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Relationship of internet addiction with cognitive style, personality, anddepression in university students

Omer Senormancı, Ozge Saraclı, Nuray Atasoy, Guliz Senormancı, FuruzanKokturk, Levent Atik

PII: S0010-440X(14)00112-6DOI: doi: 10.1016/j.comppsych.2014.04.025Reference: YCOMP 51305

To appear in: Comprehensive Psychiatry

Received date: 2 April 2014Revised date: 29 April 2014Accepted date: 30 April 2014

Please cite this article as: Senormancı Omer, Saraclı Ozge, Atasoy Nuray, SenormancıGuliz, Kokturk Furuzan, Atik Levent, Relationship of internet addiction with cognitivestyle, personality, and depression in university students, Comprehensive Psychiatry (2014),doi: 10.1016/j.comppsych.2014.04.025

This is a PDF file of an unedited manuscript that has been accepted for publication.As a service to our customers we are providing this early version of the manuscript.The manuscript will undergo copyediting, typesetting, and review of the resulting proofbefore it is published in its final form. Please note that during the production processerrors may be discovered which could affect the content, and all legal disclaimers thatapply to the journal pertain.

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Title: Relationship of internet addiction with cognitive style, personality, and depression

in university students

Ömer Şenormancı, Psychiatrist. (Corresponding author) (reprint request)

Assistant Professor, Bülent Ecevit University School of Medicine, Psychiatry, Zonguldak –

Turkey

Address: Bülent Ecevit University School of Medicine Esenler-Kozlu, Zonguldak, TR 67600

Phone number: +90 505 794 20 52

Fax number: +90 212 572 95 95

E-mail address: senorman_7@hotmail.com

Özge Saraçlı, Psychiatrist

Assistant Professor, Bülent Ecevit University School of Medicine, Psychiatry, Zonguldak –

Turkey

Nuray Atasoy, Psychiatrist

Associate Professor, Bülent Ecevit University School of Medicine, Psychiatry, Zonguldak –

Turkey

Güliz Şenormancı, Psychiatrist

Zonguldak Atatürk State Hospital

Fürüzan Koktürk

Assistant Professor, Bülent Ecevit University School of Medicine, Biostatistics, Zonguldak,

Turkey

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Levent Atik, Psychiatrist

Associate Professor, Bülent Ecevit University School of Medicine, Psychiatry, Zonguldak –

Turkey

Author Disclosure Statement

No competing financial interests exist.

Abstract

Background: The aim of the study was to investigate the relationship of dysfunctional

attitudes, self-esteem, personality, and depression with internet addiction in university

students.

Methods: A total of 720 university students participated in the study in Bülent Ecevit

University English Preparatory School which offers intensive English courses. Students were

evaluated with a sociodemographic data form, Beck Depression Inventory (BDI),

Dysfunctional Attitudes Scale form A (DAS-A), Internet Addiction Scale (IAS), Rosenberg

Self-Esteem Scale (RSES), Eysenck Personality Questionnaire Revised/Abbreviated Form

(EPQR-A).

Results: The results indicated that 52 (7.2%) of the students had internet addiction. There

were 37 (71.2%) men, 15 (28.8%) women in the addicted group. While the addicted groups’

BDI, DAS-A perfectionistic attitude, need for approval, RSES, EPQR-A neuroticism,

psychoticism scores were significantly higher, EPQR-A lie scores were significantly lower

than the non addicted group. According to the multiple binary logistic regression analysis,

being male, duration of internet usage, depression, perfectionistic attitude have been found as

predictors for internet addiction. It has been found that perfectionistic attitude is a predictor

for internet addiction even depression, sex, duration of internet were controlled.

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Conclusions: To the knowledge of the researchers, this study is the first study to show the

dysfunctional attitudes in internet addiction. It can be important to evaluate dysfunctional

attitudes, personality, self-esteem and depression in people with internet addiction. These

variables should be targeted for effective treatment of people with internet addiction in

cognitive behavioral therapy.

1. Introduction

The internet which was developed to increase communication and facilitate

information exchange has grown beyond expectations, but some users are unable to control

their internet use, and thus experience some problems in their functioning at work and in

social and private life [1]. In the literature, various terms are used for overuse or uncontrolled

use of internet. ‘Internet addiction’ which is also used in our article, is the most popular term

used for this disorder that should be considered as non-substance behavioral addictions in

Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) system [2]. Recently,

the disorder has been included in DSM-5. The problem of internet overuse was included as

‘internet use gaming disorder’ in DSM-5 section 3 which highlights the need for more

research to diagnose the formal disorders. According to DSM-5 addiction criteria, there is no

difference between ‘chemical’ and ‘behavioral’ addiction. DSM-5 focuses on personal

experiences rather than drug types [3].

