Psychiatr Q DOI 10.1007/s11126-015-9351-9 ORIGINAL PAPER
Psychometric Validation of the Chinese Compulsive Internet Use Scale (CIUS) with Taiwanese High School Adolescents Amandeep Dhir • Sufen Chen • Marko Nieminen
Ó Springer Science+Business Media New York 2015
Abstract The recent development of internet infrastructure has fuelled a popular concern that young Asian internet users are experiencing Internet addiction due to excessive Internet use. In order to understand the phenomenon, psychometric validation of a 14-item Compulsive Internet Use Scale (CIUS), with 417 Chinese adolescents has been performed. Compared to other instruments for use with Chinese populations, e.g. the 20-item Internet Addiction Test (IAT) and the 26-item Chen Internet Addiction Scale, the CIUS is relatively concise, and easy to use for measuring and diagnosing Internet addiction. The present psychometric validation has found good factorial stability with a one-factor solution for the CIUS. The internal consistency and model fit indices were very good, and even better than any previous CIUS validations. The Chinese CIUS is a valid and reliable self-reporting instrument for examining compulsive Internet use among Chinese adolescents. Other findings included: male adolescents tend to experience more compulsive Internet use than their female counterparts, and CIUS scores were positively correlated with the daily Internet use time and negatively correlated with the academic performance of the participants. No significant relationships between the CIUS, ICT accessibility, family economic condition, parental occupation or religion were found.
A. Dhir (&) Institute of Behavioral Psychology, University of Helsinki, Helsinki, Finland e-mail: [email protected]; [email protected] A. Dhir Department of Computer Science and Engineering, Aalto University, Espoo, Finland S. Chen Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taipei, Taiwan e-mail: [email protected] M. Nieminen Department of Computer Science and Engineering, Aalto University, Espoo, Finland e-mail: [email protected]
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Keywords Adolescents Compulsive Internet Use Scale Cross-sectional survey Psychometric validation
Introduction Society has witnessed exponential growth in Internet connectivity in the recent past, and it is predicted that continuing growth will lead to rising levels of Internet addiction (IA) among the Asian population [1–5]. By the end of 2012, there were approximately 564.8 million Chinese Internet users residing in Asia [6]. From this total, there were 538 million in Mainland China, 17.5 million in Taiwan, 5.3 million in Hong Kong, and 4 million in Singapore. Since the beginning of the new millennium, Mainland China has witnessed a 23.9-fold increase in Internet users, while Taiwan, Hong-Kong, and Singapore witnessed 2.80-, 2.33- and 3.3-fold increases [7]. These rapid increases in the percentages of active Internet users have fuelled a popular concern that Internet users might also be experiencing problems due to Internet overuse. Internet overuse has led to psychosocial problems such as loneliness, alienation, disturbance in social relationships [8–10], declines in academic performance [8, 10–13], and even mental disorders [14]. Research on IA started almost two decades ago, but despite this, there is not yet agreement on the suitable terminology to describe this phenomenon. Various terminologies do exist, including IA [15, 16], compulsive Internet use [17, 18], problematic Internet use [19], pathological Internet use [20], and Internet dependency [21]. On this note, Barke et al. [22] remarked that even though different terminologies exist, there is general agreement on the core elements of this concept, referred to as the diagnostic criteria of IA. These core elements include utilizing the Internet excessively at the expense of one’s own life (social and personal relationships, personal well-being, and work or study life), and experiencing anger, tension, negative emotions, alienation, and even loss of sleep as a result of internet use [22]. The development of diagnostic instruments to measure IA has been an important breakthrough, not only for IA research in general, but also for easy and timely screening of Internet addicts and related policymaking. In addition to the development of a diagnostic instrument, there is a need to examine the psychometric properties of different diagnostic instruments with Internet users of different cultures, languages, and demographic profiles. To address this gap in the existing literature, the present study has examined the psychometric properties of a diagnostic instrument, the Chinese version of the Compulsive Internet Use Scale (CIUS). In addition to this, the present study has examined the possible relationships between the CIUS, the demographic profiles of adolescents and external criteria, as well as how demographics and external criteria predict adolescents’ CIUS scores.
Background Literature At present, three widely used IA instruments are the Internet Addiction Test (IAT) [16], the Chen Internet Addiction Scale (CIAS) [23], and the Compulsive Internet Use Scale (CIUS) [18]. Of these instruments, the IAT has received the highest number of psychometric validations with different demographics, cultures, and languages. However, previous psychometric validations of the IAT did not conclude with homogeneous results. Forexample, English IAT validations with a UK population resulted in a six-factor solution
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(salience, excessive use, neglecting work, anticipation, self-control and neglect of social life) [24] and a three-factor (emotional/psychological conflict, time management issues, and mood modification) solution [25], whereas with a US population, two factors (dependent use and excessive use) were identified [26]. The results were also inconsistent in other languages, with the number of factors ranging from one to six. The Italian IAT reported having six factors (compromised social quality of life, compromised individual quality of life, compensatory usage of the internet, compromised time control, compromised academic/working careers, and excitatory usage of the internet) [27], whereas the Malay and the Korean IATs concluded with five (lack of control, neglect of duty, problematic use, social relationship disruption, and email primacy) [28] and four factors (excessive use, dependence, withdrawal, and avoidance of reality) [29], respectively. In comparison with these, the Japanese and Greek IATs resulted in three factors, but with different names: absorption, difficulties in setting priorities, and conflicts for the Japanese IAT [30], and psychological/emotional conflict, time management, and neglect of work for the Greek IAT [31]. Most recently, the German IAT validation resulted in two factors, namely loss of control and pre-occupation [22]. At present three IAT validations exist in the Chinese language where two validations have concluded a three-factor structure (withdrawal and social problems, time management and performance, and reality substitute) [32, 33], while a third Chinese IAT validation by Ngai [34] concluded with a four-factor (interference with family neglecting daily relationships, salience and withdrawal, overindulgence in online relationships, and tolerance and routines) solution. Contradictory to these psychometric validations, the Arabic [35], Finnish [36], French [37], Portuguese and Cypriot IATs all resulted in single-factor solutions. The utilization of different techniques for data sampling and statistical analyses, differences in the sample size and demographic profile of the participants (age, male to female ratio, and socio-economic condition), differences in technology accessibility, language, and culture are all possible reasons for differences in the factor solutions of the previous IAT validations. The heterogeneity in the factorial structure of the IAT poses a major concern for IA researchers and practitioners, not due to the differences in the number of factors, but because of the diverse factor names representing different concepts of IA. Due to these different factor names, it is likely that IA researchers in the previous IAT validations have interpreted the results in different ways, even though all of them have utilized the same IAT. Therefore, we argue that due to the aforementioned heterogeneities of factorial structure and factor names in the previous IAT validations, a state of confusion and complexity prevails in the correct utilization of the IAT. Additionally, this state of confusion also acts as a potential hurdle to better understanding of the IA phenomenon and the efficiency and effectiveness of the IA instruments. A second popular IA instrument validated with Chinese internet users is the 26-item CIAS, originally developed in 2003 with five factors (compulsive use, withdrawal, tolerance, interpersonal and health related problems, and time management problems) [23] Later, a 19-item revised CIAS-R version was developed, which resulted in a four-factor model, where the factor regarding symptoms of IA was omitted, based on data from Mainland Chinese college students [38]. The CIAS is a reliable instrument that measures the core symptoms and related problems of IA among the Chinese population. In the most recent psychometric validation of the CIAS, tested with 860 Grade 7–13 students in Hong Kong, Kwok-Kei et al. [39] concluded the same four factors. Almost all of the previous psychometric validations for the CIAS and CIAS-R were carried out with Chinese internet users, and its applicability to populations other than Chinese is as of yet underdeveloped.
