Content uploaded by Kagan Kircaburun
Author content
All content in this area was uploaded by Kagan Kircaburun on Aug 31, 2016
Content may be subject to copyright.
Journal of Education and Practice www.iiste.org
ISSN 2222-1735 (Paper) ISSN 2222-288X (Online)
Vol.7, No.24, 2016
64
Self-Esteem, Daily Internet Use and Social Media Addiction as
Predictors of Depression among Turkish Adolescents
Kağan Kırcaburun
Duzce University, Faculty of Education, Computer Education and Instructional Technology
Abstract
In this study, direct and indirect effects of self-esteem, daily internet use and social media addiction to depression
levels of adolescents have been investigated by testing a model. This descriptive study was conducted with 1130
students aged between 12 and 18 who are enrolled at different schools in southern region of Aegean. In order to
collect data, “Children's Depression Inventory”, “Rosenberg Self-esteem Scale” and “Social Media Addiction
Scale” have been used. In order to test the hypotheses Pearson's correlation and structural equation modeling were
performed. The findings revealed that self-esteem and social media addiction predict %20 of the daily internet use.
Furthermore, while depression was associated with self-esteem and daily internet use directly, social media
addiction was affecting depression indirectly. Tested model was able to predict %28 of the depression among
adolescents.
Keywords: depression, self-esteem, daily internet use, social media addiction, structural equation modeling
1. Introduction
Depression is one of the major health problems among modern society. Research conducted by World Health
Organization (WHO) in 2016 revealed that depression is affecting approximately 350 million people from all
around the World. Different from general mood change and short term emotional reactions against daily struggles,
depression becomes a very dangerous and serious health problem especially when intense depressive feelings and
symptoms are shown for extended period of time. It is a common and serious psychological disorder and affects
individuals’ daily routine activities and abilities such as thinking, feeling, sleeping, eating, working and studying
(NIMH, 2016). Duran (1999) defined depression as a syndrome that includes symptoms of retardation and
fuzziness in speech and movement; worthlessness, pettiness, weakness and reluctance; pessimistic emotions,
thoughts and psychological states.
Depression is a mental illness that also affects adolescents prevalently. While depressive symptoms can
be seen among children uncommonly, beginning from early stage of adolescence, symptoms are escalating acutely
(Fleming & Offord, 1990). Major depressive illnesses are usually commencing in adolescence and because of their
chronic and repetitive character there is a high probability of iteration in adulthood (Jacob et al., 2008). Kim-Cohen
et al. (2003) reported that 75% of the adults that are going through major depressive disorder have experienced
their first depressive episode in childhood or adolescence.
It was reported that 2.8 million American adolescents who are between ages of 12 to 17 have gone through
at least one major depressive episode in 2014. This was the 11.4% of the total population of age of 12-17
adolescents. In recent years, these numbers have been going up regularly (Abuse, 2014). Depression has been seen
among Turkish adolescents as well. A study conducted by Eskin et al. (2008) has shown that among high school
students, 61.5% of the participants were having mental health problems. In other research, Toros et al. (2005)
conducted a study with the students aged between 10 and 20; findings revealed that 12.5% of the participants had
high levels of depression.
There is number of psychical, social and psychological factors thought to be effective on depression. One
of them is self-esteem which expresses the individuals’ respect towards themselves. Self-esteem is self-
appreciation and self-approval which originated from self-assessment (Yavuzer, 2003; as cited in Sarıkaya, 2015).
Individuals who are lack of self-liking and seeing themselves lower than actually who they are, feel and perceive
themselves inadequate against their surroundings. They tend to be more sensitive against criticism and rather focus
on how people see them. As a result of this, they avoid taking risks, they usually demonstrate self-enclosed
behavior and they feel alone and left-off (Rosenberg & Owens, 2001; as cited in Sowislo & Orth, 2013).
Vulnerability model is one of the models that was developed to define the relationship between depression
and self-esteem. According to this model, lower self-esteem is a serious risk factor for future depression (Orth,
Robins ve Roberts, 2008). Individuals with low self-esteem usually are in a need for constant approval from their
friends and loved ones in order to feel valuable and precious. This situation is lifting the probability of being
rejected by their close friends and consequently the probability of being depressed (Joiner, Alfano & Metalsky,
1992). In many research, relationship between depression and low self-esteem have been revealed (Burwell &
Shirk, 2006; Conti, Adams & Kisler, 2014; Erözkan, 2009; Eskin et al., 2008; Kernis et al., 1998; Li et al., 2015;
Lin, 2015; Orth et al., 2008; Orth et al., 2014; Orth et al.,2016; Steiger, Fend & Allemand, 2015; Wouters et al.,
2013).