Depression is the most comorbid disorder with internet addiction [4,5]. In a follow up

study, dysfunctional attitudes that are known to be related to depression have been found as a

predictor for problematic alcohol use when current depressive symptoms, gender, and

influence of alcohol consumption were controlled. This conclusion has emphasized the

cognitive structure as a risk factor for problem drinking [6]. Considering the problematic

behavioral model, internet addiction is related to problematic alcohol use [7]. It has been also

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found that maladaptive perfectionism attitude that is not direct relationship with internet

addiction is related to internet addiction [8].

Self-esteem is described as person’s attitude to himself. It can be positive or negative.

It is accepted that while a person perceives himself as positively, self-esteem is high, but if the

person perceives himself as negatively, self-esteem is low [9]. It has been suggested that low

self-esteem is a principal component of depression [10]. As a coping strategy, people who

have negative beliefs about themselves like low self-esteem may tend to be addict to relieve

from the negative beliefs [11,12]. Self-esteem has a relationship with perfectionism.

Maladaptive perfectionism was found as a negative predictor of self-esteem [13].

Internet addiction is seen with some personality traits [14]. A study that used Eysenck

Personality Questionnaire has shown that students addicted to the Internet showed higher

neuroticism/stability scores, higher psychoticism/socialization scores, and lower lie scores

[15]. Although these traits have been found to be related to internet addiction, neuroticism

trait has one of the main roles for internet addiction [16]. People who have high neuroticism

traits tend to be internet addicts [17,18]. People who have neurotic traits use internet for

expressing their ideas and feelings and showing their hidden skills [19,20]. They have more

depression risk [21,22]. They are hypervigilant to emotional stimulants. For this reason, they

show inadaptable reactions, and they have traits which can cause depression [23].

There is a relationship between personality traits and self-esteem. This relationship has

a role to develop internet addiction [24]. Neuroticism and low self-esteem are predictors for

depression together, and they should be evaluated in depressive illness [25]. Also, in some

studies, a positive correlation has been found between maladaptive perfectionism and

neuroticism [26,27]. Thus, it is possible that there can be a relationship between dysfunctional

attitudes, neuroticism, self-esteem, depression, and internet addiction.

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The aim of the study was to investigate the relationship of dysfunctional attitudes, self-

esteem, personality, and depression with internet addiction in university students.

2. Methods

2.1. Participants

The study was conducted at Bülent Ecevit University English Preparatory School

which offers intensive English courses in October 2013. The necessary permissions to

conduct the study were received from the Bülent Ecevit University School of Medicine Ethics

Committee prior to the initiation of the research. The school has 1026 students. We could not

reach 277 students, and 29 students’ data were excluded due to incomplete measurements.

Thus, a total of 720 university students participated in the study. Before submitting the

questionnaires, the students were informed about measurements and study protocol, and their

written consents were gathered. The students answered the measurements anonymously.

2.2. Instruments

2.2.1. Sociodemographic data form: A form was developed by the researchers to collect the

sociodemographic data from the participants considering the aim of the study.

2.2.2. Beck Depression Inventory (BDI): This is a 21-item self-report scale measuring the

emotional, cognitive, somatic, and motivational symptoms of depression. Each item is scored

on a scale from 1 to 3, and total scores are calculated by summing the scores on all items [28].

The cut-off score was set at 17 in a Turkish validity and reliability study. The internal

consistency reliability Chronbach‘s alpha was 0.80 [29].

2.2.3. Dysfunctional Attitudes Scale form A (DAS-A): DAS-A is a self-report scale, consisting

of 40 items on a 7-point Likert scale, which is developed for measuring dysfunctional

attitudes and beliefs. Ten items are reversely coded (2, 6, 12, 17, 24, 29, 30, 35, 37 and 40)

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since they point to functional attitudes. Each item is scored on a scale from 1 to 7. Higher

scores correspond to more frequent dysfunctional attitudes [30]. Four factors including

‘perfectionistic attitude’, ‘need for approval’, ‘independent attitude’ and ‘variable attitude’

were reported in the reliability and validity studies of the Turkish version of the measurement.

