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ADICCIONES, 2015 · VOL. 27 ISSUE 1 · PAGES 47-63
47
original adicciones vol. 27, issue 1 · 2015
Adolescents’ use of the Internet is becoming a matter of great concern
for different sectors of society. The psychological and behavioural
consequences of problematic Internet use in young people demands
quick and effective answers. One of the major challenges in this
context is the development of empirically validated tools, which would
facilitate early detection and screening for potential risk cases. This is
precisely the aim of this paper. Based on a sample of 1,709 secondary-
school students from Galicia (a region in northern Spain) aged 11
to 17 (M = 13.74, SD = 1.43), the analysis carried out permitted us to
present a brief and simple tool (with just 11 items). It has substantial
theoretical support, since both the existing background information
and the views of experts from the academic and professional spheres
were taken into account in the course of its development. The scale
is adapted to the Spanish cultural context and to the language of
young people. It has satisfactory psychometric properties in terms of
reliability of the scores (α = .82), evidence of its internal structure
(tested via a Conrmatory Factorial Analysis), sensitivity (81%),
and specicity (82.6%). Moreover, its use enables the gradation of
adolescents on a risk or problematic Internet use continuum. In
our view, all of this lends it enormous applied potential in both the
educational and clinical contexts.
Key words: addiction, adolescents, early detection, Internet,
problematic use, screening.
Abstract
Received: May 2014; Accepted: November 2014
Address for correspondence:
Antonio Rial Boubeta. Facultad de Psicología, C/ Xosé María Suárez Núñez, s/n. Campus Vida-Universidad de Santiago de Compostela.
15782- Santiago de Compostela (Spain). E-mail: antonio.rial.boubeta@usc.es
PIUS-a: Problematic Internet Use Scale in adolescents.
Development and psychometric validation
EUPI-a: Escala de Uso Problemático de Internet en
adolescentes. Desarrollo y validación psicométrica
A R B*; P G S*; M I F**;
M A G*; J V M*
*Universidad de Santiago de Compostela
**Universidad de Vigo
El uso que los adolescentes hacen de Internet viene suscitando una
enorme preocupación en diferentes sectores de la sociedad. Las
consecuencias a nivel psicológico y conductual que el uso problemático
de la Red provoca entre los más jóvenes demandan una respuesta tan
rápida como ecaz. Uno de los grandes retos en este contexto es el
desarrollo de herramientas validadas empíricamente, que permitan
hacer un cribado o detección precoz de posibles casos de riesgo. Ese
es precisamente el objetivo de este trabajo. A partir de una muestra de
1709 escolares de Enseñanza Secundaria Obligatoria de la comunidad
gallega, de edades comprendidas entre los 11 y los 17 años (M
= 13,74; DT = 1,43), los análisis realizados permiten presentar una
herramienta breve y sencilla (compuesta por solo 11 ítems), que goza
de un importante aval teórico, ya que para su elaboración se tuvieron
en cuenta tanto los antecedentes existentes en la literatura, como las
opiniones de expertos del ámbito académico y profesional. Dicha
escala, además de estar adaptada al contexto cultural español y al
lenguaje de los adolescentes, presenta unas propiedades psicométricas
satisfactorias, tanto en términos de abilidad de las puntuaciones
(α = ,82) y evidencias de su estructura interna (probada a través de
un Análisis Factorial Conmatorio), como de sensibilidad (81%) y
especicidad (82,6%), permitiendo “escalar” a los adolescentes en un
continuum de riesgo o uso problemático de Internet. Todo ello le
conere, a nuestro modo de ver, un notable potencial a nivel aplicado,
tanto en el contexto educativo como clínico.
Palabras clave: adicción, adolescentes, cribado, detección precoz,
Internet, uso problemático.
Resumen
adicciones vol. 27, issue 1 · 2015original
ADICCIONES, 2015 · VOL. 27 ISSUE 1
48
PIUS-a: Problematic Internet Use Scale in adolescents. Development and psychometric validation
One of the dening characteristics 21st-cen-
tury society is the generalized use of so-ca-
lled New Technologies (NT) or Information
and Communications Technology (ICT). Accor-
ding to Spain’s National Institute of Statistics (Instituto
Nacional de Estadística [INE], 2014), in Spain 92% of
minors aged 10 to 15 are Internet users. The Web provi-
des access to multiple sources of information, learning,
leisure and personal, academic and professional develo-
pment, as well as to innovative forms of communication,
relation and expression. Without questioning its benets,
we should not overlook the fact that its use also involves
certain associated risks and dangers (loss of privacy, con-
tact with strangers, isolation), in addition to disconcer-
ting practices such as cyberbullying, grooming or sexting,
which are affecting increasing numbers of adolescents,
considered one of the most vulnerable groups in this new
environment (Castellana, Sánchez-Carbonell, Graner, &
Beranuy, 2007; Echeburúa & Corral, 2010; Yang & Tung,
2007).
The use that teenagers make or are able to make of In-
ternet is a topic of concern in Spain and elsewhere (Oliva
et al., 2012; Puerta-Cortés & Carbonell, 2014; Ruiz-Oli-
vares, Lucena, Pino, & Herruzo, 2010; Smahel et al.,
2012; Tsitsika, Tzavela, Mavromati and EU consortium
NET ADB, 2012; Valedor do Pobo, 2011). This general
concern has been heightened in recent years by the so-
metimes sensationalist way in which the subject is treated
in the media. This has helped to create some degree of
social alarm and no little scepticism among researchers
and professionals, who do not consider it appropriate to
speak of Internet addiction per se (Estallo, 2001; Grohol,
1999; Matute, 2001). However, others have indeed taken
this step, attempting to scientic credibility to the use of
this term (Cao & Su, 2007; Young, 1998). Various studies
have tried to provide evidence that the behaviour of some
individuals in relation to the Web fulls the general crite-
ria of an addiction (Echeburúa, 1999; Grifths, 1998), or
even proposed diagnostic criteria (Ko, Yen, Chen, Chen,
& Yen, 2005; Tao et al., 2010; Young, 1996). And while it
is true that neither of the diagnostic manuals of reference
(CIE-10 and DSM-5) currently includes such a category,
it is interesting to mention two innovations that appear
in the DSM-5 (American Psychiatric Association [APA],
2013): on the one hand, Internet Gaming Disorder has been
included in Section III of the manual, reserved for po-
tential new diagnostic categories that require further re-
search and evidence from clinical contexts; on the other
hand, compulsive gambling has been classied as a behaviou-
ral addiction, which leads us to think, in line with other
authors (Cía, 2014; Petry & O’Brien, 2013), that other
behaviours capable of generating the psychopathology
characteristic of addiction could also be fully incorpora-
ted into coming editions of the manual.
