Evaluation of the Psychometric Properties
of the Italian Internet Addiction Test
Giulia Fioravanti, PhD, and Silvia Casale, PhD
Abstract
Since the diffusion of Internet addiction has emerged in several cultural contexts, it seems relevant to study the
properties of the Internet Addiction Test (IAT)—the most widely used screening instrument—across various
cultures. In Italy, only one study has examined the IAT factor validity, and a comprehensive investigation of its
psychometric characteristics is so far lacking. The purpose of this study was to perform a psychometric analysis
of the Italian IAT. A total of 840 students (
M
age=
18.65 years,
SD
=
3.85 years; 59% female) were recruited.
Pertaining to scale dimensionality, the best-fit measurement model includes two factors: ‘‘Emotional and
cognitive preoccupations with the Internet and social consequences’’ and ‘‘Loss of control and interference with
daily duties’’ (
v
2/
df
=
3.38; comparative fit index
=
0.88; Tucker–Lewis Index
=
0.87; root mean square error of
approximation
=
0.07), which together explained 45.59% of the variance. Internal consistency Cronbach’s alpha
values ranged from 0.83 to 0.86. Convergent validity was demonstrated, with significant correlations between
IAT and Generalized Problematic Internet Use Scale 2 scores. The Italian version of the IAT was found to have
good psychometric properties and a two-factorial structure. Identification of the IAT dimensions may help to
define the construct better and develop intervention strategies.
Introduction
I
n recent years, there has been increasing interest in the negative effects of Internet use, and a variety ofterms, such as Internet Addiction (IA),1problematic
Inter-net use (PIU),2and Internet dependency,3have been used to
describe them. Despite some differences, these terms share the assumption that the Internet has the potential to create psychological, social, school, and/or work difficulties in a
person’s life.4
In the extant literature, the most widespread conceptual and research approach is the IA perspective, which defines IA as a behavioral addiction, classifying it as an impulse control disorder that is characterized by an inability to con-trol Internet use, leading to negative consequences in
ev-eryday functioning (for a review, see Morahan-Martin5and
Young et al.6). Within this framework, Young,7borrowing
from the DSM-IV criteria for pathological gambling, de-veloped an eight-item ‘‘yes/no’’ Internet Addiction Diag-nostic Questionnaire (IADQ) in order to survey the existence and prevalence of IA. Her criteria for PIU included: preoc-cupation with the Internet (thinking about previously online activity or anticipating future online sessions); the need to spend increasingly long periods online in order to achieve satisfaction; repeated unsuccessful attempts to control or
stop Internet use; suffering withdrawal symptoms (feeling restless, depressed, or irritable) when attempting to cut down or stop Internet use; time management problems (staying online longer than originally intended); environmental dis-tress (family, school/work, friends) because of the Internet; deception regarding time spent online (lying to others to conceal the extent of involvement with the Internet); and mood modification (using the Internet as a way of escaping from problems or relieving a dysphoric mood). Individuals who met five of eight criteria over a 6 month period were
qualified as Internet addicts. In a later study, Young1
ex-panded the IADQ and developed the Internet Addiction Test (IAT), a 20 item questionnaire that aims to measure the symptoms and the severity of IA. The items cover Internet use habits, preoccupation with Internet use, ability to control online use, and the extent of lying or hiding about online use and related problems in everyday functioning. Respondents are asked to consider only the time spent online for nonac-ademic or nonjob purposes when answering. For each item, a 5-point scale response (from ‘‘rarely’’ to ‘‘always’’) can be se-lected; the higher the item scores, the greater the levels of IA. Several other questionnaires assessing IA have also been
de-veloped along these lines (for a review, see Lortie and Guitton8).
