Educational Research and Reviews

  • Abbreviation: Educ. Res. Rev.
  • Language: English
  • ISSN: 1990-3839
  • DOI: 10.5897/ERR
  • Start Year: 2006
  • Published Articles: 2019

Full Length Research Paper

Examining internet addiction levels of high school last-grade students

Mustafa Çınar
  • Mustafa Çınar
  • Mustafa Çınar, Fırat Ãœniversitesi, EÄŸitim Programları ve Öğretim Programı, 23100 Elazığ. Eposta, Turkey.
  • Search for this author on:
  • Google Scholar
Ferhat Bahçeci
  • Ferhat Bahçeci
  • Mustafa Çınar, Fırat Ãœniversitesi, EÄŸitim Programları ve Öğretim Programı, 23100 Elazığ. Eposta, Turkey.
  • Search for this author on:
  • Google Scholar
Semih Dikmen
  • Semih Dikmen
  • Mustafa Çınar, Fırat Ãœniversitesi, EÄŸitim Programları ve Öğretim Programı, 23100 Elazığ. Eposta, Turkey.
  • Search for this author on:
  • Google Scholar


  •  Received: 03 January 2020
  •  Accepted: 14 May 2020
  •  Published: 31 May 2020

 ABSTRACT

Technological developments in the 21th century have enabled the emergence of tools that enable mass communication. This communication environment has brought about a continuing passion for technology in individuals and, with this passion, a communication pollution and addiction have begun to emerge. In this study, Internet addiction of high school last-grade students studying in YeÅŸilyurt district of Malatya city was analyzed and investigated according to gender and family monthly income.  The population of the study consisted of 3442 last-grade students studying in 37 public high schools located in YeÅŸilyurt district of Malatya city in 2016 to 2017 academic year.  The sample of the study was composed of 606 last-grade students from 17 high schools randomly selected from the schools in the population. The study model was the survey model.  In the study, “Internet Addiction Scale” developed by Günüç (2009) was used to determine the Internet addiction levels of the students. This scale is composed of “withdrawal”, “controlling difficulty”, “disorder in functionality”, and “social isolation” subscales. In the analysis of the data, arithmetic mean (xÌ„)  frequency (f), standard deviation (sd), k-mean set method, t-test and one-way ANOVA test were used. When these results were taken into consideration, it was observed that majority of the students in the sample were in the non-addicted group (43.3%). A significant difference was determined between gender and Internet addiction mean scores of the students. On the other hand, no significant difference was found between family monthly income and the internet addiction mean scores of the students.
 
Key words: Internet, Internet addiction, addiction, technology, technology dependence.


 INTRODUCTION

The Internet, which has been developing and changing rapidly globally since its emergence, has greatly affected our lives and paved the way for advances in many areas. Internet  has  influentially  shown  its  existence  in   many fields such as economy, education, art, science, and daily life and even today it has become a must. The benefits of the Internet and its reflections on our daily lives are of course beyond measure. In addition, it has also led to the development of some negative behaviors. Internet addiction is the first of these. In general, addiction and  substance use are thought to fulfill the function of helping an individual to overcome difficulties in daily life (Flores, 2004: 1). Addiction, which often refers to repeated behavioral routines mostly to obtain chemical substance, sometimes without purpose, is a psychiatric disorder in which the individual exhibits repetitive obsessions or imperative behaviors (Marks, 1990, 1389). Although it is traditionally seen as a phenomenon caused by psychotropic substances affecting human behaviors such as alcohol or cocaine, studies conducted in the last 30 years have shown that individuals can get harmed due to their behaviors and habits showing addiction signs. Overeating, gambling, shopping, sex and Internet usage can create similar problems with psychotropic substances (Padwa and Cunningham, 2010: 1). Therefore, the concept of addiction has started to be increasingly used to explain the behavior of many people (Netherland, 2012: 11). 
 
The technology dependence which was defined as a non-chemical addiction type involving human and machine communication in these times when computers started to be used extensively was first introduced by Griffiths (1995: 14,15). With the spread of the Internet around the world from the mid-90s, Internet addiction has been defined as an important legal psychological disorder affecting cognitive, emotional, and social development of individuals (Price, 2011: 7). It was found that 6% of online users in America in 1998 are faced with this problem (Brenner, 2000: 452). However, unlike chemical dependency, excessive internet use has come to the forefront with some technological benefits that it provides to society rather than being criticized as addictive (Young, 2009: 217). When the first signs of Internet addiction appeared, it led to discussions among clinicians and academicians. Excessive Internet use has been considered by some as a type of pathological, addictive and technological addiction (Widyanto and Griffiths, 2006: 31).
 
The Internet use, one of the realities of the information age, has affected not only almost every field of life but also significantly the structure and presentation of education programs in education and school system. The Internet has made not only access to information easier, but also information independent of time and space. As a natural result of this situation, access to information seems to have ceased to be a problem (Aydemir et al., 2013: 1073). Proper definition of the concept of Internet addiction has shown variation depending on the perspectives. It is generally characterized by impulses or behaviors related to computer and Internet use which lead to distortion and distress along with uncontrollable engagement (Shaw and Black, 2008: 353). While some researchers have associated Internet addiction with dependencies including alcohol and substance use (Griffiths, 1999: 246), some others have associated it with recurrent obsessions or compulsive (impulse) control disorders (Belsare et al., 1997). The expressions of pathological Internet use (Davis, 2001:187) and problematic Internet use (Caplan, 2003: 625) have also been used to describe this problem.
 
The concept of Internet addiction, the last link of technological dependence, was first mentioned by Ivan (Goldberg, 1996; Suler, 1999). Internet addiction is an uncontrollable, significantly time-consuming process resulting in problematic or social and professional difficulties (Shapira et al., 2000. s. 268). According to Young (2004),Internet addiction as a rapidly growing phenomenon is a concept including a wide range of behavior variety and impulse control disorder associated with gambling addiction (p. 402). Griffiths (1999) stated that Internet may not be an addiction for most of excessive users and other addictions can be a tool of satisfaction. 
 
