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Advances in Social Sciences Research Journal – Vol. 8, No. 8
Publication Date: August 25, 2021
DOI:10.14738/assrj.88.10698. Chen, E. S. L. (2021). The Relation of Personality Traits to Internet Addiction and Internet Social Behaviour in Chinese Secondary
School Students. Advances in Social Sciences Research Journal, 8(8). 550-563.
Services for Science and Education – United Kingdom
The Relation of Personality Traits to Internet Addiction and
Internet Social Behaviour in Chinese Secondary School Students
Eve Siu Ling Chen
School of Education and Languages
The Open University of Hong Kong
ORCID: 0000-0002-3747-1987
ABSTRACT
The present study attempts to study the relationship between internet addiction
and internet social behaviour with personality. The sample consisted of 365 Grade
10 students from two secondary schools in Mainland China. Four instruments were
used: Young’s Internet Addiction Test, Chen Internet Addiction Scale, Ma, Li & Pow’s
Adolescent Internet Use Questionnaire, and Eysenck Personality Questionnaire.
Boys scored significantly higher than girls in the two Internet addiction
questionnaires. Results also supported the following hypotheses: (1) Internet
addiction is positively associated with psychoticism, neuroticism, and frequency of
Internet use; and it is also negatively associated with social desirability and self- concept. (2) Holistic Internet social behavior is positively associated with
extroversion, social desirability, self-concept, and frequency of Internet use; and it
is also negatively associated with psychoticism and neuroticism. A high (low) rating
of Holistic Internet Social Behavior refers to a high (low) rating of Prosocial Internet
Behaviour and a low (high) rating of Antisocial Internet Behaviour.
INTRODUCTION
Characteristics and Prevalence of Internet Addiction
Various research studies in the past decade concluded internet addiction as a quickly growing
and highly alarming social issue. Substantial attention had been attracted to a variety of
problematic online applications which include a sexual online chat room, general online chat
room, online pornography, online gambling, online gaming, online auction houses, and online
shopping (e.g., Young, 2007). Thereafter, attention has also been drawn to the issue of internet
movie/television streaming addiction (Allen, 2014). Even though excessive gaming, sexual
preoccupations, and text-messaging have long been regarded as subtypes of internet addiction
among many researchers (Block, 2008) and neurophysiological psychiatric research on people
such as those with excessive and problematic viewing of online sexual images using the
technology of fMRI further suggested the existence of internet porn addiction as a specific type
of internet addiction (Voon et al., 2014), the American Psychiatric Association (APA) based on
the empirical evidence from Asian societies which centers on male youths and adolescents,
singled out internet gaming as a mental disorder in Section III of the fifth edition of the
Diagnostic and Statistical Manual of Mental Disorders (i.e., DSM-V) in 2013 and named it as
“Internet Gaming Disorder” (American Psychiatric Association, 2013). So far, “Internet Gaming
disorder” was not yet an official psychiatric diagnosis for the reason that internet gaming
addiction and the much broader concept and prevalent occurrence of internet addiction “has
not been around long enough or sufficiently studied to warrant official disorder status”
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Chen, E. S. L. (2021). The Relation of Personality Traits to Internet Addiction and Internet Social Behaviour in Chinese Secondary School Students.
Advances in Social Sciences Research Journal, 8(8). 550-563.
URL: http://dx.doi.org/10.14738/assrj.88.10698
(Techaddiction, 2021). The naming of this new phenomenon has long been controversial (e.g.,
Kuss, Griffiths, & Pontes, 2017) and has not been resolved so far. Researchers used to adopt a
variety of alternative names to refer to the newly emerged mental health problem, for example,
“pathological computer use”, “virtual addiction”, “internet addiction disorder”, “excessive
internet use”, “compulsive internet use”, “problematic internet use”, and “pathological internet
use”. However, terms such as “pathological computer use” could be quite misleading and
outdated. With the ever-evolving technologies in the world, internet users could access the
internet with a great number of different kinds of electronic devices such as their smartphones
and tablets. By the end of 2020, there are 989 million internet users in Mainland China. Among
which, students got the largest share of Internet users in China, accounting for 23.7% of the
total number of netizens in the Mainland of China (China Daily 2021; China Internet Network
Information Centre, 2020). Moreover, 97.6% of secondary school students in Mainland China
(total number around 25 million) have been internet users, and they attributed to 60.9% of
internet users who are students according to a national survey (China Internet Network
Information Centre, 2019). Needless to say, people could go online to download different data
and files from the internet, storing them in various kinds of electronic devices, and making
excessive and problematic use of the downloaded data and files offline. According to Block
(2008), internet addiction is conceptualized as “a compulsive-impulsive spectrum disorder that
involves online and/or offline computer usage” (p.306). Referring to (Starcevic, 2017), “The
addiction model of IGD is constraining and does not contribute to a better understanding of the
various patterns of problematic gaming.” The corresponding diagnostic criteria need a
thorough overhaul, which should be based on a model of problematic gaming that can
accommodate its disparate aspects”. IGD refers to Internet Gaming Disorder. On the other hand,
major characteristic symptoms of Internet addiction involve the features and negative costs
which are very similar to substance abuse or addiction. For instance, the prolonged excessive
compulsive internet use despite choices and efforts to quit, the tolerance, dependence, and
withdrawal with physical discomforts, and the school or career failure, the family conflict with
parents or spouse, and the loss of friendship, etc. (American Psychiatric Association, 2013;
Young, 1998a). Functional magnetic resonance imaging (fMRI) studies repeatedly pointed out
that internet addiction shared the neurobiological abnormalities and neurobiological
mechanism with some other well-known addiction disorders such as substance use disorder
(Ko, et al., 2009). Moreover, brain scans with fMRI shown that psychiatric medication for the
treatment of substance abuse could change the brain activity and decrease the craving for
internet gaming in internet game addicts similarly in ways that could be observed among the
drug-craving addicts (Han, Hwang & Renshaw, 2011). Pertinent literature substantiated the
notion that internet addicts jeopardized many aspects of themselves. It has been evidenced that
internet addiction leads to psychological problems, social problems, occupational problems,
and neurological problems of the addicts (Cash, Rae, Steel, & Winkler, 2012), and psychiatrists
worried that internet addiction could turn into the “greatest psychiatric epidemic of all time”
(Allen, 2014). Hence, the operational definition of internet addiction would broadly include
various online and offline excessive compulsive use of the internet via any electronic device in
addition to internet gaming addiction in the study. As psychosocial well-being was negatively
associated with symptoms of Internet gaming disorder across 20 countries (Cheng, Cheung, &
Wang, 2018), and psychopathy such as alexithymia, depression, anxiety is positively associated
with internet gaming disorder in young adults (Bonnaire & Baptista, 2019), the Eysenck
Personality Questionnaire (Eysenck & Eysenck, 1975) was adopted to study the association of
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psychoticism, neuroticism, extroversion and social desirability with internet addiction in this
study.
