5 sub-dimensions, with eigenvalues
greater than 1. However, the items numbered 1, 3, 4, 7, 8, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21,
22, 24, 25, 26, 27 with factor loadings below 0.30 and that were overlapping were gradually
removed from the scale. Factor analysis was made again by removing one item at each step. As
a result, 20 items were removed from the scale and the remaining 10 items were collected in 3
sub-dimensions. The items of each sub-dimension were examined and it was found that they
were grouped under the factor to which they were related. To clarify the relationship among
factors, direct oblimin rotation (the oblique rotation technique of Principal Component
Analysis) wasused. Within this context, it was determined that the first factor explained 26.2%,
the second factor explained 25.26% and the third factor explained 19.5% of the total variance.
The total variance explained by the scale was found 70.96%. The findings obtained as a result
of the factor analysis performed for SNAS Adult Application reveal that the validity of the scale
was sufficient. In addition, it was determined that it had the same factor structure with the
adolescent application. The factors formed after EFA for SNAS Adult Application and the
factor loadings are given in Table 5.
As can be seen inTable 5, the first factor is composed of three items (28, 29, 30) and the factor
loadings vary between .88 and .90. The second factor is composed of four items (2, 5, 6, 9) and
the factor loading values vary between .71 and .81. The third factor is composed of three items
(16, 17, 23) and the factor loadings vary between .76 and .80.
Int. J. Asst. Tools in Educ., Vol. 6, No. 3, (2019) pp. 396–414
407
Table 5. Factor Loadings of SNAS Adult Application
Factors
Items
Factor Loadings
1
2
3
Control
Difficulty
Q5
I make an effort to use social networks every day.(2)
.842
.023
-.167
Q4
I find myself surfing the social networks in most of my
daily life.(5)
.817
.057
.050
Q6
I spend most of my time in social networks. (9)
.737
.022
.150
Q7
I do not give up using social networks even if they
affect my daily life. (6)
.692
.020
.186
Negativenes
s in Social
Relations
Q8
I feel happy to share my ideas on social networks.(23)
.075
.826
.008
Q9
I express myself better on social networks.(17)
.001
.792
.080
Q10
I prefer to share my daily activities on social
networks.(16)
.107
.766
.095
Decrease in
Functions
Q1
My performance at work/school have decreased since I
began to use social networks. (28)
.049
.011
.925
Q2
I have begun to have problems focusing on my
work/school since I began to use social networks.(30)
.038
.043
.898
Q3
The time I allocate for my work/lessons have decreased
since I began to use social networks.(29)
.097
.012
.878
Notes: N=310 * p<.05 **p<.01
Figure 4. SNAS Adult Application Scree Plot Graph
When the “Scree Plot” graph is examined, it can be seen that the curve shows a sharp decrease
till the third factor and that the curve proceeds horizontally after the third factor (Figure 4). The
results are consistent with the previous results showing that the scale has a three-factor
structure. After this process, it was analyzed whether there were any significant relationships
between the factors forming the scale. As a result of Pearson Product-Moment Correlation
Analysis conducted to test whether there was a significant relationship among the sub-
dimensions of the scale, consistent with the literature (Andreassen, 2012; Esgi, 2017; Şahin,
2018; Şahin & Yağcı, 2017; Ülke et al., 2017), it was found that there were positive significant
relationships among all the factors of the scale (p<.001). It was determined that the relationship
of “Control Difficulty” factor with “Decrease in Functions” and “Negativeness in Social
Relations” factors we as .40 and .47, respectively, and the relationship between “Decrease in
Functions” and “Negativeness in Social Relations” was .23.
First level CFA was performed to determine whether the 10-item, 3-factor structure of the scale
achieved after EFA performed for SNAS Adult Application would be verified.