For individuals who are having difficulties in expressing themselves, feeling alone and left-off, it may be
Journal of Education and Practice www.iiste.org
ISSN 2222-1735 (Paper) ISSN 2222-288X (Online)
Vol.7, No.24, 2016
65
thought that since virtual platforms such as internet and social media allow individuals to be anonymous by
disguising their identity, they can act as a person they are not and express themselves more comfortably (Kurtalan,
2008). In a recent study, it was reported that, over 7.3 million people are living in the world, 3.4 million of them
are using internet and 2.3 million of them are active social media users. These results show that number of internet
and active social media users were up 10% compared to 2015 results (Chaffey, 2016).
Statistics suggest that majority of internet users are also social media users. Social media enable
individuals to communicate with one another through internet. It also contains communion platforms such as
Facebook, Twitter and YouTube that provides availability to online interactions (Corbeil & Corbeil, 2011). Using
social media platforms such as Facebook and Twitter have become widespread among adolescents. According to
recent study conducted with American students aged between 13 and 17, while 90% of them were social media
users, 75% of them were using social networking sites and nearly 35% of them were logging in to their social
media profiles several times a day (Rideout, 2012). As a result of their study with the students aged between 13
and 19, Kırık et al. (2015) also reported that social media was effective on students’ lives and social media use and
addiction levels were increasing among adolescents.
Social media is the most commonly used platform on the internet and it is becoming rapidly the most
important communication and interaction tool. Number of users of social networking platforms is growing in
accordance with the increasing use of internet (Çam & İşbulan, 2012). However, although social media enables
individuals to interact with a wide range of people, these superficial and artificial interactions stay inadequate for
replacing the face-to-face communications. Excessive use of internet and social networking platforms could
weaken the connections between individuals and their families, friends and loved ones. As a result of that,
individuals may feel lonelier and depressed (Pantic, 2014; Yellowless & Marks, 2007).
When the literature reviewed, number of research regarding subjects of depression, self-esteem, excessive
internet use and social media addiction have been noticed. As a result of their study with adolescents and teenagers
aged between 15 and 21, Orth et al. (2008) concluded that low self-esteem was causing depression. This finding
supports the vulnerability model that suggests lower self-esteem is significant predictor of depression. Another
finding that supports this model was found by Kernis et al. (1998). According to this study, unstable self-esteem
causes vulnerable feelings of self-worthiness and this leads to depressive symptoms. In other study, Erözkan (2009)
who has investigated the predictors of depression among 8 graders reported that low self-esteem was an important
predictor of depression. In a similar study, Eskin et al. (2008) found out that low self-esteem was causing
depression among high school students. In a meta-analysis conducted by Chen, Chiu & Huang (2013), 50 studies
containing more than 32,000 participants were examined. As a result of the study, findings revealed that there was
a moderate but significant relationship between self-esteem and depression. Aside from these studies, Shrier et al.
(2001) conducted a research with 6,583 adolescent students who were in grades of 7th to 12th. As a result of the
study it was stated that there was no significant relationship between depressive syndrome and self-esteem.
In the literature, it can be seen that there is number of studies which have investigated the relationships
between daily internet use, pathological internet use, internet addiction and depression. For instance, according to
Sanders et al. (2000), increase on internet use was causing weakening effect on social relations, however that
wasn’t affecting the depression levels of the students significantly. In similar studies, Banjanin et al. (2015) and
Tan et al. (2016) stated that there was a significant positive correlation between internet use and depression.
According to them, as the time spent online increased depression level of individual was rising. In another study,
Ayas & Horzum (2013) investigated the relations between internet addiction, loneliness, self-esteem and
depression among high school students. Findings revealed that while there was a significant positive relationship
between depression and internet addiction, no relations have been found between self-esteem and internet
addiction. In a recent study, Budak et al. (2015) stated that internet addiction levels of the university students were
causing elevation on psychopathological symptoms and drop on their self-esteem.
As the use of social media and social networking sites widened rapidly, studies that were investigating
the effects of this increase to depression have been conducted. Pantic et al. (2012) reported that daily time spent
on social media was causing adjuvant effect on depression levels of the high school students. Steers, Wicham &
Acitelli (2014) have reported a similar result to the findings of Pantic et al. (2012). According to this study, staying
longer on Facebook was making students more depressed. In contrast to the finding of Pantic et al. (2012),
Jelenchick, Eickhoff & Moreno (2013) stated that there was no significant relationship between daily time spent
on social networking platforms and depression among university students. In other study, De Choudhury, Counts
& Horvitz (2013) have developed a model that was aiming to analyse the tweets of individuals in order to determine
their depression levels. As a result of the study, it was reported that social media platforms such as Twitter could
be consistent, reliable sources for measuring the depression levels of the individuals. It was also stated that
detecting any sign of depression at early stages would enable specialists to prevent and intervene symptoms before
escalating. Another study, that has investigated the Facebook status updates of the university students, revealed
that 25% of the students were showing depressive symptoms and 2.5% of them were determined as in a major
depressive episode.