The IAT is the first validated instrument to measure IA. Its psychometric properties have been evaluated in several
Department of Health Sciences, Psychology and Psychiatry Unit, University of Florence, Florence, Italy. Volume 18, Number 2, 2015
ªMary Ann Liebert, Inc. DOI: 10.1089/cyber.2014.0493 120
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international studies, including ones from the United States,9
the United Kingdom,10,11 China,12 France,13 Finland,14
Germany,15,16Italy,17Portugal,18Cyprus,19and Lebanon.20
Whereas the internal consistency of the IAT has been good in these studies, the dimensionality assessment of the IAT factor structure has provided ambiguous results (for a sum-mary of previous findings, see the Appendix). Indeed, while the IAT was developed as a unidimensional scale, the number of extracted factors and factor arrangements varied across studies, thus suggesting a multidimensional nature of the IA construct. The proposed factorial solutions comprise
one main factor,13,14,19,20two factors,9,15,16three factors,11,12
and six factors.10,17The amount of explained variance ranges
from 41% to 91%. With regard to factor arrangements, previous studies have described different aggregations of items. They covered three macro-areas: failure to control or reduce the amount of time spent online, negative outcomes in social life and in academic/work performance, and salient use and use of the Internet for relieving real life problems and for modifying negative mood states.
Such diverse factor structures can be explained by several different causes, including, most notably, the inconsistent definition of the construct itself. Generally speaking, the addiction framework has been criticized because it lacks conceptual or theoretical specificity. For instance, Da-vis2(p187) argues that the current addiction perspective is
‘‘loosely described.’’ Shaffer et al.21(p164) similarly argue
that ‘‘Internet addiction may be misleading as a category in which to group all problems with excessive computer or Internet use,’’ without identifying what people are actually doing online. In this sense, differentiation between single applications does not emerge in IAT items that fail to ac-count for what it actually is that people are addicted to. A second potential reason for the divergent findings are cultural and age-related differences, as the samples came from dif-ferent countries and from difdif-ferent age groups. Finally, methodological issues could be at least partially responsible for the variations of the proposed factor structures. Different sample sizes (ranging from 86 to 1,825 in the aforementioned studies), and most importantly the different factor analytic techniques (principal component analysis, exploratory factor analysis [EFA], confirmatory factor analysis [CFA]) and decision heuristics used (Kaiser’s criterion, Cattell’s scree test, maximum likelihood method, Horn’s parallel analysis, and minimum average partial) can influence the factor structure obtained.
With respect to the validity of the IAT, particularly its convergent validity, earlier studies have examined how the IAT and its dimensions correlate with a number of criterion variables. For example, age significantly correlated with IAT total and factor scores, with younger users showing high
levels of IA.10,13,15,16Some studies have found that females
had lower IAT scores than males.11,14–16 However, other
studies did not find significant differences between males and
females in IAT total and dimension scores.10,12Time spent
online per day and per week significantly correlated with
IAT scores.10,11,13,15–17 Differences in IAT scores among
people involved in different types of Internet use (e.g., in-formation searching and playing games) were not found by
some studies,10whereas other studies have found that the use
of specific Internet applications (e.g., cyber relationships, online gambling, and playing games) is strongly related to
IAT scores.12–14 Moreover, academic performance,12
psy-chiatric symptoms, impulsivity, neuroticism, and
conscien-tiousness were correlated with IAT total and factor scores.15
Finally, Barke et al.16 found that IAT scores correlated
with the Generalized Problematic Internet Use Scale 2
(GPIUS2),22a measure of pathological involvement in the
unique communicative context available online.
Since the diffusion of PIU has emerged in several different
cultural contexts,23–32it seems relevant to study the properties
of the IAT across various cultures. In Italy, a recent study33
assessed the prevalence of PIU in a sample of 2,853 high school students, finding that 1.2% of the participants were addicted to the Internet. However, the psychometric characteristics of the Italian version of the IAT were not extensively studied. Only
Ferraro et al.17have examined the IAT for its factor validity in
a sample of 236 Italian chatters, suggesting a six factor solu-tion. Nevertheless, data about its dimensionality explored with a confirmatory approach, its reliability, and its validity are not available. Given that the IAT is the most frequently used di-agnostic instrument for measuring symptoms of IA, it is worth investigating its psychometric properties carefully. Moreover, identifying the dimensionality of the scale may help to focus treatment on different aspects of IA.
For these reasons, the aims of the present study are to ex-amine, refine, and validate the dimensionality of the Italian version of the IAT while using a confirmatory approach and investigating its internal consistency and convergent validity.