Despite the enormous amount of scientic work that
this area has been generating for almost two decades now,
there is still a degree of controversy (García, Beltrán, &
Pérez, 2012; Douglas et al., 2008; Rial, Gómez, Braña, &
Varela, 2014; Sánchez-Carbonell, Beranuy, Castellana, Cha-
marro, & Oberst, 2008; Spada, 2014). An example of this
is the disparity in the prevalence gures estimated by diffe-
rent studies. In the Spanish context the data range from
0.76% of adolescents and young people with severe level of
Internet addiction to 21.88% with moderate addiction (Oliva
et al., 2012), or 3.3% of problematic users and 43.3% of risk
users in young people aged 14 to 18 (Estévez, Bayón, de
la Cruz, & Fernández-Líria, 2009), to 19.9% of problematic
users among secondary-school students (Gómez, Rial, Bra-
ña, Varela, & Barreiro, 2014).
In the case of Europe-wide studies, the data range
from 1% of pathological levels of Internet use found in chil-
dren aged 11 to 16 (Smahel et al., 2012), or the 1.2% of
adolescents aged 14 to 17 with Internet addiction behaviours
and 12.7% at risk (Tsitsika et al., 2012), to 4.4% of patholo-
gical Internet users and 13.5% of maladaptive users (Durkee
et al., 2012)
The gures in studies outside Europe also show dispa-
rities: Cao, Sun, Wan, Hao and Tao (2011) report 8.1%
of problematic users of Internet among Chinese adolescents,
whilst Lam, Peng, Mai and Jing (2009) speak, in relation
to Chinese secondary-school students, of 10.2% modera-
tely addicted and 0.6% severely addicted to the Web. At the
same time, the range of prevalence found in studies with
adolescents and university students in the USA is from
0% to 26.3% (Moreno, Jelenchick, Cox, Young, & Chris-
takis, 2011).
In sum, although more and more research is being
done on this issue, the data are confusing, and at times
even contradictory. The “risk” estimated in each case
tends to be an excessively diffuse term, given the under-
lying conceptual controversy that must be resolved rst:
what do we actually want to assess? It is necessary to cla-
rify what we are talking about: Internet addiction (Chou
& Hsiao, 2000; Young, 1996), compulsive use (Greeneld,
1999; Meerkerk, Van Den Eijnden, Vermulst, & Garret-
sen, 2009), pathological use (Davis, 2001; Morahan-Martin
& Schumacher, 2000), problematic use (Caplan, 2002; Sha-
pira et al., 2003), excessive use (Hansen, 2002), unregulated
use (LaRose, Lin, & Eastin, 2003) or Internet dependence
(Anderson, 2001; Scherer, 1997). Do these terms form
part of a single continuum of risk? And if so, what sequen-
ce do they follow? Which of them accounts for the hi-
ghest level of risk? Where should we set the boundaries
between one concept and another? The heterogeneity of
terms makes it seriously difcult to compare and integra-
te results, so that the rst task for research teams and the
scientic-professional community is to reach a consensus
on both the term to use and its dening criteria. Thus,
ADICCIONES, 2015 · VOL. 27 ISSUE 1
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Antonio Rial Boubeta, Patricia Gómez Salgado, Manuel Isorna Folgar, Manuel Araujo Gallego, Jesús Varela Mallou
although it might be acceptable to speak of addiction to
Internet, in the name of rigour and orthodoxy (and pen-
ding the necessary consensus), a prudent solution would
be to employ the term problematic use. Authors such as
Ceyhan, Ceyhan and Gürcan (2007), Pulido-Rull, Esco-
to-de la Rosa and Gutiérrez-Valdovinos (2011) or That-
cher and Goolam (2005) advocate the use of this expres-
sion.
But beyond this controversy, what is certain is that sig-
ns are increasing of the existence of the problem, so that
it must be addressed as soon as possible. In this context,
one of the greatest challenges for research is the deve-
lopment of screening instruments, permitting early de-
tection or the identication of possible risk cases, which
would redound to the benet of prevention initiatives.
The Appendix to this article includes a list of the prin-
cipal instruments or tests developed to date. Its length
serves only to highlight the enormous degree of hetero-
geneity there is, from both the conceptual and methodo-
logical points of view. Despite the large number of scales
available, if our goal is to develop a tool with sound gua-
rantees that permits early detection of cases of proble-
matic Internet use among adolescents, the truth is that
many of them present some kind of limitation: (1) they
are not adapted to the adolescent population or the items
are unsuitable for the specic reality of this age group
(Armstrong, Phillips, & Saling, 2000; Nichols & Nicki,
2004); (2) they do not provide sufcient data on their
psychometric properties, or they are not as reliable as
they should be (Frangos, Frangos, & Sotiropoulos, 2012;
García et al., 2008; Orman, 1996); (3) the samples used
for their empirical validation are too small (Lam-Figue-
roa et al., 2011; Morahan-Martin & Schumacher, 2000);
(4) their factor structure is unclear (Chang & Law, 2008;
Widyanto & McMurran, 2004); (5) they are difcult to
use as screening tools, given their large numbers of items
(Davis, Flett, & Besser, 2002; Li & Yang, 2007) or because
they do not provide cut-off points (Beranuy, Chamarro,
Graner, & Carbonell, 2009; Meerkerk et al., 2009); (6)
there is no suitably adapted version in Spanish (Deme-
trovics, Szeredi, & Rózsa, 2008); or (7) they were develo-
ped in a culture very different from that of Spain (Chen,
Weng, Su, Wu, & Yang, 2003; Huang, Wang, Qian, Zhong,
& Tao, 2007).
The objective of the present work is precisely to de-
velop a screening scale for problematic Internet use in ado-
lescents, with sufcient theoretical and empirical gua-
rantees, and that is both brief and easy to use. Such a
scale must integrate the different antecedents from the
literature and present acceptable psychometric proper-
ties, with regard to score reliability, evidence of validity,
sensitivity and specicity. In addition to being brief and
simple, its items must be in accordance with the language
and cultural context of adolescents.