The number of Internet users which was 360 million people worldwide in 2000 (Internet World Status [IWS], 2019) exceeded 4.02 billion people in 2018 (Bayrak, 2018). According to data in 2018, the rate of Internet use among individuals in the age group of 16-74 years was 59.6 and 72.9%, respectively (Turkish Statistical Institute [TÜÄ°K], 2018). The number of users was recorded as approximately 44 million people (IWS, 2019). This means that nearly one in two people is an active user. With such increase in the place of Internet in daily life, the relationship between many variables, especially psychological factors, andInternet addiction has been investigated. These studies have revealed that there was a correlation between problematic Internet use and psychological disorders such as attention deficit and hyperactivity disorder (Dalbudak and Evren, 2013; Ko, 2009; Öztürk et al., 2013; Yen et al., 2007), depression (Choi et al., 2014; Koronczai et al., 2013; Åžahin et al., 2013; Åženormancı et al., 2014; Yang et al., 2014), loneliness (Yao and Zhong, 2014), neurotic personality (Tsai et al., 2009; Wang et al., 2011), low self-esteem (Armstrong et al., 2000; Aydın and Sarı, 2011; Sariyska et al., 2014), low self-control (Özdemir et al., 2014), academic failure (Stavropoulos et al., 2013), feelings of hostility (Koç, 2011) and insomnia (Anderson, 2001; Lam, 2014).
 
In this context, the aim of the study is to determine the Internet addiction status of the students with related literature and feedbacks from the students, identify the relationship of Internet addiction with the gender variable and family’s monthly income, and to make contribution to the literature.
 


 METHODOLOGY

In this study, the survey model, which aims to describe the situation as it is, was used. The survey models are the survey conducted on the whole population or a group; example or sample to be taken from the population in order to make a general judgment about the population in a population composed  of  many  elements  (Karasar, 2011: 110).
 
Study group
 
While conducting the sampling, first, it is necessary to define the study population by limiting the population in which the results are intended to be generalized in line with the purposes of the study. There is a study population which is the most appropriate one according to the purposes of the The population of the study consisted of 3442 students studying in the 4th grade in 37 high schools in YeÅŸilyurt District of Malatya city in the 2016-2017 academic year. 
 
According to certain rules, the sample is a small cluster selected from a certain population and accepted as a adequacy of representation of the population in which it was selected. Studies are mostly carried out on sample sets and the obtained results are generalized to relevant populations (Karasar, 2011). The sample of the study consisted of 606 last-grade students who were randomly selected from 17 high schools.
 
Data collection tool
 
In order to determine the Internet addiction levels of the students in the study, “Internet Addiction Scale” developed by Günüç (2009) was used. The scale consists of 35 items including “withdrawal”, “controlling difficulty”, “disorder in functionality” and “social isolation” subscales.
 
Data analysis
 
In the study, the data related to the participants were analyzed by using The Statistical Package for Social Sciences(SPSS)  22.0 packaged software. After uploading the data obtained with scales into the computer environment, Test of Normality was conducted to determine whether they had normal distribution or not. In a statistical study, the distribution should be normal or close to normal for many tests to be performed (Kalaycı, 2006). While many features show a normal distribution in the population, deviations from the normal distribution will occur if the measurements of a property of interest are obtained from a small group (n<30). As the size of the group increases, the distribution will approach normal (cited by Büyüköztürk et al., 2014: 63 from Ravid, 1994). Tabachnick and Fidell (2005) have accepted that the distribution is normal when skewness and kurtosis values vary between +1.500 and -1.500 (p. 81). As a result of the applied test of normality, it can be asserted that the distribution in the study was normal since the skewness (0.762) and kurtosis (0.074) values of the scale items were between the values of +1.500 and -1.500. For this reason, arithmetic mean ( , frequency (f), standard deviation (sd), k-mean set method, t-test and one-way ANOVA test were used to analyze the data in the study. The significance value was taken as (p<0.05) in the data analysis. 


 RESULTS

Information related to the distribution of the students in terms of their demographic characteristics are given in frequency and percentage in Table 1. As seen in Table 1, 52% (51.6%) of the students in the sample group were female and 48% (48.4%) were male students. Besides, it was found that the monthly income of the families of the students  was  mostly   (39.2%)  between  1501-3000 TL.  
 
The score distribution of the students from their answers to the scale was examined and Table 2 shows the analysis results. 
 
As seen in Table 2, the lowest mean score of the participants from Internet Addiction Scale was 1.00 and their highest mean score was 4.94. The arithmetic mean obtained from the scale was X Ì…= 2.37 and standard deviation was sd= 0.89. 
 
In order to determine the group with or without Internet addiction and to obtain more detailed results about the addiction statuses of the individuals, “clustering analysis” technique from sample classification techniques was applied. The general purpose of the clustering analysis is to reveal the similarities of the units according to their certain characteristics and to classify the units into the correct categories based on these similarities (Çokluk et al., 2014: 139). This method has also allowed to reveal some extreme values found to be implicit in the sample. With this clustering method, addiction levels of individuals can be classified in a healthier way (Günüç and Kayri, 2009: 171).
 
In order to determine a more detailed result in the determination of the addiction statuses of individuals, the clustering analysis was applied and it was observed to consist of three sub-clusters. Accordingly, as seen in Table 2, “addicted group” was in the first cluster, “group with the addiction risk” was in the second cluster and “non-addicted group” was in the third cluster. In the naming of clusters, Günüç (2009)'s classification was taken as an example. Table 3 shows frequency and percentage distributions of the Internet addiction scores of students by considering the students’ scores obtained from the scale total related to their Internet addiction levels. 
 
