Akin, Iskender (2011). Internet addiction and depression, anxiety and stress.pdf

I nternational O nline J ournal of E ducational S ciences, 2011, 3(1), 138-148

www.iojes.net

Internet Addiction and Depression, Anxiety and Stress

Ahmet AKIN 1 and Murat İSKENDER 2

Abstract

The purpose of this study is to examine the relationships between internet addiction and depression,

anxiety, and stress. Participants were 300 university students who were enrolled in mid-size state University,

in Turkey. In this study, the Online Cognition Scale and the Depression Anxiety Stress Scale were used. In

correlation analysis, internet addiction was found positively related to depression, anxiety, and stress.

According to path analysis results, depression, anxiety, and stress were predicted positively by internet

addiction. This research shows that internet addiction has a direct impact on depression, anxiety, and stress.

Key Words: Internet addiction, depression, anxiety, stress, path analysis

Introduction

The internet is a new tool that is evolving into an essential part of everyday life all over the

world (Nalwa & Anand, 2003) and its use increases especially among young people. In

spite of the widely perceived merits of this tool, psychologists and educators have been

aware of the negative impacts of its use, especially the over or misuse and the related

physical and psychological problems (Greenfield, 2000). One of the most common of these

problems is internet addiction (Murali & George, 2007; Shapira, Lessig, Goldsmith et al.,

2003; Young, 1998). This problem is a raising phenomenon affecting people with varying

frequency around the world and has produced negative impacts on the academic,

relationship, financial, and occupational aspects of many lives (Chou & Hsiao, 2000;

Griffiths, 2000; Young, 1998). Internet addiction is typically characterized by psychomotor

agitation anxiety craving (Ferraro Caci D’Amico et al 2007) depression hostility,

substance experience (Ko, Yen, Chen et al., 2006; Yen, Ko, Yen et al., 2007), preoccupation,

loss of control, withdrawal, impairment of function, reduced decision-making ability (Ko,

1 Sakarya University, aakin@sakarya.edu.tr

2 Sakarya University, iskender@sakarya.edu.tr

International Online Journal of Educational Sciences, 2011, 3(1), 138-148

Yen, Chen et al., 2005), and constant online surfing despite negative effects on social and

psychological welfare (Shaw & Black, 2008; Tao et al., 2010).

More recently, the importance of research on internet addiction has grown. Studies have

utilized various methods to identify, internet addicts, and have used numerous terms such

as internet dependents, problematic internet users, or pathological internet users (Davis,

2001; Lin & Tsai, 2002). Research on internet addiction demonstrated that the greater use

of the internet is associated with some social and psychological variables such as, declines

in the size of social circle, depression, loneliness (Kraut et al., 1998), lower self-esteem and

life satisfaction (Ko, Yen, Chen et al., 2005), sensation seeking (Lin & Tsai, 2002), poor

mental health (Yang, 2001;Young & Rogers, 1998), and low family function (Armstrong,

Phillips, & Saling, 2000).

Internet Addiction and Affect

The excessive growth of the internet has had a huge influence on psychological research in

understanding its role in emotional states and there has been increased interest in the

addictive potential of the internet (Griffiths, 1998). The authors report that there are a

number of emotional factors which may be related to college students’ internet addiction

(Kandell, 1998). Among these factors the most remarkable are depression, anxiety, and

stress. Research on internet addiction and depression demonstrated that the overuse of the

internet, which results in a disruption of the normal lives of an individual and the people

around him, was associated with an increase in the frequency of depression (Kraut et al.,

1998, 2002; McKenna & Bargh, 2000; Nie, Hillygus, & Erbring, 2002). Because, excessive

internet use can displace valuable time that people spend with family and friends, which

leads to smaller social circles and higher levels of loneliness and stress (Nie et al., 2002).

Other conclusions of excessive usage have been documented as neglect of academic, work,

and domestic responsibilities, disruption of relationships, social isolation, and financial

problems (Griffiths, 2000; McKenna & Bargh, 2000).

Internet addiction also may contribute to anxiety and stress (Egger & Rauterberg, 1996;

Yu, 2001). Those who suffer from anxiety and stress often have a great deal of trouble

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Ahmet AKIN & Murat İSKENDER

communicating and interacting with others in a healthy, positive, and meaningful way.

These human characteristics are viewed as important determinants of internet addiction.