Method
Participants
To achieve our objective we used purposive sampling,
in an effort to access the largest and most heteroge-
neous sample possible. Through contact with 11 secon-
dary schools in 7 different municipalities (both urban
and rural) of the province of A Coruña (north-western
Spain), it was possible to assemble a sample of 1709 ado-
lescents (835 girls and 874 boys) aged 11-17 (M = 13.74;
SD = 1.43). Of these, 30.2% were in the rst grade (1º
de ESO), 25.2% in the second grade, 23.8% in the third
grade and 20.8% in the fourth grade. As regards parents’
educational level, 4% of fathers and 3.2% of mothers had
no formal education, whilst 36.9% of fathers and 34.5%
of mothers had primary education. Those with secon-
dary/high-school education accounted for 48.6% of the
fathers and 46.3% of the mothers, and 10.4% of fathers
and 15.9% of mothers had a university education.
Instruments
For the construction and development of the scale we
followed the phases set down in the American Educatio-
nal Research Association’s Standards (American Psycholo-
gical Association and National Council on Measurement
in Education, 1999). First of all, we dened the purpose
of the scale, which was to produce a screening instrument
for problematic Internet use in adolescents, as well as de-
ning the scope of the construct or domain to be measu-
red. We next specied certain aspects of the scale, such
as item format, response format and the procedure for
calculating the scores obtained by each participant. Spe-
cically, it was decided that the items would be statements
in the rst person, and that the response format would
be based on a Likert-type scale with 5 options, since this
maximizes score reliability and improves the chances of
obtaining good validity evidence. For calculating the sco-
res we established a range of 0 to 4 in the 5 categories of
the agreement scale, 0 signifying Totally disagree and 4 sig-
nifying Totally disagree. Next, we implemented the phase
of development, assessment and selection of the items,
and nally, we drew up the nal version of the scale and
carried out its empirical evaluation.
These four phases were developed through three com-
plementary strategies. The rst of these involved a review
of the extensive literature on the subject, summarized
in the Appendix, which lists the main scales previously
proposed and their dening characteristics. We also took
into account the DSM-5 diagnostic criteria for pathologi-
cal gambling and Internet Gaming Disorder.
The second strategy was the development of a quali-
tative study, which involved the creation of a multidisci-
plinary team of experts consisting of 12 professionals (3
clinical psychologists, 3 psychiatrists, 3 community edu-
ADICCIONES, 2015 · VOL. 27 ISSUE 1
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PIUS-a: Problematic Internet Use Scale in adolescents. Development and psychometric validation
cation workers and 3 experts in drug-dependence pre-
vention), with three specic objectives: (1) To carry out a
critical review of the existing literature, highlighting the
current limitations in this area; (2) To provide evidence
of the content validity of the scale and the items making
it up; and (3) To establish criteria for analyzing its discri-
minative capacity, given the absence of consensus-based
diagnostic criteria.
For the work with the experts we used the Delphi tech-
nique, structured in three phases: the initial meeting was
for discussing the state of the issue and the possible crite-
ria indicating the problem; the experts then individually
presented their reections on the problem and their con-
sidered proposals for the items to be included in the scale
and the response format to be used; nally, they agreed
on the indicators or reference criteria for considering an
adolescent’s behaviour as “risk”, as well as other, more te-
chnical elements such as the order of presentation of the
items.
Furthermore, we took into account the results of the
preliminary study by Gómez et al. (2014), which presen-
ted an 8-item scale that can be considered the starting
point of the scale used in the present study. Nevertheless,
it should be noted that, given its additional contributions,
the present scale can be considered a priori a more com-
prehensive and rigorous instrument, with greater theo-
retical support (thanks to the extensive literature review
and the work carried out with the experts).
As a result of the four phases mentioned above and
the three complementary strategies used, we drew up an
initial version of the 14-item scale, as shown in Table1.
Table 1
Items of the scale and sources
ITEMS OF THE SCALE SOURCES
1. WhenI’m onlineI feel that time fliesandhours pass without me realizing it - Beranuy et al., 2009
- Huang et al., 2007
- Preliminary study
- Expert group
2. I’ve sometimes tried to control or reduce my Internet use, but I couldn’t - Echeburúa, 1999
- Young, 1996
- Internet Gaming Disorder
- Gambling Disorder
3. I sometimes prefer to be online than to be with people (family or friends) - Chen et al., 2003
- García et al., 2008
- Young, 1998
- Preliminary study
- Expert group
4. I’ve sometimes even managed to neglect certain tasks or perform below par (in exams, sport, etc.) - De García et al., 2008
because I put connecting to Internet first - Internet Gaming Disorder
- Preliminary study
5. I’m starting to like more and more spending hours connected to Internet - Chen et al., 2003
- Greenfield, 1999
- Internet Gaming Disorder
- Gambling Disorder
6. I sometimes get irritated or in a bad mood because I can’t connect to Internet - Demetrovics et al., 2008
or because I have to disconnect - Young, 1998
- Internet Gaming Disorder
- Gambling Disorder
- Preliminary study
7. I prefer that my parents don’t know how long I spend online because they would think it was too much - Huang et al., 2007
- Morahan-Martin & Schumacher, 2000
- Internet Gaming Disorder
- Gambling Disorder
- Expert group
8. I’ve stopped going to placesordoing things thatinterested mebeforeso as toconnect to the Internet - Armstrong et al., 2000
- Internet Gaming Disorder
- Preliminary study
- Expert group
9. Connectingto the Internethelps me to notthink about problemsand to relax - Beranuy et al., 2009
- Huang et al., 2007
- Internet Gaming Disorder
- Gambling Disorder
10. I’ve even putrelationships or important thingsat risk becauseof the Internet - Beranuy et al., 2009
- De Gracia et al., 2002
- Internet Gaming Disorder
- Gambling Disorder
- Preliminary study
11. I’ve sometimes got into trouble because of the Internet - Caplan, 2002
- Morahan-Martin& Schumacher, 2000
- Expert group
(continues)
ADICCIONES, 2015 · VOL. 27 ISSUE 1
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Antonio Rial Boubeta, Patricia Gómez Salgado, Manuel Isorna Folgar, Manuel Araujo Gallego, Jesús Varela Mallou
Procedure
Data were collected in the classrooms of the schools
participating in the study, in small groups (20 or less),
after the corresponding instructions had been given. Co-
llection of the information was carried out by the experts
in drug-dependence prevention who developed the edu-
cation for health programmes at the different schools, as
an extra activity of the same programme, within the mo-
dule on responsible use of New Technologies. A training
session was held with technicians who assisted with data
collection in order to standardize the procedure as much
as possible and resolve any doubts at the technical level.