Current literature indicated that surveys on Internet addiction in the past decade have shown
the percentage of Internet addicts generally varies from 1.5% to 8.2% of the sampled
population in the United States and countries in Europe (Weinstein & Lejoyeux, 2010). In 2007,
it was estimated that approximately two hundred and ten thousand children and adolescents
(2.1%; ages 6–19) are Internet-addicted in South Korea while another 1.2 million South Korean
children and adolescents (12.0%; ages 6–19) are believed to be at risk of addiction (Block,
2008). Concurrently, about 10 million teenaged Internet users in Mainland China (13.7% of the
Chinese adolescents) meet internet addiction diagnostic criteria (Block, 2008). Despite the
Government of China began to further restrict the entry of children and adolescents aged under
18 to internet bars and cyber cafes, and proactively regulate prolonged computer game use
over 3 hours among those aged under 18 via mandating Internet game operators to install anti- addiction software on their game products by law from 2007 (e.g., Wang, 2007), the epidemic
was not stopped. In 2009, the number of Internet-addicted children and adolescents in
Mainland China slightly increased to 14.1% and the percentages of Internet Addiction ranged
from about 8% in the more developed cities like Beijing and Shanghai to up to around 30% in
the less developed cities such as Guiyang and Nanchang (China Youth Association for Network
Development, 2010). Since 2012, the Central Government has made the indigenous research,
prevention, and intervention of internet addiction in Chinese children and adolescents a
national strategy and policy in response to the deteriorating situation among the youths in
Mainland China (Xinhua, 2013). Nevertheless, the 26.5% prevalence of internet addiction with
severe addiction being 0.96% among Mainland Chinese students between 10 to 18 (Xin, et.al.,
2017) and the recent political-economic incidence of a state-run media in Mainland China
openly condemned internet gaming as the “spiritual opium” that is greatly jeopardizing
children’s education (Shen, Qu & Hu, 2021) have similarly revealed that the addiction problem
among Chinese students is cautiously worsening. The research on characteristics and
psychopathology of the problematic Internet users in Chinese adolescents is still not thorough
enough, in-depth, and multi-faceted investigations to heighten the alertness to and enable the
feasibility of prevention of internet addiction in the general public of Mainland China remains
highly important and desirable.
An updated review of the existing measuring scales of Internet addiction and their
psychometric properties (Laconi, Rodgers, & Chabrol, 2014) concluded that 45 research
instruments for internet addiction in 23 different languages were identified in the literature
while 17 out of the 45 scales had more than one study evaluating their psychometric properties
and that only ten had three or more evaluating studies on their psychometric properties. The
big ten well-established measuring scales were listed as follows: (1) the 20-item Internet
Addiction Test (IAT) (Young, 1998b) with a 5-point Likert self-reporting scale; (2) the 20-item
Internet Addiction Test (IAT) (Young & Nabuco de Abreu, 2011) with a 6-point Likert self- reporting scale; (3) the 14-item Compulsive Internet Use Scale (CIUS) (Meerkerk et al.,2009)
with a 5-point Likert self-reporting scale; (4) the 8-item Internet Addiction Diagnostic
Questionnaire (IADQ) (Young, 1996) with Yes/No response; (5) the 26-item Chen Internet
Addiction Scale (CIAS) (Chen, Weng, Su, Wu, & Yang, 2003) with a 4-point Likert self-reporting
scale; (6) the 36-item Online Cognition Scale (OCS) (Davis, Flett, & Besser, 2002) with a 7-point
Likert self-reporting scale; (7) the 15-item Generalized Problematic Internet Use Scale 2
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Chen, E. S. L. (2021). The Relation of Personality Traits to Internet Addiction and Internet Social Behaviour in Chinese Secondary School Students.
Advances in Social Sciences Research Journal, 8(8). 550-563.
URL: http://dx.doi.org/10.14738/assrj.88.10698
(GPIUS-2) (Caplan, 2010) with a 8-point Likert self-reporting scales; (8 ) the 29-items
Generalized Problematic Internet Use Scale (GPIUS) (Caplan, 2002) with an 8-point Likert self- reporting scale; (9) the 18-item Problematic Internet Use Questionnaire (PIUQ) (Demetrovics
et al., 2008) with a 5-point Likert self-reporting scale; (10) the 20-item Internet Related
Problem Scale (IRPS) (Armstrong, Phillips, &Saling, 2000) with a 10-point Likert self-reporting
scale (see Laconi, Rodgers, & Chabrol, 2014, pp.195-197).