Gokdas & Kuzucu
408
Figure 5. SNAS Adult 1stLevel CFA
Figure 6. SNAS Adult 2ndLevel CFA
As a result of the first level (Figure 5) and second level (Figure 6) CFA performed for SNAS
Adult Application, χ2/sd ratio was calculated as 1.68 (χ2/sd=53.89/32) and these values
correspond to good fit. It was determined that, of the fit indexes, AGFI was .91, GFI was .95,
standardized RMR fitindex value was .056, NFI fit index value was .97, and CFI fit index value
was .99. RMSEA fit index value for both levels was found as .059. When all the values related
to data fit of the model are considered, it can be seen that the model formed shows adequate
level of fit with the data.
To compare 1-factor and multifactorial structure, an additional CFA was performed Scale was
assumed one dimensional and the obtained fit values ((χ2/sd=440.25/35=12.5, NFI=0.76,
GFI=0.69, CFI=0.78, RMSEA=0.24) indicated that the 1-factor structure had poorer fit values
than the 3-factor structure. This result supports to the multifactorial structure of SNAS Adult
Application.
In order to determine whether the properties of the scale are invariant in different groups,
measurement invariance was examined. While the measurement invariance of the factor
structure of the scale was being measured for the adolescent and adult sample, multiple-group
confirmatory factor analysis was used. For this purpose, 4 hierarchical models; structural
invariance, metric invariance, strong invariance and strict invariance, which are commonly used
in the literature were tested. In this study, it was examined whether the invariance conditions
of ΔCFI ≤ -0.01 for multiple group confirmatory factor analysis study files which are
compatible with the data of were obtained. The fact that ΔCFI value obtained as a result of the
comparison of the two models is equal to -.01 or below can be used as the evidence that the
measurement equivalence is achieved (Wu et al., 2007).
The findings regarding the invariance steps tested are present in Table 6. “The Structural
Invariance Model” in the table represents the factor loads, regression constant and the error
variances free model; “The Weak Invariance Model” in the table represents the factor loads
constant, regression constants and error variances free model; “The Strong Invariance Model”
in the table represents the factor loads, regression constants and error variance free model; and
Int. J. Asst. Tools in Educ., Vol. 6, No. 3, (2019) pp. 396–414
409
“The Strict Invariance Model” in the table represents the factor loads, regression constants and
error variances constant model.
Table 6. Fit statistics regarding measurement invariance
Steps
χ2
d
CFI
GFI
RMSEA
ΔCFI
Structural Invariance
87.84
67
.99
.97
.031
-
Weak (Metric) Invariance
44.76
32
1.00
1.00
.036
0.01
Strong (Scalar) Invariance
44.76
32
1.00
1.00
.036
0.01
Strict Invariance
87.84
87
1.00
.97
.006
0.01
As can be seen in Table 6, the fit indexes obtained as a result of multi-group CFI and ΔCFI
values obtained as a result of CFI difference test can be interpreted for each step as follows.
According to the results, it is seen that the structural invariance is provided and this finding
shows that the measured structures use the same conceptual perspectives in responding to the
scale items of the adolescents and adults. The finding regarding the metric invariance indicates
that the factor structures of the variables taken in the model are the same in the adolescent and
adult groups. It is confirmed that the strong invariance is provided and the constant number in
the regression equations formed for the items is invariant between the groups. In the last stage,
considering the ΔCFI value calculated with the fit indexes, it is accepted that the error terms
regarding the items forming the measurement tool are invariant between the comparison groups.
Hierarchical analysis results, factor structure and pattern of the scale, factor loads, regression
constants, and error variances are seen to be invariant for the adolescent and adult groups.
For the criterion validity of SNAS Adult Application, the relationship with Internet Addiction
Scale was examined. As a result of Pearson Product-Moment Correlation Analysis performed
for the criterion validity, it was determined that there was a positive (r=.65) and statistically
significant relationship (p≤.001) between the scales.