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The third popular IA instrument is the 14-item CIUS, the most recent to be introduced. The CIUS is known for its brief and concise structure that measures the severity of the core elements of compulsive internet use among the target population. The CIUS was developed based on the seven criteria for substance dependence and ten criteria for pathological gambling specified in the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) [40], and behavioral addiction criteria given by Griffiths [41], Brown [42] and Marks [43]. The CIUS measures five core elements of IA behavior, namely withdrawal symptoms, preoccupation, loss of control, conflict, and mood modifications— coping and escaping. To date, six psychometric validations of the CIUS are available, but none are in the Chinese language or have been tested with Chinese internet users. Four of the six validations, namely the Arabic [44], Dutch [18], German [45], and French CIUS [37] evaluations resulted in single-factor solutions. The CIUS validations in Persian [46] (factor names unavailable) and Japanese [30] resulted in three-factor solutions (mood regulation, absorption, and priorities). However, possible reasons for differences in the obtained factorial structure could be over-estimation of factors due to reliance on the Kaiser criterion [47]. In the present study, the psychometric validation of the Chinese version of the CIUS was performed, mainly due to two reasons: 1) The CIUS is a relatively new instrument; hence it requires psychometric validations in different languages and cultures in order to finalize its factorial structure; and 2) Most existing IA diagnostic instruments validated with Chinese internet users are long, e.g. the 20-item IAT and the 26-item CIAS; thus there is a need to validate a shorter item instrument, which is easy to use and economical for clinical settings. In the present study, psychometric validation of the Chinese CIUS was performed with Taiwanese adolescents aged 15–18 years. Currently, Chinese internet users account for roughly one-fourth of the world’s total internet users [6, 7]. Therefore, our present psychometric validation of the CIUS could potentially provide a brief, concise, reliable, tested, and validated diagnostic instrument for the Chinese internet user population.
Research Methodology Aims and Research Questions of the Study In the present study, the following research questions are explored: RQ1
RQ2
RQ3
RQ4
What do the exploratory and confirmatory factor analyses of the Chinese version of the CIUS reveal about the factorial structure of the CIUS? Is the factorial structure comparable to those reported by earlier CIUS psychometric validations (i.e., the Dutch, French, Arabic, and German versions)? What is the internal consistency and homogeneity of the Chinese version of the CIUS? Does the Chinese version of the CIUS satisfy the various criteria of instrument validity and reliability? What are the relationships between the CIUS overall scores, the demographic profiles of the adolescents (age, gender, religion, academic performance, parental occupation, and family economic condition), and external criteria (internet connectivity at home, mobile phone ownership, internet use on mobile phones, duration of daily internet use, and number of years of internet use)? How can demographics and the external criteria predict the CIUS scores of the study participants?
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Data Collection A total of 425 Taiwanese adolescents (aged 15–18 years) were surveyed. The data collection was carried out in three public schools located in Taipei, Taiwan using a paper-andpencil survey. The survey was organized in December 2013 in standard Mandarin Chinese, the official language of Taiwan. The English version of the CIUS was translated into Mandarin Chinese using back translation procedures. A team of three, inclusive of a native Chinese professor, a bilingual expert translator in English–Chinese–English translation and an English-speaking researcher, were involved in this process. Table 2 presents the English translation of the Chinese version of CIUS. Participation in the study was voluntary and anonymous, and participants were given the opportunity to withdraw from the study at any time. A copy of the survey was submitted to the respective schools, and the necessary approvals to organize the study were obtained. A short pilot study was organized with the target user group on the school premises in order to understand if any of the translated CIUS items were difficult for the students to interpret. Additionally, teachers from the participating schools also reviewed the questionnaire survey in order to locate any difficult or confusing terms. The feedback of the students and teachers was utilized to update the wording of the survey. The updated survey was finally tested with 425 adolescent internet users. It should be noted that the pilot study data were not considered in the final pool of 425 cases. Study Measures Compulsive Internet Use Scale (CIUS) A 14-item translated version of the CIUS was answered on a five-point Likert scale that ranged from never = 1; rarely = 2; occasionally = 3; frequently = 4; always = 5. The original CIUS was modified in order to address the target user group, i.e. adolescents aged 15–18 years, as per the recommendations given by previous validations [11, 48]. These modifications included Items 3 and 4, where ‘‘others (e.g., partner, children, parents)’’ were illustrated as friends, classmates, and family, Items 10 and 11, ‘‘home-work’’ and ‘‘daily obligations (work, school, or family life)’’ were replaced with homework/school work. Adding the scores for all 14 CIUS items gives the cumulative CIUS score, which is the determinant of compulsive internet use. The mean CIUS score was 38.14 (SD = 9.60), while the minimum and maximum CIUS scores were 14 and 70 respectively. Internet Addiction Test (IAT) A 20-item translated version of the Chinese IAT was utilized in the study. The IAT was also answered on a five-point Likert scale that ranged from ‘‘strongly disagree’’ = 1 to ‘‘strongly agree’’ = 5. The IAT was utilized in the study to evaluate the concurrent validity of the Chinese CIUS; that is, high correlation between the IAT and the CIUS would suggest that both instruments are measuring the same underlying concepts or phenomena, thus ensuring high concurrent validity of the CIUS. Demographics A total of six demographic questions were included in the survey in order to investigate their relationship with the CIUS. These included age, gender (Male = 1, Female = 2),
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religion, academic performance (referring to the grade or percentage received in the last annual school examination by the study participant), parental occupation, and family’s economic condition. The mean age of the participants was 16.33 (SD = 0.84), where 58.8 % (n = 245) were male and 41.2 % (n = 172) were female adolescents (Table 1). External Criteria A total of six items represented external criteria, namely internet connectivity at home (yes = 1, no = 2), mobile phone ownership (yes = 1, no = 2), use of internet on mobile phones (yes = 1, no = 2), duration of daily internet use, and number of years of internet use (see Table 1). The mean daily time spent on the internet was 2.82 (SD = 2.66) hours, minimum and maximum daily times spent were 0 and 14 h per day. Similarly, the mean
Table 1 Descriptive statistics for demographics and external criteria Variables
Category
Percentage (frequency)
Age
15
15.6 (65)
16
44.6 (186)
17
30.9 (129)
18 Gender Academic performance
Family economic condition
58.8 (245)
Female
41.2 (172)
Bottom 10
23.0 (96)
Below average
34.8 (145)
Above average
29.7 (124)
Top 10
12.5 (52)
Difficult
1.4 (6)
So-So
50.8 (212)
Middle-class
46.8 (195)
Rich Religion
Parental occupation
Internet at home Mobile phone ownership Mobile phone internet
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8.9 (37)
Male
1.0 (4)
Christian
7.0 (29)
Buddhist
22.1 (92)
Taoist
16.1 (67)
Other
54.9 (229)
Both working
69.5 (290)
Father alone working
24.2 (101)
Mother alone working
4.8 (20)
None of them working
1.4 (6)
Yes
97.1 (405)
No
2.9 (12)
Yes
95.9 (400)
No
4.1 (17)
Yes
63.1 (263)
No
36.9 (154)
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years of internet use experience was 7.94 (SD = 2.69) years, and the minimum and maximum numbers of years of internet use experience were 0.5–14 years. Statistical Analysis The software programs IBM SPSS 21.0 and AMOS 21.0 for Windows and Factor tool1 were utilized to perform the various statistical procedures in this study. First, missing value analysis (MVA) was performed in order to examine the pattern of missing values in the collected sample data. It was found that none of the cases had more than 20 % missing data. Little’s MCAR test [49] returned a statistically significant value indicating that data was not missing completely at random. A thorough review of different techniques for handling missing data was performed, and it was found that the traditional methods of handling missing data, namely pairwise deletion, list-wise deletion and mean substitution, are considered substandard [50, 51]. The Expectation–Maximization (EM) algorithm is the recommended technique for handling missing data even if the data is not missing completely at random (Schafer and Graham 2002) [52]. Therefore, the missing entries were imputed using EM [53, 54]. A total of 8 cases were detected as possible outliers, based on the Z score criteria, i.e. Z score \3.27 [55–57]. Hence, the effective sample size after deleting the outliers was 417. Later, the effective sample data were examined to check the normal distribution of the CIUS items by calculating skewness and kurtosis. It was found that both skewness and kurtosis were in the acceptable range, i.e. ±1 for a symmetrical or normal distribution as suggested by previous literature [58–60].
Study Results Reliability and Homogeneity of CIUS The Chinese version of the CIUS returned a Cronbach’s alpha value (a) of 0.91 showing that the CIUS items had excellent internal consistency [61, 62]. The a value was used to calculate the index of measurement error in the CIUS using the formula: 1 - [Square of (a)] = index of measurement error [63]. The Chinese CIUS returned a very low measurement error of 0.17. The ‘‘alpha if item deleted’’ for the CIUS items ranged from 0.90 to 0.91. This showed that the a value for the CIUS did not change significantly and remained stable when any of the items were deleted. The corrected item-total correlation value for the CIUS items ranged from 0.45 to 0.75. Results of Exploratory Factor Analysis (EFA) Exploratory factor analysis (EFA) is usually performed when no priori factorial structure exists for the considered instrument. Although the majority of the earlier CIUS validations have agreed on a single factor solution, the present study still examined the factorial structure for the Chinese CIUS. The reasons were: (1) The Persian and Japanese CIUS validations resulted in three factor solutions, meaning that not all prior CIUS validations unanimously endorsed a single factor solution; and (2) The CIUS has not yet received validation in the Chinese language. Due to these two reasons, it was safe to assume that no priori factorial structure exists for the Chinese CIUS; hence it required EFA examination. 1
http://psico.fcep.urv.es/utilitats/factor/index.html.