Young's Internet Addiction Test (IAT) (Young, 1998b) is the first and the most well-known
instrument for the measuring of internet addiction though researchers in the field also
acknowledge the observations and input of Ivan Goldberg (e.g., Laconi, Rodgers, & Chabrol,
2014; Shek, Tang, & Lo, 2009). Many measuring instruments in different languages were
developed in different countries based on the IAT (Laconi, Rodgers, & Chabrol, 2014). The
contribution of Dr. Kimberly Young in the research areas of internet addiction is unprecedented
and undeniable. The characteristics and symptoms the IAT looks for are not narrowly restricted
to internet gaming addiction only. Nonetheless, this single test might not be able to portray the
full picture of internet-related addiction. For instance, Dr. Allen Frances, the chairman of DSM- IV maintained that more research on internet addiction should further investigate what
separates internet addiction from a bad habit of internet use (Tinker, 2013). Therefore, the
instruments such as the Adolescent Internet Use Questionnaire (Ma, Li & Pow, 2011) could
supplement the desirable data in this aspect because it was developed based on the Adolescent
Behavior Questionnaire (ABQ) (Ma, 1988), a well-established self-reporting questionnaire that
measures the frequency of a variety of real-life pro-social behaviors, neutral behaviors, and
anti-social behaviors in adolescents. Without timely revision, the sensitivity of IAT to internet- related addiction could diminish after nearly two decades. For example, the IAT asks the
subjects “how often do you check your email before something else that you need to do?” In our
world today, most people may not even be adequately informed about the things they need to
do if they do not check emails before they start to work each morning in the day. According to
a clinical study on internet addiction (Kim, Park, Ryu, Yu, & Ha, 2012), the IAT was queried as
having limited clinical utility because only around 40% of the clinically diagnosed internet
addicts who participated in the study were successfully detected and identified by the IAT. The
study suggested that no significant association between IAT scores and the stages and forms of
their internet addiction (i.e., course of the disease) was evident, and an insignificant association
was only found between the IAT scores of the internet addicts and their differential severity of
the addiction. It was suggested that some other popular research instruments for internet
addiction such as Ko’s Internet Addiction Scale (Ko, Yen, Chen, Chen, & Yen, 2005) and the Chen
Internet Addiction Scale (CIAS) (Chen, Weng, Su, Wu, & Yang, 2003) could serve the purpose of
reliable identification when the study is targeted for internet-related addiction but not limited
to internet gaming addiction. We, therefore, chose Young’s IAT in this study because of its
popularity and impact in the study of internet addiction. In addition, we also chose CIAS because
of its relevance to the Chinese situation. In addition, the Adolescent Internet Use Questionnaire
(AIUQ) (Ma, Li & Pow, 2011) was adopted in this study to measure the self-perception and
social behaviours in cyberspace, and negative outcomes in the daily life of adolescents that are
associated with generalized problematic internet use.
The major aim of the present study is to investigate the relationship between personality traits
such as psychoticism, neuroticism, extroversion, social desirability, and self-concept with
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Internet addiction and Internet social behavior (prosocial and antisocial behavior). Based on
the above literature review, we set up the following hypotheses.
Hypothesis1:
a) Internet addiction is positively associated with psychoticism.
b) Internet addiction is positively associated with neuroticism.
c) Internet addiction is negatively associated with social desirability.
d) Internet addiction is negatively associated with self-concept
e) Internet addiction is positively associated with Internet use.
Hypothesis 2:
a) Holistic Internet social behavior is positively associated with extroversion.
b) Holistic Internet social behavior is negatively associated with psychoticism.
c) Holistic Internet social behavior is negatively associated with neuroticism.
d) Holistic Internet social behavior is positively associated with social desirability.
e) Holistic Internet social behavior is positively associated with self-concept.
f) Holistic Internet social behavior is positively associated with Internet use.
Holistic Internet Behavior (HIU) is measured by (NIU – DIU). A high score of HIU means a high
score of prosocial Internet behavior and a low score of delinquent Internet behavior, whereas
a low score of HIU means a low score of prosocial Internet behavior and a high score of
delinquent Internet behavior. We did not set up any hypothesis on the relation between
Internet addiction and extroversion because there is not clear-cut literature on this topic. We
have left it to be addressed by a research question instead.
METHODS
Participants
Three hundred and eighty-two Grade 10 students (195 boys and 187 girls) from two secondary
schools in Jiangsu Province of Mainland China participated in this quantitative study. The mean
of age is 16.44 and 4 research instruments which included Young’s Internet Addiction Test
(YIAT), Chen Internet Addiction Scale (CIAS), Ma, Li & Pow’s Adolescent Internet Use
Questionnaire (AIUQ), and the Revised Short Version of Eysenck Personality Questionnaire for
Chinese (EPQ-SRC) were used to measure aspects of the characteristics and psychopathology
of the problematic internet users among the participating Chinese adolescents.
Instruments
Young’s Internet Addiction Test (YIAT)
The IAT was used to measure the degree of internet addiction among the participating Chinese
adolescents. It is a 20-item Internet Addiction Test (YIAT) (Young, 1998b) with a 5-point Likert
self-reporting scale (ranging from 0 = not applicable to 5 = always).For example, the IAT asks
the subjects “how often do others in your life complain to you about the amount of time you
spend online?” and “how often do you become defensive or secretive when anyone asks you
what you do online?”An extensive review of the existing measuring scales of Internet addiction
and their psychometric properties (Laconi, Rodgers, & Chabrol, 2014) ranked the IAT as the
first of the big ten well-established measuring scales in the research area. The use of IAT should
enable a convenient cross-cultural comparison of the results of this study.
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Chen, E. S. L. (2021). The Relation of Personality Traits to Internet Addiction and Internet Social Behaviour in Chinese Secondary School Students.
Advances in Social Sciences Research Journal, 8(8). 550-563.
URL: http://dx.doi.org/10.14738/assrj.88.10698
Chen Internet Addiction Scale (CIAS)
The Chen Internet Addiction Scale (CIAS) (Chen, Weng, Su, Wu, & Yang, 2003) consists of five
subscales (compulsive use, withdrawal, tolerance symptoms of Internet addiction,
interpersonal and health-related problems, and time management problems) was also
employed to measure the degree of internet addiction in terms of the core symptoms and
related problems of Internet addiction among the participating Chinese adolescents. It is a 26-
item with a 4-point Likert self-reporting scale (ranging from 1 = does not match my experience
at all to 4 = definitely match my experience). For example, the CIAS asks the subjects “more than
once, I have slept less than four hours due to being online” and “my life would be joyless without
the Internet”. Other than the reasons that it was ranked the fifth of the big ten well-established
measuring scales in the research area (Laconi, Rodgers, & Chabrol, 2014) and it was and was
developed with studies in the Chinese population(Chen et al, 2003), it was also suggested to be
one of the more sensitive instruments which are of higher clinical utility that could satisfactorily
identify the internet addicts and their differential severity of the addiction as well (Kim, Park,
Ryu, Yu, & Ha, 2012).
