A Study on Turkish Adolescent's Internet Use: Possible Predictors of Internet Addiction

Abstract

The purpose of this study is to investigate the internet use of Turkish adolescents, with a (particular) focus on the risk of internet addiction. A web-based questionnaire was completed by a total of 4,311 adolescents attending public high schools in grades 9–12, in a small-sized city in western Turkey. Ages ranged from 15 to 19 years, 54 percent were female and 46 percent male. The questionnaire included items on sociodemographic information, internet usage, and a Turkish version of the Young's Internet Addiction Test. The data were analyzed in SPPS 15.0 program using the t test, the Mann–Whitney U test, correlation and hierarchic regression analysis. The findings show that, regardless of gender, Facebook ranked highest in the classification of students' purpose of internet use; it was also found that females mainly used the internet for communication, whereas males were more interested in playing online games and reading newspapers and magazines. The results of hierarchic regression analysis indicated that the significant predictors of the internet addiction were the presence of internet access at home, gender, and family income levels.

Introduction

The internet, a multi-user communication network, is increasingly becoming an essential part of our life and the number of users is rising dramatically. While the internet addresses numerous business and other needs, such as access to data, communication, children, adolescents, and adults may be adversely affected if it is used inappropriately. Besides the fact that the internet is an essential part of students' lives, most use it in a useful and productive manner, according to Chou and Hsiao¹; some students, however, develop a pathological internet use model. When we analyze the literature, we observe that this kind of negative use is labeled in various ways, including terms, such as internet addiction, cyberspace addiction, internet addiction disorder, online addiction, net addiction, internet addicted disorder, pathologic internet use, and high internet dependency.^2–4 However, the most common term is “internet addiction.”^{2, 5–10}

According to Öztürk, Eraslan, Odabaşıoğlu, Genç, and Kalyoncu,¹¹ internet addiction starts at a relatively early age when compared to other addictions, such as those of psychoactive substances, alcohol, and gambling. The highest risk group is adolescents between the ages of 12 and 18. In fact, during the transition from childhood to adulthood, anxiety and the search for one's real identity can cause serious problems. In the critical period of adolescence, knowledge of the frequency and the purpose of internet use of individuals has the potential to provide a considerable degree of support in the manipulation of adolescents' difficulties. The degree of manipulation of the media in adolescents difficulties is also very important since internet access is available at homes, schools, and internet cafes, which make adolescents the most vulnerable group.^12,13 Negative consequences of the internet addiction like reduced sleep time, skipping meals, and decreased time management skills may have negative effects on adolescents' academic achievement (i.e., missing school, deteriorated performance). Therefore, the identification of predictors of internet addiction of students in the age 15 to 19 groups, and also the identification of the variables, which cause such addiction are important factors in the definition of the scope of the psychological counseling and guidance services provided for the development of students' personality and academic performance. This study investigates the internet use of adolescents over a large research group, and provides an analysis of possible predictors of internet addiction.

The purpose of this study is to investigate the Turkish adolescent's internet use with a focus on the risk of addiction. With this aim in mind, first of all the students' reasons for internet use were investigated in terms of gender and internet addiction; Following this, the study explores the extent to which gender and socioeconomic variables are predictors related to internet addiction.

We formulated the following research questions:

(a) Does the purpose of internet use vary by gender?

(b) To what extent are the factors of gender, family income, access to privacy, access to a computer, and an internet connection at home predictors related to internet addiction?

Method

Participants

The participants are comprised of 4,311 adolescents, 2,310 female (53.6 percent) and 2,011 male (46.4 percent), attending public high schools in grades 9–12, in a small-sized city in western Turkey. The average age was 17 years. The distribution of the students was as follows: 9th grade, 1,102 students (25.6 percent); 10th grade, 1,254 students (29.1 percent); 11th grade, 1,333 students (30.9 percent); and 12th grade, 622 students (14.4 percent).