3.2.3.Reliability Studies
Reliability analyses were performed both for the overall and for the factors of SNAS Adult
Application. On the condition that the items are congeneric measurements, McDonald Omega
coefficient is used (McDonald, 1999). McDonald Omega coefficient of the overall scale was
calculated as .91. McDonald Omega value for the first factor was .83; item total correlation
coefficients varied between .78 and .84. For the second factor, McDonald Omega value was
.76; item total correlation coefficients varied between .54 and .73. For the third factor,
McDonald Omega value was .91; item total correlation coefficients varied between .52 and .55.
As all the values in the reliability analysis both for the overall and for the factors of SNAS Adult
Application are above 0.70, itcan be said that the reliability of the scale is high. In the reliability
analysis for the 10 items included in the scale, the item total correlation coefficients of the items
varied between .37 and .66.
Item analysis was performed to determine whether there was a difference between the responses
of the individuals with low scores (the lower 27% group) and high scores (the upper 27%
group). As a result of the ttest performed for this purpose, it was observed that tvalues of the
items varied between 6.09 (p<.001) and21.03 (p<.001) and there was a significant difference.
Within the framework of Item Response Theory, item difficulty (a), item discrimination power
(b) and item-model fit (X2) was examined and the results are given in Table 7.
When Table 7 is analyzed, the fact that all the values are above 1 reveals that the items have
good level of discrimination. High level of "b" value exhibits that the item is functional or it
measures among the individuals with high addiction levels, whereas low level of "b" value
indicates that the item is functional or it measures among the individuals with low addiction
Gokdas & Kuzucu
410
levels. Item difficulty value varies between -2.32 and 3.26. It was noted that while the first
threshold value of the scale (Likert 1 and 2 interval) was between 0 and 1, the second threshold
value varied between 0 and 1, the third threshold value varied between 1 and 2, and the fourth
threshold value varied between 2 and 3. This suggests that the scale is better discriminated in
the individuals with high social network addiction. The fact that all theX2values indicating item
model fit are insignificant shows that all the items meet the model fit.
Table 7. Parameter values in terms of SNAS Adult Application according to IRT
Item
a
s.e.
b1
s.e.
b2
s.e.
b3
s.e.
b4
s.e.
X2
df
p
1
1.53
0.22
-2.32
0.33
-1.35
0.22
-0.15
0.14
0.59
0.15
54.65
45
0.153
2
2.08
0.28
-0.77
0.16
0.09
0.12
1.11
0.14
1.72
0.19
53.93
44
0.144
3
2.43
0.34
-1.14
0.16
-0.38
0.12
0.40
0.11
1.45
0.16
60.30
44
0.051
4
3.00
0.43
-0.61
0.13
0.29
0.11
0.97
0.12
1.71
0.17
48.88
38
0.110
5
1.27
0.20
-1.01
0.22
0.78
0.16
1.96
0.28
3.26
0.51
49.03
45
0.314
6
1.35
0.22
-0.21
0.16
0.83
0.16
1.92
0.27
3.10
0.47
51.97
46
0.252
7
1.01
0.18
-0.82
0.23
0.65
0.19
1.91
0.33
3.20
0.56
70.70
52
0.053
8
1.73
0.35
0.66
0.13
1.76
0.23
2.38
0.34
3.16
0.54
33.63
30
0.295
9
1.63
0.31
0.21
0.13
1.34
0.18
2.35
0.33
2.91
0.45
52.26
37
0.049
10
1.70
0.33
0.47
0.12
1.35
0.18
2.07
0.28
2.83
0.43
44.54
39
0.249
4. DISCUSSION and CONCLUSION
The presence of social network addiction can be discussed depending on the definition of
addiction used. However, there are evidences that some social network users are experiencing
addiction-like symptoms due to excessive use. Besides, many studies have revealed that social
networks are addictive (eg. Echeburúa & de Corral, 2010; Grffiths, Kuss, & Demetrovics, 2014;
Pantic, 2014; Ryan et al., 2014). It can be seen in the literature that the researchers investigating
social network addiction focus primarily on Facebook addiction (Andreassen, 2015). However,
it has been discussed that Facebook is just a social network and therefore, there is a need for
valid scales involving other social network sand measuring social network addiction (Griffiths
et al., 2014). Although social networks, which can be considered as the sub-dimension of the
internet, have some similar characteristics in terms of their intended use, they differ in the uses
specific to individual and purpose (Kuşay, 2013; Van den Eijnden et al., 2016).