Ma, Li & Pow’s Adolescent Internet Use Questionnaire (AIUQ)
The Adolescent Internet Use Questionnaire (Ma, Li & Pow, 2011) was developed based on the
Adolescent Behavior Questionnaire (ABQ) (Ma, 1988), and a well-established self-reporting
questionnaire that measures the frequency of a variety of real-life pro-social behaviours,
neutral behaviours, and anti-social behaviours in adolescents. This 43-item AIUQ is a
comprehensive questionnaire that also measures the cognition, behaviors, and negative
outcomes associated with Davis’s (2001) concept of generalized Pathological Internet Use. The
internal consistency reliabilities of the major indices of the AIUQ were in the range of .84 to .88,
and the predictive validities were also good (Ma, Li, & Pow, 2011). In this study, only two scales
of the AIUQ were used: the Normal or Prosocial Internet Use and the Delinquent Internet Use.
The two scales measured the prosocial and antisocial/delinquent behavior of adolescents in the
virtual internet world and revealed their prosocial and antisocial internet use. Participants
were asked to rate the items on a 5-point Likert scale (ranging from 1 = strongly disagree to 5
= strongly agree). For example, the Normal or Prosocial Internet Use Scale (NIU) asked “I would
use the net when I’m down” and “I seek others online when I feel isolated”, and the Delinquent
or Antisocial Internet Use Scale (DIU) asked “I have gambled on the Internet” and “I have missed
family and school meetings because of online activities”. Moreover, scores of the Holistic
Internet Use Scale (HIU) were generated using the formula HIU = NIU – DIU. A high (low) rating
of HIU refers to a high (low) rating of NIU and a low (high) rating of DIU, and vice versa. In other
words, a high value of HIU means more prosocial and less antisocial use of the Internet; a low
value of HIU refers to less prosocial and more antisocial use of the Internet. PAT (Total score of
the perceived Internet activities) measures the frequency of various Internet activities such as
surfing, email, blogging, and Internet purchase. PLT (Total score of the perceived self-concept
in daily life) measures one’s self-concept in real life such as “I am a reliable and honest person
in other people’s eyes” and “I like the I in real life”.
Eysenck Personality Questionnaire (EPQ-RSC)
While most subjects took the first three questionnaires, only about one-third of the sample took
the Eysenck Personality Questionnaire (Short Form Revised Chinese Version, EPQ-RSC). The
48-item EPQ-RSC (Qian, Wu, Zhu, & Zhang, 1999) was adapted based on the revised 48-item
short version of the Eysenck Personality Questionnaire (EPQ-R Short Scale) to assess one’s
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personality traits. In the same line with EPQ-R (Eysenck, Eysenck, & Barrett, 1985), there are
four scales in EPQ-RSC: Extraversion vs. Introversion (E), Neuroticism or emotionality (N),
Psychoticism or toughmindedness (P), and Lie (L) or social desirability. The EPQ-RSC measures
the four aspects of personality accordingly. For example, the Extraversion vs. Introversion (E)
scale asks “Are you a talkative person?”, the Neuroticism or emotionality (N) scale asks “Are
you feeling easily hurt”, the Psychoticism or toughmindedness (P) scale asks “Do you try not to
be rude to people?”, and Lie (L) or social desirability asks “Have you ever cheated at a game?”
The Eysenck Personality Questionnaire (Short Form Revised Chinese Version) was
standardized with the participations of 8637 subjects and a big team of psychologists in
Mainland China (Qian, Wu, Zhu, Zhang, 2002) and the internal consistency reliabilities of these
four scales ranged from .61 to .88 (p.321) and was concluded as a valid and reliable
psychological instrument for Chinese.
Procedure
The questionnaires were group-administered to participants during their normal class period.
The experimenter went through the test instruction with the participating secondary school
students before they answered the questions.
RESULTS
The means and standard deviations of the test scores by sex are presented in Table 1. The mean
CIAS, YIAT, NIU, DIU, and PAT scores for boys were significantly higher than those for girls. In
other words, the degree of internet addiction of boys is higher than that of girls. In addition, as
far as prosocial internet behavior, antisocial internet behavior, and the use of the internet are
concerned, boys also scored significantly higher than girls. On the other hand, the mean L score
for girls was significantly higher than that for boys.
Table 1. Mean and SD of test scores
Score Total Sample Boy Girl t
M SD M SD M SD
Chen Internet Addiction Scale (N = 368; 185 boys, 183 girls)
CIAS 2.30 .27 2.34 .30 2.27 .22 2.353*
Young’s Internet Addiction Scale (N = 368; 185 boys, 183 girls)
YIAT 2.05 .65 2.15 .67 1.95 .61 2.923**
Ma, Li & Pow’s Adolescent Internet Use Questionnaire (N = 368; 185 boys, 183 girls)
NIU 3.22 .51 3.28 .56 3.16 .44 2.417*
DIU 1.76 .44 1.83 .46 1.70 .41 2.800**
PLT 3.46 .65 3.44 .72 3.48 .58 -.583
PAT 2.48 .44 2.54 .44 2.42 .43 2.588*
Eysenck Personality Questionnaire (N = 123; 60 boys, 63 girls)
E .64 .22 .60 .25 .66 .19 -1.520
P .20 .15 .23 .16 .18 .15 1.784
N .53 .28 .51 .27 .56 .29 -.995
L .41 .24 .35 .24 .46 .23 -2.584*
Note. The number of cases for PLT is 367, with one missing.
*p < .05. **p < .01.