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The CIUS data were found to be suitable for factor analysis since they returned acceptable inter-item correlation matrix values between 0.20 and 0.72, a Kaiser–Meyer– Olkin (KMO) [47] test value of 0.92 (very good), and a statistically significant Bartlett’s statistic [64], v2 = 2841.3, df = 91, p \ 0.0001. The inter-item correlation matrix values suggest that the CIUS data were evaluating similar phenomena or behaviors of the study participants. Afterwards, EFA was performed using ‘‘Maximum Likelihood (ML)’’. The loadings for all 14 CIUS items were well above the minimum threshold of 0.50, except Item 4 which was 0.47, thus showing good factor loading for the collected data (Table 2). The analysis returned a two-factor solution that explained 46.8 % of the variance based on the eigenvalues known as the Kaiser Criterion [47], where the eigenvalues were 6.55 and 1.31. It should be noted that relatively recent research studies (e.g., the Arabic and French CIUS validations) have shown that reliance alone on the Kaiser criterion to decide the number of factors may eventually overestimate the factor number. Due to this reason, to reconfirm the results obtained from the Kaiser criterion, the obtained factorial structure for the CIUS was re-examined using the Factor tool and two well-known statistical tests, namely Parallel Analysis (PA) [65] with optimal implementation, and Velicer’s Minimum average partial (MAP) [66]. It was found that MAP returned one dimension, with an average partial value of 0.022, while PA returned two dimensions. Careful examination of the PA results showed that the second eigenvalue of 1.31 was only slightly higher than the criterion value of 1.24 generated by PA, and was within one standard deviation. Hawi [35] recommended that if the eigenvalue falls within one standard deviation of the criterion value, then it should be rejected. Therefore, the PA also resulted in a one-factor solution. Finally, Catell’s scree test [67] was examined, and the scree plot also confirmed the presence of a one-factor solution for the Chinese CIUS. This also shows that the Kaiser criterion actually overestimated the number of factors for the CIUS. Results of Confirmatory Factor Analysis (CFA) Confirmatory factor analysis (CFA) was performed to verify the one-dimensional factorial structure. The CFA was performed using a one-factor model, where the latent variable was labeled ‘‘Compulsive internet use’’, and 14 observed variables were the CIUS items. In order to determine the model fit, various goodness of fit indices were examined. The CFA involves testing two models, namely Model A (where correlation between residual error terms was not permitted), and Model B (where correlation between residual error terms was permitted), as suggested by previous psychometric validation studies [18, 45]. Model A did not pass several of the criteria, including the v2 and df ratios, the RMSEA was too high, and the GFI and AGFI were too low; hence this model was rejected (Table 2). Afterwards, the fit for Model B was examined using CFA. The correlation between residual error terms should be performed if the standard residual covariance exceeds the cutoff value of 1.96 [68] and meets the criteria given by Kline [69]. According to Kline [69], the Pearson correlation of error variances should be greater than 0.30, and connecting residual errors should be theoretically grounded. Eleven pairs of residual error terms were connected, namely: Items 1 and 2 (r = 0.72), Items 2 and 3 (r = 0.48), Items 6 and 7 (r = 0.59), Items 8 and 9 (r = 0.61), Items 10 and 11 (r = 0.67), Items 12 and 13 (r = 0.72), Items 13 and 14 (r = 0.47), Items 2 and 11 (r = 0.61), Items 12 and 14 (r = 0.41), Items 2 and 8 (r = 0.49), and Items 7 and 12 (r = 0.40). Model B returned improved goodness of fit indices: CFI = 0.99, GFI = 0.97, AGFI = 0.95, and RMSEA = 0.04 (see Table 2). This shows that Model B has a good model fit since it meets the cut-off scores for all of the goodness of fit indices [69–71].
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Psychiatr Q Table 2 Factor loadings, goodness of fit indices and reliability coefficients Items
Chinese CIUS
EFA
Model A
Model B
Item 1
Do you find it difficult to stop using the INTERNET when you are online?
0.73
0.63
0.61
Item 2
Do you often continue to use the INTERNET despite your intention to stop?
0.74
0.70
0.65
Item 3
Do others (e.g., friends and family) say you should use the INTERNET less?
0.56
0.62
0.62
Item 4
Do you prefer to use the INTERNET instead of spending time with others (e.g. friends and family)?
0.47
0.42
0.43
Item 5
Are you short of sleep because of the INTERNET?
0.66
0.68
0.70
Item 6
Do you think about the INTERNET, even when not online?
0.71
0.64
0.65
Item 7
Do you look forward to your next INTERNET session?
0.66
0.67
0.67
Item 8
Do you think you should use the INTERNET less often?
0.62
0.67
0.64
Item 9
Have you unsuccessfully tried to spend less time on the INTERNET?
0.80
0.87
0.87
Item 10
Do you rush through your (homework)/(schoolwork) in order to go on the INTERNET?
0.72
0.77
0.76
Item 11
How often do you neglect your daily obligations (work, school, or family life) because you prefer to go on the INTERNET?
0.77
0.79
0.77
Item 12
How often do you go on the INTERNET when you are feeling down?
0.50
0.51
0.46
Item 13
How often do you use the INTERNET to escape from your sorrows or get relief from negative feelings?
0.53
0.58
0.55
Item 14
How often do you feel restless, frustrated, or irritated when you cannot use the INTERNET?
0.59
0.58
0.58
Fit indices
Recommended values
2
v
521.17
101.51
v2 ratio df
B3.0
6.77
1.54
CFI
C0.95
0.94
0.99
GFI
C0.95
0.85
0.97
AGFI
C0.95
0.80
0.95
RMSEA
\0.05
0.12
0.04
a
p [ 0.70
0.91
0.91
Validity and Reliability In the present study, different forms of instrument validities and reliabilities were examined in the context of the Chinese CIUS. These include: (1) Content validity This evaluates the extent of correspondence between the CIUS items and the underlying syndrome the items are measuring (i.e., compulsive internet use). To ensure the content validity, all the CIUS items were derived from the previous CIUS validations. This shows that an expert panel of International IA researchers, reviewers and audience interested in IA research had already reviewed the CIUS items; (2) Face validity This examines whether the instrument items appear valid or not for the study participants. A brief pilot study was organized with the target user group of adolescents in order to revise any confusing or difficult CIUS items. The pilot study therefore ensured the face validity of the Chinese CIUS; (3)
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Concurrent validity This examines the degree to which the CIUS is related to other constructs or scales, and whether their relationship is consistent with the earlier findings from the IA research. It was found that the CIUS was highly correlated with the Internet Addiction Test (IAT) (r = 0.847**) and daily time spent on internet use (r = 0.232**). This shows that the Chinese CIUS fulfills the concurrent validity; (4) Construct reliability The Chinese CIUS returned a very good Cronbach’s alpha value of 0.913, representing the internal consistency of the measure. This shows that the Chinese CIUS possesses very good construct reliability. Relation Between the CIUS, Demographics and External criteria Pearson correlation with 95 % confidence interval was utilized to investigate the relationship between the CIUS and other variables. The CIUS scores were in significant correlation with daily internet use (r = 0.23**) and academic performance (r = -0.14**). An independent samples t test (t = 2.20, sig = 0.03) revealed that male adolescents (M = 39.00, SD = 9.92) tend to experience more compulsive internet use than female adolescents (M = 36.91, SD = 9.02). The correlation analysis found no significant relationship between CIUS scores and number of years of internet use (r = -0.03), internet connectivity at home (r = 0.05), mobile phone ownership (r = 0.02), internet use on mobile phones (r = -0.002), or family economic condition (r = -0.02). A one-way ANOVA test revealed that the CIUS scores did not share any significant relationship with the age of the participants (F = 0.73, p = 0.91), religion (F = 1.08, p = 0.34), or parental occupation (F = 0.58, p = 0.99). Predicting CIUS Scores Using Demographic and External Criteria Hierarchical regression was performed in order to predict the relative influences of various demographic variables and external criteria on the CIUS scores of the participants (see Table 3). It was found that daily time spent on the internet was a significant positive predictor of the CIUS scores. On the other hand, the gender variable (male = 1, female = 2) and the academic performances of the participants were significant negative predictors of the CIUS scores. Demographic variables explained 3.3 % and external criteria explained 8.2 % variance in the CIUS scores among the adolescents.