When the literature was examined, it could be seen that the scales developed differed in terms
of factor structures and target groups and the total variance range explained varied between
35% and 59% (Esgi, 2016; Fırat, & Barut, 2018; Şahin, 2018; Şahin, & Yağcı, 2017; Taş, 2017;
Tutgun-Ünal, 2015; Tutgun-Ünal, & Deniz, 2015; Ülke, Noyan, & Dilbaz, 2017; Van den
Eijnden et al., 2016). The factor structures of these scales, which were developed for different
age groups differed from each other. On the other hand, the factor structure of the social media
addiction scale, which was developed by Bakır Ayğar & Uzun (2018) and whose target group
was university students, was similar to the factor structure of SNAS. As could be seen, each
scale was structured according to different age groups and their factor structures differed from
each other. Besides, for the criterion validity in the measurement tools developed to measure
social network addiction, Bakır Ayğar & Uzun (2018) used the problematic internet use scale;
and Van den Eijnden et al. (2016) used compulsive internet use scale. In both studies, it was
determined that the correlation with the scale used for the criterion validity was high. The
criterion validity was not examined in the other scales developed for social network addiction
(Eşgi, 2017; Şahin, 2017; Şahin & Yağcı, 2016).
Int. J. Asst. Tools in Educ., Vol. 6, No. 3, (2019) pp. 396–414
411
The scales in the literature are generally dispersed in terms of target groups and factor
structures. This situation is thought to have a negative effect on the comprehensive
comparability between the developmental periods regarding social network addiction. SNAS
developed in this study is significant in terms of sorting out this problem.
The factor structures of SNAS, which was composed of a total of 10 items, were named as
“Control Difficulty”, “Decrease in Functions” and “Negativeness in Social Relations”.
As a result of the factor analysis performed, the total variance explained and factor load values
were high in adolescent and adult form. The first level CFA and second level CFA results of
the scale revealed that the model showed adequate fit with the data. McDodalds Omega
reliability coefficient values for each sub-dimension, which was conducted to determine the
reliability of the scale, also showed that the scale was reliable. Furthermore, IRT was used to
confirm the reliability results obtained. As a result of this analysis, it was found that only the
item number 9 in the adolescent form was weak in terms of discrimination. However, due to
the fact that it was too close to the acceptable value and that both the factor load value and the
total correlation coefficient were high, the item was not excluded from the scale. As a result of
Pearson Product Moment Correlation Analysis performed for the criterion validity of
adolescent and adult forms, a positive and statistically significant relationship was found
between the scales. The highest score that can be obtained from the scale is 50 and the lowest
score is 10. As the score obtained from the scale goes up to 50, addiction level increases, too.
The scale developed involves adolescents, young adults and adults between 14-45 years of age.
The scale has great power of explaining the variable it intends to measure with a small number
of well-working items. With this feature, the scale will provide researchers convenience and
flexibility with the researches targeting different age groups and for possible comparisons.
Orcid
İbrahim GÖKDAŞ https://orcid.org/0000-0001-7019-8735
Yaşar KUZUCU https://orcid.org/0000-0002-8487-9993
5. REFERENCES
Aksu, G., Eser, M. T., & Güzeller, C. O. (2017). Açımlayıcıve DoğrulayıcıFaktör Analizi ile
Yapısal Eşitlik Modeli Uygulamaları. Ankara: Detay yayıncılık.
Allen, K. A., Ryan, T., Gray, D. L., Mclnerney, D. M., & Waters, L. (2014). Social media use
and social connectedness in adolescents: The positives and the potential pitfalls. The
Australian Educational and Developmental Psychologist, 31, 18 - 31. https://doi.org/10.