Journal of Education and Practice www.iiste.org
ISSN 2222-1735 (Paper) ISSN 2222-288X (Online)
Vol.7, No.24, 2016
66
Despite there is a number of research that have investigated the effects of self-esteem, internet use and
social media addiction on the depression levels of the individuals, number of studies conducted with Turkish
adolescents thought to be inadequate. Therefore, this study was seemed to be needed in order to study the effects
of psychological and technological factors on the depression levels of Turkish adolescents. Furthermore,
contributions of the studies which are investigating the effects of excessive use of internet and social media on
individuals’ psychology considered to be beneficial to the literature.
Hypotheses of this research are:
1- There is a positive association between depression and daily internet use.
2- There is a positive association between depression and social media addiction.
3- There is a negative association between depression and self-esteem.
4- There is a positive association between daily internet use and social media addiction.
5- There is a negative association between daily internet use and self-esteem.
2. Method
2.1 Research Design
In this study, relational survey design has been used. Relational survey design enables researchers to investigate
the change and the level of the variation of two or more variables together (Karasar, 2015). In order to determine
the relationship between depression, self-esteem, daily internet use and social media addiction, structural equation
modeling analysis have been utilized.
2.2 Participants
Adolescents aged between 12 and 18 constitute the population of the study. Purposeful sampling method was used
in order to choose the participants. 1130 students who are in grades between 6 to 11 in secondary and high schools
in southern region of Aegean had participated in this study. Demographic features of the participants are shown in
Table 1.
Table 1
Characteristics of the Participants
n %
Gender Female 659 58.7
Male 463 41.3
Grade Level
6
th
grade 154 13.6
7
th
grade 201 17.8
8
th
grade 143 12.7
9
th
grade 294 26
10
th
grade 177 15.7
11
th
grade 161 14.2
Age
12 137 12.1
13 224 19.8
14 143 12.7
15 212 18.8
16 228 20.2
17 154 13.6
18 32 2.8
Online via Mobile Yes 865 80.5
No 210 19.5
Daily Internet Use
Less than 1 Hour 384 34
Between 1-3 Hours 467 41.3
More than 3 Hours 279 24.7
2.3 Instruments
Children’s Depression Inventory (CDI): The CDI consists of 27 items labeled from A to Z. For every item there
are 3 possible statements which are given points between 0 and 2. 13 of the items are reverse coded. Highest total
score that can be taken from the scale is 54 and pathologic cutpoint is 19. The scale was developed by Kovacs in
1981. Adaptation into Turkish of the scale was conducted by Öy (1991). As a result of reliability test, Cronbach
Alfa co-efficient was found .86. In this study, calculated reliability co-efficient alpha was .85.
Rosenberg Self-esteem Scale (RSES): The scale was developed by Rosenberg (1965). Adaptation into
Turkish of the scale was conducted by Çuhadaroğlu (1986). Scale consists of 10 items on a 4-point likert scale. 5
of the items are reverse scored. Consistency of the scale was .71. In this study, reliability co-efficient was found .78.
Daily Internet Use: Daily internet use was one of the variables in this study. In order to identify the
Journal of Education and Practice www.iiste.org
ISSN 2222-1735 (Paper) ISSN 2222-288X (Online)
Vol.7, No.24, 2016
67
characteristics of internet usage among participants, personal information form was used. Participants have chosen
between 3 items which are "Less than 1 hour", "Between 1 to 3 hours" and "More than 3 hours".
Social Media Addiction Scale: In this study, addiction factor of “Social Networking Status Scale” was
used. The scale was developed by Arslan & Kırık (2013). Scale consists of 25 items on a 5-point likert scale such
as “I lose track of time while i am on Facebook”, “I prefer social media over Tv.” and “From the early times of
the day i feel desire to log in to social networking sites”. Calculated Cronbach Alfa reliability co-efficient for this
scale was .93. In this study, consistency of the scale was .94.
2.4 Data Analysis
In the analyses, descriptive statistics, Pearson correlation test and structural equation modeling was used. Structural
equation modeling is an extensive statistical approach that is used to test models which consist of hypotheses about
direct and indirect associations between observed and latent variables (Hoyle, 1995). Analyses were conducted by
using Amos 23.0 and Spss 23.0 package programs.