Instruments

A 25-item questionnaire was used in this study. The first five items collected data about, age, gender, high school grade, family socioeconomic status, and purpose of internet use. The remaining 20 items were the Internet Addiction Test (IAT). The IAT, developed by Young¹⁴ and adapted to Turkish by Bayraktar,¹⁵ is a 6-point Likert scale designed for determining adolescents' level of internet addiction. Participants responded to the 20 IAT items on a 6-point Likert measure (“does not apply” to “always”), which produced an overall score between 0 and 100. IAT author guidelines recommend that participants scoring less than 40 be categorized as average users, those scoring between 40 and 59 be categorized as moderate internet users, and those scoring 60 or above be categorized as excessive users. The Cronbach alpha coefficient of the scale is 0.91 and the Spearman–Brown value is 0.87. Also, the model was found statistically significant at the regression analysis of the points obtained from test [F(11, 29)=26.15, p<0.01]. Furthermore, Cronbach alpha coefficient for scale reliability was calculated as 0.93 in this study.

Data analysis

The data were analyzed with SPSS 15.0 program using correlation and hierarchic regression analysis and the t-test. In making comparisons that relate to the students' purpose of internet use, the Mann–Whitney U test was used, since analyses were made on mean ranks. The Pearson's Correlation Coefficient was used to determine whether there was a significant correlation between the students' total IAT scores and socioeconomic variables. Hierarchic regression analysis was used to determine whether gender and such socioeconomic variables as family income, access to a private space, access to a computer, and an internet connection at home acted as a predictor of adolescents' IAT scores.

Results

When students' IAT scores are categorized according to the IAT author guidelines, it is shown that 5 percent of students are excessive users and 13 percent of students are moderate users, while 82 percent of students are average users. Frequencies and percentages that relate to gender and socioeconomic variables, such as access to a private space, access to a computer, and an internet connection at home are given in Table 1.

Table 1.

Frequencies and Percentages That Relate to Gender and Socioeconomic Variables

	f	%	Mean	SD
Gender
Female	2310	53.6	20.25	17.03
Male	2001	46.4	27.56	19.05
Availability of a private room at home
Yes	3036	70.4	25.24	18.60
No	1275	29.6	19.83	17.20
Availability of a computer at home
Yes	3288	76.3	26.47	18.45
No	1023	23.7	14.56	14.79
Availability of Internet connection at home
Yes	2760	64.0	27.73	18.68
No	1551	36.0	16.36	15.29
Family Income
Low	1100	25.6	18.07	16.89
Medium	1636	37.9	22.97	17.63
High	1575	36.5	27.63	18.77

As seen in Table 1, the participants were composed of 2,310 females (53.6 percent) and 2,001 (46.4 percent) males. The mean of the females' IAT scores is 20.25, while that of the males is 27.56. 3,036 students (70.4 percent) reported having access to their own private space at home, whereas 1,275 students (29.6 percent) did not. The mean of IAT scores of the students with access to privacy is 25.24 and 19.83 for those without. 3,288 students (76.3 percent) said there was a computer at home, in contrast to the 1,023 (23.7 percent) without. The mean of IAT score of those with computer access at home is 26.47 compared to 14.56 for those without. The number of students with and without internet access are 2,760 (64.0 percent) and 1,551 (36.0 percent), respectively. The mean IAT score of those with internet access is 27.73 compared to 16.36 for those without. The number of students who have low, medium, and high family monthly income are 1,100 (25.6 percent), 1,636 (37.9 percent), and 1,575 (36.5 percent), respectively. The mean IAT score of those who have a low family monthly income is 18.07, compared to 27.63 for those who have a high family monthly income. A correlation analysis was made to examine the relationship between variables. The correlations, means and standard deviations between total IAT points and variables are given in Table 2.

Table 2.