In  Table 3, the majority (43.3%) of 606 high school fourth-grade students participating in the study were seen to be in non-addicted group. This was followed by Risk Group (42.6%) and Addicted Group (14.1%), respectively. In the literature review conducted based on these data, it was observed in the study by Özdemir (2016) that 1.5% of the sample were addicted Internet users. Addicted group forms 7% of the sample in the study by Ä°ÅŸleyen (2013), 10.1% in the study by Günüç (2009), 0.4% of the study by Ä°nan (2010), 0.2% in the study by Çalışgan (2013), 23.2% in the study by Balcı and Gülnar (2009), 17% in the study by Durualp and ÇiçekçioÄŸlu (2013) and there was no addicted group in the studies by Yücelten (2016) and Döner (2011). These rates were found in some other studies as 4% (Wang et al., 2011), 1.1% (Bayraktar, 2001), 3.1% (Kaltiala-Heino et al., 2004), 2% (Johansson and Götestam, 2004), 20.7% (Yen et al., 2007), 2.4% (Cao and Su, 2007), 8%  (Elizabeth and Tee, 2007), and 4.3% (Jang et al., 2008) (Cited., by Günüç, 2009; p. 89). It was observed that 14% of the sample in the study by Özdemir (2016) were risky Internet users, 9% of the sample in the study by Ä°nan (2010)   were   the   group   showing   Internet    addiction symptom, 28.4% of the sample in the study by Balcı and Gülnar (2009) were risky Internet users, 11% of the sample in the study by Yücelten (2016) were Internet addiction risk group, 9% in the study by Döner (2011)  were those showing limited symptom, 23% of the sample in the study by Ä°ÅŸleyen (2013) were risk group, 14% of the sample in the study by Åžahin (2011) were those showing limited symptoms, 66% of the study of Durualp and ÇiçekçioÄŸlu (2013) were risk group, and 29% of the sample in the study by Günüç (2009) were risk group.
 
 
Table 4 shows the mean scores used in the determination of these three groups obtained as a result of the applied clustering analysis method.  When Table 4 was examined, it was determined that the mean score of the students in the addicted group was 3.58, the mean score of the students in the risk group was 2.40 and the mean score of non-addict students was 1.51.  Table 5 shows the results of “t-test” conducted to determine whether there is a significant difference between the Internet addiction scores of high school last-grade students by their gender. When Table 5 was examined, no differentiation was determined in withdrawal subscale of the Internet addiction scale of high school last-grade students based on gender variable 0.11 (p>0.05).
 
Internet addiction mean scores significantly differentiated in terms of gender at the value of p<0.05 in overall Internet addiction scale (p=0.04), controlling difficulty (p=0.00), disorder in functionality (p=0.03) and social isolation subscales (p=0.04). When the arithmetic means were examined, it was observed that the male students caused the significant difference. The study results revealed that male students were under more risk in terms of Internet addiction compared to female students. In the literature review conducted in this context, a large number of studies supporting the study results were found. The correlation between the Internet addiction and gender was examined in the study conducted by Usta (2016) and a significant correlation was found between Internet addiciton and gender variable. As a result of the analysis, it was concluded that male students showed more Internet addiction behavior than female students. Similarly, Gencer (2017) stated that male students showed more Internet addiction behaviors than female students. AyaroÄŸlu (2002) examined the correlation between Internet uses of the university students and their loneliness levels and concluded that men spend more time than women in the fields of surfing on web and file transfer.  Scherer  (1997)  examined  531 students in terms of Internet usage and determined that the majority of students (71%) determined to be Internet addicts were male students. Similarly,Döner (2011) reached a total of 624 students including 282 females and 342 males in her study and, according to the results of the study, Internet addiction of the male students differed significantly compared to female students and this difference was observed in favor of men. Similarly, Morahan-Martin and Schumacher (2000), Chou and Hsiao (2000), Bayraktar (2001), Koch and Pratarelli (2004), AktaÅŸ (2005), Yang and Tung (2007), Balta and Horzum (2008), Ögel and Cömert (2009), Günüç (2009), Kelleci et al. (2009), Tsai et al. (2009), Esen (2010), Gürcan (2010), Yıldız (2010), Taçyıldız (2010), Gençer (2011), Liberatore et al. (2011), Carli et al. (2012), Gökçearslan and Günbatar (2012), Yılmaz (2013), Zorbaz (2013), TürkoÄŸlu (2013), Azher et al., (2014), Waldo (2014), Ceyhan (2016), Ä°ÅŸsever (2016) and Ünsal (2016) also found that male students had higher Internet addiction levels than female students in terms of gender variable.  These results support the data obtained concerning the variable of gender in the study.
 
 
 

It is also possible to find studies in the literature that show that there is no significant differentiation between Internet addiction and gender.  Brenner (2000), Batıgün (2011), Kaya (2011), Jelenhick et al. (2012), Hawii (2012),   Çalışgan   (2013),   Andreou   and   Svoli (2013), Dikme (2014), Dalgalı (2016), and Yücelten (2016) have also found that gender has no effect on Internet addiction. A limited number of studies have revealed that Internet addiction is in favor of female students (BeÅŸaltı; 2016; Griffiths, 1995).

The differentiation between the gender variable and Internet addiction is thought to be caused by the measurement type of Internet addiction level in the studies or the cultural differences due to different countries (Balta and Horzum, 2008: 187-205). When the studies are examined in general, the reasons why men have higher Internet addiction level than women are that there is gender inequality in the society, men are left more comfortable and free in the society, and men can go Internet cafés more than women (ÇavuÅŸ and GökdaÅŸ, 2006: 57; TaÅŸpınar and GümüÅŸ, 2005: 80). On the other hand, female students can be deprived (Atlasma and GökdaÅŸ, 2006), their spare time is taken away by taking many responsibilities at home or their areas of freedom is reduced by interfering (Cited, Yılmaz, 2013: 75).

In Turkish society, males can be more comfortable and free compared to females due to the reasons such as tendency of men to move away from family after a certain age, adolescent period syndromes and friend environment. Besides, the general family structure of the Malatya province may be one of the reasons of higher Internet  addiction   scores   of   men.   Table   6    shows  arithmetic mean and standard deviation of the students related to Internet addiction according to the family monthly income.