Cronbach alpha reliability coefficient was found as 0.79, and the average of item total score

correlations was found as 0.34 in the Turkish adaptation study [31].

2.2.4. Internet Addiction Scale (IAS): This is a 31-item self-report scale to measure

pathological internet use. Each item is scored on a scale from 1 to 5 [32]. Due to the low

correlation, item 4 was deleted. The higher score a person gets, the more likely that he/she is

an ‘internet addict’ The cut-off score was set at 90. In other words, 90 or higher scores means

‘internet addiction’. The internal consistency reliability Chronbach‘s alpha was 0.80 [33].

2.2.5. Rosenberg Self-Esteem Scale (RSES): This is a 63-item self-report scale to measure

global feelings of self-worth or self-acceptance. Higher scores on the scale items indicate

lower levels of self-esteem [9]. The scale consists of 12 sub-categories. The aim of the current

study was considered, and the first 10 questions of the original scale that measure self-esteem

were used. The correlation between the scale and psychiatric interview results was found 0.71

for Turkish validity and reliability study [34].

2.2.6. Eysenck Personality Questionnaire Revised/Abbreviated Form (EPQR-A): This is an

abbreviated form of the Eysenck personality questionnaire. It is a 24-item self-report scale

which includes one validity scale and three personality scales. The EPQR-A was used to

identify the three extraversion, neuroticism and psychoticism dimensions of personality, and it

includes 6 items in each domain [35]. Four factors were found for Turkish validity and

reliability study. Alpha coefficients of the scales for extraversion, neuroticism, psychoticism

and lie were found to be 0.78, 0.65, 0.42, 0.64, respectively, and the test-retest reliabilities

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were found to be .84, .82, .69, and .69, respectively. It is important to note that lie sub-scale

does not evaluate a personality dimension, but it measures validity of the other subscales [36].

2.3. Statistical analysis

SPSS 18 was used for all statistical analyses. Normal distribution of the data was

evaluated with the Kolmogorov–Smirnov distribution test. Mann–Whitney U-tests was used

to compare the quantitative variables that were not normally distributed. In all tables, the

numeric variables are presented as mean ± SD or median (Min - Max), and the categorical

variables are presented in terms of both numbers of observations and percentages (%).

Significance levels were set at p < 0.05 and p < 0.001. Logistic regression analysis was

performed.

3. Results

Our sample age was 19 (17 - 35). Duration of internet usage was 2.5 (0 - 16). There

are 362 (50.3%) female and 358 (49.7%) male students in the study. Sociodemographic and

clinical characteristics of study sample were shown in table 1. Comparison measurements

scores between non-addicted and addicted groups was shown in table 2.

The results indicated that 52 (7.2%) of the students who received 90 or higher IAS

score had internet addiction. There were 37 (71.2%) men, 15 (28.8%) women in the addicted

group.

Age, duration of internet usage, sex, living condition, divorced parents, siblings,

having own room, having own computer, family income, psychiatric treatment history, family

history of alcohol/drug abuse, history of self-injury behaviour, suicide attempt, depression,

perfectionistic attitude, need for approval, independent attitude, variable attitude, self-esteem,

extraversion, neuroticism, psychoticism, lie were entered in multiple binary logistic regression

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analysis to detect the internet addiction predictors. After the analysis, sex, duration of internet

usage, depression, perfectionistic attitude were found to be the predictors for internet

addiction (Table 3).

4. Discussion

This study aimed to investigate predictive effects of dysfunctional attitudes, self-

esteem, personality, and depression on internet addiction. The results indicated that 7.2% of

the students have internet addiction. There are some internet addiction prevalence studies in

our country that IAS was used. In these studies, internet addiction rate was found as 11.6% in

high school students [37]. While a study reported the rate as 12.2% [38], Dalbudak et al.s’

study presented the rate as 7.2% in university students [39]. Our prevalence rate is similar

with the findings of Dalbudak et al.s’ and lower than the rate in other studies. It is possible

that these two study samples may include conservative students (e. g. low rate of illicit drug

use), for this reason, lower rates may have been found [39]. In the current study, males have

2.5 fold higher internet addiction than females. In general, in these studies it is found that

males have 3 or 4 fold higher internet addiction than females [40-42].