Correlations, Means and Standard Deviations Between Variables

Variables	(1)	(2)	(3)	(4)	(5)	(6)
(1) IAT point	1
(2) Gender	0.199^***	1
(3) Availability of a private room	0.131^***	0.015	1
(4) Availability of a computer	0.275^***	0.30^*	0.25^***	1
(5) Internet connection	0.295^***	0.005	0.220^***	0.729^***	1
(6) Family income	0.178^***	0.005^**	0.212^***	0.276^***	0.272^***	1
Mean	23.70	0.53	0.29	0.23	0.36	1684.27
SD	18.28	0.50	0.45	0.42	0.48	1413.28

N=4099,^*p<0.05, ^**p<0.01, ^***p<0.00

IAT, Internet Addiction Test.

Table 2 shows that the correlation between total IAT scores and such variables as gender, availability access to privacy, access to a computer and an internet connection at home, and family income is significant at the p<0.00 level. Based on these findings, we can conclude that the correlation between total IAT scores and other variables is high.

The purpose of internet use and gender

Students were asked to rank their eight main reasons for internet use. The overall rank order and means are as follows: (a) “Facebook” (mean 2.47); (b) “research/homework” (mean 2.99); (c) “MSN (Windows Live Messenger)” (mean 3.01); (d) “reading newspapers/magazines” (mean 3.70); (e) “music downloads” (mean 4.04); (f) “e-mail” (mean 4.83); (g) “video download” (mean 5.16); (h) “Online game playing” (mean 5.23). According to these findings, we could conclude that students mainly use the internet for communication and research purposes. The results of the Mann–Whitney U test regarding purpose of internet use according to gender are shown in Table 3.

Table 3.

Results of Mann–Whitney U Test Regarding Purpose of Internet Use According to Gender

Purpose of Internet Use	Gender	n	Mean Rank	Sum of Ranks	U	p
Facebook	Male	1727	1696.8	29304	14383	0.769
	Female	1675	1706.3	28581
Research/homework	Male	1456	1976.9	28773	10721	0.00^***
	Female	1984	1532.9	30412
MSN	Male	1499	1643.0	24630	11301	0.00^***
	Female	1647	1510.2	24873
E-mail	Male	921	825.9	76066	33607	0.388
	Female	748	846.2	63296
Online game	Male	1016	707.64	71896	20232	0.00^***
	Female	576	953.24	54907

***

p<0.00

As seen in Table 3, the difference between the mean ranks for internet connection for “Facebook” purpose according to gender is not significant (U=14,383, p>0.05). This finding demonstrates that the mean ranks for access to the internet for “Facebook” does not change according to gender. The difference between mean ranks for internet access for “research/homework” purpose according to gender is significant (U=10,721, p<0.00). When mean ranks are considered, it is possible to conclude that “research/homework” as a purpose for internet use ranks higher for females than males. The difference between mean ranks for internet access for the purpose of using “MSN” according to gender is significant (U=11,301, p<0.00). When mean ranks are considered, it is possible to conclude that “MSN” as a purpose for internet use ranks higher for females than males. The difference between mean ranks for internet access for the purpose of “online games” according to gender is significant (U=20,232, p<0.00). When mean ranks are considered, it may be suggested that “online games” as a purpose for internet use ranks higher for males than females.

Table 4 shows the results of the t test conducted to analyze the difference between the purpose of internet use ranked highest by individual students, and their total IAT scores.

Table 4.

Results of T Test Regarding the Purpose of Internet Use, Which the Students Ranked First and Total Internet Addiction Test Points

Purpose of Internet Use		n	Mean	SD	t	df	p
Facebook	Yes	1255	29.42	17.91	6.59	2641	0.00^***
	No	2143	25.22	18.04
Online Game	Yes	156	34.53	18.34	3.00	195	0.00^***
	No	1436	29.87	19.55

***

p<0.00

As can be concluded from Table 4, IAT scores for the students whose primary purpose of internet use is Facebook is higher than the scores of those who have a different primary purpose (t=6.59, p<0.00). On the other hand, it was shown that total IAT scores of those whose main use is online games is higher than total IAT scores of those who had some other main purpose (t=3.00, p<0.00).