The number of cases in the correlations of CIAS with YIAT, NIU/DIU, E/P/N/L, and PLT is 368,
381, 127, and 367. The number of cases in the correlations of YIAT with NIU/DIU, E/P/N/L, and
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Chen, E. S. L. (2021). The Relation of Personality Traits to Internet Addiction and Internet Social Behaviour in Chinese Secondary School Students.
Advances in Social Sciences Research Journal, 8(8). 550-563.
URL: http://dx.doi.org/10.14738/assrj.88.10698
PLT is 369, 124, and 368. The number of cases in the correlations of NIU/DIU with E/P/N/L,
and PLT is 123, and 367.
The correlations of the personality (E, P, N, L), PLT, and PAT scores with the Internet Addiction
(CIAS, YIAT) and Internet Social Behavior (NIU, DIU, HIU) scores are given in Table 2. The
correlations of the CIAS and YIAT with N and PAT were significant and positive, and the
correlations of CIAS and YIAT with L and PLT were significant and negative. In addition, the
correlation of YIAT with P is also significant and positive. The correlations of NIU with E, PLT,
and PAT were significant and positive. The correlations of DIU with P, N, and PAT were
significant and positive, and the correlations of DIU with L and PLT were significant and
negative. The correlations of HIU with E, L, PLT, and PAT were significant and positive, and the
correlations of HIU with P and N were significant and negative.
In addition, the correlations of CIAS with YIAT, NIU, DIU and HIU were .52 (p < .001), .09, .39 (p
< .001), -.22 (p < .001), and the correlations of YIAT with NIU, DIU and HIU were .24 (p < .001),
.63 (p < .001), -.22 (p < .001). The correlations of NIU with DIU and HIU were .15 (p < .01) and
.71 (p < .001), and the correlation of DIU with HIU was -.59 (p < .001).
Table 2. Correlations of Internet Addiction and Internet Social Behavior Scores with other Test
Scores
Score E P N L PLT PAT
CIAS -.02 .15 .35*** -.51** -.33*** .15**
YIAT -.09 .32*** .34*** -.63*** -.32*** .29***
NIU .29** .01 -.15 -.05 .30*** .44***
DIU -.10 .29** .22* -.61*** -.30*** .25***
HIU .28** -.23* -.29** .47*** .46*** .19***
*p < .05. **p < .01. ***p < .001.
Six multiple regression analyses were conducted with P, E, N, L, PLT, and PAT as dependent
variables. The following scores were entered as independent variables for each multiple
regression analysis: CIAS, YIAT, NIU, and DIU. The results are presented in Table 3. The findings
indicated that the CIAS, YIAT, NIU, and DIU are good predictors of each of the dependent
variables (P, E, N, L, PLT, and PAT). In particular, the regression of L, PLT, and PAT on the four
independent variables are robust and very significant. The results also showed that internet
addiction and internet social behavior are good predictors of extroversion (E), psychoticism
(P), and neuroticism (N).
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Table 3. Multiple Regression of the Personality Scores on the Internet Addiction and Internet
Social Behavior Scores
Independent
Variable
B SE β t Significance of F,
R, R2, and Adjusted R2
Dependent Variable : P (n = 123)
CIAS -.099 .072 -.164 -1.377 F(4, 118) = 4.730 (p < .001)
R = .372
R2= .138
Adjusted R2= .109
YIAT .076 .028 .347 2.686**
NIU -.020 .036 -.048 -.548
DIU .054 .041 .160 1.307
Dependent Variable : N (n = 123)
CIAS .307 .124 .281 2,473* F(4, 118) = 7.895 (p < .001)
R = .459
R2= .211
Adjusted R2= .184
YIAT .113 .049 .286 2.314*
NIU -.179 .061 -.243 -2.909**
DIU -.070 .071 -.115 -.987
Dependent Variable : E (n = 123)
CIAS .066 .103 .077 .643 F(4, 118) = 3.880 (p < .01)
R = .341
R2= .116
Adjusted R2= .086
YIAT -.051 .040 -.165 -1.262
NIU .185 .051 .321 3.625***
DIU -.034 .059 -.072 -.585
Dependent Variable : L (n = 123)
CIAS -.113 .088 -.121 -1.280 F(4, 118) = 24.769 (p < .001)
R = .676
R2= .456
Adjusted R2= .438
YIAT -.109 .035 -.322 -.3132**
NIU .047 .044 .074 1.065
DIU -.172 .051 -.328 -3.384**
Dependent Variable : PLT (n = 367)
CIAS -.475 .124 -.193 -3.833*** F(4, 362) = 39.455 (p < .001)
R = .551
R2= .304
Adjusted R2= .296
YIAT -.260 .060 -.257 -4.337***
NIU .515 .059 .398 8.765***
DIU -.189 .082 -.127 2.300*
Dependent Variable: PAT (n = 368)
CIAS .040 .087 .024 .463 F(4, 363) = 28.478 (p < .001)
R = .489
R2= .239
Adjusted R2= .230
YIAT .076 .042 .111 1.798
NIU .347 .041 .397 8.360***
DIU .110 .058 .110 1.906
DISCUSSION
Sex Differences in Internet Addiction, Internet Social Behavior, and Personality Traits
Results indicated that boys scored significantly higher than girls in the two internet addiction
(CIAS and YIAT) questionnaires. It was quite a clear cut in this study and was very much agreed
with previous studies in internet gaming addiction in Chinese and other cultures (e.g., Tsitsika
et al., 2014; Xin, et.al., 2017). In addition, boys scored higher than girls in the delinquent
internet behavior score and the use of the internet which is in good agreement with previous
findings by Ma, Li and Pow (2011), Ma, Shek, Cheung & Lee (1996), and Ma (2005). On the other
hand, girls scored higher than boys in social desirability (L) score which is also similar to a
previous study in Hong Kong (Ma, Shek, Cheung, & Lee, 1996). Overall, it seems that boys tend
to be more internet-addicted, more antisocial, and less socially desirable than girls. On the other
hand, boys have reported a slightly and significantly higher frequency of Internet Prosocial
Behavior (NIU) in this study, and that is a bit at odds with the social expectation of Chinese girls
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Chen, E. S. L. (2021). The Relation of Personality Traits to Internet Addiction and Internet Social Behaviour in Chinese Secondary School Students.