Method
Participants and procedure
A total of 840 students between the ages of 14 and 26 were
recruited (M=18.65 years, SD=3.85; 59% females).
Ap-proximately half of the sample (50.5%) was recruited from public high schools in Florence. The remaining sample was recruited in the study rooms of the Universities of Florence and Perugia, Italy. The students were approached at the end of the lectures by a female research assistant. General in-formation about the purposes of the study was announced to the participants. Participation was voluntary and anonymous. Participants younger than 18 years of age needed written consent from their parents. Students filled out questionnaires in a classroom setting.
Measures
Demographic information as well as self-reports regarding the number of hours spent online in a typical week (ex-cluding study-related use of the Internet) were collected. The
Italian version17of the IAT1and the Italian version34of the
GPIUS222 were administered. The GPIUS2 contains 15
Likert-type items rated on an 8-point scale (from ‘‘definitely disagree’’ to ‘‘definitely agree’’). The items form five sub-scales: Preference for Online Social Interaction (POSI), Mood Regulation (MR), Cognitive Preoccupation (CP), Compulsive Use (CU), and Negative Outcome (NO). The Italian version of the GPIUS2 showed good internal
con-sistency (Cronbach’sa=0.78–0.89).
Statistical analysis
In order to explore the psychometric properties of the
Italian version of the IAT, the original data set (n=840) was
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randomly divided into two equal subsamples: one for EFA and the other for CFA. The EFA was conducted first to identify the underlying factor structure of the IAT scale. The CFA was then performed in order to validate the results of the EFA.
The EFA was conducted using SPSS v19.0 with principal axis factor analysis employed as an extraction method with promax rotation. The suitability of the data for factor anal-ysis was tested with the Kayser–Meyer–Olkin (KMO) mea-sure of sampling adequacy and Bartlett’s test of sphericity. The number of factors to be extracted was determined by the examination of the scree plot in combination with the
con-ventional cutoff of eigenvalues >1. The promax rotation, an
oblique rotation, was used because it is reasonable to assume that any extracted factors relevant to IA might be inter-correlated. The internal consistency of each factor was ex-amined by calculating Cronbach’s alpha coefficient.
The CFA was performed to test the fit of the factor structure identified through EFA and the one-, two-, and six-factor solutions proposed in previous European stud-ies.10,13,14,16,17,19 Analysis of Moment Structures (AMOS) v19 was used to conduct the CFA. The criteria for assessing overall model fit were mainly based on practical fit measures:
the ratio of chi square to its degree of freedom (S–Bv2/df), the
comparative fit index (CFI),35 the Tucker–Lewis Index
(TLI),36 and the root mean square error of approximation
(RMSEA). 37 For the ratio of chi square to its degree of
freedom (S–Bv2/df), values<3 were considered to reflect fair
fit.38We considered CFI and TLI valuesq0.90 to reflect fair
fit.35For the RMSEA, valuesp0.08 were considered to
re-flect adequate fit.39Finally, in order to test the IAT’s
con-vergent validity, a series of correlation analyses were conducted using the entire sample. Correlations between the IAT score and sex, age, online-time in a typical week, and the GPIUS2 and its subscales were computed. The correla-tion between sex and IAT score is point biserial, whereas all other correlations are based on Pearson’s product moment coefficient.
Results
EFA
Data from the first subsample (n=403) were submitted to
EFA in order to investigate the dimensionality of the IAT scale. Principal axis factor analysis with promax rotation was used. According to the KMO criterion, sampling adequacy
was excellent (KMO=0.91). Bartlett’s test of sphericity
showed that the correlation matrix was suitable for factor
analysis (v2=3,214.41, df=190, p<0.001). Using the
con-ventional criterion for retaining factors with eigenvalues
>1.0 and the scree plot, a two-factor solution was identified,
with the extracted factors explaining 45.59% of the total variance. All items loaded at 0.30 or above.
As shown in Table 1, the first factor contains 11 items and relates to the use of the Internet to alleviate negative mood states, to obsessive thoughts about Internet when offline, and to negative outcomes in social life and in social relationships due to Internet use; this factor was called ‘‘Emotional and cognitive preoccupations with the Internet and social con-sequences.’’