Results about the predictors of internet addiction

Hierarchic regression analysis was conducted to investigate the extent to which students' mean IAT scores are predicted by the following factors: gender, access to private space, availability of a computer and internet connection at home, and the level of family income. Results of the hierarchic regression analysis are shown in Table 5.

Table 5.

Results of the Hierarchic Regression Analysis

	β	Standart Error	T	R	R²	R² change	F change
IAT points
Step 1				0.295	0.087	0.087	391.33^***
Internet connection	0.295	0.570	19.78^***
Step 2				0.357	0.127	0.040	188.58^***
Gender	0.200	0.535	13.73^***
Step 3				0.369	0.136	0.009	40.95^***
Family income	0.097	0.000	6.40^***
Step 4				0.375	0.141	0.005	21.74^***
Computer	0.100	0.923	4.66^***
Step 5				0.377	0.142	0.002	8.56^**
Private Room	0.044	0.608	2.93^***

N=4099, ^**p<0.01, ^***p<0.00

The equation at regression analysis was formulated so as to investigate the correlation of total IAT scores with access to the internet at home, gender, family income levels, availability of a computer at home, and access to a private space. The first variable, home internet access, was entered to the equation and the variance was statistically controlled. The other variables: gender, family income, availability of computer, and access to privacy were entered in the second, third, fourth, and fifth steps, respectively. Based on the findings, home internet access, the first control variable, had a significant positive correlation with total IAT scores [β=0.295, t(4099)=19.78, p<0.00]. Thus, a notable rise is observed in total IAT scores of those with home internet access. This variable explained 8.7 percent total variance [F(1, 4099) 391.33, p<0.00]. The gender variable demonstrated a significant correlation with total IAT scores [β=0.200, t(4099)=13.73, p<0.00]. Thus, the total IAT scores are higher for females than males. Because gender additionally explained 7 percent of the variance [F(1, 4099)=188.58, p<0.00], total variance rose to 12.7 percent. The family monthly income variable demonstrated a positive significant correlation with total IAT scores [β=0.097, t(4099)=6.40, p<0.00]. Thus, it can be noted that the total IAT scores rise with the family income. Because the family monthly income additionally explained 0.9 percent of the variance [F(1, 4099)=40.95, p<0.00], the total variance rose to 13.6 percent. The variable access to a computer at home demonstrated a positive significant correlation with total IAT scores [β=0.100, t(4099)=4.66, p<0.00]. Thus, higher total IAT scores are noted in students with computers at home. Because this variable additionally explained 0.5 percent of the variance [F(1, 4099)=21.74, p<0.00], total variance rose to 14.1 percent. Access to privacy, the last variable to be entered into the equation, demonstrated a positive significant correlation with total IAT scores [β=0.044, t(4099)=2.93, p<0.00]. Thus, higher total IAT scores are seen for those with their own private space. Because access to a private space additionally explained 0.2 percent of the variance [F(1, 4099)=8.56, p<0.00], total variance rose to 14.2 percent. The results of the t test, which relate to the significance of regression coefficients, shows that the variables included in the regression analysis are significant predictors of internet addiction.

Discussion and Conclusion

This study investigated adolescent internet use with, a specific focus on the risk of internet addiction. It found a correlation that was significant at the p<0.00 level between the total IAT scores of the adolescent participants, and a number of variables: gender, access to privacy, access to a computer and an internet connection at home, and the family monthly income level.

The study first investigated students' purpose of internet use in relation to gender and total IAT scores. It was observed that, regardless of gender, students indicated that the use of Facebook was the highest ranked purpose. Bayraktar and Gün¹⁶ correlated the most common reason for using the internet, that is, peer communication, to the fact that this type of communication has an important role during the adolescent period. Thus, the high ranking of Facebook use is likely to be due to the importance of peer communication for this age group, and also the fact that it is the most convenient type of internet communication.