 

When Table 6 was examined, a significant difference was determined between Internet addiction status of the students and their family income level at the level of p<0.05 (p=0.23) for the overall Internet addiction scale. When the subscales of the scale were examined, no significant difference was observed in all of the subscales at the level of p<0.05.  No significant difference was determined between Internet addiction of the students and the monthly income level of their families.  

 
The fact that Internet access is cheap and comfortable for individuals from all socio-economic levels can be shown as the reason why there was no significant difference between Internet addiction and the families’ monthly income levels. This result supports most of the results in the literature conducted in terms of the variable of family monthly income level. According to the study by Song (2003) and Balta and Horzum (2008), it was found that there was no correlation between the Internet addiction and socio-economic level. Bakken et al. (2009) also found no significant difference between the income level and Internet addiction. Esen (2010), Ä°nan (2010), Gençer (2011), BeÅŸaltı (2016), Ceyhan (2016) and Dalgalı (2016) have also found similar results.  
 
In the literature, it is possible to see studies contrary to the findings from the present  research.  In  the  study  by Yılmaz (2013) it was found that students with high economic level were more Internet addicted than the students with moderate economic level. According to Åžahin (2011) as the families’ income level increased, the students’ tendency to Internet addiction increased. Similarly, Bayraktar (2001), Batıgün and Kılıç (2011), and Sevindik (2011) also found a significant and positive difference between economic level and Internet addiction. The study by Kayri and Günüç (2016) revelaed that “the children of families with high socioeconomic levels are more likely to have Internet addiction”. 
 
 
 

 


 CONCLUSION

Based on the results obtained from the study, the following conclusions were reached.
 
(i) As a result of the clustering analysis conducted to investigate the Internet addiction levels of high school last-grade students, 14% of the students were in addicted Group, 42% were in the group with addiction risk and 43% were in non-addicted group. 
(ii) It was determined that the students’ mean scores from Internet addiction scale differed according to the variable of gender.  This differentiation was observed in favor of male students. The mean scores of male students were higher than the mean scores of female students. 
(iii) The family’s income levels of the students were found to be mostly between 1501-3000 TL and there was no significant correlation between their mean scores of Internet addiction scale.
 
The following recommendations may be given in accordance with the findings and results obtained from this study: 
 
(i) When considering that 42% of the students were involved in the risk group, high school students should be informed about Internet addiction in both information courses and in other related courses and necessary contents can be added into these courses. 
(ii) Families and children can be aware of addiction through various channels using the developing technological possibilities and mass media in order to stand out that the Internet addiction has an important place like other substance addiction. 
(iii) Although high school students are open to new ideas and innovations, it is generally accepted that they do not have enough experience to question the validity of these ideas and innovations. Therefore, parents should limit and guide theInternet use of their children. 
(iv) Parents should be informed about the family protection programs and the necessary support for the effective use of the program should be provided by the relevant institutions. 
(v) This study was carried out with high school students studying in YeÅŸilyurt District of Malatya city and similar studies can be conducted in other regions with larger population and sample. 
 


 CONFLICT OF INTERESTS

The authors have not declared any conflict of interests.

 



 REFERENCES

AktaÅŸ Y (2005). 3-18 YaÅŸ Grubu Çocuk ve Gençlerin Ä°nteraktif Ä°letiÅŸim Araçlarını Kullanma Alışkanlıklarının DeÄŸerlendirilmesi. The Turkish Online Journal of Educational Technology 4(4):59-66.
 

Anderson KJ(2001). Internet Use Among College Students: An Exploratory Study. Journal of American College Health 50(1):21-26
Crossref

  
 

Andreou E, Svoli H (2013). The Association between Internet User Characterictics and Dimensions of Internet Addiction among Greek Adolescents. International Journal of Mental Health and Addiction 11(2):139-148.
Crossref

  
 

Armstrong L, Phillips JG, Saling LL (2000). Potential determinants of havier internet usage. International Journal of Human-Computer Studies 53(4):537-550.
Crossref

  
 

AyaroÄŸlu NS (2002). The Relationship Between Internet Use and Loneliness of University Students. Ankara: Orta DoÄŸu Teknik Üniversitesi EÄŸitim Bilimleri Bölümü, Yayımlanmamış Yüksek Lisans Tezi.

  
 

Aydemir H, Benzer AÄ°, Karahan O, Akmençe E (2013). The Evaluation of University Students' Views on Internet Resources. Procedia - Social and Behavioral Sciences 103:1067-1074.
Crossref

  
 

Aydın M (2011). EÄŸitim Yönetimi. Ankara: Gazi Kitabevi.

  
 

Aydın B, Sarı SV (2011). Internet addiction among adolescents: the role of self-esteem. Procedia Social and Behavioral Sciences 15:3500-3505.
Crossref

  
 

Azher M, Khan RB, Salim M, Bilal M, Hussain A and Hseeb M (2014). The Relationship between Internet Addiction and Anxiety among students of University of Sargodha. International Journal of Humanities and Social Science 4(1):288-293.

  
 

Bakken IJ, Wenzel HG, GötestamKG, Johansson A, Ören A (2009). Internet addiction among norwegian adults a stratified probability sample study. ScandinavianJournal of Psychology 50(2):121-127.
Crossref

  
 

Balcı Åž, Gülnar B (2009). Üniversite ÖÄŸrencileri Arasında Ä°nternet Bağımlılığı ve Ä°nternet Bağımlılarının Profili. Selçuk Ä°letiÅŸim pp.5-22.

  
 

Balta ÖÇ, Horzum MB (2008). The factors that affect internet addiction of students in a web based learning environment. Ankara University Journal of Faculty of Educational Sciences 41(1):187-205.