The second factor consists of nine items and relates to the attempts to control the amount of time spent online and to the
negative consequences of the Internet use on daily func-tioning; this factor was named ‘‘Loss of control and in-terference with daily duties.’’ There was a high linear
correlation between the two factors (r=0.69). Internal
con-sistency of the two factors is reported in Table 1. Cronbach’s alpha values did not increase when an item was deleted, and all item-corrected total correlations were above 0.30.
CFA
To verify the factor structure identified through EFA, CFA
was performed on the second subsample (n=437).
Mod-ification indices suggested adding the error covariance be-tween item 3 and item 19, item 17 and item 18 (referring to factor 1), item 1 and item 16, and item 6 and item 8 (referring to factor 2). After adding these constraints (due to their similar content), an acceptable fit for the two-factor solution was obtained (see Table 2). The path diagram and the stan-dardized path coefficients are shown in Figure 1. Standar-dized factor loadings ranged from 0.25 to 0.73, all of which were significant at the 0.001 level, as well as the estimated correlations among errors. The results of the CFA conducted on the current data to test the factor solutions proposed in the literature are shown in Table 2. The comparison of the
one-factor model (found by Khazaal et al.,13 Panayides
and Walker,19and Korkeila et al.14), the two-factor solution
(found by Barke et al.16), and two different six-factor
solu-tions (found by Widyanto and McMurran10 and Ferraro
et al.17) with our factor solution showed that our
two-factor solution achieved the best fit (Table 2).
Convergent validity
Correlations between the IAT total score and gender, age, online time in a typical week, and the GPIUS2 and its subscales were computed. IAT score and gender were not
significantly correlated (rpb=–0.05; p=0.11). IAT score
correlated slightly with age (r=–0.12; p<0.001) and
mod-erately with the time spent online in a typical week (r=0.29;
p<0.001). IAT score correlated highly with the GPIUS2
total score and with the GPIUS2 subscales scores (Table 3). Regarding GPIUS2 dimensions, the highest correlations were found for the subscales CU and CP and the lowest correlation for POSI.
Discussion
The Italian version of the IAT was found to have good psychometric properties. It possessed good internal consis-tency, which would not have benefitted from removing any item from the questionnaire. However, it is worth noting that item 7 showed the lowest item-total correlation coefficient and the lowest factor loading, probably because this item refers to a common behavior (i.e., e-mail checking) that cannot be necessarily considered a behavioral symptom of PIU.
A two-factor solution for the Italian IAT was found that explained 45.59% of the total variance. The first factor, ‘‘Emotional and cognitive preoccupations with the Internet and social consequences,’’ encompasses items related to the emotional and cognitive salience of Internet use, such as the presence of negative feelings (e.g., feeling bored, depressed, or nervous) and obsessive thoughts about the Internet when
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Table1. Item Descriptive Statistics, Factor Loadings, and Internal Consistency
Factor loading
Item wording M(SD)
Corrected item-total
correlation Factor 1 Factor 2
(1) How often do you find that you stay online longer than you intended?
2.84 (1.05) 0.43 0.28 0.78
(2) How often do you neglect household chores to spend more time online?
1.71 (0.88) 0.62 0.11 0.59
(3) How often do you prefer the excitement of the Internet to intimacy with your partner?
1.23 (0.63) 0.44 0.49 0.01
(4) How often do you form new relationships with fellow online users?
2.10 (1.02) 0.40 0.01 0.43
(5) How often do others in your life complain to you about the amount of time you spend online?
2.03 (1.17) 0.65 0.24 0.48
(6) How often do your grades or school work suffer because of the amount of time you spend online?
1.89 (1.06) 0.67 0.17 0.61
(7) How often do you check your e-mail before something else that you need to do?
2.10 (1.15) 0.34 0.01 .39
(8) How often does your job performance or productivity suffer because of the Internet?
1.68 (.95) 0.66 0.26 .51
(9) How often do you become defensive or secretive when anyone asks you what you do online?