Special emphasis was placed on the condentiality of in-
formation and anonymity of responses was guaranteed,
since at no time were the adolescents asked about their
names or personal information. The cooperation and
consent of both the schools’ head teachers and parents’
associations were obtained. Participation in the study was
completely voluntary and unpaid. Rate of refusal to par-
ticipate in the study was 1.2%. Finally, it should also be
pointed out that the study was approved by the Bioethics
Committee of the University of Santiago de Compostela.
Data analysis
First of all we carried out a missing values analysis.
Once conrmed the low percentage of missing values for
each of the variables (ranging between 0% and 1.8%),
and the randomness of such values, we decided to remove
from the analysis those participants with missing values
in any of their answers. Thus, an initial sample of 1772
adolescents became a nal sample of 1709 adolescents for
the analysis. We then calculated the descriptives (M, SD,
skewness and kurtosis) for each scale item, as well their
corrected homogeneity indices (HIc). The multivariate
normal distribution was evaluated by means of the Mardia
coefcient and the internal consistency with Cronbach’s
alpha. To study the dimensionality or factor structure of
the scale we carried out rst of all an Exploratory Fac-
tor Analysis (EFA), followed by a rst-order Conrmatory
Factor Analysis (CFA). Given the absence of consensus-ba-
sed diagnostic criteria with which to dene problematic
Internet use among adolescents, in order to explore the
scale’s capacity for screening, we rst divided the total
sample into two groups: (a) a rst group whose use of
Internet could be considered moderate, and (b) a second
group whose use could be considered problematic (they
go online every day, usually for more than 5 hours a day,
and report frequent arguments with their parents for this
reason). Next, based on this categorization as moderate
or problematic users, we calculated the sensitivity and
specicity values for different cut-off points, and comple-
mentary to this, we carried out a ROC curve analysis. Fina-
lly, in addition to providing the descriptive statistics of the
nal scale for the total sample, we made comparisons of
means by the adolescents’ sex (through application of the
Student t test) and age (through a one-factor ANOVA and
a Tukey post-hoc comparison). All the analyses were carried
out using IBM SPSS Statistics 20.0 (IBM Corp. Released,
2011) and IBM SPSS AMOS 21.0 (Arbuckle, 2012).
A large part of the decisions made on a methodologi-
cal level took as a reference studies such as those of Cuen-
ca-Royo, Torrens, Sánchez-Niubó, Suelves and Domin-
go-Salvany (2013) or Muñiz and Fonseca-Pedrero (2008).
Results
Table2showsthedescriptivestatistics foreachoneof
the 14 items in the initial version. The highest avera-
gescorrespondto items 1 (When I’m onlineI feel that
time ies and hours pass without me realizing it) with
a mean of2.62, followed by item9 (Connecting to the
Internet helps me to not think about problems and to
relax),with an average of1.69.The lowestaverage corres-
ponds to item8 (I’ve stopped going to places ordoing
things thatinterested mebefore so as to connect to the
Internet), with a mean of 0.27, and item 10 (I’ve even
putrelationships or important things at risk because of
the Internet), with0.39.Asregards thevariability of res-
ponses, the item that presents the most heterogeneous
responses(with a standard deviation of1.45) is item9,
whileitem 8is the most homogeneous, with a standard
deviation of0.71. As regards the standardized skewness
values, we can observe a marked positive skewness in all
the items, except in the case of the rst one, which pre-
sents marked negative skewness. Regarding the kurtosis,
many of the items show a leptokurtic distribution (items
12. It annoys me to spend hours without connecting to Internet - Young, 1998
- Internet Gaming Disorder
- Gambling Disorder
- Preliminary study
- Expert group
13. When I can’t connect I can’t stop thinking that I might be missing something important - Caplan, 2002
- Labrador, Becoña& Villadangos, 2008
- Internet Gaming Disorder
- Gambling Disorder
14. I say or do things on Internet that I wouldn’t be capable of saying/doing in person - Caplan, 2002
- Carbonell et al., 2012
- Expert group
(Continued from previous page)
ADICCIONES, 2015 · VOL. 27 ISSUE 1
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PIUS-a: Problematic Internet Use Scale in adolescents. Development and psychometric validation
2, 3, 6, 7, 8, 10, 11, 12 and 14), though some have a pla-
tykurtic distribution (items 1, 5, 9 and 13). However, only
four of the items have a value above 10, the rest falling
within reasonable limits (Kline, 2005). The Mardia multi-
variate normality coefcient is 77.97, which leads to rejec-
tion of the multivariate normality hypothesis. The correc-
ted homogeneity index (HIc) of the items ranges from
.37 to .59 and the internal consistency of the initial scale
is in general highly acceptable, with a Cronbach’s alpha
coefcient of .83. As Pardo and Ruíz (2001) state, “values
of over .80 are generally considered sound, and those of
over .90, excellent” (p. 598).
To study the factor structure of the scale we began by
dividing the aggregate sample into two random halves of
the same size. With the rst we carried out an Explora-
tory Factor Analysis (EFA), and with the second, a Conr-
matory Factor Analysis (CFA), in an effort to conrm or
validate the structure found. It was also found that there
were no signicant differences in the composition of the
two subsamples, or by sex (χ2 = 0.02; p = .88), or age (t =
1.27; p = .20).
For carrying out the EFA we used the Principal Compo-
nents Method. The KMO index value was .88, and that of
the Bartlett Sphericity Test, 2539.47 (p <.01). The analy-
sis provided three factors, which together accounted for
49.40% of the variance of the data, even though the rst
one explained 32.90%, the other two factors showing a
much more residual character, which ts at a theoretical
level with the unidimensional nature of the Gomez et al.
(2014) scale that was used as a basis. Following this rst
analysis we conducted a CFA, starting out from a theore-
tical model with a single dimension. Despite the absence
of normality, for parameter estimation we used the Maxi-
mum Likelihood (ML) method, since works such as those
of Curran, West and Finch (1996) or Thomas and Oliver
(1998) have indicated that this method is sufciently ro-
bust against non-fullment of this assumption when sam-
ples are large, as in this case (n = 1709). In any case, and
in accordance with Levy, Martin and Norman (2006), we
used complementarily other methods, such as Generalized
Least Squares (GLS), Unweighted Least Squares (ULS)
and Asymptotically Distribution Free (ADF), obtaining
very similar results. The estimated parameters were statis-
tically signicant (p <.01) and the factor loadings greater
than .40, except in the case of item 9 (see Figure 1).