1017/edp.2014.2
Andreassen, C. S. (2015). Online social network site addiction: A comprehensive review.
Technology and Addiction (M Griffiths, Section Editor). Current Addiction Reports. 2,
175–184. DOI 10.1007/s40429-015-0056-9
Augner, C., & Hacker, G.W. (2012). Associations between problematic mobile phone use and
psychological parameters in young adults. International Journal of Public Health.57(2),
437-41. DOI: 10.1007/s00038-011-0234-z
APA (American Psychiatric Association). (2013). Diagnostic and Statistical Manual of Mental
Disorders (DSM-5). American Psychiatric Association Publishing.
Ayğar Bakır, B. & Uzun, B. (2018). Sosyal Medya Bağımlılığı Ölçeği’nin geliştirilmesi:
Geçerlik ve güvenirlik çalışmaları. [Developing the Social Media Addiction Scale:
Validity and Reliability Studies] Addicta: The Turkish Journal on Addictions,5, 507‒
525. http://dx.doi.org/10.15805/addicta.2018.5.3.0046
Gokdas & Kuzucu
412
boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship.
Journal of Computer - Mediated Communication, 13, 2010 - 2030. https://doi.org/10.11
11/j.1083-6101.2007.00393.x
Brunborg, G. S., Mentzoni, R. A., Molde, H., Myrseth, H., Skouverøe, K. J. M., Bjorvatn, B.,
& Pallesen, S. (2011). The relationship between media use in the bedroom, sleep habits,
and symptoms of insomnia. Journal of Sleep Research, 20, 569-575.
https://doi.org/10.1111/j.1365-2869.2011.00913.x
Cakir Balta, O. & Horzum M. B. (2008). İnternet bağımlılığı testi. [Internet addiction test].
Eğitim Bilimleri ve Uygulama, 7(13), 87-102
Can, L., & Kaya, N. (2016). Social networking sites addiction and the effect of attitude towards
social network advertising. Procedia-Social and Behavioral Sciences,235, 484-492.
https://doi.org/10.1016/j.sbspro.2016.11.059
Cao, F., & Su, L., (2006). Internet addiction among Chinese adolescents: prevalence and
psychological features. Child Care Health and Development. 33(3), 275–281.
https://doi.org/10.1111/j.1365-2214.2006.00715.x
Cecilia, C., & Yee-lam L. A., (2014). Internet addiction prevalence and quality of (real) life: a
meta-analysis of 31 nations across seven world regions, Cyberpsychology, Behavior, and
Social Networking. 17(12), 755-760.
Cheung, L. M., & Wong, W. S. (2011). The effects of insomnia and internet addiction on
depression in Hong Kong Chinese adolescents: An exploratory cross-sectional analysis.
Journal of Sleep Research, 20, 311–317. http://dx.doi.org/10.1111/j.1365-
2869.2010.00883.x
Choi, S. B., & Lim, M. S. (2016). Effects of social and technology overload on psychological
well-being in young South Korean adults: The mediatory role ofsocial network service
addiction. Computers in Human Behavior, 61, 245-254.
Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2012). Sosyal Bilimler için Çok Değişkenli
İstatistik SPSS ve LISREL Uygulamaları. Ankara: Pegem Akademi.
De Beer, M. (2004). Use of differential item functioning (DIF) analysis for bias analysis intest
construction. South African Journal of Industrial Psychology, 30(4), 52-58.
Dewald, J. F., Meijer, A. M., Oort, F. J., Kerkhof, G. A., & Bögels, S. M. (2010) The influence
of sleep quality, sleep duration and sleepiness on school performance in children and
adolescents: a meta-analytic review. Sleep Medicine Reviews, 14, 179-189.
Echeburúa, E., & De, Corral. P. (2010). Addiction to new technologies and to online
socialnetworking in young people: A new challenge. Adicciones,22(2), 91-95.
Erkuş, A. (2012). Psikolojide Ölçme ve Ölçek Geliştirme-1 temel kavramlar ve işlemler.