Advances in Social Sciences Research Journal, 8(8). 550-563.
URL: http://dx.doi.org/10.14738/assrj.88.10698
as the more obedient and well-behaved non-troublemakers from the early years (e.g., Chen &
Rao, 2011). However, this unexpected finding of gender difference in internet prosocial
behaviours is consistent with the non-significant gender difference in real-life prosocial
behaviours between Chinese boys and girls (e.g., Ma, 2005; Ma, Shek, Cheung & Lee, 1996).
Overall speaking, the Chinese boys tended to outperform Chinese girls in the internet world by
acting more actively and prosocially, but are more at risk of trouble-making by acting more
antisocially in cyberspace at the same time. These findings should be followed up in future
studies.
Correlation Analyses
The correlations of two internet addiction scales with Eysenck's Personality Scores are given in
Table 2. Results indicated that the correlations of YIAT with P and N were .32 and .34 (both
with p < .001); the correlation of CIAS with N was also .35 (p < .001). In addition, the
correlations of CIAS and YIAT with L were -.51 and -.63 (both with p < .001). Internet addiction
is found to be significantly and positively associated with neuroticism and psychoticism, but
negatively associated with the Lie score (social desirability). The present findings indicated that
internet addiction is associated with negative personality (Allen, 2014; Block, 2008; Bonnair &
Baptista, 2019; Cash, Rae, Steel, & Winkler, 2012; Cheng, Cheung, & Wang, 2018; Park et.al.,
2013). The present findings supported the following hypotheses: 1(a) Internet addiction is
positively associated with psychoticism, 1(b) Internet addiction is positively associated with
neuroticism, and 1(c) Internet addiction is negatively associated with social desirability. In
addition, the correlation of CIAS and YIAT with PLT were -.33 and -.32 (both with p < .001), and
with PAT were .15 (p < .01) and .29 (p < .001). The findings supported the hypothesis: 1(d)
Internet addiction is negatively associated with self-concept, and 1(e) Internet addiction is
positively associated with Internet use.
The P and N scores correlate with DIU positively and significantly and with HIU scores
negatively and significantly. That is, delinquent internet behavior is related to psychoticism (P)
and neuroticism (N). Extroversion (E) is positively related to normal/prosocial internet
behavior (NIU) and holistic Internet Behavior (HIU). In addition, Social desirability (Lie score)
is negatively related to DIU and positively with HIU, which is logical. The findings supported the
following hypotheses: 2(a) Holistic Internet social behavior is positively associated with
extroversion, 2(b) Holistic Internet social behavior is negatively associated with psychoticism,
2(c) Holistic Internet social behavior is negatively associated with neuroticism, and 2(d)
Holistic Internet social behavior is positively associated with social desirability.
PAT only correlated with L, but PLT correlated significantly with all the scores, positively with
E and L and negatively with P and N. In addition, HIU correlated positively and significantly
with PLT and PAT; which supported the following two hypotheses: 2(e) Holistic Internet social
behavior is positively associated with self-concept, and 2(f) Holistic Internet social behavior is
positively associated with Internet use.
Finally, the correlations of two internet addiction scales (CIAS and YIAT) with prosocial and
antisocial behavior in cyberspace (NIU and DIU) are also given in the Result section. Results
indicated that the correlations of CIAS and YIAT with DIU were .39 and .63 (both with p < .001).
In other words, Internet addiction measured by CIAS and YIAT was found to be significantly
and positively associated with the delinquent use of the internet (DIU). In addition, Young’s
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internet addiction score (YIAT) also associated significantly and positively with prosocial
internet use (NIU) (r = .244, p < .001), but the strength of association between internet
addiction and delinquent internet use is much stronger than that between internet addiction
and prosocial internet use.
Multiple Regression Analysis
We have conducted six multiple regression analyses with each of the following variables as
dependent variables: P, N, E, L, PLT, and PAT. The following variables were entered as
independent variables: CIAS, YIAT, NIU, and DIU. The percentage of variances of P, N, E, L, PLT,
and PAT explained by the four independent variables (CIAS, YIAT, NIU, DIU) were13.8%, 21.1%,
11.6%, 45.6%, 30.4%, and 23.9%. The overall result of each of the regression analyses indicated
that the regressions of each of the dependent variables on the four independent variables fitted
nicely because all F-ratios are significant at p < .001 except with E where p < .01. In summary,
our findings indicated that the Internet addiction scores (CIAS and YIAT) and Internet social
behavior scores (NIU and DIU) predicted quite well the personality trait scores (P, N, E, L, and
PLT) and Internet use score (PAT). This finding lays a sound foundation for future studies on
the relation of Internet Behavior and personality trait.
CONCLUSION
The present findings indicated that there are sex differences in internet addiction in favor of
boys. Boys also scored higher in the Prosocial and Antisocial Internet Behaviour than girls. The
findings supported the argument that boys tend to spend more time in the use of the internet
and usually in a more negative way in comparison with girls (Ma, Li, & Pow, 2011). As suggested
by the literature that the gender-related cultural values significantly affect the psychopathology
of internet addiction (Cheng, Cheung, & Wang, 2018), future studies should be conducted to
further investigate how gender-role identity affects internet addiction, anti-social internet
behaviour, and prosocial internet behaviour in different cultures.
The present results supported the following hypotheses: 1(a) Internet addiction is positively
associated with psychoticism, (b) Internet addiction is positively associated with neuroticism,
(c) Internet addiction is negatively associated with social desirability, (d) Internet addiction is
negatively associated with self-concept, and (e) Internet addiction is positively associated with
Internet use; and 2(a) Holistic Internet social behavior is positively associated with
extroversion, (b) Holistic Internet social behavior is negatively associated with psychoticism,
(c) Holistic Internet social behavior is negatively associated with neuroticism, (d) Holistic
Internet social behavior is positively associated with social desirability, (e) Holistic Internet
social behavior is positively associated with self-concept, (f) Holistic Internet social behavior is
positively associated with Internet use.