Goodness of t of the model was assessed by means
of different indices, as recommended by Byrne (2009) or
Kline (2005): χ2, χ2/df, Goodness of Fit Index (GFI), Adjusted
Goodness of Fit Index (AGFI), Comparative Fit Index (CFI),
Normed Fit Index (NFI), Tucker Lewis Index (TLI) and Root
Mean Square Error of Approximation (RMSEA). Following
the recommendations of Steiger (1998), we also included
the 90% condence intervals in the case of RMSEA. The
different indices show that the scale ts only moderately
with the unidimensional theoretical model (see Table 3).
Although the GFI and AGFI values were over .90, those of
the NFI, TLI and CFI were around .85, and the RMSEA
value was .074. The low HIc values of some items (3, 9
and 10) and their low factor loadings (.46, .35, and .45,
respectively), together with the modication indices pro-
vided by the program, advised a respecication of the ini-
tial model, deleting the 3 items mentioned.
Table 2
Descriptive Statistics for the Elements of the Initial Scale
Ítem M SD Skewness Kurtosis HIc
1 2,62 1,27 -6,983 -5,694 0,39
2 0,81 1,13 18,378 5,584 0,43
3 0,53 0,94 26,332 21,018 0,42
4 1,00 1,21 14,567 ,138 0,49
5 1,22 1,24 10,801 -2,533 0,56
6 0,88 1,20 17,896 4,108 0,57
7 0,91 1,33 17,708 1,808 0,51
8 0,27 0,71 44,471 74,563 0,40
9 1,69 1,45 4,210 -8,748 0,37
10 0,39 0,91 35,935 41,721 0,42
11 0,48 1,01 31,099 28,493 0,38
12 0,96 1,20 15,854 1,807 0,57
13 1,11 1,35 13,674 -2,362 0,59
14 0,93 1,29 16,994 1,629 0,42
Figure 1. Estimated standardized parameters of the Initial CFA
Model
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Antonio Rial Boubeta, Patricia Gómez Salgado, Manuel Isorna Folgar, Manuel Araujo Gallego, Jesús Varela Mallou
Next, on the re-specied model with only 11 elements,
a new CFA was conducted, giving a better overall t of
the scale, with a GFI value of .96, AGFI, NFI, TLI and CFI
values of between .92 and .95, and an RMSEA value of
under .06, as recommended by Hu and Bentler (1999). It
should be borne in mind that even differences below 0.1
in the GFI or AGFI can be considered as relevant (Levy,
Martin, & Norman, 2006). All estimated parameters were
statistically signicant (p <.01) (Figure 2). Finally, the in-
ternal consistency of the nal scale was analyzed, yielding
a Cronbach’s alpha of .82. Additionally, since the respon-
se format used was an ordered categorical scale, the ordi-
nal alpha index (Elosúa & Zumbo, 2008) was calculated,
yielding a slightly higher value (83).
As regards the total descriptive statistics for the nal
11-item scale, for a theoretical minimum score of 0 and a
maximum of 44, the average score attained for the whole
sample was 11.18, and the standard deviation was 7.70. As
shown in Table 4, no signicant differences were found
by sex, but there were differences according to age group
(F = 10.32; p <.001), with a higher average on the scale in
the older age groups.
To study the scale’s capacity for screening, in the ab-
sence of consensus-based diagnostic which could enable
us to have a clinical sample, the total sample was divided
into two groups: (a) a rst group whose Internet use could
be considered moderate, and (b) a second group whose
use could be considered problematic. This second group
would be made up those adolescents who: 1) connect the
Internet every day, 2) are usually online for more than
5 hours a day, and 3) report frequent arguments with
their parents for this reason. The sensitivity and speci-
city values obtained for different cut-off points are shown
in Figure 3. As it can be seen, values 15 and 16 permit
the attainment of a balance between these two indicators.
Specically, if we use as a cut-off point a score of 16, we
obtain a sensitivity of 81% and a specicity of 82.6%. In
other words, the screening instrument is capable of de-
tecting true positives in 81% of cases and of rejecting true
negatives in 82.6% of cases, both results being highly ac-
ceptable. By way of a complement, we carried out a ROC
curve (Receiver Operating Characteristic) analysis, obtai-
ning an area under the curve of 0.88.
Finally, participants’ response pattern was analyzed by
comparing the prole of adolescents who make modera-
te use of the Internet with those who use is problematic.
As can be seen in Table 5, at both the global score level
and that of each of the items individually, the two groups
showed statistically signicant differences (p <.01). This
conrms the capacity of each of the items for detecting
problematic Internet use and justies their presence in
the nal version of the scale.
Discussion
One the main concerns among professionals and re-
searchers in the eld of addictions today is adolescents’
problematic use of the Internet. In this context, various
authors have highlighted the need to reach a consensus,
from both the conceptually and methodological points of
view, on the denomination, denition and evaluation of
Table 3
Goodness of fit indicators of the CFA Model for the Screening Tool
χ2 df p χ2/df GFI AGFI NFI TLI CFI RMSEA [CI]*
Initial model 325.38 77 <.001 4.22 .93 .90 .84 .85 .87 .074 [.066-.083]
Respecified model 118.39 44 <.001 2.69 .96 .95 .92 .94 .95 .054 [.042-.065]
Note: * 90% Confidence Interval for the RMSEA statistic
Figure 2. Estimated standardized parameters of the Final CFA
Model
Table 4
Comparison of total scores on the scale by sex and age
M SD t p
Sex Men 10,76 7,24 -1,87 ,062
Women 11,60 8,13
M SD t p
Age group 11 – 13 years 10,18 7,43 10,321 < ,001
14 – 15 years 11,78 7,92
16 – 17 years 13,18 7,61
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PIUS-a: Problematic Internet Use Scale in adolescents. Development and psychometric validation
the problem (Byun et al., 2009; Gómez et al., 2014; Ko-
ronczai et al., 2011). Although there is considerable con-
troversy insofar as it is not entirely clear whether we can
really speak of cyberdependence or Internet addiction,
the progressive emergence of cases of abuse or problema-
tic use at increasingly younger ages means that those wor-
king in the eld of prevention need scientically valida-
ted instruments for the early detection of possible cases of
risk. The aim of the present study was to make progress in
the search for practical solutions in this context, through
the development and validation of a new screening tool,
backed up by the knowledge accumulated over the past
two decades and with proven psychometric properties.