Ankara: Pegem Akademi.
Esgi, N. (2016). Development of social media addiction test (SMART17). Journal of education
and training studies.4(10), 174-181
Gao, W., Liu, Z., & Li, J. (2017). How does social presence influence SNS addiction? A
belongingness theory perspective. Computers in Human Behavior 77 (2017) 347-355.
http://dx.doi.org/10.1016/j.chb.2017.09.002
Griffiths, M. D. (2013). Social networking addiction: emerging themes and issues. Journal of
Addiction Research & Therapy, 4(5). http://dx.doi.org/10.4172/2155-6105.1000e118
Griffiths, M. D., Kuss, D. J., & Demetrovics, Z. (2014). Social networking addiction:
anoverview of preliminary findings. In Behavioral addictions. Criteria, evidence, and
treatment (pp. 119-141). New York: Elsevier.
Int. J. Asst. Tools in Educ., Vol. 6, No. 3, (2019) pp. 396–414
413
Gültaş, M. (2014). Work Discipline Compound Personality Scale Development with Item
Response Theory. Unpublished dissertation thesis. Middle East Technical University,
Graduate School of Social Sciences, Ankara.
Huang, H., & Leung, L. (2009). Instant messaging addiction among teenagers in China:
Shyness, alienation, and academic performance decrement. Cyberpsychology and
Behavior,12(6), 675-679. http://dx.doi.org/10.1089/cpb.2009.0060
Huang, X., Zhang, H., Li, M., Wang, J., Zhang, Y., & Tao, R., (2010). Mental health,
personality, and parental rearing styles of adolescents with internet addiction disorder.
Cyberpsychology, Behavior, and Social Networking. 13(4), 401-406. DOI:
10.1089=cyber.2009.0222
Kang, I., Shin, M. M., & Park, C. (2013). Internet addiction as a manageable resource:A focus
on social network services. Online Information Review,37(1), 28-41.
Kim, E. J., Namkoong, K., Ku, T., & Kim, S.J., (2008). The relationship between online game
addiction and aggression, self-control and narcissistic personalitytraits. European
Pschiatry, 23, 212-218.
Kim, K., Ryu, E., Chon, M. Y., Yeun, E. J., Choi, S. Y., Seo, J. S., &Nam, B.W., (2006).
Internet addiction in Korean adolescents and its relation to depression andsuicidal
ideation: a questionnaire survey. Intertional Journal of Nursing Studies, 43, 185-192.
Kuss, D. J., & Griffiths, M. D. (2011). Online social networking and addiction-A review of the
psychological literature. International Journal of Environmental Research and Public
Health, 8, 3528-3552. DOI:10.3390/ijerph8093528
Kuss, D. J., & Griffiths, M. D. (2012). Internet gaming addiction: A systematic reviewof
empirical research. International Journal of Mental Health and Addiction, 10(2), 278-
296.
Kuşay, Y. (2013). Sosyal Medya Ortamında Çekicilik ve Bağımlılık- Facebook Üzerine Bir
Araştırma.İstanbul: Beta Yayıncılık.
LaRose, R., Connolly, R., Lee,H., Li, K., & Hales, K. D. (2014). Connection overload? Across
cultural study of the consequences of social media connection. Information Systems
Management, 31(1), 59-73. https://doi.org/10.1080/10580530.2014.854097
Lavin, M. J., Yuen, C.N., Weinman, M., & Kozak, K., (2004). Internet dependence in the
collegiate population: The role of shyness. CyberPsychology & Behavior. 7, 379-383.
Pantic, I. (2014). Online social networking and mental health. Cyberpsychology, Behavior, and
Social Networking, 17(10), 652-657. DOI: 10.1089/cyber.2014.0070
Park, S. K., Kim, J. Y., & Cho, C. B. (2008). Prevalence of Internet addiction and correlations
with family factors among South Korean adolescents. Adolescence,43(172), 895-909.