The investigation of the purpose of internet use by adolescents according to gender revealed that females used internet mostly for communication, whereas for males, online game playing, and newspaper and magazines were most important. In the internet addiction studies conducted by Chou, Condron and Belland,¹⁷ it was concluded that purpose of internet use varied according to gender. This finding agrees the results of other studies which demonstrated differences in internet use between young females and males.^14,18–25

A review of the correlation between the highest ranked purpose of internet use and the total IAT scores determined that those students who ranked either Facebook use or online games first were those with higher IAT scores. This finding seems to support the result of Ceyhan's²⁶ hierarchic regression analysis, that internet use for “entertainment” and “communication” purposes are important predictors of internet addiction.

According to Chak and Leung,²⁷ the internet addicted mainly spends time engaged with synchronous/asynchronous communication media and interactive online game playing, rather than research. A study by Whang, Lee and Chang²⁸ revealed that students classified as internet-addicted make more use of internet communication facilities and online game services when compared with probable internet-addicted and nonaddicted groups. In the light of these findings, it can be concluded that the purpose of internet use is also likely to be an important predictor for internet addiction. However, because this study examined the purpose of internet use in a specific way, that is, through ranking, purpose is not included in the variables subject to a regression analysis. However, the influence of the purpose of adolescent internet use is a useful area for analysis and exploration in future studies. It is important to determine whether the purpose of internet use is a predictor of adolescent internet addiction.

The regression analysis demonstrated that access to the internet at home, and gender were the significant predictors. Home access to the internet and being male further increased IAT scores. Higher levels of family income and access to a computer at home were also factors that raised IAT scores. Although availability of a private room at home had a lower influence when compared with other variables, it still caused an increase in the total internet addiction scores of the students. At the end of the study, it can be concluded that, in addition to such variables as the availability of internet connection at home, an adolescent's gender will be a predictor of internet addiction; and family income, as a predictor, could be considered a determiner variable of the availability of both a computer and an internet connection at home.

It can be concluded that gender and such socioeconomic variables as availability of an internet connection and a computer at home, and the family income are the significant predictors of adolescent internet addiction. Considering also the findings in the literature that there is a negative correlation between family and teacher support and internet addiction,²⁹ it will be useful for families and teachers to follow and monitor adolescents with respect to the variables considered here, and purpose of internet use, and to raise their awareness regarding internet use. Some findings in the literature suggest that some psychological variables could also influence the purpose of internet use, and therefore be a predictor of internet addiction. One of the limitations of this current work is that only gender and socioeconomic variables have been considered, and psychological variables have been excluded. Therefore, we deem it important in future research to study the relationship between internet addiction and such psychological variables as depression, self-respect, and loneliness.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

References

Chou

, Hsiao

. Internet addiction, usage, gratifications, and pleasure experience: the Taiwan college students' case. Comput Educ, 2000; 35:65–80.

Byun

, Ruffini

, Mills

. Internet addiction: metasynthesis of 1996–2006 quantitative research. Cyberpsychol Behav, 2009; 12:203–207.

Davis

, Flett

, Besser

. Validation of a new scale for measuring problematic internet use: implications for pre-employment screening. Cyberpsychol Behav, 2002; 5:331–345.

Hur

. Demographic, habitual, and socioeconomic determinants of internet addiction disorder: an empirical study of Korean teenagers. Cyberpsychol Behav, 2006; 9:514–525.

Huang

. Internet addiction: stability and change. Eur J Psychol Educ, 2010; 25:345–361.

Saville

, Gisbert

, Kopp

et al. Internet addiction and delay discounting in college students. Psychol Rec, 2010; 60:273–286.