Future studies should include the following: (1) More personality traits such as loneliness,
depression, and gender role identity should be investigated, (2) longitudinal studies should be
conducted to find out the possible causes of Internet addiction in children and adolescents, (3)
cross-cultural studies should be carried out to see whether culture is a significant factor in
explaining the development of Internet addiction, (4) enhancement of self-concepts as a
prevention and intervention strategy for internet addiction in adolescents.
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Advances in Social Sciences Research Journal, 8(8). 550-563.
URL: http://dx.doi.org/10.14738/assrj.88.10698
References
Allen, N. (2014). Help at last for the addicts enslaved by the internet. The Telegraph, 29 December 2014.
Armstrong, L., Phillips, J. G., & Saling, L. L. (2000). Potential determinants of heavier internet usage. International
Journal of Human Computer Studies, 53, 537–550.
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders: DSM-5.
Arlington, VA: American Psychiatric Association.
Bonnaire, C., & Baptista, D. (2019). Internet gaming disorder in male and female young adults: The role of
alexithymia, depression, anxiety and gaming type. Psychiatry Research, 272, 521–530. doi:
10.1016/j.psychres.2018.12.158
Block, J. (2008). Issues for DSM-V: Internet Addiction. The American Journal of Psychiatry, 165(3), pp.306-307.
Caplan, S. E. (2002). Problematic Internet use and psychosocial well-being: Development of a theory-based
cognitive-behavioral measurement instrument. Computers in Human Behavior, 18(5), 553–
575. https://doi.org/10.1016/S0747-5632(02)00004-3
Caplan, S. E. (2010). Theory and measurement of generalized problematic Internet use: A two-step
approach. Computers in Human Behavior, 26(5), 1089–1097. doi: 10.1016/j.chb.2010.03.012
Cash, H., Rae, C. D., Steel, A. H., & Winkler, A. (2012). Internet Addiction: A Brief Summary of Research and
Practice. Current Psychiatry Reviews, 8, 292-298.
Chen, E. S. L., & Rao, N. (2011). Gender socialization in Chinese kindergartens: Teachers’ contributions. Sex Roles,
64, 103-116. Retrieved https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3016075/
Chen, S. H., Weng, L.C., Su, Y.J., Wu, H. M., Yang, P. F. (2003). Development of Chinese Internet Addiction Scale and
its psychometric study. Chinese Journal of Psychology, 45(3), pp.279–294.
Cheng, C., Cheung, M. W.-L., & Wang, H.-Y. (2018). Multinational comparison of internet gaming disorder and
psychosocial problems versus well-being: Meta-analysis of 20 countries. Computers in Human Behavior, 88, 153–
167. doi: 10.1016/j.chb.2018.06.033
China Daily (2021). Students 21% of 989m internet users in China. Retrieved from
https://global.chinadaily.com.cn/a/202102/03/WS601a60baa31024ad0baa70c3.html
China Internet Network Information Centre (2020). The 46th Statistical Report on Internet Development in
China. Retrieved from
https://www.cnnic.com.cn/IDR/ReportDownloads/202012/P020201201530023411644.pdf
China Internet Network Information Centre (2019). 2019 National Report on Internet Use among minors in
China (In Chinese). Retrieved from http://www.cac.gov.cn/cms/pub/interact/downloadfile.jsp?
China Youth Association for Network Development (2010). 2009 Report on the Survey of internet addiction in
adolescents (In Chinese). Retrieved from http://mat1.gtimg.com/edu/pdf/wangyinbaogao.pdf
Davis, R. A. (2001). Cognitive-behavioral model of pathological Internet use. Computers in Human Behavior, 17(2),
187–195.
Davis, R. A., Flett, G. L., & Besser, A. (2002). Validation of a New Scale for Measuring Problematic Internet Use:
Implications for Pre-employment Screening. CyberPsychology & Behavior, 5(4), 331–345. doi:
10.1089/109493102760275581
Demetrovics, Z., Szeredi, B., & Rózsa, S. (2008). The three-factor model of Internet addiction: The development of
the Problematic Internet Use Questionnaire. Behavior Research Methods, 40(2), 563–574. doi:
10.3758/brm.40.2.563
Eysenck, H. J., & Eysenck, S. B. G. (1975). Manual of the Eysenck Personality Questionnaire (Junior & Adult). Kent,
UK: Hodder & Stoughton.
Eysenck, S. B. G., Eysenck, H. J., & Barrett, P. (1985). A revised version of the Psychoticism scale. Personality and
Individual Differences, 6(1), 21-29.
Page 13 of 14
562
Advances in Social Sciences Research Journal (ASSRJ) Vol. 8, Issue 8, August-2021
Services for Science and Education – United Kingdom
Han, D. H., Hwang, J. W., & Renshaw, P. F. (2011). Bupropion sustained release treatment decreases craving for
video games and cue-induced brain activity in patients with internet video game addiction. Psychology of Popular
Media Culture, 1(S), 108–117.
Kim S. J., Park, D. H., Ryu S. H., Yu, J., & Ha, J. H. (2012).Usefulness of Young's Internet Addiction Test for clinical
populations.Nordic Journal of Psychiatry, 67(6), pp.393-399.
Ko, C. H., Yen, J. Y., Chen, S. H., Chen, C. F., & Yen, C. F. (2005). Proposed diagnostic criteria of internet addiction
for adolescents. Journal of Nervous and Mental Disease, 193(11), 728-733.
Ko, C. H., Liu, G. C., Hsiao, S., Yang, M. J., Lin, W. C., & Yen, C. F. (2009). Brain activities associated with gaming urge
of online gaming addiction. Journal of Psychiatric Research, 43(7), pp.739-747.