The Present Study

Although the relationships of internet addiction with social, educational, and physical

variables have received extensive scholarly attention, documenting its strong associations

with emotional variables such as depression, anxiety, and stress have received less

attention. Thus, the aim of the present research is to examine the relationships between

internet addiction and depression, anxiety, and stress. In this study depression is

operationalized as an abnormal state of the organism manifested by signs and symptoms

such as low subjective mood, pessimistic and nihilistic attitudes, loss of spontaneity and

specific vegetative signs, anxiety is operationalized as an emotional state of subjective

worry, along with heightened arousal of the autonomic nervous system, and stress is

operationalized as an emotional state of bodily or mental tension resulting from factors

that tend to alter an existent equilibrium . We hypothesized that internet addiction would

be associated positively with depression, anxiety, and stress.

Method

Participants

Participants were 300 university students enrolled in various undergraduate programs at

the Sakarya University, Turkey. Of the participants, 65 were first-year students, 41 were

second-year students, 56 were third-year students, and 138 were fourth-year students. 96

of the participants (32%) were males and 204 (68%) were females. A large majority of the

students (91%) were between 17 and 24 years of age (mean; 21.24, sd; 1.49).

Measures

The online cognition scale (OCS). Internet addiction was measured using OCS.

This scale contains 36 items on a 7-point Likert-type scale. It was developed by Davis,

Flett, and Besser (2002) to assess internet addiction and it has four sub-dimensions:

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International Online Journal of Educational Sciences, 2011, 3(1), 138-148

Loneliness/depression, diminished impulse control, distraction, and social comfort. The

internal consistency coefficient of Turkish form was .93 and the test–retest reliability

coefficient was .87. Turkish adaptation of this scale had been done by Ozcan (2004). The

internal consistency coefficient of Turkish form was .93 and the test–retest reliability

coefficient was .90.

The depression anxiety stress scale (DASS). Depression, anxiety, and stress were

measured by using a Turkish version of the DASS (Lovibond & Lovibond, 1995). Turkish

adaptation of the DASS had been done by Akın and Çetin (2007) The DASS is a 42-item

self-report inventory that provides scores on three subscales: Depression (14-items),

anxiety (14-items), and stress (14-items). Each item was rated on a 5-point scale. The

language validity findings indicated that correlation between Turkish and English forms

was .96. Factor loadings of the subscales ranged from .39 to .88. The internal consistency

alpha coefficients were found for depression, anxiety, and stress .90, .92, and .92

respectively. The test-retest reliability scores after three weeks were found .98 for three

subscales. Related with the criterion-related validity of the scale, correlation coefficients

between the DASS and the Beck Depression Inventory (Beck, Steer, & Brown, 1996), and

the Beck Anxiety Inventory (Beck, Steer, & Garbin, 1988) were computed as .87 and .84,

respectively.

Procedure

Permission for participation of students was obtained from related chief departments and

students voluntarily participated in the research. Completion of the questionnaires was

anonymous and there was a guarantee of confidentiality. Measurement items were

administered to the students in groups in the classrooms. The measures were

counterbalanced in administration. Prior to administration of measures, all participants

were told about purposes of the study. In this research, Pearson correlation coefficient and

structural equation modeling was utilized to determine the relationships between internet

addiction and depression, anxiety, and stress. These analyses were carried out via LISREL

8.54 (Joreskog & Sorbom, 1996) and SPSS 13.0.

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Ahmet AKIN & Murat İSKENDER

Findings

Descriptive Data and Inter-correlations

Table 1 shows the means, standard deviations, inter-correlations, and internal consistency

coefficients of the variables used.

Table 1. Descriptive Statistics, Alphas, and Inter-correlations of the Variables

Internet

Variables

Depression

Anxiety

Stress

Addiction

Internet Addiction

1.00

Depression

.672**

1.00

Anxiety

.629**

.806**

1.00

Stress

.627**

.810**

.826**

1.00

Mean

85,15

10,48

11,83

15,07

Standard deviation

41,01

9,01

8,90

9,60

Alpha

.95

.91

.88

.90

**p<.01

When Table 1 is examined, it is seen that there are significant correlations between internet

addiction and depression, anxiety, and stress. Internet addiction related positively to

depression ( r =.67, p <.01), anxiety ( r =.63, p <.01), and stress ( r =.63, p <.01).

Structural Equation Modeling

Hypothesized model was examined via structural equation modeling (SEM). Figure 1

presents the results of SEM analysis, using maximum likelihood estimations. The model

fitted well (χ2 = 123 df = 1 p = 26745 GFI = 100 AGFI = 98 CFI = 1.00, NFI = 1.00, RFI =

.99, IFI = 1.00, and RMSEA = .028) and also accounted for 45% of the depression, 40% of the

anxiety, and 40% of the stress variances.

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