In the current study, addicted group’s depression scores were significantly higher than

non-addicted group. Studies on comorbidity of internet addiction and depression did not have

a pattern for demonstrating a causal relationship between these disorders until Dong et al.s’

study. This follow up study could not find a solid pathological predictor for internet addiction

disorder. The authors of this have suggested that internet addiction disorder may bring some

pathological problems to the addicts [43]. Loneliness and social isolation that is caused by

excessive use of internet may trigger comorbid depression in people with internet addiction.

Perfectionism is a cognitive schema that reflects high personal standarts, according to

which people evaluate their own performance. Perfectionist people have some cognitive

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features that trigger, develop, and maintain stress; for this reason, they tend to have

psychiatric disorders [44]. People with maladaptive perfectionism may have alcohol problems

to cope stressful life events [45]. It has been also suggested that the disparity between an

individual’s actual self and ideal self causes maladaptive perfectionism. Creating online

perfect persona can cause individuals’ developing internet addiction [46]. In the current study,

the perfectionistic attitude scores of the addicted group were significantly higher than the non

addicted group.

The need for approval refers to a person’s attempts to gain approval by pretending as if

he/she possessed socially desirable characteristics [47]. It has been suggested that people who

have high needs for social approvals are more likely to be alcohol or drug addicts than the

people with low needs for social approvals [48,49]. The tendency has been explained by

observational learning [48]. The relationship between low self esteem that may be related

with the need for approval and internet addiction have been known well [50,51]. Internet has

suitable environment for people with low self esteem, low motivation, fear of rejection, and

need for approval. They can explain themselves by taking less risks than ‘real life’. Internet is

a kind of safety tool for them. In the current study, the addicted group has significantly more

perfectionistic attitude and less self esteem than non-addicted group. To the knowledge of the

researchers, this is the first study that showed dysfunctional attitudes in internet addiction.

According to Eysenc and Eysenc theory (1975), extraversion represents being social,

assertiveness, and enjoying risk taking. People with extravert trait like to socialize with others

and prefer to spend their time with their friends [52]. People high on extraversion are

comfortable with face to face communication. They do not need to make alternative

relationships using internet. They are satisfied with ‘real’ life activities [53]. In the current

study, there is no difference between the two groups in terms of the extraversion domain as

expected [15,54].

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Neuroticism represents emotional instability or excessive reactivity. People who have

neurotic traits can be anxious, depressive, nervous, shy, and have low self-confidence [52].

Considering neuroticism domain, people high on neuroticism have difficulties in making

healthy relationships. Extraverted people can be content with face to face interactions, and

they can also meet new people on the internet [53]. Thanks to internet, people high on

neuroticism can relieve their loneliness that is caused by anxiety, shyness, and insecurity [55].

Internet can be a suitable social tool and can work for emotional regulation for people high on

neuroticism. In the current study, the neuroticism scores of the addicted group were

significantly higher than the scores of the non-addicted group. This result is consistent with

the previous studies [15,54].

Psychoticism represents unusual personality traits like aggressiveness, making cold

relationship with others, weirdness, being non-emphatic, insensitiveness [52]. People high on

psychoticism use internet deviantly rather than social-communal use [56]. In the current

study, the psychoticism scores of the addicted group were significantly higher than the scores

of the non-addicted group. This result is also consistent with the previous studies [15,54].

Although lie subscale measures validity of the other subscales in EPQR-A, it has been

suggested that there is a negative relationship between lie personality trait and social maturity.

Hence, the students who are less mature may be more inclined to becoming internet addicts.

In the current study, the lie scores of the addicted group were significantly higher than the

scores of the non-addicted group. This result is consistent with the previous studies, too

[15,54].

In the present study, regression model was used to determine the predictors of internet

addiction. According to the model, being male, duration of internet usage, depression,

perfectionistic attitude have been found as the predictors for internet addiction. It has been

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acknowledged in the literature that being male, duration of internet usage, depression are

important predictors for internet addiction. We also found that perfectionistic attitude is

another predictor for internet addiction, even when depression, sex, duration of internet were

controlled. Although the evidence of cognitive behavioral therapy is not strong, It is the most

promising treatment approach for internet addiction [57,58]. It can be important to evaluate

the perfectionistic attitude in people with internet addiction. The perfectionistic attitude

should be targeted for effective treatment of people with internet addiction in cognitive

behavioral therapy.