Discussion The present study has examined the psychometric properties of the Chinese version of the CIUS with adolescents aged 15–18 years. Psychometric validation resulted in a one-factor solution with a good model fit. These results confirm the findings of previous CIUS validations in Arabic, Dutch, German, and French. The Chinese CIUS possesses excellent internal consistency, as indicated by a value of 0.91. The a value for the Chinese CIUS was better than that of the Arabic and French CIUS, the same as that of the Dutch CIUS, and comparable to that of the German CIUS validation (a = 0.93). Furthermore, the goodness of model fit indices for the Chinese CIUS were better than those of all previous CIUS validations. Until now, three diagnostic instruments, namely the IAT (20-item), the CIAS (26-item) and the CIAS-R (19-item) have been psychometrically validated with Chinese internet
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Psychiatr Q Table 3 Predicting the influence of demographics and external criteria on CIUS score Predictors (stepwise regression)
Compulsive Internet Use Scale (CIUS) Score Beta
t value
Block 1: demographics Age
-0.04
-0.89
Gender (male = 1; female = 2)
-0.10**
-2.14
Religion
-0.05
-0.99
Parental occupation
-0.02
-0.37
Academic performance
-0.13**
Family economic condition
0.00
R2 value
0.03
2.57 -0.01
Block 2: external criteria Internet connectivity at home Mobile phone ownership
0.05
0.98
-0.02
-0.39
Internet on mobile phone
0.04
0.72
Daily time spent on internet
0.22**
4.50
Number of years of internet use
-0.03
Final R2 value
0.08
Increment in R2 value
0.05
-0.53
The table represents standardized beta coefficients and t value from the final hierarchical regression carried out using dependent and independent variables of our sample data ** p \ 0.01
users. All previous validations have resulted in multiple-factor solutions ranging from three to four factors. In contrast to previous validations, our present study not only resulted in a single-factor solution with a good model fit, but it also provided an easy to use, concise and brief (only 14 items) instrument. The study results show that male adolescents tend to experience more compulsive internet use than female adolescents. Similarly, adolescents who spend more time on the internet daily and have lower academic performance (bottom 10 = 1, top 10 = 4) tend to score high in compulsive internet use. These findings were also supported by the results of the hierarchical regression, where daily time spent, academic performance, and gender were the three strongest predictors of compulsive internet use. Similar findings were revealed by the earlier IA research, that is, male internet users tend to experience higher compulsive internet use compared to female adolescents [5, 27, 72, 73], and time spent on internet use is directly proportional to experiencing compulsive internet use [18, 44]. In addition to this, it was found that those adolescents who tend to increasingly utilize the internet actually do not exhibit good academic performance. Furthermore, adolescents experiencing compulsive internet use belong to the ‘‘below average’’ or ‘‘bottom 10’’ category of academic performance. The study results did not find any significant relationship between the CIUS, number of years of internet use, internet connectivity at home, mobile phone ownership, internet use on mobile phones, or family economic condition. These results suggest that technology accessibility among compulsive and non-compulsive users is similar in the case of Taiwanese adolescents. The possible reasons could be: (1) Taiwan has experienced a very high
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penetration of ICT use (97.1 % internet connectivity at home and 95.9 % mobile phone ownership in the sample) due to which compulsive and non-compulsive internet users possess the same level of ICT penetration. Furthermore, due to the high ICT penetration, compulsive and non-compulsive internet users did not differ in the total number of years of internet use experience; (2) Similar findings were reported by Leung [9] who found no difference between internet addicts and non-addicts in their economic status and education variables. The possible reasons were availability of low cost computing devices and cheaper internet data plans. These results are in agreement with the findings of the multiple hierarchical regressions. The CIUS scores did not share any significant relationship with age, religion, or the parental occupation of the study participants. To the best of our knowledge, none of the previous studies have examined the relationship between the aforementioned variables and the CIUS, or even any other internet addiction instrument. Therefore the possible reasons could be: (1) The age group of the study respondents was narrow, i.e. 15–18 years, and use of the internet is emphasized in all grades of Taiwanese schools. These could be possible reasons behind there being no relationship between age and the CIUS; and (2) Due to the high ICT penetration in Taiwanese society, low cost computing devices and cheaper internet data plans, no significant relationship between religion or parental occupation and the CIUS was found. Similar results were found in the multiple hierarchical regressions.
Study Implications The present study has the following theoretical and practical implications: (1) internet use among Chinese internet users is increasing. Due to this, IA researchers and practitioners will require validated, reliable and tested diagnostic instruments for compulsive internet use among internet users. This need is fulfilled by the present study to some extent; (2) The majority of the existing diagnostic instruments for IA screening among Chinese internet users are long, e.g. the 20-Item IAT, the 26-item CIAS and the 19-item CIAS-R. In comparison, the present study has provided a validated and tested scale which is the shortest scale to date; (3) The Chinese version of the CIUS is valid, reliable, economical, and suitable for both research and clinical applications. Furthermore, due to having fewer items, the Chinese CIUS might ease the problem of participant fatigue while answering, which IA researchers often try to control to make tests suitable for online surveys; (4) The CIUS is a handy instrument for schools, students, researchers and practitioners that can provide timely and economical screening of compulsive internet use among internet users; (5) The present CIUS validation has confirmed the findings of earlier CIUS validations in the Dutch, German, French and Arabic versions; and finally (6) The present study has revealed new knowledge for IA researchers and practitioners by examining the relationship between compulsive internet use and adolescents’ demographic profile and external criteria.
Study Limitations and Future Work The present study has the following limitations: (1) It was organized with a sub-set of the adolescent population, i.e. 15–18 year old adolescents, so the findings of our study might not be applicable to the entire range of adolescents aged between 10 and 19 years. Furthermore, it is not known at this stage if the present study findings are applicable to adults
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or young adults; (2) The present study was organized with participants from only three schools in Taipei; therefore we cannot guarantee the applicability of the study findings to adolescent internet users throughout the whole of Taiwan; and (3) Two study variables, namely academic performance and family economic condition, were unitary (i.e., they were single-item constructs). It is quite possible that these variables are not able to address the complex notion behind academic performance and economic status. Therefore, the present study might also involve bias related to both these variables. For future studies, IA researchers may investigate and compare the prediction power of the CIUS with other instruments, such as the CIAS-R and the IAT, to predict time spent and frequency of internet use, behavioral changes, and problematic behavior due to internet overuse. The process of comparing various instruments should examine their similarities and differences. Such comparisons could result in recommendations for IA researchers and practitioners for the possible utilization of the instruments. Future studies may validate the Chinese CIUS with internet users from Mainland China, Hong Kong, and Singapore. In addition to this, we suggest that IA researchers perform similar validation studies with various age groups, not just adolescents. This will ensure the generalizability of the study results. Furthermore, it is worth examining the relationship between new constructs and CIUS, and comparing those findings with the present study results. Finally, at present, no statistically proven cut-off score for the CIUS exists for dichotomizing compulsive and non-compulsive internet use. We suggest that IA researchers should develop statistical cutoff scores for the CIUS. Acknowledgments This research was conducted in the Future Industrial Services (FutIS) research program (Project No. 2113194), managed by the Finnish Metals and Engineering Competence Cluster (FIMECC), and funded by the Finnish Funding Agency for Technology and Innovation (TEKES), research institutes and companies. Their support is gratefully acknowledged. The support received from Academy of Finland in the form of researcher’s mobility grant to Taiwan (Decision No. 265969) and South Africa (Decision No. 277571) is acknowledged. Additionally, we would like to acknowledge the support received from Ministry of Science and Technology, Taiwan, under grant number NSC 102-2628-S-011-001-MY4. Conflict of interest The authors declare that they have no conflict of interest.
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Amandeep Dhir, Msc (Tech) is a project researcher at Department of Computer Science and Engineering, Aalto University and also a doctoral candidate at Institute of behavioral science, University of Helsinki, Finland. Sufen Chen, PhD is a Professor at Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology, Taiwan. Marko Nieminen, PhD is a professor of human–computer interaction, School of Science, Aalto University (formerly Helsinki University of Technology), Finland.