1.98 (1.13) 0.55 0.32 .29
(10) How often do you block out disturbing thoughts about your life with soothing thoughts of the Internet?
1.54 (.85) 0.57 0.60 .05
(11) How often do you find yourself anticipating when you will go online again?
1.62 (.93) 0.61 0.58 .12
(12) How often do you fear that life without the Internet would be boring, empty, and joyless?
1.55 (.91) 0.51 0.73 .14
(13) How often do you snap, yell, or act annoyed, if someone bothers you while you are online?
1.83 (1.06) 0.56 0.46 .17
(14) How often do you lose sleep due to late-night log ins? 1.76 (1.06) .52 0.15 .44
(15) How often do you feel preoccupied with the Internet when offline, or fantasize about being online?
1.36 (.73) .57 0.71 .05
(16) How often do you find yourself saying ‘‘just a few more minutes’’ when online?
2.64 (1.26) 0.60 0.11 0.79
(17) How often do you try to cut down on the amount of time you spend online and fail?
1.72 (1.05) 0.60 0.48 0.21
(18) How often do you try to hide how long you have been online?
1.55 (.92) 0.61 0.48 0.22
(19) How often do you choose to spend more time online over going out with others?
1.36 (.74) 0.50 0.63 0.06
(20) How often do you feel depressed, moody, or nervous, when you are offline, which goes away once you are back online?
1.30 (.77) 0.60 0.86 0.17
Explained variance (%) 37.61 7.99
Cronbach’s alpha 0.86 0.83
Note.The higher of the two-factor loadings are shown in bold.
Table2. Fit Indices of the Confirmatory Factor Analysis
Model v2 df v2/df RMSEA (IC 90) RMR TLI CFI
2-factor model 557.71 165 3.38 0.07 (0.06–0.08) 0.05 0.87 0.88
1-factor model1 607.12 166 3.66 0.08 (0.07–0.08) 0.05 0.85 0.87
2-factor model2 664.79 167 3.98 0.08 (0.07–0.09) 0.06 0.83 0.85
6-factor model3 700.43 155 4.52 0.09 (0.08–0.09) 0.07 0.80 0.84
6-factor model4 569.59 155 3.67 0.08 (0.07–0.08) 0.05 0.85 0.87
Comparison of our two-factor solution with the factor structures suggested in the literature. 1Khazaal et al. 2008; Panayides and Walker 2012; Korkeila et al. 2010.
2
Barke et al. 2012.
3Widyanto and McMurran 2004. 4
Ferraro et al. 2007.
RMSEA, root mean square error of approximation; RMR, root mean square residual; TLI, Tucker–Lewis index; CFI, comparative fit index.
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offline, and items concerned with the negative social con-sequences due to Internet use, such us preferring online ac-tivities to social interactions with others. The second factor, ‘‘Loss of control and interference with daily duties,’’ con-tains items related to unsuccessful attempts to control the amount of time spent online (e.g., staying online longer than intended) and to the negative consequences of the Internet use on daily functioning (e.g., sleep disturbances and low school/job performance or productivity). In the literature, different factor solutions were proposed. Both the number of factors extracted and the item aggregations varied across studies, probably because of the inconsistent conceptual
specificity of the IA construct, cultural and age-related dif-ferences, and methodological issues. Comparing our two-factor solution with the two-factor structures found in other European countries, the greatest agreement was found with
the German IAT factor solution.16Only three items (4, 5, and
17) were grouped into a different factor.
The two-factor solution proved itself superior to the other factor solutions proposed in the literature. The fact that a two-factor solution fitted better than a one-factor model suggests that the IA construct has a multidimensional na-ture. Compared with the six factors identified by Widyanto
and McMurran10 and by Ferraro et al.,17 the present
find-ings suggest that IA dimensions clustered together more strongly in this study’s sample. This result is in line with the
cognitive–behavioral model of PIU,2,22which postulates an
interplay between the dimensions that constitute IA. The first factor, ‘‘Emotional and cognitive preoccupations with the Internet and social consequences,’’ is comprised of two as-pects: the emotional and cognitive salience of Internet use, and the negative social consequences that arise due to In-ternet use. The results of the current study suggest that these two dimensions load on a single factor, demonstrating the strong interplay between both. The presence of negative feelings (e.g., feeling bored, depressed, or nervous) and ob-sessive thoughts about the Internet when offline can add stress to interpersonal relationships by leading to a prefer-ence for online activities over social interactions with others. FIG. 1. Path diagram for
the confirmatory factor ana-lysis of the Italian IAT.