Despite the existence of numerous previous scales, a
large part of them have certain shortcomings or limita-
tions, especially if our aim is to have available a tool with
guarantees that would permit early detection of proble-
matic Internet use among Spanish adolescents. Many of
the scales developed previously have not been validated
in our country, are not adapted to the adolescent popu-
Figure 3. Sensitivity and Specificity values for the different cut-off points
Table 5
Comparison between adolescents with moderate use and problematic use
Item Moderate Problematic t p
use use
1 WhenI’m onlineI feel that time fliesandhours pass without me realizing it 2,50 3,25 -4,59 < ,001
2 I’ve sometimes tried to control or reduce my Internet use, but I couldn’t 0,72 1,43 -3,22 < ,01
3 I’ve sometimes even managed to neglect certain tasks or perform below par (in exams, sport, etc.) 0,86 2,09 -5,67 < ,001
because I put connecting to Internet first
4 I’m starting to like more and more spending hours connected to Internet 1,12 2,32 -4,99 < ,001
5 I sometimes get irritated or in a bad mood because I can’t connect to Internet or because I have to disconnect 0,69 2,16 -6,13 < ,001
6 I prefer that my parents don’t know how long I spend online because they would think it was too much 0,72 2,32 -6,37 < ,001
7 I’ve stopped going to placesordoing things thatinterested mebeforeso as toconnect to the Internet 0,19 0,67 -2,88 < ,01
8 I’ve sometimes got into trouble because of the Internet 0,36 1,28 -3,66 < ,001
9 It annoys me to spend hours without connecting to Internet 0,82 1,89 -4,56 < ,001
10 When I can’t connect I can’t stop thinking that I might be missing something important 0,97 2,41 -6,32 < ,001
11 I say or do things on Internet that I wouldn’t be capable of saying/doing in person 0,81 1,84 -4,09 < ,001
TOTAL 9,67 21,62 -9,70 < ,001
lation, or fail to provide data on their psychometric pro-
perties. Sometimes the samples used for their validation
are too small, their factor structure is unclear, they are too
long, or they or simply do not provide the cut-off points
needed for their used as screening instruments.
Analyses carried out with a sample of 1709 schoolchil-
dren in the Spanish region of Galicia made it possible to
present a new scale (PIUS-a) that may prove extremely
useful for practitioners and researchers in this eld.
This scale was developed on the basis of a thorough re-
view of the literature and enriched by the contributions
from a multidisciplinary team of experts; moreover, it
has highly acceptable psychometric properties, in terms
of both internal consistency (
α
= .81), and evidence of
internal structure and content, attaining an interesting
balance between levels of sensitivity (81%) and speci-
city (82.6%), as far as screening capacity is concerned.
In short, the work carried out has made available to re-
searchers, clinicians and community education workers
a brief and simple scale (with just 11 items), adapted to
the cultural context of our country and the language of
young people – qualities that lend it great potential for
everyday practice.
Although in this paper we have been prudent and cho-
sen to use the term problematic Internet use, it would
not be unreasonable, as various authors have proposed,
to employ terms such as pathological Internet use or In-
ternet addiction (Durkee et al., 2012; Tsitsika et al., 2012;
Young, 1996). The facts that the selection of scale items
was made on the basis of previous work along these lines,
that we took into account the judgements of experts in
the clinical and educational spheres, and that we used as
references the criteria for diagnostic categories of a simi-
lar nature, makes the use of these terms quite plausible.
Ofcial recognition of a pathology associated with the
use of Internet and the availability of clinical samples, as
is the case in the Asian context (Huang et al., 2007; Ko,
Yen, Yen et al., 2005), would help resolve these types of
controversy.
Moreover, despite the unidimensionality of the scale
developed, we cannot discard the possibility of a multi-
dimensional approach, given the complexity of the pro-
ADICCIONES, 2015 · VOL. 27 ISSUE 1
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Antonio Rial Boubeta, Patricia Gómez Salgado, Manuel Isorna Folgar, Manuel Araujo Gallego, Jesús Varela Mallou
blem at hand. However, many of the works that adopt a
multidimensional approach (Günüç & Kayri, 2010; Wid-
yanto & McMurran, 2004) provide a global
α
, which im-
plicitly entails some unidimensionality. Furthermore, the
fact that the calculation of an overall score and the setting
of a cut-off point are necessary to enable screening for
possible cases of risk makes it preferable to opt initially
for the existence of a single factor.
However, this study does have some limitations. First,
all the variables are self-reported, so it is impossible to
know the extent to which the adolescents may have un-
derestimated or overestimated their Internet use. On the
other hand, self-report questionnaires on the use of alco-
hol and other drugs have shown themselves to be relia-
ble and even comparatively better than other detection
methods in the eld of substance use (Babor, Kranzler, &
Lauerman, 1989; Winters, Stincheld, Henly, & Schwartz,
1990), so that self-report measures may well be relevant
in this context also. It is also true that the use of a social
desirability scale or an instrument for the detection of
random response patterns could of great use. Moreover,
given the still-unresolved conceptual controversy and lack
of consensus on the dening criteria of the problem, the
sensitivity and specicity values were calculated based on
criteria established by the authors, even though it must
be said that they have been used in works and endorsed
by the group of experts. The availability in the future of
duly consensus-based criteria will be what nally permits
the validation of the instruments developed, which from a
purely psychometric perspective is not currently possible.
It is important to note, nally, that the scale presented
constitutes a tool for screening, and never for diagnosis,
as the latter must be based on the clinical act itself. Such
instruments would play a complementary role, facilita-
ting the early detection of adolescents whose use of the
Internet could constitute a problem, on interfering in a
crucial way in their everyday life. The scale is specically
designed for use by counsellors, community education
workers and drug-prevention experts in the school con-
text, in which it has been validated empirically. Future
research would need to test its behaviour in clinical set-
tings, linked in (as occurs in other countries) to primary
care services.
Acknowledgements
This study was supported by the Valedor do Pobo ins-
titution, a part of the Galician Regional Administration
(Xunta de Galicia).
Conflicts of interests
The authors declare that there are no conicts of in-
terests.