Ryan, T., Chester, A., Reece, J., & Xenos, S. (2014). The uses and abuses of Facebook: A
review of Facebook addiction. Journal of Behavioral Addictions 3(3), 133-148. DOI:
10.1556/JBA.3.2014.016
Salehan, M., & Negahban, A. (2013). Social networking on smartphones: Whenmobile phones
become addictive. Computers in Human Behavior,29(6), 2632-2639.
Satici, S. A., & Uysal, R. (2015). Well-being and problematic Facebook use. Computers in
Human Behavior,49, 185-190. DOI:10.1016/j.chb.2015.03.005
Suganuma, N., Kikuchi, T., Yanagi, K., Yamamura, S., Morishima, H., Adachi, H., Kumanogo,
T., Mikami, A., Sugita, Y., & Takeda, M. (2007). Using electronic media before sleep
can curtail sleep time and result in self-perceived insufficient sleep. Sleep and Biological
Rhythms, 5, 204-214.
Gokdas & Kuzucu
414
Şahin, C. (2018). Social media addiction scale- Student form: The reliability and validity study,
TOJET: The Turkish Online Journal of Educational Technology,17(1), 169-182. ERIC
Number: EJ1165731
Şahin, C., & Yağcı, M. (2017). Sosyal medya bağımlılığı ölçeği- Yetişkin formu: Geçerlilik ve
güvenirlik çalışması[Social Media Addiction Scale - Adult Form: The Reliability and
Validity Study Social Media Addiction Scale - Adult Form: The Reliability and Validity
Study], Ahi Evran Üniversitesi Kırşehir Eğitim Fakültesi Dergisi (KEFAD), 18(1), 523-
538.
Taş,İ. (2017). Ergenler için sosyal medya bağımlılığı ölçeği kısa formunun (SMBÖ-KF)
geçerlik ve güvenirlik çalışması, [The Study of Validity And Reliability of The Social
Media Addiction Scale Short Form For Adolescents], Online Journal of Technology
Addiction & Cyberbullying,4(1), 27-40.
Tekin, C., Gunes, G., & Colak, C. (2014). Adaptation of problematic mobile phone use scale
to Turkish: a validity and reliability study. Medicine Science,3(3), 1361-81. DOI:
10.5455/medscience.2014.03.8138
Turel, O., & Serenko, A. (2012). The benefits and dangers of enjoyment with socialnetworking
websites. European Journal of Information Systems, 21(5), 512-528.
Tutgun-Ünal, A. (2015). Sosyal medya bağımlılığı: üniversite öğrencileri üzerine bir
araştırma. [Social media addiction: a research on university Students], (Yayınlanmamış
Doktora Tezi) Marmara Üniversitesi Sosyal Bilimler Enstitüsü, Gazetecilik Ana Bilim
Dalı, Bilişim Bilim Dalı.İstanbul.
Van den Eijnden, R. J. J. M., Lemmens, J. S., & Valkenburg, P. M. (2016). The social media
disorder scale. Computers in Human Behavior, 61, 478-487.
Vilca, L. W., & Vallejos, M. (2015). Construction of the risk of addiction to socialnetworks
scale (Cr. ARS). Computers in Human Behavior, 48, 190-198.
We are Social (2017). Digital in 2017 Global Overview Report.
https://wearesocial.com/uk/special-reports/digital-in-2017-global-overview
Wu, A. D., Li, Z. ve Zumbo, B. D. (2007). Decoding the meaning of factorial invariance and
updating the practice of multi-group confirmatory factor analysis: A demonstration with
TIMSS data. Practical Assessment, Research & Evaluation,12, 1-26.
Yang, S., Liu, Y., & Wei, J. (2016). Social capital on mobile SNS addiction: A perspective
from online and offline channel integrations. Internet Research, 26(4), 982-1000.
Young, K. S. (1998). Internet addiction: The emergence of a new clinical disorder.
Cyberpsychology and Behavior, 1(3), 237-244. https://doi.org/10.1089/cpb.1998.1.237