3. Results
3.1 Descriptive Statistics and Inter-correlations
Significant relationships between depression, self-esteem, daily internet use and social media addiction have been
observed. While depression was negatively but moderately associated with self-esteem, it was positively but
weakly correlated with daily internet use and social media addiction.
Table 2
Mean Scores, Standard Deviations and Correlation Co-efficients of The Variables
Variables X Sd 1 2 3 4
1.Depression 16.61 8.37 .
2.Self-Esteem 29.28 5.39 -.50** .
3.Daily Internet Use 1.90 .76 .27** -17**
4.Social Media Addiction 75.12 22.32 .13** -09** ..44** .
**p<.001
3.2 Structural Equation Model
Good fit, acceptable values (Hu & Bentler, 1999) and model results are given in Table 3.
According to Table 3., χ2/df and RMSEA results are at acceptable levels and SRMR, GFI, NFI, NNFI,
CFI, IFI, AGFI results are at good fit levels. These results suggest that model is accepted.
Table 3
Model Indexes
Indexes Model Results Good Fit Acceptable Values
X
2
/df 4.35 X
2
/df < 2 X
2
/df < 5
RMSEA
.05 RMSEA <. 05 .05 < RMSEA <. 08
SRMR .03 SRMR < .05 .05 < SRMR < .10
GFI .99 .95 < GFI < 1 .90 < GFI < .95
CFI .99 .95 < CFI < 1 .90 < CFI < .95
NFI .99 .95 < NFI < 1 .90 < NFI < .95
IFI .99 .95 < IFI < 1 .90 < IFI < .95
AGFI .98 .90 < AGFI < 1 .85 < AGFI < .90
NNFI .97 .95 < NNFI < 1 .90 < NNFI < .95
Journal of Education and Practice www.iiste.org
ISSN 2222-1735 (Paper) ISSN 2222-288X (Online)
Vol.7, No.24, 2016
68
Chi-square= 8.69, df= 2, p-value= .013, RMSEA= .054
Figure 1. Standardized Values Between Variables
As the t values and standardized values of the variables were examined, it was found that while social
media addiction was positively affecting daily internet use (t=16.18, standardized=.43); daily interent use was
negatively associated with self-esteem (t=-4.93, standardized= -.13). Furthermore, depression was affected by self-
esteem negatively (t=-18.5, standardized= -.47) and it was predicted by daily internet use positively (t=7.39,
standardized=.19). Structural equations and R
2
values are presented in Table 4.
Table 4
Structural Equations and R
2
Values
Structural Equation R
2
Daily Internet Use = .43*SocialMediaAddiction - .13*SelfEsteem .20
Depression = .08*SocialMediaAddiction + .19*DailyInternetUse - .50*SelfEsteem .28
Social media addiction and self-esteem were predicting %20 of the daily internet use and %28 of the
depression among adolescents was predicted by social media addiction, daily internet use and self-esteem. In the
model, daily internet use and self-esteem was affecting depression directly and social media addiction indirectly.
4. Discussion
In this study, variables that are thought to be effective on depression levels of the adolescents have been examined
by using correlation analysis and structural equation modeling. Findings revealed that there were significant
correlations between depression, self-esteem, daily internet use and social media addiction. It was also found that
depression was directly negatively associated with self-esteem and positively with daily internet use. Social media
addiction was affecting depression levels of adolescents indirectly (positively).
According to results, self-esteem was affecting daily internet use of adolescents negatively. This finding
coincides with some studies (Aydm & San, 2011; Bahrainian et al., 2014; Kim et al., 2016; Mei et al., 2016;
Zhang, 2015), as well as it contradicts the other ones (Ayas & Horzum, 2013; Reisoğlu, Gedik & Göktaş, 2013).
This result may be explained by that individuals who have low self-esteem are avoiding real interactions and
escape to virtual world where they can behave anonymously and act as whoever they want.
In the structured model, most significant predictor of depression was self-esteem. According to this, as
the self-esteem levels of students decreased, probability of showing depressive symptoms were going up. Although
there is a large number of studies supporting this finding (Burwell & Shirk, 2006; Conti, Adams & Kisler, 2014;
Erözkan, 2009; Eskin et al., 2008; Kernis et al., 1998; Li et al., 2015; Lin, 2015; Orth et al., 2008; Orth et al., 2014;
Orth et al., 2016; Steiger, Fend & Allemand, 2015; Wouters et al., 2013), some researchers reported different
results (Gana et al., 2015; Shrier et al., 2001).