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Appendix. Table A1
Compilation of the most relevant assessment tools or screening instruments published
YEAR AUTHORS INSTRUMENTS COUNTRY VALIDATION SAMPLE CONSTRUCT Nº ITEMS Nº FACTORS
1995 Goldberg Internet Addiction Disorder scale
– Qualitative scale
USA - Internet addiction 7 criteria -
1996 Orman Internet Stress Scale (ISS) USA - Internet addiction 9 YES/NO items -
1996 Young Young’s Diagnostic Questionnaire
(YDQ)
USA 496 participants online (Adult popula-
tion)
Internet addiction 8 YES/NO items 1
1997 Brenner Internet-Related Addicitive Be-
havior Inventory (IRABI)
USA 563 users (Mean age: 34 years) Internet addiction 32 TRUE/FALSE items 1
1997 Scherer Clinical symptoms of Internet
dependency (CSID)
USA 531 university students Internet dependence 10 clinical symptoms -
1998 Chou, Chou & Tyan Chinese Internet-Related Addic-
itive Behavior Inventory version I
(C- IRABI- I)
Taiwan 104 participants online (Mean age:
22,3 years; SD= 3.13)
Internet addiction 32 TRUE/FALSE items 1
1998 Griffiths Addiction Core components
criteria
UK 5 case studies (ages 15 to 35 years) Addiction 6 principal components
of addiction
-
1998 Young Internet Addiction Test (IAT) USA - Internet addiction 20 items (Likert 5-point scale) -
1999 Pratarelli, Browne
& Johnson
Computer Use Survey USA 341 university students (Mean age: 22.8
years; SD= 5.88)
Internet addiction 55 items 4
1999 Greenfield Virtual Addiction Survey (VAS) USA
& Canada
17251 participants online Compulsive
Internet use
36 YES/NO items -
1999 Echeburúa Test de Adicción a Internet Spain - Internet addiction 9 YES/NO items -
2000 Chou & Hsiao Chinese Internet-Related Addici-
tive Behavior Inventory version II
(C- IRABI- II)
Taiwan 910 university students (Mean age:
21.11 years; SD= 210)
Internet addiction 37 items (Likert 4-point agreement
scale)
6
2000 Morahan-Martin &
Schumacher
Pathological Internet Use Scale
(PIUs)
USA 277 university students (Mean age:
20.72 years; SD= 2.35)
Pathological
Internet use
13 TRUE/FALSE items -
2000 Armstrong et al. Internet Related Problem Scale
(IRPS)
Australia 50 participants (75% aged 25 to 30
years)
Internet addiction 20 questions (Likert 10-point
agreement scale)
-
2001 Tsai & Lin Internet Addiction Scale for Tai-
wan high school students (IAST)
Taiwan 753 secondary-school students (Age
range: 16-17 years)
Internet addiction 29 items (Likert 4-point scale) 4
2001 Anderson Sin nombre USA 1302 university students Internet dependence 7 YES/NO items -
2001 Del Miglio, Gamba &
Cantelmi
Use, Abuse and Dependence
on the Internet inventory (UADI
scale)
Italy 244 participants (Age range: 13-57
years; Mean age: 28.7 years)
Internet dependence 75 items (Likert 5-point scale) 5
2002 Caplan Generalized Problematic Internet
Use Scale (GPIUS)
USA 386 university students (Age range: 18-
57 years; Mean age: 20 years; SD= 2.22)
Generalized problema-
tic Internet use
29 items (Likert 5-point agreement
scale)
7
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YEAR AUTHORS INSTRUMENTS COUNTRY VALIDATION SAMPLE CONSTRUCT Nº ITEMS Nº FACTORS
2002 Davis et al. Online Cognition Scale (OCS) Canadá 211 Psychology students (Mean age:
21.73 years; SD= 4.4)
Problematic
Internet use
36 items (Likert 7-point
agreement scale)
4
2002 De Gracia, Vigo, Fer-
nández & Marcó
Problemas Relacionados con
Internet (PRI)
Spain 1664 Self-selected internauts (Age
range: 15- 54 years)
Problematic
Internet use
19 items
(Likert frequency scale)
-
2003 LaRose et al. Deficient Internet self-regulation USA 465 university students Unregulated
Internet use
7 items (Likert 7-point scale) -
2003 Rotunda, Kass, Sut-
ton & Leon
Internet Use Survey (IUS) USA 393 university students (Age range: 18-
81 years; Mean age: 276 years)
Abuse of Internet Impairment index of the IUS:
32 items (Likert 5-point
frequency scale)
4
2003 Chen et al. Chinese Internet Addiction Scale
(CIAS)
Taiwan - Internet addiction 26 items (Likert 4-point scale) 5
2004 Yuen & Lavin No name USA 283 university students (adults Internet dependence 7 items adapted from the DSM- IV
criteria for substance dependence
(Likert 5-point agreement scale)
-
2004 Nichols & Nicki Internet Addiction Scale (IAS) Canada 233 university students Internet addiction 31 items (Likert 5-point frequency
scale)
1
2004 Widyanto
& McMurran
Internet Addiction Test (IAT) UK 86 participants online (Age range:
13-67 years; Mean age men:
25.45 years (SD = 8.91); Mean age
women: 31.44 years (SD = 10.34))
Internet addiction 20 items (Likert 5-point scale) 6
2005 Beard Screening Interview Assessing
Problematic Internet Use
USA - Problematic
Internet use
72 questions -
2005 Thatcher & Goolam Problematic Internet Use Ques-
tionnaire (T- PIUQ)
South Africa Pilot study: 279 participants; Validation:
1795 participants
Problematic
Internet use
20 items (Likert 5-point scale) 3
2007 Fortson, Scotti, Chen,
Malone & Del Ben
Reported Behaviors Related to
Internet Abuse and Dependence
USA 411 university students
(Age range: 18-56 years;
Mean age: 20.4 years; SD= 3.2)
Internet abuse and/or
dependence
9 items (Likert scale) -
2007 Li & Yang Adolescent Pathological Internet
Use Scale
China 1331 secondary-school students and 30
Adolescents diagnosed as pathological
users
Pathological Internet
use
38 items (Likert 5-point scale) 6
2007 Ceyhan et al. Problematic Internet Usage Scale
(PIUS)
Turkey 1658 university students Problematic
Internet use
33 items (Likert 5-point scale) 3
2007 Huang et al. Chinese Internet Addiction
Inventory (CIAI)
China Study 1: 516 university students
(Age range: 17-24 years; mean age:
20.5 years; SD= 1.47); Study 2:
513 university students (Age range: 17-