The current study has some limitations. The use of self-rating measurement to detect

internet addicition can be considered as a limitation of the study. As the study was cross-

sectional, we could not establish a causal relationship. The current study represents

preparatory school students’ cognitive styles and personalities, for this reason, the findings

should be treated with caution because the results cannot be generalized to different age

groups. This study can be replicated in another setting or with participants from different age

groups and backgrounds to reach at more generalizable findings.

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Behav 2008;24:346-360.

[56]. Amiel T, Sargent SL. Individual differences in Internet usage motives. Comput Human

Behav 2004;20:711-726.

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[57]. Young K. Cognitive behavior therapy with Internet addicts: Treatment outcomes and

implications. Cyberpsychol Behav Soc Netw 2007;10:671-679.

[58]. Du Y, Jiang W, Vance A. Longer term effect of randomized, controlled group cognitive

behavioral therapy for Internet addiction in adolescent students in Shanghai. Aust N Z J

Psychiatry 2010;44:129-134.

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Table 1 Sociodemographic and clinical characteristics of study sample (n=720)

Age, med (min - max) 19 (17 - 35)

Duration of internet usage

(hours /day), med (min - max)

2.5 (0 - 16)

Sex, n (%)

Female 362 (50.3)

Male 358 (49.7)

Living conditions, n (%)

Alone 27 (3.8)

With someone (family,

friend)

695 (96.3)

Divorced parents, n (%)

Yes 39 (5.4)

No 681 (94.6)

Siblings, n (%)

Yes 677 (94.0)

No 43 (6.0)

Having own room, n (%)

Yes 520 (72.2)

No 200 (27.8)

Having own computer, n (%)

Yes 452 (62.8)

No 268 (37.2)

Mostly used internet activity, n

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(%)

Internet surfing 461 (64)

E-mailing 70 (9.7)

Video sharing sites 72 (10)

Chat rooms 56 (7.8)

Online gaming 28 (3.9)

Academic activities 33 (4.6)

Family income, n (%)

0-499 47 (6.5)

500-999 171 (23.8)

1000-1499 178 (24.7)

1500-1999 142 (19.7)

2000 ve üstü 182 (25.3)

Alcohol/drug use, n (%)

No 621 (86.3)

Alcohol 91 (12.6)

Drug 4 (0.6)

Alcohol + drug 4 (0.6)

Psychiatric treatment history, n

(%)

Yes 48 (6.7)

No 672 (93.3)

Family history of alcohol/drug

abuse, n (%)

Yes 20 (2.8)

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No 700 (97.2)

History of self-injury

behaviour, n (%)

Yes 25 (3.5)

No 695 (96.5)

Suicide attempt

Yes 6 (0.8)

No 714 (99.2)

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Table 2 Comparison of measurement scores between the nonaddicted and addicted groups

Non-addicted

group (n = 668)

Addicted group

(n = 52)

Z p

BDI 7 (0 - 46) 15 (0 - 39) -4.941 <0.001

DAS-A

Perfectionistic

attitude

44 (17 - 93) 60 (26 - 101) -5.831 <0.001

Need for approval 41 (11 - 74) 48 (20 - 71) -4.442 <0.001

Independent

attitude

20 (6 - 83) 23 (6 - 35) -1.121 0.262

Variable attitude 19 (5 - 31) 18 (9 - 42) -0.885 0.376

RSES 0.8 (0 - 5) 1.25 (0 - 4) -2.942 0.003

EPQR-A

Extroaversion 4 (0 - 6) 3 (0 - 6) -1.809 0.070

Neuroticism 3 (0 - 6) 4 (0 - 6) -2.727 0.006

Psychoticism 1 (0 - 6) 2 (0 - 5) -4.049 <0.001

Lie 4 (0 - 6) 3.5 (0 - 6) -2.880 0.004

Mann-Whitney U test, p < 0.05, p < 0.001 level of significance

BDI = Beck Depression Inventory, DAS-A = Dysfunctional Attitudes Scale form, RSES =

Rosenberg Self-Esteem Scale, EPQR-A = Eysenck Personality Questionnaire

Revised/Abbreviated Form.

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Table 3 Predictors for internet addiction by multiple binary logistic regression analysis

Predictors Odds ratio %95 CI p

Being male 3.202 1.518 – 6.753 0.002

Duration of internet

usage

1.229 1.115 – 1.355 <0.001

Depression 1.071 1.036 – 1.107 <0.001

Perfectionistic

attitude

1.045 1.022 – 1.069 <0.001

Constant 0.001 <0.001