There are various models in the literature that are trying to illuminate the relationship between depression
and self-esteem. One of them is vulnerability model which claims low self-esteem is leading individuals to
depression. Different from that, scar model asserts that depressive episodes leave certain scars on individuals'
psychology and this leads to lower self-esteem. In their study, Steiger, Fend & Allemand (2015) aimed to
determine which model was better at explaining the relationship between depression and self-esteem. As a result,
findings which are supporting vulnerability model was higher than the findings of scar model. In current study,
Journal of Education and Practice www.iiste.org
ISSN 2222-1735 (Paper) ISSN 2222-288X (Online)
Vol.7, No.24, 2016
69
similar results were observed as Steiger, Fend & Allemand (2015). According to this, depression was not predicting
the low self-esteem levels of adolescents, however, low self-esteem was a significant risk factor and was leading
individuals to depression. This may be explained by that individuals with low self-esteem are more sensitive and
fragile to negativity around their surroundings. Because of that, this may lead to psychological and emotional
damage on person’s life by having permanent bad mood.
Another important result of the study is that depression was affected by daily internet use positively.
Students who have stayed longer on the internet had higher scores on depression inventory. While this finding is
parallel with the result of some studies (Banjanin et al., 2015; Morrison & Gore, 2010; Ostovar et al,. 2016; Tan
et al., 2016), it differs from some other studies (Cotton et al., 2014; Moreno, Jelenchick & Breland, 2015; Sanders
et al., 2000). As a result of their research that has investigated the association between depression, social isolation
and internet use, Sanders et al. (2000) reported that while increased time spent online was weakening the social
relations, it was not significantly affecting the depression levels of adolescents. Opposing to Sanders et al. (2000),
findings of this study suggest that daily time spent online was predicting depression weakly but significantly. This
may be interpreted by that the more students stay away from real life and interactions, the more they get lonely
and depressed. As much as there is online communication with other people, since human beings also need physical
interactions, these artificial and superficial relations might not be enough to feel fulfilled and satisfied emotionally
and psychologically.
Parallel to daily internet use, social media addiction was also positively correlated with depression and
analyses revealed that depression levels of adolescents was associated with social media addiction indirectly. It
means as the time spent on social media increases, risk of depression is going up. As some studies resulted similarly
(Pantic et al., 2012; Sagioglou et al., 2014; Sidani et al., 2016; Woods & Scott, 2016), some of them did not
(Banjanin et al., 2015; Jelenchick et al., 2013; Tandoc, Ferrucci & Duffy, 2015). These diversified results suggest
that more studies may be needed to shed light on the relationship between social media addiction and depression.
Finding of this study suggest that social media addiction causes increased time spent online and as a result of it,
there is a higher probability for depressive symptoms to be seen.
As a result of this study, it may be concluded that self-esteem, daily internet use and social media
addiction are significant predictors of depression among Turkish adolescents. Adolescence is a transitional period
between childhood and adulthood that brings number of psychological, physiological and cognitive alterations
into the individuals' lives. In this vulnerable period, harmful factors such as depression which could affect students’
development negatively must be avoided as much as possible. Furthermore, conducting preventive studies would
be beneficial for healthy development of adolescents. In accordance with this purpose, in order to be able to prevent
possible damages, it may be suggested that other possible risk factors that can cause depression among adolescents
should be examined. Also, teachers, psychological counselors and adults should gain more consciousness about
negative factors affecting adolescents.
This study has some limitations. First limitation is that, sample of the study was consisted of students
only from a single city in southern region of Aegean. Conducting future studies with samples from various cities
and different age groups would be beneficial in order to generalize the model that was tested in this study. Another
limitation is that data was collected by using quantitative scales and self-reports. It may be suggested that new
studies may be conducted by using more detailed qualitative instruments to shed further light on these associations.
Acknowledgements
I especially thank to Ismet Kırcaburun, who have kindly contributed to data collection process.
References
Abuse, S. (2014). Mental Health Services Administration,(SAMHSA). Results from the 2012 national survey on
drug use and health: Summary of national findings. Rockville, MD: Substance Abuse and Mental Health
Services Administration; 2013. NSDUH Series H-46, HHS Publication No.(SMA) 13–4795. Substance
Abuse and Mental Health Services Administration.
Arslan, A., & Kırık, A. M. (2013). Validity and reliability study of the social networking status scale. Journal of
Marmara University Social Sciences Institute/Öneri, 10(40), 223-231.
Ayas, T., & Horzum, M. (2013). Relation between depression, loneliness, self-esteem and internet addiction.
Education, 133(3), 283-290.
Aydm, B., & San, S. V. (2011). Internet addiction among adolescents: the role of self-esteem. Procedia-Social and
Behavioral Sciences, 15, 3500-3505.