Shaw

, Black

. Internet addiction: definition, assessment, epidemiology and clinical management. CNS Drugs, 2008; 22:353–365.

Soule

, Shell

, Kleen

. Exploring internet addiction: demographic characteristics and stereotypes of heavy internet users. J Comput Inf Syst, 2003; 4:64–73.

Thatcher

, Goolam

. Development and psychometric properties of the problematic internet use questionnaire. S Afr J Psychol, 2005; 35:793–809.

10.

Young

. Internet addiction: diagnosis and treatment considerations. J Contemp Psychother, 2009; 39:241–246.

11.

Öztürk

, Odabaşıoğlu

, Eraslan

et al. İnternet Bağımlılığı: Kliniği ve tedavisi/internet addiction: clinical aspects and treatment strategies. Bağımlılık Derg, 2007; 8:36–41.

12.

Ferraro

, Caci

, D'Amico

et al. Internet addiction disorder: an Italian study. Cyberpsychol Behav, 2007; 10:170–175.

13.

Hawi

. Internet addiction among adolescents in Lebanon. Comput Hum Behav, 2012; 28:1044–1053.

14.

Young

. Internet addiction: the emergence of a new clinical disorder. Cyberpsychol Behav, 1998; 1:237–244.

15.

Bayraktar

. İnternet kullanımının ergen gelişimindeki rolü/the role of the internet use on the devlopments of adolescents. Unpublished master's thesis. Ege University: Izmir, 2001.

16.

Bayraktar

, Gün

. Incidence and correlates of internet usage among adolescents in North Cyprus. Cyberpsychol Behav, 2007; 10:191–197.

17.

Chou

, Condron

, Belland

. A review of the research on internet addiction. Educ Psychol Rev, 2005; 17:363–388.

18.

Bayraktar

, Amca

. Interrelations between virtual-world and real-world activities: comparison of genders, age groups, and pathological and nonpathological internet users. Cyberpsychol Behav Soc Netw, 2012; 15:263–269.

19.

Chen

. 2000. Gender differences of internet addiction in Taiwan. Poster presented at the 108th American Psychology Association Annual Convention: Washington, DC.

20.

Frangos

, Frangos

, Sotiropoulos

. Problematic internet use among greek university students: an ordinal logistic regression with risk factors of negative psychological beliefs, pornographic sites, and online games. Cyberpsychol Behav Soc Netw, 2011; 14:51–58.

21.

Jackson

, Zhao

, Qiu

et al. Culture, gender, and information technology use: a comparison of Chinese and US children. Comput Hum Behav, 2008; 24:2817–2829.

22.

, Zhang

, Li

et al. Stressful life events and problematic internet use by adolescent females and males: a mediated moderation model. Comput Hum Behav, 2010; 26:1199–1207.

23.

Mazhari

. Association between problematic internet use and impulse control disorders among Iranian university students. Cyberpsychol Behav Soc Netw, 2012; 15:270–273.

24.

Tahiroğlu

, Ccedil;elik

, Uzel

et al. Internet use among Turkish adolescents. Cyberpsychol Behav, 2008; 11:537–543.

25.

Yates

, Gregor

, Haviland

. Child maltreatment, alexithymia, and problematic internet use in young adulthood. Cyberpsychol Behav Soc Netw, 2012; 15:219–225.

26.

Ceyhan

. Predictors of problematic internet use on Turkish university students. Cyberpsychol Behav, 2008; 11:363–366.

27.

Chak

, Leung

. Shyness and locus of control as predictors of internet addiction and internet use. Cyberpsychol Behav, 2004; 7:59–70.

28.

Whang

, Lee

, Chang

. Internet over-users psychological profiles: a behavior sampling analysis on internet addiction. Cyberpsychol Behav, 2003; 6:143–150.

29.

Kıran-Esen

, Gündoğdu

. The relationship between internet addiction, peer pressure and perceived social support among adolescents. Int J Educ Res, 2010; 2:26–33.