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Psychometric Validation of the Chinese Compulsive Internet Use Scale (CIUS) with Taiwanese High School Adolescents Amandeep Dhir • Sufen Chen • Marko Nieminen
Ó Springer Science+Business Media New York 2015
Abstract The recent development of internet infrastructure has fuelled a popular concern that young Asian internet users are experiencing Internet addiction due to excessive Internet use. In order to understand the phenomenon, psychometric validation of a 14-item Compulsive Internet Use Scale (CIUS), with 417 Chinese adolescents has been performed. Compared to other instruments for use with Chinese populations, e.g. the 20-item Internet Addiction Test (IAT) and the 26-item Chen Internet Addiction Scale, the CIUS is relatively concise, and easy to use for measuring and diagnosing Internet addiction. The present psychometric validation has found good factorial stability with a one-factor solution for the CIUS. The internal consistency and model fit indices were very good, and even better than any previous CIUS validations. The Chinese CIUS is a valid and reliable self-reporting instrument for examining compulsive Internet use among Chinese adolescents. Other findings included: male adolescents tend to experience more compulsive Internet use than their female counterparts, and CIUS scores were positively correlated with the daily Internet use time and negatively correlated with the academic performance of the participants. No significant relationships between the CIUS, ICT accessibility, family economic condition, parental occupation or religion were found.
A. Dhir (&) Institute of Behavioral Psychology, University of Helsinki, Helsinki, Finland e-mail: [email protected]; [email protected] A. Dhir Department of Computer Science and Engineering, Aalto University, Espoo, Finland S. Chen Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taipei, Taiwan e-mail: [email protected] M. Nieminen Department of Computer Science and Engineering, Aalto University, Espoo, Finland e-mail: [email protected]
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Keywords Adolescents Compulsive Internet Use Scale Cross-sectional survey Psychometric validation
Introduction Society has witnessed exponential growth in Internet connectivity in the recent past, and it is predicted that continuing growth will lead to rising levels of Internet addiction (IA) among the Asian population [1–5]. By the end of 2012, there were approximately 564.8 million Chinese Internet users residing in Asia [6]. From this total, there were 538 million in Mainland China, 17.5 million in Taiwan, 5.3 million in Hong Kong, and 4 million in Singapore. Since the beginning of the new millennium, Mainland China has witnessed a 23.9-fold increase in Internet users, while Taiwan, Hong-Kong, and Singapore witnessed 2.80-, 2.33- and 3.3-fold increases [7]. These rapid increases in the percentages of active Internet users have fuelled a popular concern that Internet users might also be experiencing problems due to Internet overuse. Internet overuse has led to psychosocial problems such as loneliness, alienation, disturbance in social relationships [8–10], declines in academic performance [8, 10–13], and even mental disorders [14]. Research on IA started almost two decades ago, but despite this, there is not yet agreement on the suitable terminology to describe this phenomenon. Various terminologies do exist, including IA [15, 16], compulsive Internet use [17, 18], problematic Internet use [19], pathological Internet use [20], and Internet dependency [21]. On this note, Barke et al. [22] remarked that even though different terminologies exist, there is general agreement on the core elements of this concept, referred to as the diagnostic criteria of IA. These core elements include utilizing the Internet excessively at the expense of one’s own life (social and personal relationships, personal well-being, and work or study life), and experiencing anger, tension, negative emotions, alienation, and even loss of sleep as a result of internet use [22]. The development of diagnostic instruments to measure IA has been an important breakthrough, not only for IA research in general, but also for easy and timely screening of Internet addicts and related policymaking. In addition to the development of a diagnostic instrument, there is a need to examine the psychometric properties of different diagnostic instruments with Internet users of different cultures, languages, and demographic profiles. To address this gap in the existing literature, the present study has examined the psychometric properties of a diagnostic instrument, the Chinese version of the Compulsive Internet Use Scale (CIUS). In addition to this, the present study has examined the possible relationships between the CIUS, the demographic profiles of adolescents and external criteria, as well as how demographics and external criteria predict adolescents’ CIUS scores.
Background Literature At present, three widely used IA instruments are the Internet Addiction Test (IAT) [16], the Chen Internet Addiction Scale (CIAS) [23], and the Compulsive Internet Use Scale (CIUS) [18]. Of these instruments, the IAT has received the highest number of psychometric validations with different demographics, cultures, and languages. However, previous psychometric validations of the IAT did not conclude with homogeneous results. Forexample, English IAT validations with a UK population resulted in a six-factor solution
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(salience, excessive use, neglecting work, anticipation, self-control and neglect of social life) [24] and a three-factor (emotional/psychological conflict, time management issues, and mood modification) solution [25], whereas with a US population, two factors (dependent use and excessive use) were identified [26]. The results were also inconsistent in other languages, with the number of factors ranging from one to six. The Italian IAT reported having six factors (compromised social quality of life, compromised individual quality of life, compensatory usage of the internet, compromised time control, compromised academic/working careers, and excitatory usage of the internet) [27], whereas the Malay and the Korean IATs concluded with five (lack of control, neglect of duty, problematic use, social relationship disruption, and email primacy) [28] and four factors (excessive use, dependence, withdrawal, and avoidance of reality) [29], respectively. In comparison with these, the Japanese and Greek IATs resulted in three factors, but with different names: absorption, difficulties in setting priorities, and conflicts for the Japanese IAT [30], and psychological/emotional conflict, time management, and neglect of work for the Greek IAT [31]. Most recently, the German IAT validation resulted in two factors, namely loss of control and pre-occupation [22]. At present three IAT validations exist in the Chinese language where two validations have concluded a three-factor structure (withdrawal and social problems, time management and performance, and reality substitute) [32, 33], while a third Chinese IAT validation by Ngai [34] concluded with a four-factor (interference with family neglecting daily relationships, salience and withdrawal, overindulgence in online relationships, and tolerance and routines) solution. Contradictory to these psychometric validations, the Arabic [35], Finnish [36], French [37], Portuguese and Cypriot IATs all resulted in single-factor solutions. The utilization of different techniques for data sampling and statistical analyses, differences in the sample size and demographic profile of the participants (age, male to female ratio, and socio-economic condition), differences in technology accessibility, language, and culture are all possible reasons for differences in the factor solutions of the previous IAT validations. The heterogeneity in the factorial structure of the IAT poses a major concern for IA researchers and practitioners, not due to the differences in the number of factors, but because of the diverse factor names representing different concepts of IA. Due to these different factor names, it is likely that IA researchers in the previous IAT validations have interpreted the results in different ways, even though all of them have utilized the same IAT. Therefore, we argue that due to the aforementioned heterogeneities of factorial structure and factor names in the previous IAT validations, a state of confusion and complexity prevails in the correct utilization of the IAT. Additionally, this state of confusion also acts as a potential hurdle to better understanding of the IA phenomenon and the efficiency and effectiveness of the IA instruments. A second popular IA instrument validated with Chinese internet users is the 26-item CIAS, originally developed in 2003 with five factors (compulsive use, withdrawal, tolerance, interpersonal and health related problems, and time management problems) [23] Later, a 19-item revised CIAS-R version was developed, which resulted in a four-factor model, where the factor regarding symptoms of IA was omitted, based on data from Mainland Chinese college students [38]. The CIAS is a reliable instrument that measures the core symptoms and related problems of IA among the Chinese population. In the most recent psychometric validation of the CIAS, tested with 860 Grade 7–13 students in Hong Kong, Kwok-Kei et al. [39] concluded the same four factors. Almost all of the previous psychometric validations for the CIAS and CIAS-R were carried out with Chinese internet users, and its applicability to populations other than Chinese is as of yet underdeveloped.