Kuss, D. J., Griffiths, M. D., & Pontes, H. M. (2017). Chaos and confusion in DSM-5 diagnosis of Internet Gaming
Disorder: Issues, concerns, and recommendations for clarity in the field. Journal of Behavioral Addictions, 6(2),
103–109. doi: 10.1556/2006.5.2016.062
Laconi, S., Rodgers, R. F., & Chabrol, H. (2014). The measurement of Internet addiction: A critical review of
existing scales and their psychometric properties. Computers in Human Behavior, 41, pp.190–202.
Ma, H. K. (1988). Adolescent Behavior Questionnaire: An Introduction. Hong Kong: The Chinese University of Hong
Kong.
Ma, H. K. (2005). The Relation of Gender-Role Classifications to the Prosocial and Antisocial Behavior of Chinese
Adolescents. Journal of Genetic Psychology, 166, 189-201.
Ma, H. K., Shek, D. T. L., Cheung, P. C., & Lee, R. Y. P. (1996). The Relation of Prosocial and Antisocial Behavior to
Personality and Peer Relationships of Hong Kong Chinese Adolescents, Journal of Genetic Psychology, 157, 255-
266.
Ma, H. K., Li, S. C., & Pow, J. W. C. (2011). The Relation of Internet Use to Prosocial and Antisocial Behavior in
Chinese Adolescents. Cyberpsychology, Behavior, and Social Networking, 14(3), pp.123-130.
Meerkerk, G.-J., Van Den Eijnden, R. J. J. M., Vermulst, A. A., & Garretsen, H. F. L. (2009). The Compulsive Internet
Use Scale (CIUS): Some Psychometric Properties. CyberPsychology & Behavior, 12(1), 1–6. doi:
10.1089/cpb.2008.0181
Park, S. M., Park, Y. A., Lee, H. W., Jung, H. Y., Lee, J-Y, & Choi, J-S. (2013). The effects of behavioral
inhibition/approach system as predictors of Internet addiction in adolescents. Personality and Individual
Differences, 54, 7-11.
People’s Daily online (2007). China's revised minor protection law to prevent internet addiction: newspaper.
Retrieved from http://en.people.cn/n3/2021/0601/c90000-9856415.html
Qian, M. Y., Wu, G. C., Zhu, R. C., & Zhang, S. (2002). Development of the revised Eysenck Personality
Questionnaire Short Scale for Chinese (EPQ-RSC). Acta Psychologica Sinica, 32 (3), 317-323. (in Chinese).
Qian, M. Y., Wu, G. C., Zhu, R. C., & Zhang, S. (1999). User Manual of the revised Eysenck Personality Questionnaire
Short Scale for Chinese (EPQ-RSC). Beijing: Department of Psychology, Peking University. (in Chinese).
Shek, D. T. L., Tang V. M. Y., & Lo, C.Y. (2009). Evaluation of an Internet addiction treatment program for Chinese
adolescents in Hong Kong. Adolescence, 44(174), 359-373.
Shen, X., Qu, T., & Hu, M. (2021). China’s Big Tech crackdown: Will Beijing’s efforts kill the country’s most vibrant
economic sector? (2021, July 31). South China Morning Post.
Starcevic, V. (2017). Internet gaming disorder: Inadequate diagnostic criteria wrapped in a constraining
conceptual model. Journal of Behavioral Addictions, 6(2), 110–113. doi: 10.1556/2006.6.2017.012
Techaddiction (2021). Internet Addiction Treatment: Instant Help & Other Effective Options. Retrieved from
http://www.techaddiction.ca/internet-addiction-treatment.html
Tinker, B. (2013). Four Beds Ready to Treat Internet Addicts. CNN, 7 September 2013.
Page 14 of 14
563
Chen, E. S. L. (2021). The Relation of Personality Traits to Internet Addiction and Internet Social Behaviour in Chinese Secondary School Students.
Advances in Social Sciences Research Journal, 8(8). 550-563.
URL: http://dx.doi.org/10.14738/assrj.88.10698
Tsitsika, A., Janikian, M., Schoenmakers, T. M., Tzavela, E. C., Ólafsson K., Wójcik, S., Macarie G. F., Tzavara, C., The
EU NET ADB Consortium, & Richardson, C. (2014). Internet Addictive Behavior in Adolescence: A Cross- Sectional Study in Seven European Countries. Cyberpsychology, Behavior, and Social Networking, 17(8), 528-535.
Voon V, Mole TB, Banca P, Porter L, Morris L, et al. (2014) Neural Correlates of Sexual Cue Reactivity in
Individuals with and without Compulsive Sexual Behaviours. PLOS ONE 9(7): e102419.
Wang, X. (2007). The more they play, the more they lose. Retrieved 2020, from
http://www.chinadaily.com.cn/china/2007-04/10/content_846715.htm
Weinstein, A. & Lejoyeux, M. (2010) Internet addiction or excessive Internet use. The American Journal of Drug
and Alcohol Abuse, 36(5), 277-283.
Xin, M., Xing, J., Pengfei, W., Houru, L., Mengcheng, W., & Hong, Z. (2017). Online activities, prevalence of Internet
addiction and risk factors related to family and school among adolescents in China. Addictive behaviors reports, 7,
14–18. https://doi.org/10.1016/j.abrep.2017.10.003
Xinhua (2013). Our country will research on systematic prediction of internet addiction: Preventing internet
gaming addiction in adolescents. (In Chinese). The Central People’s Government of the People’s Republic of China.
Retrieved from http://www.gov.cn/jrzg/2013-02/17/content_2333445.htm
Young, K. S. (1996). Internet addiction: The emergence of a new clinical disorder. Paper presented at the 104th
annual meeting of the American Psychological Association, August 18, 1996. Toronto, Canada.
Young, K. (1998a). Internet addiction: the emergence of a new clinical disorder. Cyberpsychology and Behavior, 1,
pp.237–244.
Young, K. (1998b). Caught in the Net: How to Recognize the Signs of Internet Addiction and a Winning Strategy for
Recovery. New York, US: Wiley.
Young, K. & Nabuco de Abreu, C. (Eds.) (2011). Internet Addiction: A Handbook and Guide to Evaluation and
Treatment. John Wiley & Sons.