  
 

Batıgün AD, Kılıç N (2011). Ä°nternet bağımlılığı ile kiÅŸilik özellikleri, sosyal destek, psikolojik belirtiler ve bazı sosyo-demografik deÄŸiÅŸkenler arasındaki iliÅŸkiler. Türk Psikoloji Dergisi 26(67):1-10.

  
 

Bayrak H (2018, Åžubat 1). Dijilopedi. Dünyada Ä°nternet Kullanımı ve Sosyal Medya Ä°statistikleri: 

View

  
 

Bayraktar F (2001). Ä°nternet Kullanımının Ergen GeliÅŸimindeki Rolü. Ä°zmir: Ege Üniversitesi Sosyal Bilimler Enstitüsü.

  
 

Belsare TJ, Gaffney GR, Black DW (1997). Compulsive computer use. The American Journal of Psychiatry 154(2):289.
Crossref

  
 

BeÅŸaltı M (2016). Ergenlerde Ä°nternet Bağımlılığı ve KiÅŸisel Özelliklerinin Bazı Sosyo-DemoÄŸrafik Özelliklere Göre Ä°ncelenmesi. Gaziantep: Gaziantep Üniversitesi EÄŸitim Bilimleri Enstitüsü EÄŸitim Bilimleri Ana Bilim Dalı.

  
 

Büyüköztürk Åž, Çokluk Ö, Köklü N (2014). Sosyal Bilimler Ä°çin Ä°statistik. Ankara: Pegem Akademi.

  
 

Cao F, Su L (2007). Internet addiction among Chinese adolescent: Prelavence and psychological features. Child: Care, Health and Developmant 33(3):275-281.
Crossref

  
 

Caplan EC (2003). Preference for Online Social Interaction A Theory of Problematic Internet Use and Psychosocial Well-Being. Communication Research, pp. 625-648.
Crossref

  
 

Carli V, Durkee T, Kaess M, Brunner R, Wasserman C, Sarchiapone M, Wasserman D (2012). Prevelance of pathological internet use among adolescent in Europa: Demographic and social factors. Society for the Study of Addiction, pp.2210-2222.
Crossref

  
 

Ceyhan E (2016). Ä°nternet Bağımlılığının BaÄŸlanma Stilleri ve Duygu Düzenlemeyle Ä°liÅŸkisi. Van: Yüzüncü Yıl Üniversitesi EÄŸitim Bilimleri Enstitüsü EÄŸitim Bilimleri Ana Bilim Dalı Psikolojik Danışmanlık ve Rehberlik Bilim Dalı.

  
 

Choi JS, Park SM, Roh MS, Lee JY, Park CB, Hwang JY (2014). Dysfunctional inhibitory control and impulsivity in Internet addiction. Psychiatry Research 215(2):424-428.
Crossref

  
 

Chou C, Hasiao MC (2000). Internet addiction, usage, gratifications and pleasure experience: "The Taiwan College Students" case. Computer Education 35(1): 65-80.
Crossref

  
 

Çalışgan H (2013). Ä°lköÄŸretim ÖÄŸrencilerinde Ä°nternet Bağımlılığı ve Siber Zorbalık. Ä°stanbul: Yeditepe Üniversitesi Sosyal Bilimler Enstitüsü, Yayımlanmamış Yüksek Lisans Tezi.

  
 

ÇavuÅŸ H, GökdaÅŸ Ä° (2006). EÄŸitim Fakültesinde ÖÄŸrenim Gören ÖÄŸrencilerin Ä°nternetten Yararlanma Nedenleri ve Kazanımları. Yüzüncü Yıl Üniversitesi EÄŸitim Fakültesi Dergisi 3(2):56-78.

  
 

Çokluk Ö, ÅžekercioÄŸlu G, Büyüköztürk Åž (2014). Sosyal Bilimler Ä°çin Çok DeÄŸiÅŸkenli Ä°statistik SPSS ve Lisrel Uygulamaları. Ankara:Pegem Akademi.

  
 

Dalbudak E, Evren C (2013). The relationship of internet addiction severity with attention deficit hiperactivity. Comprehensive Psychiatry 55(3):497-503.
Crossref

  
 

Dalgalı FN (2016). Lise ÖÄŸrencilerinde Yeme Tutumları ve Ä°nternet Bağımlılığının Bazı DeÄŸiÅŸkenlere Göre Ä°ncelenmesi. Ä°stanbul: Beykent Üniversitesi Sosyal Bilimler Enstititüsü Psikoloji Ana Bilim Dalı Klinik Psikolojisi Bilim Dalı.

  
 

Davis RA (2001). A cognitive-behavioral model of pathological Internet use. Computers in Human Behavior 17(2):187-195.
Crossref

  
 

Dikme E (2014). Meslek Lisesi ÖÄŸrencilerinin Ä°nternet Bağımlılıklarının ÇeÅŸitli DeÄŸiÅŸkenler BaÄŸlamında Ä°ncelenmesi. Ä°stanbul: Yeditepe Üniversitesi Sosyal Bilimler Enstitüsü.

  
 

Döner C (2011). Ä°lköÄŸretim ÖÄŸrencilerinde Ä°nternet Bağımlılığının Farklı DeÄŸiÅŸkenlere Göre Ä°ncelenmesi. Ankara: Ankara Üniversitesi EÄŸitim Bilimleri Enstitüsü.

  
 

Durualp E, ÇiçekçioÄŸlu P (2013). YetiÅŸtirme Yurdunda Kalan Ergenlerin Yalnızlık Düzeylerinin Ä°nternet Bağımlılığı ve ÇeÅŸitli DeÄŸiÅŸkenler Açısından Ä°ncelenmesi. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 15(1):29-46.

  
 

Elizabeth H, Tee MY (2007). Excessive internet use: The role of personality, linoliness and social support networks in internet addiction. Australian Journal of Emerging Technologies and Society 5(1):34-47.