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The third popular IA instrument is the 14-item CIUS, the most recent to be introduced. The CIUS is known for its brief and concise structure that measures the severity of the core elements of compulsive internet use among the target population. The CIUS was developed based on the seven criteria for substance dependence and ten criteria for pathological gambling specified in the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) [40], and behavioral addiction criteria given by Griffiths [41], Brown [42] and Marks [43]. The CIUS measures five core elements of IA behavior, namely withdrawal symptoms, preoccupation, loss of control, conflict, and mood modifications— coping and escaping. To date, six psychometric validations of the CIUS are available, but none are in the Chinese language or have been tested with Chinese internet users. Four of the six validations, namely the Arabic [44], Dutch [18], German [45], and French CIUS [37] evaluations resulted in single-factor solutions. The CIUS validations in Persian [46] (factor names unavailable) and Japanese [30] resulted in three-factor solutions (mood regulation, absorption, and priorities). However, possible reasons for differences in the obtained factorial structure could be over-estimation of factors due to reliance on the Kaiser criterion [47]. In the present study, the psychometric validation of the Chinese version of the CIUS was performed, mainly due to two reasons: 1) The CIUS is a relatively new instrument; hence it requires psychometric validations in different languages and cultures in order to finalize its factorial structure; and 2) Most existing IA diagnostic instruments validated with Chinese internet users are long, e.g. the 20-item IAT and the 26-item CIAS; thus there is a need to validate a shorter item instrument, which is easy to use and economical for clinical settings. In the present study, psychometric validation of the Chinese CIUS was performed with Taiwanese adolescents aged 15–18 years. Currently, Chinese internet users account for roughly one-fourth of the world’s total internet users [6, 7]. Therefore, our present psychometric validation of the CIUS could potentially provide a brief, concise, reliable, tested, and validated diagnostic instrument for the Chinese internet user population.
Research Methodology Aims and Research Questions of the Study In the present study, the following research questions are explored: RQ1
RQ2
RQ3
RQ4
What do the exploratory and confirmatory factor analyses of the Chinese version of the CIUS reveal about the factorial structure of the CIUS? Is the factorial structure comparable to those reported by earlier CIUS psychometric validations (i.e., the Dutch, French, Arabic, and German versions)? What is the internal consistency and homogeneity of the Chinese version of the CIUS? Does the Chinese version of the CIUS satisfy the various criteria of instrument validity and reliability? What are the relationships between the CIUS overall scores, the demographic profiles of the adolescents (age, gender, religion, academic performance, parental occupation, and family economic condition), and external criteria (internet connectivity at home, mobile phone ownership, internet use on mobile phones, duration of daily internet use, and number of years of internet use)? How can demographics and the external criteria predict the CIUS scores of the study participants?
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Data Collection A total of 425 Taiwanese adolescents (aged 15–18 years) were surveyed. The data collection was carried out in three public schools located in Taipei, Taiwan using a paper-andpencil survey. The survey was organized in December 2013 in standard Mandarin Chinese, the official language of Taiwan. The English version of the CIUS was translated into Mandarin Chinese using back translation procedures. A team of three, inclusive of a native Chinese professor, a bilingual expert translator in English–Chinese–English translation and an English-speaking researcher, were involved in this process. Table 2 presents the English translation of the Chinese version of CIUS. Participation in the study was voluntary and anonymous, and participants were given the opportunity to withdraw from the study at any time. A copy of the survey was submitted to the respective schools, and the necessary approvals to organize the study were obtained. A short pilot study was organized with the target user group on the school premises in order to understand if any of the translated CIUS items were difficult for the students to interpret. Additionally, teachers from the participating schools also reviewed the questionnaire survey in order to locate any difficult or confusing terms. The feedback of the students and teachers was utilized to update the wording of the survey. The updated survey was finally tested with 425 adolescent internet users. It should be noted that the pilot study data were not considered in the final pool of 425 cases. Study Measures Compulsive Internet Use Scale (CIUS) A 14-item translated version of the CIUS was answered on a five-point Likert scale that ranged from never = 1; rarely = 2; occasionally = 3; frequently = 4; always = 5. The original CIUS was modified in order to address the target user group, i.e. adolescents aged 15–18 years, as per the recommendations given by previous validations [11, 48]. These modifications included Items 3 and 4, where ‘‘others (e.g., partner, children, parents)’’ were illustrated as friends, classmates, and family, Items 10 and 11, ‘‘home-work’’ and ‘‘daily obligations (work, school, or family life)’’ were replaced with homework/school work. Adding the scores for all 14 CIUS items gives the cumulative CIUS score, which is the determinant of compulsive internet use. The mean CIUS score was 38.14 (SD = 9.60), while the minimum and maximum CIUS scores were 14 and 70 respectively. Internet Addiction Test (IAT) A 20-item translated version of the Chinese IAT was utilized in the study. The IAT was also answered on a five-point Likert scale that ranged from ‘‘strongly disagree’’ = 1 to ‘‘strongly agree’’ = 5. The IAT was utilized in the study to evaluate the concurrent validity of the Chinese CIUS; that is, high correlation between the IAT and the CIUS would suggest that both instruments are measuring the same underlying concepts or phenomena, thus ensuring high concurrent validity of the CIUS. Demographics A total of six demographic questions were included in the survey in order to investigate their relationship with the CIUS. These included age, gender (Male = 1, Female = 2),
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religion, academic performance (referring to the grade or percentage received in the last annual school examination by the study participant), parental occupation, and family’s economic condition. The mean age of the participants was 16.33 (SD = 0.84), where 58.8 % (n = 245) were male and 41.2 % (n = 172) were female adolescents (Table 1). External Criteria A total of six items represented external criteria, namely internet connectivity at home (yes = 1, no = 2), mobile phone ownership (yes = 1, no = 2), use of internet on mobile phones (yes = 1, no = 2), duration of daily internet use, and number of years of internet use (see Table 1). The mean daily time spent on the internet was 2.82 (SD = 2.66) hours, minimum and maximum daily times spent were 0 and 14 h per day. Similarly, the mean
Table 1 Descriptive statistics for demographics and external criteria Variables
Category
Percentage (frequency)
Age
15
15.6 (65)
16
44.6 (186)
17
30.9 (129)
18 Gender Academic performance
Family economic condition
58.8 (245)
Female
41.2 (172)
Bottom 10
23.0 (96)
Below average
34.8 (145)
Above average
29.7 (124)
Top 10
12.5 (52)
Difficult
1.4 (6)
So-So
50.8 (212)
Middle-class
46.8 (195)
Rich Religion
Parental occupation
Internet at home Mobile phone ownership Mobile phone internet
123
8.9 (37)
Male
1.0 (4)
Christian
7.0 (29)
Buddhist
22.1 (92)
Taoist
16.1 (67)
Other
54.9 (229)
Both working
69.5 (290)
Father alone working
24.2 (101)
Mother alone working
4.8 (20)
None of them working
1.4 (6)
Yes
97.1 (405)
No
2.9 (12)
Yes
95.9 (400)
No
4.1 (17)
Yes
63.1 (263)
No
36.9 (154)
Psychiatr Q
years of internet use experience was 7.94 (SD = 2.69) years, and the minimum and maximum numbers of years of internet use experience were 0.5–14 years. Statistical Analysis The software programs IBM SPSS 21.0 and AMOS 21.0 for Windows and Factor tool1 were utilized to perform the various statistical procedures in this study. First, missing value analysis (MVA) was performed in order to examine the pattern of missing values in the collected sample data. It was found that none of the cases had more than 20 % missing data. Little’s MCAR test [49] returned a statistically significant value indicating that data was not missing completely at random. A thorough review of different techniques for handling missing data was performed, and it was found that the traditional methods of handling missing data, namely pairwise deletion, list-wise deletion and mean substitution, are considered substandard [50, 51]. The Expectation–Maximization (EM) algorithm is the recommended technique for handling missing data even if the data is not missing completely at random (Schafer and Graham 2002) [52]. Therefore, the missing entries were imputed using EM [53, 54]. A total of 8 cases were detected as possible outliers, based on the Z score criteria, i.e. Z score \3.27 [55–57]. Hence, the effective sample size after deleting the outliers was 417. Later, the effective sample data were examined to check the normal distribution of the CIUS items by calculating skewness and kurtosis. It was found that both skewness and kurtosis were in the acceptable range, i.e. ±1 for a symmetrical or normal distribution as suggested by previous literature [58–60].
Study Results Reliability and Homogeneity of CIUS The Chinese version of the CIUS returned a Cronbach’s alpha value (a) of 0.91 showing that the CIUS items had excellent internal consistency [61, 62]. The a value was used to calculate the index of measurement error in the CIUS using the formula: 1 - [Square of (a)] = index of measurement error [63]. The Chinese CIUS returned a very low measurement error of 0.17. The ‘‘alpha if item deleted’’ for the CIUS items ranged from 0.90 to 0.91. This showed that the a value for the CIUS did not change significantly and remained stable when any of the items were deleted. The corrected item-total correlation value for the CIUS items ranged from 0.45 to 0.75. Results of Exploratory Factor Analysis (EFA) Exploratory factor analysis (EFA) is usually performed when no priori factorial structure exists for the considered instrument. Although the majority of the earlier CIUS validations have agreed on a single factor solution, the present study still examined the factorial structure for the Chinese CIUS. The reasons were: (1) The Persian and Japanese CIUS validations resulted in three factor solutions, meaning that not all prior CIUS validations unanimously endorsed a single factor solution; and (2) The CIUS has not yet received validation in the Chinese language. Due to these two reasons, it was safe to assume that no priori factorial structure exists for the Chinese CIUS; hence it required EFA examination. 1
http://psico.fcep.urv.es/utilitats/factor/index.html.