24 years; Mean age: 20.7
years; SD= 1.51); Study 3: 54 partici-
pants
(27 clinical sample)
Internet addiction 31 items (Likert 5-point frequency
scale)
3
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YEAR AUTHORS INSTRUMENTS COUNTRY VALIDATION SAMPLE CONSTRUCT Nº ITEMS Nº FACTORS
2007 Ferraro, Caci,
D’amico & Di Blasi
Internet Addiction Test (IAT) IItaly 236 participants online (Age range:
13-50 years; Mean age: 23.9 years;
SD = 6.5)
Internet addiction 20 items (Likert 5-point scale) 6
2007 Jenaro, Flores,
Gómez-Vela, Gonzá-
lez-Gil & Caballo
Internet Over-use scale (IOS) Spain 377 university students Pathological
Internet use
23 items (Likert 6-point scale) -
2008 Demetrovics et al. Problematic Internet Use
Questionnaire (D- PIUQ)
Hungary 1037 participants online (Mean age:
23.3 years; SD= 9.1)
Problematic
Internet use
18 items (Likert 5-point scale) 3
2008 Labrador, Becoña &
Villadangos
Cuestionario de Detección de
Nuevas Adicciones (DENA)
Spain Pilot study: 140 secondary-school
students; Study 2: 1710 minors (Age
range: 12-17 years)
Internet addiction 50 items. Block of 12 items
referring to Internet use
-
2008 García et al. Cuestionario de Uso
y Abuso de Internet
Spain 391 university students (Age range:
18-47 years; Mean age: 19.59 years;
SD= 2.83)
Internet abuse 47 items (Likert 5-point scale) -
2008 Chang & Law Internet Addiction Test (IAT) China 410 university students Internet addiction 20 items (Likert 5-point scale) 3
2009 Chow, Leung, Ng
& Yu
Internet-user Assessment
Screen
China Phase 1: 378 adolescents (Mean age:
12.84 years; SD= 1.53); Phase 2: 3523
adolescents (Mean age men: 12.33
years; SD= 1.66. Mean age women: 12.5
years; SD= 1.6)
Maladaptive Internet
use
26 items (20 items with Likert
5-point agreement scale)
9
2009 Meerkerk et al. The Compulsive Internet Use
Scale (CIUS)
Holland Study 1: 447 intensive Internet users
(Mean age: 38.5 years; SD=12.5); Study
2: 229 participants from the previous
study; Study 3: 16,925 participants
online (Age range: 11-80 years; Mean
age: 25.3; SD= 10.0)
Compulsive
Internet use
14 items (Likert 5-point
frequency scale)
1
2009 Beranuy et al. Cuestionario de Experiencias
Relacionadas con Internet (CERI)
Spain 1879 secondary-school and university
students (Mean age: 15.52; SD=2.434)
Internet addiction 10 items (Likert 4-point scale) 2
2009 Mitchell, Sabina,
Finkelhor & Wells
Index of Problematic Online
Experiences (I-POE)
USA 563 university students (Mean age:
19.86 years)
Problematic
Internet use
26 YES/NO items -
2010 Caplan Generalized Problematic Internet
Use Scale 2 (GPIUS2)
USA 785 participants (Age range: 18-70
years. Mean age: 33.14 years; SD=
15.25)
Generalized problema-
tic Internet use
15 items (Likert 8-point
agreement scale)
5 first-order fac-
tors (2 of them
form a 2nd-
order factor)
2010 Günüç & Kayri Tukish Internet Addiction Scale Turkey 754 secondary-school students Internet addiction 35 items (Likert 5-point scale) 4
2011 Frangos, Frangos &
Sotiropoulos
Problematic Internet Use Diag-
nostic Test (PIUDT)
Greece 2293 university students (adults) Problematic
Internet use
38 items 4
2011 Lam- Figueroa et. al. Escala de la Adicción a Internet
de Lima (EAIL)
Peru 248 secondary-school students Internet addiction 11 items (Likert 4-point
frequency scale)
2
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YEAR AUTHORS INSTRUMENTS COUNTRY VALIDATION SAMPLE CONSTRUCT Nº ITEMS Nº FACTORS
2011 Pulido-Rull et al. Cuestionario de Uso Problemáti-
co de Internet (CUPI)
Mexico 697 university students (Mean age:
22.68 years; SD= 4)
Problematic
Internet use
18 items (Likert 5-point scale) 5
2013 Lopez-Fernandez,
Freixa-Blanxart &
Honrubia-Serrano
Problematic Internet Entertain-
ment Use Scale for Adolescents
(PIEUSA)
Spain 1131 adolescents (Age range: 12-18
years. Mean age: 14.55 years;
SD= 1.816)
Problematic
Internet use
30 items (Likert 7-point
agreement scale)
5
2013 Labrador, Villadan-
gos, Crespo & Becoña
Cuestionario de Uso Problemáti-
co de Nuevas Tecnologías (UPNT)
Spain 2747 students (from 5th-grade Primary
to 5th-year degree)
Problematic
Internet use
26 items (Internet subscale:
7 items)
7
2013 Watters, Keefer,
Kloosterman, Sum-
merfeldt & Parker
Internet Addiction Test (IAT) Canada 1948 secondary-school students (Age
range: 16-18 years; Mean age: 17.07
years; SD = 0.84)
Internet addiction 20 items (Likert 5-point scale) 2
2013 Puerta-Cortés, Car-
bonell & Chamarro
Internet Addiction Test (IAT) Colombia 1117 participants online (Age range:
14-67 years; Mean age = 20.93 years;
SD = 4.84)
Internet addiction 20 items (Likert 5-point scale) 3
2013 Hawi Internet Addiction Test (IAT) Republic of
Lebanon
817 middle and secondary school
students (Age range: 10-22 years; Mean
age: 15 years; SD = 2.12)
Internet addiction 20 items (Likert 5-point scale) 1
2013 Lee, Lee, Gyeong, Yu,
Song & Kim
Korean version of the Internet
Addiction Test (KIAT)
Republic of
Korea
279 university students (Mean age:
19.9 years; SD = 2.7)
Internet addiction 20 items (Likert 5-point scale) 4
2014 Gómez et al. Screening Scale of Problematic
Internet Use in adolescents
Spain 2339 secondary-school students (Age
range: 11-18 years. Mean age:
13.77; SD= 1.34)
Problematic
Internet use
8 items (Likert 5-point
agreement scale)
1
2014 Cho et al. Internet Addiction Scale (IAS)
based on the Internet Gaming
Disorder Criteria (DSM-5)
Republic of
Korea
1082 secondary-school students
(Age range: 13-14 years)
Internet addiction 26 items 7
2014 Jelenchick et al. Problematic and Risky Internet
Use Screening Scale (PRIUSS)
USA 714 university students (Age range: 18-
25 years; Mean age: 19.7 years;
SD = 1.4)
Problematic
Internet use
18 items (Likert 5-point
frequency scale)
3