Table3. Pearson’s Correlation Coefficients Between IAT Score and GPIUS2 Scores
IAT total score
GPIUS2 total score 0.78*
Preference for Online Social Interaction 0.41*
Mood Regulation 0.51*
Cognitive Preoccupation 0.70*
Compulsive Use 0.71*
Negative Outcomes 0.58*
*p<0.001.
IAT, Internet Addiction Test; GPIUS2, Generalized Problematic Internet Use Scale 2.
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Similarly, the second factor, ‘‘Loss of control and interfer-ence with daily duties,’’ contains the compulsive use di-mension and the negative outcomes at school/work due to Internet use. The relationship between these two dimen-sions was already reported: compulsive use was found to be a predictor of negative consequences due to Internet use (e.g.,
Young1and Caplan22).
In the analysis, the first factor, ‘‘Emotional and cognitive preoccupations with the Internet and social consequences,’’ explains most of the variance in IAT score, suggesting that the cognitive and emotional salience of Internet use and the preference for online activities to social interactions with others are better indicators of IA than the loss of control of time spent online and the interference of Internet use on daily functioning. This finding can be useful in understanding the interplay between various problematic dimensions when assessing IA. This was also demonstrated by the intercorre-lation found in the current data between the two factors. In accordance with expectations, the extracted factors that were relevant to IA were found to be strongly intercorrelated. This
finding was also in line with previous studies.9,12,14–16
The Italian IAT showed good convergent validity. In line
with previous studies,16,34high correlations were found
be-tween IAT score and the GPIUS2 scores. Regarding GPIUS2 dimensions, the highest correlations were found for the CU
and CP subscales, both of which were identified by Caplan22
as constituting a second-order factor that has come to be known as Deficient Self-regulation. Thus, within Caplan’s multidimensional perspective of PIU, the IAT items seem to reflect the manifestation of a diminished self-regulation ca-pability. On the other hand, the lowest correlation was found with the GPIUS2 POSI subscale. This is theoretically plau-sible, since the GPIUS2 has been specifically developed for the assessment of that form of problematic use deriving
from the unique communicative context available online,22
whereas the IAT does not distinguish between the compul-sive use of different applications.
A moderate correlation was found between IAT score and the time spent online in a typical week. This result is comparable to previous findings reported in the
litera-ture.10,11,13,15–17 In accordance with some authors,2,22 the
moderate strength of this association suggests that experi-encing problems due to Internet use should be conceptual-ized as something more than just using the Internet for an excessive amount of time.
With regards to gender, no differences emerged in IAT score. Inconsistent results were previously found regarding
the association between gender and IA,40with some studies
reporting higher IA levels among males,11,14–16,41and other
studies finding no gender difference.10,12,17Finally, younger
users showed higher levels of IA, which was consistent with
previous studies.10,13,15,16
There are potential limitations with the present study. First, the convenience sampling technique prevents the re-sults from being treated as representative of the entire pop-ulation. Moreover, because the study focuses on college and undergraduate students, generalizing results to nonstudent and adult populations may not be warranted. However, since students are the most at-risk population in terms of
devel-oping an addiction to the Internet,42,43it is important to study
the psychometric properties of the IAT for this group. Sec-ond, further studies should be conducted to strengthen the
validity of the scale. For example, the relationship between IAT and other variables associated with IA (e.g., the use of different type of Internet applications, psychological symp-toms as depression, social anxiety, impulsivity, and person-ality variables such as extraversion and neuroticism) needs to be evaluated. Third, reliability needs to be further explored for determining test–retest reliability.