  
 

Esen E (2010). Ergenlerde Ä°nternet Bağımlılığını Yordayan Psiko-Sosyal DeÄŸiÅŸkenlerin Ä°ncelenmesi. Ä°zmir: Dokuz Eylül Üniversitesi EÄŸitim Bilimleri Enstitüsü.

  
 

Flores PJ (2004). Addiction as an Attachment Disorder. Northvhale/New Jersey: Jason Aronson Press.

  
 

Gencer H (2017). Ortaokul ÖÄŸrencilerinde Ä°nternet Bağımlılığı ve Siber Zorbalık Davranışları ile Ä°liÅŸkili DeÄŸiÅŸkenlerin Ä°ncelenmesi. Sivas: Cumhuriyet Üniversitesi EÄŸitim Bilimleri Enstitüsü EÄŸitim Bilimleri Ana Bilim Dalı.

  
 

Gençer SL (2011). OrtaöÄŸretim ÖÄŸretmen Adaylarının Ä°nternet Bağımlılık Durumlarının Ä°nternet Kullanım Profilleri ve Demografik DeÄŸiÅŸkenlere Göre Ä°ncelenmesi. Isparta: Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü, Yayımlanmamış Yüksek Lisans Tezi.

  
 

Goldberg I (1996). Internet Addiction Support Group. The Psychology of Cyberspace.

  
 

Gökçearslan Åž, Günbatar MS (2012). OrtaöÄŸretim ÖÄŸrencilerinde Ä°nternet Bağımlılığı. EÄŸitim Teknolojisi Kuram ve Uygulama 2(2):10-24.

  
 

Griffiths M (1995). Technological addictions. Clinical Psychology Forum, pp. 14-19.

  
 

Griffiths M (1999). Ä°nternet Addiction. The Psychologist 12(5):246-250.

  
 

Günüç S (2009). Ä°nternet Bağımlılık ÖlçeÄŸinin GeliÅŸtirilmesi Ve Bazı Demografik DeÄŸiÅŸkenler Ä°le Ä°nternet Bağımlılığı Arasındaki Ä°liÅŸkilerin Ä°ncelenmesi. Van: Yüzüncü Yıl Üniversitesi Sosyal Bilimler Enstitüsü.

  
 

Günüç S, Kayri M (2009). The Adapdation of Internet Addiction Scale into Turkish: The Study of Validitiy and Reliability. Ankara University Journal of Faculty of Educational Sciences, pp. 157-175.
Crossref

  
 

Gürcan N (2010). Ergenlerin Problemli Ä°nternet Kullanımları ile Uyumları Arasındaki Ä°liÅŸkinin Ä°ncelenmesi. Konya: Selçuk Üniversitesi EÄŸitim Bilimleri Enstitüsü, Yayımlanmamış Yüksek Lisans Tezi.

  
 

Hawii NS (2012). Internet Addiction among Adolescents in Lebanon. Computers in Human Behavior, pp. 1044-1053.
Crossref

  
 

Internet World Status (IWS) (2019, Ocak 13). 

  
 

Ä°nan A (2010). Ä°lköÄŸretim II. kademe ve ortaöÄŸretim öÄŸrencilerinde internet bağımlılığı. Erzurum: Atatürk Üniversitesi Sosyal Bilimler Enstitüsü.

  
 

Ä°ÅŸleyen F (2013). Lise öÄŸrencilerinin internet bağımlılığı düzeylerinin

  
 

kiÅŸisel ve ailesel faktörlerle iliÅŸkisinin incelenmesi. Ä°zmir: Ege Üniversitesi Sosyal Bilimler Enstitüsü.

  
 

Ä°ÅŸsever NK (2016). Ä°nternet Bağımlılığının Üniversite ÖÄŸrencilerinde Yaygınlığının Ä°ncelenmesi. Ä°stanbul: Üsküdar Üniversitesi Sosyal Bilimler Enstitüsü Klinik Psikoloji Ana Bilim Dalı.

  
 

Jang KS, Hwang SY, Choi JY (2008). Internet Addiction and Psychiatric Symptoms among Korean Adolescents. The Journal of School Health 78(3).
Crossref

  
 

Jelenhick LA, Becker T, Moreno MA (2012). Assessing the Psychometric Properties of the Internet Addiction Test (IAT) in US College Student. 
Crossref

  
 

Johansson A, Götestam GK (2004). Internet addiction: Characteristics of a questoinnaire an prevalence in Norwegian Youth (12-18 years). Scandinavian Journal of Psycholog 45:223-229.
Crossref

  
 

Kalaycı Åž (2006). SPSS Uygulamalı Çok DeÄŸiÅŸkenli Ä°statistik Teknikleri. içinde Ankara: Asil Yayıncılık.

  
 

Kaltiala-Heino R, Lintonen T, Rimpela A (2004). Internetaddiction? Potentially problematic use of the internet in a population of 12-18 year-old adolescents. Addiction Research and Theory 12(1), 89-96.
Crossref

  
 

Karasar N (2011). Bilimsel AraÅŸtırma Yöntemi. Ä°stanbul: Nobel Yayın Dağıtım.

  
 

Kaya A (2011). ÖÄŸretmen adaylarının sosyal aÄŸ sitelerini kullanım durumları ve internet bağımlılığı düzeyleri. Ä°zmir: Ege Üniversitesi Fen Bilimleri Enstitüsü.

  
 

Kelleci M, Güler N, Sezer H, Gölbaşı Z (2009). Lise ÖÄŸrencilerinde Ä°nternet Kullanma Süresinin Cinsiyet ve Psikiyatrik Belirtiler ile Ä°liÅŸkisi. TAF Preventive Medicine Bulletin 8(3):223-230.

  
 

Ko CH (2009). Psychiatric symptoms may predict internet addiction in teens. Brown University Child and Adolescent Behavior Letter pp. 3-4.

  
 

Koch WH, Pratarelli ME (2004). Effects of intro/extraversiyon and sex on social internet use. North American Journal of Psychology 6(3):371-382.