Bahrainian, S. A., Alızadeh, K. H., Raeisoon, M. R., Gorji, O. H., & Khazaee, A. (2014). Relationship of Internet
addiction with self-esteem and depression in university students. Journal of preventive medicine and
hygiene, 55(3), 86-89.
Banjanin, N., Banjanin, N., Dimitrijevic, I., & Pantic, I. (2015). Relationship between internet use and depression:
focus on physiological mood oscillations, social networking and online addictive behavior. Computers in
Journal of Education and Practice www.iiste.org
ISSN 2222-1735 (Paper) ISSN 2222-288X (Online)
Vol.7, No.24, 2016
70
Human Behavior, 43, 308-312.
Budak, E., Taymur, I., Askin, R., Gungor, B. B., Demirci, H., Akgul, A. I., & Sahin, Z. A. (2015). Relationship
between internet addiction, psychopathology and self-esteem among university students. The European
Research Journal, 1(3), 128-135.
Burwell, R. A., & Shirk, S. R. (2006). Self Processes in adolescent depression: The role of self‐worth
contingencies. Journal of Research on Adolescence, 16(3), 479-490.
Chaffey, D. (2016). Global social media research summary 2016. Retrived in June 15th 2016 from
http://www.smartinsights.com/social-media-marketing/social-media-strategy/new-global-social-media-
research.
Chen, S. J., Chiu, C. H., & Huang, C. (2013). Self-esteem and depression in a Taiwanese population: A meta-
analysis. Social Behavior and Personality: an international journal, 41(4), 577-586.
Conti, J. R., Adams, S. K., & Kisler, T. S. (2014). A pilot examination of self-esteem, depression, and sleep in
college women. NASPA Journal About Women in Higher Education, 7(1), 47-72.
Corbeil, J.R., & Corbeil, M.E. (2011). The birth of a social networking phenomenon. In C. Wankel (Ed.) Educating
educators with social media: Cutting-edge technologies in higher education (Volume 1, pp. 13-32).
Bingley, West Yorkshire, UK: Emerald.
Cotten, S. R., Ford, G., Ford, S., & Hale, T. M. (2014). Internet use and depression among retired older adults in
the United States: A longitudinal analysis. The Journals of Gerontology Series B: Psychological Sciences
and Social Sciences, 69(5), 763-771.
Çam, E. & İşbulan, O. (2012). A new addiction for teacher candidates: Social networks. The Turkish Online
Journal of Educational Technology (TOJET), 11 (3), 14-19.
Çuhadaroğlu, F. (1986). [Self-esteem of adolescents], (Master's Thesis), Hacettepe University Medical Faculty
Department of Psychiatry, Ankara.
De Choudhury, M., Counts, S., & Horvitz, E. (2013). Social media as a measurement tool of depression in
populations. In Proceedings of the 5th Annual ACM Web Science Conference (pp. 47-56). ACM.
Duran, A. (1999). Depresyon tedavisinde hastaya yaklaşım, farmakoterapi prensipleri, trisiklik ve tetrasiklik
antidepresanlar, ssrı’lar ve snrı’ler. Depresyon, Somatizasyon ve Psikiyatrik Aciller Sempozyumu, 93-
106.
Erözkan, A. (2009). [Predictors of depression among 8th grade students]. Elementary Education Online, 8(2), 334-
345.
Eskin, M., Ertekin, K., Harlak, H., & Dereboy, Ç. (2008). Prevalence of and factors related to depression in high
school students. Turkish Psychiatry Journal, 19(4), 382-389.
Fleming, J. E., & Offord, D. R. (1990). Epidemiology of childhood depressive disorders: a critical review. Journal
of the American Academy of Child & Adolescent Psychiatry, 29(4), 571-580.
Gana, K., Bailly, N., Saada, Y., Broc, G., & Alaphilippe, D. (2015). Relationship between self-esteem and
depressive mood in old age: Results from a six-year longitudinal study. Personality and Individual
Differences, 82, 169-174.
Hoyle, R. H. (Ed.). (1995). Structural equation modeling: Concepts, issues, and applications. Sage Publications.
Hu, L. T. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structural analysis: Conventional
criteria versus new alternatives, Structural Equation Modelling, 6 (1), 1-55.
Jacobs, R. H., Reinecke, M. A., Gollan, J. K., & Kane, P. (2008). Empirical evidence of cognitive vulnerability
for depression among children and adolescents: A cognitive science and developmental perspective.
Clinical psychology review, 28(5), 759-782.
Jelenchick, L. A., Eickhoff, J. C., & Moreno, M. A. (2013). “Facebook depression?” Social networking site use
and depression in older adolescents. Journal of Adolescent Health, 52(1), 128-130.