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The CIUS data were found to be suitable for factor analysis since they returned acceptable inter-item correlation matrix values between 0.20 and 0.72, a Kaiser–Meyer– Olkin (KMO) [47] test value of 0.92 (very good), and a statistically significant Bartlett’s statistic [64], v2 = 2841.3, df = 91, p \ 0.0001. The inter-item correlation matrix values suggest that the CIUS data were evaluating similar phenomena or behaviors of the study participants. Afterwards, EFA was performed using ‘‘Maximum Likelihood (ML)’’. The loadings for all 14 CIUS items were well above the minimum threshold of 0.50, except Item 4 which was 0.47, thus showing good factor loading for the collected data (Table 2). The analysis returned a two-factor solution that explained 46.8 % of the variance based on the eigenvalues known as the Kaiser Criterion [47], where the eigenvalues were 6.55 and 1.31. It should be noted that relatively recent research studies (e.g., the Arabic and French CIUS validations) have shown that reliance alone on the Kaiser criterion to decide the number of factors may eventually overestimate the factor number. Due to this reason, to reconfirm the results obtained from the Kaiser criterion, the obtained factorial structure for the CIUS was re-examined using the Factor tool and two well-known statistical tests, namely Parallel Analysis (PA) [65] with optimal implementation, and Velicer’s Minimum average partial (MAP) [66]. It was found that MAP returned one dimension, with an average partial value of 0.022, while PA returned two dimensions. Careful examination of the PA results showed that the second eigenvalue of 1.31 was only slightly higher than the criterion value of 1.24 generated by PA, and was within one standard deviation. Hawi [35] recommended that if the eigenvalue falls within one standard deviation of the criterion value, then it should be rejected. Therefore, the PA also resulted in a one-factor solution. Finally, Catell’s scree test [67] was examined, and the scree plot also confirmed the presence of a one-factor solution for the Chinese CIUS. This also shows that the Kaiser criterion actually overestimated the number of factors for the CIUS. Results of Confirmatory Factor Analysis (CFA) Confirmatory factor analysis (CFA) was performed to verify the one-dimensional factorial structure. The CFA was performed using a one-factor model, where the latent variable was labeled ‘‘Compulsive internet use’’, and 14 observed variables were the CIUS items. In order to determine the model fit, various goodness of fit indices were examined. The CFA involves testing two models, namely Model A (where correlation between residual error terms was not permitted), and Model B (where correlation between residual error terms was permitted), as suggested by previous psychometric validation studies [18, 45]. Model A did not pass several of the criteria, including the v2 and df ratios, the RMSEA was too high, and the GFI and AGFI were too low; hence this model was rejected (Table 2). Afterwards, the fit for Model B was examined using CFA. The correlation between residual error terms should be performed if the standard residual covariance exceeds the cutoff value of 1.96 [68] and meets the criteria given by Kline [69]. According to Kline [69], the Pearson correlation of error variances should be greater than 0.30, and connecting residual errors should be theoretically grounded. Eleven pairs of residual error terms were connected, namely: Items 1 and 2 (r = 0.72), Items 2 and 3 (r = 0.48), Items 6 and 7 (r = 0.59), Items 8 and 9 (r = 0.61), Items 10 and 11 (r = 0.67), Items 12 and 13 (r = 0.72), Items 13 and 14 (r = 0.47), Items 2 and 11 (r = 0.61), Items 12 and 14 (r = 0.41), Items 2 and 8 (r = 0.49), and Items 7 and 12 (r = 0.40). Model B returned improved goodness of fit indices: CFI = 0.99, GFI = 0.97, AGFI = 0.95, and RMSEA = 0.04 (see Table 2). This shows that Model B has a good model fit since it meets the cut-off scores for all of the goodness of fit indices [69–71].
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Psychiatr Q Table 2 Factor loadings, goodness of fit indices and reliability coefficients Items
Chinese CIUS
EFA
Model A
Model B
Item 1
Do you find it difficult to stop using the INTERNET when you are online?
0.73
0.63
0.61
Item 2
Do you often continue to use the INTERNET despite your intention to stop?
0.74
0.70
0.65
Item 3
Do others (e.g., friends and family) say you should use the INTERNET less?
0.56
0.62
0.62
Item 4
Do you prefer to use the INTERNET instead of spending time with others (e.g. friends and family)?
0.47
0.42
0.43
Item 5
Are you short of sleep because of the INTERNET?
0.66
0.68
0.70
Item 6
Do you think about the INTERNET, even when not online?
0.71
0.64
0.65
Item 7
Do you look forward to your next INTERNET session?
0.66
0.67
0.67
Item 8
Do you think you should use the INTERNET less often?
0.62
0.67
0.64
Item 9
Have you unsuccessfully tried to spend less time on the INTERNET?
0.80
0.87
0.87
Item 10
Do you rush through your (homework)/(schoolwork) in order to go on the INTERNET?
0.72
0.77
0.76
Item 11
How often do you neglect your daily obligations (work, school, or family life) because you prefer to go on the INTERNET?
0.77
0.79
0.77
Item 12
How often do you go on the INTERNET when you are feeling down?
0.50
0.51
0.46
Item 13
How often do you use the INTERNET to escape from your sorrows or get relief from negative feelings?
0.53
0.58
0.55
Item 14
How often do you feel restless, frustrated, or irritated when you cannot use the INTERNET?
0.59
0.58
0.58
Fit indices
Recommended values
2
v
521.17
101.51
v2 ratio df
B3.0
6.77
1.54
CFI
C0.95
0.94
0.99
GFI
C0.95
0.85
0.97
AGFI
C0.95
0.80
0.95
RMSEA
\0.05
0.12
0.04
a
p [ 0.70
0.91
0.91
Validity and Reliability In the present study, different forms of instrument validities and reliabilities were examined in the context of the Chinese CIUS. These include: (1) Content validity This evaluates the extent of correspondence between the CIUS items and the underlying syndrome the items are measuring (i.e., compulsive internet use). To ensure the content validity, all the CIUS items were derived from the previous CIUS validations. This shows that an expert panel of International IA researchers, reviewers and audience interested in IA research had already reviewed the CIUS items; (2) Face validity This examines whether the instrument items appear valid or not for the study participants. A brief pilot study was organized with the target user group of adolescents in order to revise any confusing or difficult CIUS items. The pilot study therefore ensured the face validity of the Chinese CIUS; (3)
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Psychiatr Q
Concurrent validity This examines the degree to which the CIUS is related to other constructs or scales, and whether their relationship is consistent with the earlier findings from the IA research. It was found that the CIUS was highly correlated with the Internet Addiction Test (IAT) (r = 0.847**) and daily time spent on internet use (r = 0.232**). This shows that the Chinese CIUS fulfills the concurrent validity; (4) Construct reliability The Chinese CIUS returned a very good Cronbach’s alpha value of 0.913, representing the internal consistency of the measure. This shows that the Chinese CIUS possesses very good construct reliability. Relation Between the CIUS, Demographics and External criteria Pearson correlation with 95 % confidence interval was utilized to investigate the relationship between the CIUS and other variables. The CIUS scores were in significant correlation with daily internet use (r = 0.23**) and academic performance (r = -0.14**). An independent samples t test (t = 2.20, sig = 0.03) revealed that male adolescents (M = 39.00, SD = 9.92) tend to experience more compulsive internet use than female adolescents (M = 36.91, SD = 9.02). The correlation analysis found no significant relationship between CIUS scores and number of years of internet use (r = -0.03), internet connectivity at home (r = 0.05), mobile phone ownership (r = 0.02), internet use on mobile phones (r = -0.002), or family economic condition (r = -0.02). A one-way ANOVA test revealed that the CIUS scores did not share any significant relationship with the age of the participants (F = 0.73, p = 0.91), religion (F = 1.08, p = 0.34), or parental occupation (F = 0.58, p = 0.99). Predicting CIUS Scores Using Demographic and External Criteria Hierarchical regression was performed in order to predict the relative influences of various demographic variables and external criteria on the CIUS scores of the participants (see Table 3). It was found that daily time spent on the internet was a significant positive predictor of the CIUS scores. On the other hand, the gender variable (male = 1, female = 2) and the academic performances of the participants were significant negative predictors of the CIUS scores. Demographic variables explained 3.3 % and external criteria explained 8.2 % variance in the CIUS scores among the adolescents.