  
 

Koç M (2011). Internet Addiction and Psychopathology. Turkish Online Journal of Educational Technology, pp.143-148.

  
 

Koronczai B, Kökönyei G, Urban R, Kun B, Papay O, Nagygyörgy K (2013). The mediating effect of self-esteem, depression and anxiety between satisfaction with body appearance and problematic internet use. American Journal of Drug and Alcohol Abuse, pp. 259-265.
Crossref

  
 

Lam LT (2014). Internet Gaming Addiction, Problematic Use of the Internet, and Sleep Problems A Systematic Review. Chıld And Adolescent Dısorders .
Crossref

  
 

Liberatore KA, Rosario K, Marti LCD, Martinez K (2011). Prevalence of Internet Addiction in Latino Adolescent with Psychiatric Diagnosis. Cyberpsychology, pp. 1-4.
Crossref

  
 

Marks I (1990). Behavioural (non-chemical) addictions. British Journal of Addiction, pp. 1389-1394.
Crossref

  
 

Morahan-Martin J, Schumacher P (2000). Incidence and correlates of pathological internet use among college students. Computers in Human Behavior 16:13-29.
Crossref

  
 

Netherland J (2012). Critical Perspectives on Addiction. HowardHouse, WagonLane, Bingley BD16 1WA. UK: Emerald Group Publishing Limited.
Crossref

  
 

Ögel K, Cömert IT (2009). Ä°nternet ve Bilgisayar Bağımlılığının Yaygınlığı ve Farklı Etkenlerle Ä°liÅŸkisi. Turkiye Klinikleri Journal of Forensic Medicine and Forensi 6(1):9-16.

  
 

Özdemir G (2016). Ondokuz Mayıs Üniversitesi Tıp Fakültesi ÖÄŸrencilerinde Ä°nternet Bağımlılığı ve Depresyon Arasındaki Ä°liÅŸkinin DeÄŸerlendirilmesi. Ankara: Ankara Üniversitesi Tıp Fakültesi.

  
 

Özdemir Y, Kuzucu Y, Ak Åž (2014). Depression, loneliness and Internet addiction: How important is low self-control. Computers in Human Behavior 34: 284-290.
Crossref

  
 

Öztürk FÖ, Ekinci M, Öztürk Ö, Canan F (2013). The Relationship of Affective Temperament and Emotional-Behavioral Difficulties to Internet Addiction in Turkish Teenagers. Hindawi Publishing Corporation ISRN Psychiatry, pp.1-6.
Crossref

  
 

Padwa H, Cunningham J (2010). Addiction A Reference Encyclopedia. Santa Barbara, California: BC-CLIO,LLC.

  
 

Price H (2011). Psychology of Emotions, Motivations and Actions Ä°nternet Addiction. New York: Nova Science Publishers, Inc.

  
 

Ravid R (1994). Practical Statistics for Educators. New York: University Press of America Inc.

  
 

Sariyska R, Reuter M, Bey K, Sha P, Li M, Chen YF (2014). Self-esteem, personality and Internet Addiction: A cross-cultural comparison study. Personality and Individual Differences 61:28-33.
Crossref

  
 

Scherer KR (1997). Profiles of Emotion-antecedent Appraisal: Testing Theoretical Pretictions across Cultures. Cognition and Emotion 11(2):113-150.
Crossref

  
 

Sevindik F (2011). Fırat Üniversitesi ÖÄŸrencilerinde Problemli Ä°nternet Kullanımı ve SaÄŸlıklı YaÅŸam Biçimi Davranışlarının Belirlenmesi. Malatya: Ä°nönü Üniversitesi SaÄŸlık Bilimleri Enstitüsü.

  
 

Shapira NA, Goldsmith TD, Keck PE, Khosla UM, McElroy SL (2000). Psychiatric features of individuals with problematic internet use. Journal of Affective Disorders 57:267-272.
Crossref

  
 

Shaw M, Black DW (2008). Internet Addiction Definition, Assessment, Epidemiology and Clinical Management. CNS Drug 22(5):353-365.
Crossref

  
 

Song I (2003). Internet gratifications, depression, self-efficacy, and internet addiction. Unpublished master's thesis, Michigan State University, Department of Telecommunication.

  
 

Stavropoulos V, Alexandraki K, Stefanidi FM (2013). Recognizing internet addiction: Prevalence and relationship to academic achievement in adolescents enrolled in urban and rural Greek high schools. Journal of Adolescence 36(3):565-576.
Crossref

  
 

Suler J (1999). Healthy and Pathological Internet Use. CyberPsychology and Behavior, pp. 385-394.
Crossref

  
 

Åžahin M (2011). Ä°lköÄŸretim okulu öÄŸrencilerindeki internet bağımlılığı." Yayımlanmamış Yüksek Lisans Tezi, Yeditepe Üniversitesi, Sosyal Bilimler Enstitüsü. Ä°stanbul.

  
 

Åžahin S, Özdemir K, Ünsal A (2013). Evaluatıon Of The Relatıonshıp Between Internet Addiction and Deppression in University Students. Medicinski glasnik Specijalne bolnice za bolesti štitaste žlezde i bolesti metabolizma'Zlatibor' 18(49):14-27.
Crossref

  
 

Åženormancı Ö, Saraçlı Ö, Atasoy N, Åženormancı G, Koktürk F, Atik L (2014). Relationship of Internet addiction with cognitive style, personality, and depression in university students. Comprehensive Psychiatry 55(6):1385-1390.
Crossref

  
 

Tabachnick BG and Fidell LS (2015). Using Multivarite Statistics (s. 81). içinde Boston: Pearson.

  
 

Taçyıldız Ö (2010). Lise ÖÄŸrencilerinin Ä°nternet Bağımlılığı Düzeylerinin Bazı DeÄŸiÅŸkenlere Göre Yordanması" Yüksek Lisans Tezi.