Despite these limitations, the present study is the first to present data about certain psychometric properties (e.g., di-mensionality, internal consistency, and convergent validity) of the Italian IAT among a large sample of students. As
recommended by Jia and Jia,44 dimensionality was
inves-tigated using a confirmatory approach, which allows the instrument’s validity to be comprehensively established. Moreover, the results of the present study highlight the po-tential dimensionality of the IA phenomenon. The identifi-cation of dimensions may help to define the construct and its psychological correlates better, both of which can inform subsequent intervention strategies.
Author Disclosure Statement
No competing financial interests exist.
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Address correspondence to: Dr. Giulia Fioravanti Department of Health Sciences Psychology and Psychiatry Unit University of Florence Via San Salvi 12- Padiglione 26 50135 Florence Italy E-mail:[email protected] (Appendix follows/)
For
Review
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Intended
for
Distribution
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Reproduction
Appendix : Previous Studies on the Psychometric Properties of the IAT Sample Factor analysis technique Factor structure Total variance Cronbach’s alpha Widyanto and McMurran 2004 (United Kingdom) 86 adults ( Mage = 25.45 – 8.91 years for males; Mage = 31.44 – 10.34 for females; range 13–67 years) recruited online EFA (orthogonal rotation) Six factors: salience, excessive use, neglect work, anticipation, lack of control, neglect social life 68.16% 0.54–0.82 Ferraro et al. 2007 (Italy) 236 Internet chatter ( Mage = 23.9 – 6.5years; range 13– 50 years) recruited online — Six factors: compromised social quality of life, compromised individual quality of life, compensatory usage of the Internet, compromised academic/ working careers, compromise time control, excitatory usage of the Internet 55.6% — Chang and Law 2008 (Hong Kong) 410 undergraduates recruited in campus, libraries, canteens, computer centers, and student hostels PCA (promax rotation), CFA Three factors: withdrawal and social problems, time management and performance, reality substitute 57.1% 0.60–0.89 Khazaal et al. 2008 (France) 246 undergraduates and volunteers from the community ( Mage = 24.11 – 9 years; range 18– 54 years) EFA,CFA One factor 45% 0.93 Korkeila et al. 2010 (Finland) 1,825 undergraduate and college students ( Mage = 24.7 – 5.7 years) recruited through a web-based survey service EFA (oblimin rotation) One factor and two factors: salient use and loss of control — One factor: 0.92; two factors: 0.91 and 0.81 Widyanto et al. 2011 (United Kingdom) 225 Internet users ( Mage = 25.2 – 9.6 years; range 16–66 years) recruited via a database created from a previous study PCA (oblimin rotation) Three factors: psychological/ emotional conflict, time management problems, mood modification 56.3% — Panayides and Walker 2012 (Cyprus) 604 randomly selected high school students (aged 17–18 years) Rash rating Scale Model One factor 41% 0.99 ( continued 127
For
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Intended
for
Distribution
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Reproduction
Appendix :( Continued ) Sample Factor analysis technique Factor structure Total variance Cronbach’s alpha Barke et al. 2012 (Germany) Offline sample: 841 undergraduates ( Mage = 23.5 – 3 years) recruited in campus. Online sample: 1,041 participants ( Mage = 24.2 – 7.2) recruited online PCA (varimax rotation), CFA Two factors: emotional and cognitive preoccupation with the Internet, loss of control, and interference with daily life 42% (offline), 46.7% (online) 0.83–0.89 Jelenchick et al. 2012 (United States) 215 undergraduates ( Mage = 18.8 years, range 18–20 years) recruited online EFA (varimax rotation) Two factors: dependent use and excessive use 91% 0.91, Pawlikowski et al. 2012 (Germany) 584 participants ( Mage = 25.62 – 6.88 years) recruited through local and Internet advertisements PCA (varimax rotation), CFA Two factors: loss of control/time management, craving/social problems 52.3% 0.87, Conti et al. 2012 (Portugal) 115 undergraduates ( Mage = 23 – 3.7 years) randomly selected — — — 0.84 Hawi 2013 (Lebanon) 817 students from intermediate and secondary public schools ( Mage = 15 – 2.12 years; range 10– 22 years) PCA (oblimin rotation), CFA One factor 40.64% 0.92 128