Joiner, T. E., Alfano, M. S., & Metalsky, G. I. (1992). When depression breeds contempt: Reassurance seeking,
self-esteem, and rejection of depressed college students by their roommates. Journal of Abnormal
Psychology, 101, 165–173.
Karasar, N. (2015). Bilimsel araştırma yöntemi: Kavramlar, ilkeler, teknikler. Ankara: Nobel Yayın Dağıtım.
Kernis, M. H., Grannemann, B. D., & Mathis, L. C. (1991). Stability of self-esteem as a moderator of the relation
between level of self-esteem and depression. Journal of Personality and Social Psychology, 61(1), 80-84.
Kernis, M. H., Whisenhunt, C. R., Waschull, S. B., Greenier, K. D., Berry, A. J., Herlocker, C. E., & Anderson,
C. A. (1998). Multiple facets of self-esteem and their relations to depressive symptoms. Personality and
Social Psychology Bulletin, 24(6), 657-668.
Kırık, A. M., Arslan, A., Çetinkaya, A., & Gül, M., (2015). A quantitative research on the level of social media
addiction among young people in Turkey. International Journal of Science Culture and Sport (IntJSCS),
3(3), 108-122.
Kim-Cohen, J., Caspi, A., Moffitt, T. E., Harrington, H., Milne, B. J., & Poulton, R. (2003). Prior juvenile
diagnoses in adults with mental disorder: Developmental follow-back of a prospective-longitudinal
Journal of Education and Practice www.iiste.org
ISSN 2222-1735 (Paper) ISSN 2222-288X (Online)
Vol.7, No.24, 2016
71
cohort. Archives of General Psychiatry,60, 709−717.
Kim, J. E., Han, K. S., Kang, H., & Hong, Y. S. (2016). Internet use of korean adolescents: a test of causal model.
International Journal of Applied Engineering Research, 11(2), 1036-1141.
Kovacs, M. (1981). Rating scales to assess depression in school-aged children. Acta Paedopsychiatrica:
International Journal of Child & Adolescent Psychiatry. 46, 305-315.
Kurtaran, G. T., (2008). Examining the variables predicted internet addiction. (Master's Thesis), Mersin University
Institute of Social Sciences, Mersin.
Li, J. B., Delvecchio, E., Di Riso, D., Salcuni, S., & Mazzeschi, C. (2015). Self-esteem and its association with
depression among Chinese, Italian, and Costa Rican adolescents: A cross-cultural study. Personality and
Individual Differences, (82), 20-25.
Lin, C. C. (2015). The relationships among gratitude, self‐esteem, depression, and suicidal ideation among
undergraduate students. Scandinavian journal of psychology, 56(6), 700-707.
Mei, S., Yau, Y. H., Chai, J., Guo, J., & Potenza, M. N. (2016). Problematic Internet use, well-being, self-esteem
and self-control: Data from a high-school survey in China. Addictive behaviors, 61, 74-79.
Moreno, M. A., Jelenchick, L. A., Egan, K. G., Cox, E., Young, H., Gannon, K. E., & Becker, T. (2011). Feeling
bad on Facebook: Depression disclosures by college students on a social networking site. Depression and
Anxiety, 28, 447–455.
Moreno, M. A., Jelenchick, L. A., & Breland, D. J. (2015). Exploring depression and problematic internet use
among college females: a multisite study. Computers in human behavior, 49, 601-607.
Morrison, C. M., & Gore, H. (2010). The relationship between excessive Internet use and depression: a
questionnaire-based study of 1,319 young people and adults. Psychopathology, 43(2), 121-126.
National Institude of Mental Health, (2016). Depression, Retrived in June 12th 2016 from ,
https://www.nimh.nih.gov/health/topics/depression/index.shtml
Orth, U., Robins, R. W., & Roberts, B. W. (2008). Low self-esteem prospectively predicts depression in
adolescence and young adulthood. Journal of personality and social psychology, 95(3), 695-708.
Orth, U., Robins, R. W., Widaman, K. F., & Conger, R. D. (2014). Is low self-esteem a risk factor for depression?
findings from a longitudinal study of mexican-origin youth. Developmental Psychology, 50(2), 622-633.
Orth, U., Robins, R. W., Meier, L. L., & Conger, R. D. (2016). Refining the vulnerability model of low self-esteem
and depression: Disentangling the effects of genuine self-esteem and narcissism. Journal of Personality
and Social Psychology, 110(1), 133-149.
Ostovar, S., Allahyar, N., Aminpoor, H., Moafian, F., Nor, M. B. M., & Griffiths, M. D. (2016). Internet addiction
and its psychosocial risks (depression, anxiety, stress and loneliness) among ıranian adolescents and
young adults: a structural equation model in a cross-sectional study. International Journal of Mental
Health and Addiction, 14(3), 257-267.