  
 

TaÅŸpınar M, GümüÅŸ Ç (2005). Ülkemizde Ä°nternet Kafelerin Ortam ve Kullanıcı Profilleri. Elektronik Sosyal Bilimler Dergisi 4(14):80-93.

  
 

Tsai HF, Cheng SH, Yeh TL, Shih CC, Chen KC, Yang YC, Yang YK (2009a). The risk factors of Internet addiction- A survey of university freshmen. Psychiatry Research 167(3):294-299.
Crossref

  
 

Tsai HF, Cheng SH, Yeh TL, Shih CC, Chen KC, Yang YC, Yang YK (2009b). Assessing the psychometric properties of the internet addiction test (IAT) in USA. 1 23, 2016 tarihinde Psychiatry Research: 
Crossref

  
 

Türkiye Ä°statistik Kurumu (TÜÄ°K) (2018, AÄŸustos 18). Hanehalkı BiliÅŸim Teknolojileri Kullanım AraÅŸtırması, 2016. Türkiye Ä°statistik Kurumu: 

View

  
 

TürkoÄŸlu S (2013). Ergenlerin Problemli Ä°nternet Kullanımları ile Siber Zorbalık EÄŸilimleri Arasındaki Ä°liÅŸkinin Ä°ncelenmesi. Ä°stanbul: Yüksek Lisans Tezi. Marmara Üniversitesi EÄŸitim Bilimleri Enstitüsü Bilgisayar ve ÖÄŸretim Teknolojileri EÄŸitimi Ana Bilim Dalı Bilgisayar ve ÖÄŸretim Teknolojileri EÄŸitimi Bilim Dalı.

  
 

Usta A (2016). Üstün Zekalı ve Yetenekli ÖÄŸrencilerde Ä°nternet Bağımlılığının Farklı DeÄŸiÅŸkenlere Göre Ä°ncelenmesi. Konya: Necmettin Erbakan Üniversitesi EÄŸitim Bilimleri Enstitüsü Özel EÄŸitim Ana Bilim Dalı Özel EÄŸitim Bilim Dalı.

  
 

Ünsal E (2016). Ortaokul ÖÄŸrencilerinin Ä°nternet Bağımlılığının Bazı DeÄŸiÅŸkenlere Göre Ä°ncelenmesi. Ä°stanbul: NiÅŸantaşı Üniversitesi Sosyal Bilimler Enstitüsü.

  
 

Waldo AD (2014). Correlates of Internet Addiction Among Adolescents. Psychology 5(18):1999-2008.
Crossref

  
 

Wang H, Zhou X, Lu C, Wu J, Deng X, Hong L (2011). Problematic internet use in high school students in Guangdong Province, China. PloS ON 6(5):1-8.
Crossref

  
 

Widyanto L, Griffiths M (2006). 'Internet Addiction': A Critical Review. International Journal of Mental Health and Addiction 4(1):31-51.
Crossref

  
 

Yang SC, Tung CJ (2007). Comparison of internet addicts and non-addicts in taiwanese high school. Computer in Human Behaviour (23):79-96.
Crossref

  
 

Yang L, Sun L, Zhang Z, Sun Y, Wu H, Ye D (2014). Internet addiction, adolescent depression, and the mediating role of life events: Finding from a sample of Chinese adolescents. International Journal of Psychology 49(5):342-347.
Crossref

  
 

Yao MZ, Zhong Z-j (2014). Loneliness, social contacts and Internet addiction: A cross-lagged panel study. Computers in Human Behavior 30:164-170.
Crossref

  
 

Yen JY, Yen CF, Chen CC, Chen SHand Ko, CH (2007). Family factor of internet addiction and substance use experience in Taiwanese Adolescents. Cyber Psychology and Behavior 10(3):323-329.
Crossref

  
 

Yıldız Ä° (2010). Lise ÖÄŸrencilerinde Problemli Ä°nternet Kullanımı Ä°le SaÄŸlıklı YaÅŸam Biçimi Davranışları Arasındaki Ä°liÅŸki. Sivas: Cumhuriyet Üniversitesi SaÄŸlık Bilimleri Enstitüsü.

  
 

Yılmaz E (2013). Lise 9. ve 10. Sınıf Sınıf ÖÄŸrencilerinin Ä°nternet Bağımlılık Düzeyleri ile Problem Çözme Becerileri Arasındaki Ä°liÅŸkinin ÇeÅŸitli DeÄŸiÅŸkenler Açısından Ä°ncelenmesi. Ä°stanbul: Marmara Üniversitesi EÄŸitim Bilimleri Enstitüsü, Yayımlanmamış Yüksek Lisans Tezi.

  
 

Young KS (2004). Internet Addiction A New Clinical Phenomenon and Its Consequences. American behavioral scientist 48(4):402-415.
Crossref

  
 

Young KS (2009). Assessment and Treatment of Internet Addiction. The Praeger International Collection on Addictions 4(3):217-235. içinde Greenwood Publishing Group.

  
 

Yücelten E (2016). Üniversite ÖÄŸrencilerinde Ä°nternet Bağımlılığı ve Akıllı Telefon Bağımlılığının BaÄŸlanma Stilleri ile Ä°liÅŸkisinin Ä°ncelenmesi. Ä°stanbul: Üsküdar Üniversitesi Sosyal Bilimler Enstitüsü Klinik Psikoloji Ana Bilim Dalı.

  
 

Zorbaz O (2013). Lise ÖÄŸrencilerinin Problemli Ä°nternet Kullanımının Sosyal Kaygı ve Akran Ä°liÅŸkileri Açısından Ä°ncelenmesi. Ankara: Hacettepe Üniversitesi Sosyal Bilimler Enstitüsü.

  

 




Copyright © 2025 Author(s) retain the copyright of this article.

This article is published under the terms of the Creative Commons Attribution License 4.0

Back to Vol. 15 No. 5
Back to articles
SUBSCRIBE TO RSS
SUBMIT MANUSCRIPT
CONFERENCES
          */?>