Discussion The present study has examined the psychometric properties of the Chinese version of the CIUS with adolescents aged 15–18 years. Psychometric validation resulted in a one-factor solution with a good model fit. These results confirm the findings of previous CIUS validations in Arabic, Dutch, German, and French. The Chinese CIUS possesses excellent internal consistency, as indicated by a value of 0.91. The a value for the Chinese CIUS was better than that of the Arabic and French CIUS, the same as that of the Dutch CIUS, and comparable to that of the German CIUS validation (a = 0.93). Furthermore, the goodness of model fit indices for the Chinese CIUS were better than those of all previous CIUS validations. Until now, three diagnostic instruments, namely the IAT (20-item), the CIAS (26-item) and the CIAS-R (19-item) have been psychometrically validated with Chinese internet
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Psychiatr Q Table 3 Predicting the influence of demographics and external criteria on CIUS score Predictors (stepwise regression)
Compulsive Internet Use Scale (CIUS) Score Beta
t value
Block 1: demographics Age
-0.04
-0.89
Gender (male = 1; female = 2)
-0.10**
-2.14
Religion
-0.05
-0.99
Parental occupation
-0.02
-0.37
Academic performance
-0.13**
Family economic condition
0.00
R2 value
0.03
2.57 -0.01
Block 2: external criteria Internet connectivity at home Mobile phone ownership
0.05
0.98
-0.02
-0.39
Internet on mobile phone
0.04
0.72
Daily time spent on internet
0.22**
4.50
Number of years of internet use
-0.03
Final R2 value
0.08
Increment in R2 value
0.05
-0.53
The table represents standardized beta coefficients and t value from the final hierarchical regression carried out using dependent and independent variables of our sample data ** p \ 0.01
users. All previous validations have resulted in multiple-factor solutions ranging from three to four factors. In contrast to previous validations, our present study not only resulted in a single-factor solution with a good model fit, but it also provided an easy to use, concise and brief (only 14 items) instrument. The study results show that male adolescents tend to experience more compulsive internet use than female adolescents. Similarly, adolescents who spend more time on the internet daily and have lower academic performance (bottom 10 = 1, top 10 = 4) tend to score high in compulsive internet use. These findings were also supported by the results of the hierarchical regression, where daily time spent, academic performance, and gender were the three strongest predictors of compulsive internet use. Similar findings were revealed by the earlier IA research, that is, male internet users tend to experience higher compulsive internet use compared to female adolescents [5, 27, 72, 73], and time spent on internet use is directly proportional to experiencing compulsive internet use [18, 44]. In addition to this, it was found that those adolescents who tend to increasingly utilize the internet actually do not exhibit good academic performance. Furthermore, adolescents experiencing compulsive internet use belong to the ‘‘below average’’ or ‘‘bottom 10’’ category of academic performance. The study results did not find any significant relationship between the CIUS, number of years of internet use, internet connectivity at home, mobile phone ownership, internet use on mobile phones, or family economic condition. These results suggest that technology accessibility among compulsive and non-compulsive users is similar in the case of Taiwanese adolescents. The possible reasons could be: (1) Taiwan has experienced a very high
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Psychiatr Q
penetration of ICT use (97.1 % internet connectivity at home and 95.9 % mobile phone ownership in the sample) due to which compulsive and non-compulsive internet users possess the same level of ICT penetration. Furthermore, due to the high ICT penetration, compulsive and non-compulsive internet users did not differ in the total number of years of internet use experience; (2) Similar findings were reported by Leung [9] who found no difference between internet addicts and non-addicts in their economic status and education variables. The possible reasons were availability of low cost computing devices and cheaper internet data plans. These results are in agreement with the findings of the multiple hierarchical regressions. The CIUS scores did not share any significant relationship with age, religion, or the parental occupation of the study participants. To the best of our knowledge, none of the previous studies have examined the relationship between the aforementioned variables and the CIUS, or even any other internet addiction instrument. Therefore the possible reasons could be: (1) The age group of the study respondents was narrow, i.e. 15–18 years, and use of the internet is emphasized in all grades of Taiwanese schools. These could be possible reasons behind there being no relationship between age and the CIUS; and (2) Due to the high ICT penetration in Taiwanese society, low cost computing devices and cheaper internet data plans, no significant relationship between religion or parental occupation and the CIUS was found. Similar results were found in the multiple hierarchical regressions.
Study Implications The present study has the following theoretical and practical implications: (1) internet use among Chinese internet users is increasing. Due to this, IA researchers and practitioners will require validated, reliable and tested diagnostic instruments for compulsive internet use among internet users. This need is fulfilled by the present study to some extent; (2) The majority of the existing diagnostic instruments for IA screening among Chinese internet users are long, e.g. the 20-Item IAT, the 26-item CIAS and the 19-item CIAS-R. In comparison, the present study has provided a validated and tested scale which is the shortest scale to date; (3) The Chinese version of the CIUS is valid, reliable, economical, and suitable for both research and clinical applications. Furthermore, due to having fewer items, the Chinese CIUS might ease the problem of participant fatigue while answering, which IA researchers often try to control to make tests suitable for online surveys; (4) The CIUS is a handy instrument for schools, students, researchers and practitioners that can provide timely and economical screening of compulsive internet use among internet users; (5) The present CIUS validation has confirmed the findings of earlier CIUS validations in the Dutch, German, French and Arabic versions; and finally (6) The present study has revealed new knowledge for IA researchers and practitioners by examining the relationship between compulsive internet use and adolescents’ demographic profile and external criteria.
Study Limitations and Future Work The present study has the following limitations: (1) It was organized with a sub-set of the adolescent population, i.e. 15–18 year old adolescents, so the findings of our study might not be applicable to the entire range of adolescents aged between 10 and 19 years. Furthermore, it is not known at this stage if the present study findings are applicable to adults
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Psychiatr Q
or young adults; (2) The present study was organized with participants from only three schools in Taipei; therefore we cannot guarantee the applicability of the study findings to adolescent internet users throughout the whole of Taiwan; and (3) Two study variables, namely academic performance and family economic condition, were unitary (i.e., they were single-item constructs). It is quite possible that these variables are not able to address the complex notion behind academic performance and economic status. Therefore, the present study might also involve bias related to both these variables. For future studies, IA researchers may investigate and compare the prediction power of the CIUS with other instruments, such as the CIAS-R and the IAT, to predict time spent and frequency of internet use, behavioral changes, and problematic behavior due to internet overuse. The process of comparing various instruments should examine their similarities and differences. Such comparisons could result in recommendations for IA researchers and practitioners for the possible utilization of the instruments. Future studies may validate the Chinese CIUS with internet users from Mainland China, Hong Kong, and Singapore. In addition to this, we suggest that IA researchers perform similar validation studies with various age groups, not just adolescents. This will ensure the generalizability of the study results. Furthermore, it is worth examining the relationship between new constructs and CIUS, and comparing those findings with the present study results. Finally, at present, no statistically proven cut-off score for the CIUS exists for dichotomizing compulsive and non-compulsive internet use. We suggest that IA researchers should develop statistical cutoff scores for the CIUS. Acknowledgments This research was conducted in the Future Industrial Services (FutIS) research program (Project No. 2113194), managed by the Finnish Metals and Engineering Competence Cluster (FIMECC), and funded by the Finnish Funding Agency for Technology and Innovation (TEKES), research institutes and companies. Their support is gratefully acknowledged. The support received from Academy of Finland in the form of researcher’s mobility grant to Taiwan (Decision No. 265969) and South Africa (Decision No. 277571) is acknowledged. Additionally, we would like to acknowledge the support received from Ministry of Science and Technology, Taiwan, under grant number NSC 102-2628-S-011-001-MY4. Conflict of interest The authors declare that they have no conflict of interest.
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Amandeep Dhir, Msc (Tech) is a project researcher at Department of Computer Science and Engineering, Aalto University and also a doctoral candidate at Institute of behavioral science, University of Helsinki, Finland. Sufen Chen, PhD is a Professor at Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology, Taiwan. Marko Nieminen, PhD is a professor of human–computer interaction, School of Science, Aalto University (formerly Helsinki University of Technology), Finland.
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