Öy, B. (1991). Children's depression inventory: reliability and validity study. Turkish Psychiatry Journal, 2(2),
132-136.
Pantic, I., Damjanovic, A., Todorovic, J., Topalovic, D., Bojovic-Jovic, D., Ristic, S., & Pantic, S. (2012).
Association between online social networking and depression in high school students: behavioral
physiology viewpoint. Psychiatria Danubina, 24(1.), 90-93.
Pantic, I. (2014). Online social networking and mental health. Cyberpsychology, Behavior, and Social Networking,
17(10), 652-657.
Reisoğlu, İ., Gedik, N., & Göktaş, Y. (2013). Relationship between pre-service teachers' levels of self-esteem,
emotional intelligance and problematic internet use. Education and Science, 38(170), 150-165
Rideout, V. J. (2012). Social media, social life: How teens view their digital lives.
Rosenberg, M. (1965). Society And The Adolescent Self-_Mage, NJ: Princeton University Pres, Princeton.
Sanders, C. E., Field, T. M., Miguel, D., & Kaplan, M. (2000). The relationship of Internet use to depression and
social isolation among adolescents. Adolescence, 35(138), 237-242.
Sagioglou, C., & Greitemeyer, T. (2014). Facebook’s emotional consequences: Why Facebook causes a decrease
in mood and why people still use it. Computers in Human Behavior, 35, 359-363.
Sarıkaya, A. (2015). The correlation between self-esteem level and pyschological resilience of adolescents aged
14-18. (Master's Thesis), İstanbul Bilim University, Institute of Social Sciences.
Shrier, L. A., Harris, S. K., Sternberg, M., & Beardslee, W. R. (2001). Associations of depression, self-esteem,
and substance use with sexual risk among adolescents. Preventive medicine, 33(3), 179-189.
Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., Hoffman, B. L., ... & Primack, B. A. (2016).
Association between social media use and depression among US young adults. Depression and anxiety,
33(4), 323-331.
Sowislo, J. F., & Orth, U. (2013). Does low self-esteem predict depression and anxiety? A meta-analysis of
longitudinal studies. Psychological bulletin, 139(1), 213-240.
Steers, M. L. N., Wickham, R. E., & Acitelli, L. K. (2014). Seeing everyone else's highlight reels: How Facebook
Journal of Education and Practice www.iiste.org
ISSN 2222-1735 (Paper) ISSN 2222-288X (Online)
Vol.7, No.24, 2016
72
usage is linked to depressive symptoms. Journal of Social and Clinical Psychology, 33(8), 701-731.
Steiger, A. E., Fend, H. A., & Allemand, M. (2015). Testing the vulnerability and scar models of self-esteem and
depressive symptoms from adolescence to middle adulthood and across generations. Developmental
psychology, 51(2), 236-247
Tan, Y., Chen, Y., Lu, Y., & Li, L. (2016). Exploring associations between problematic internet use, depressive
symptoms and sleep disturbance among Southern Chinese adolescents. International journal of
environmental research and public health, 13(3), 313-325.
Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is
facebooking depressing?. Computers in Human Behavior, 43, 139-146.
Toros, F., Bilgin, N. G., Bugdayci, R., Sasmaz, T., Kurt, O., & Camdeviren, H. (2004). Prevalence of depression
as measured by the CBDI in a predominantly adolescent school population in Turkey. European
Psychiatry, 19(5), 264-271.
Watson, D., Suls, J., & Haig, J. (2002). Global self-esteem in relation to structural models of personality and
affectivity. Journal of Personality and Social Psychology, 83, 185–197.
Woods, H. C., & Scott, H. (2016). # Sleepyteens: Social media use in adolescence is associated with poor sleep
quality, anxiety, depression and low self-esteem. Journal of Adolescence, 51, 41-49.
World Health Organization, (2016). Depression, Retrived in June 13th 2016 from
http://www.who.int/mediacentre/factsheets/fs369/en/
Wouters, S., Duriez, B., Luyckx, K., Klimstra, T., Colpin, H., Soenens, B., & Verschueren, K. (2013). Depressive
symptoms in university freshmen: Longitudinal relations with contingent self-esteem and level of self-
esteem. Journal of Research in Personality, 47(4), 356-363.
Yellowlees, P. M., Marks, S. (2007). Problematic Internet use or Internet addiction?. Computers in Human
Behavior 23, 1447–1453.
Zhang, R. (2015). Internet dependence in chinese high school students: relationship with sex, self-esteem, and
social support. Psychological reports, 117(1), 8-25.