Validation and psychometric analysis of the Internet Addiction Test in Spanish among college students

Abstract

The wide use of the Internet in the workplace, academic or social field, can have an impact on daily life. One of the most used questionnaires worldwide to analyse these problems is the Internet Addiction Test (IAT). Our aim was to validate a Spanish version of the IAT and analyse its psychometric properties.
Population of study were college students participating in the uniHcos project (Universities of Granada, Huelva, Jaén, León, Salamanca, and Vigo). The questionnaire was translated and back-translated by two native English speakers. Reliability of scores was analysed using Pearson’s correlation coefficient and agreement was analysed using the Bland-Altman and Kappa techniques. Test dimensions were analysed by exploratory and confirmatory factor analysis.
The reliability of scores was good (r = 0.899, Kappa = 0.650 and mean difference using Bland-Altman = −3.5). The psychometric assessment identified two factors (Emotional Investment; Performance and Time Management) which explained 55 % of the variance (total internal consistency of 0.91) and only 19 items. The confirmatory analysis showed an acceptable goodness of fit, especially when items 6 and 8 were related (RMSEA = 0.07 90%IC = 0.06 - 0.08; WRMR = 1.01, CFI = 0.96; TLI = 0.95). The two dimensions were negatively correlated with age and positively correlated with time spent online, especially for the purposes of leisure and entertainment.
The results show good reliability and psychometric properties of the Spanish version of IAT with a two-dimensional solution. This result is partially in concordance with previous validations of the IAT in other languages that have found uni- and multi-dimensional solutions using different methodologies. Moreover, we want to highlight the possibility that some item of this questionnaire is outdated due to the technological and lifestyles changes and should be not taken into account.
The reliability and psychometric properties obtained in this study support the conclusion that this Spanish short version of the IAT represents a useful tool for the analysis of problems arising from misuse of the Internet.

Full text

R E S E A R C H A R T I C L E Open Access
Validation and psychometric analysis of the
Internet Addiction Test in Spanish among
college students
Tania Fernández-Villa
1,2*
, Antonio J. Molina
1,2
, Miguel García-Martín
3,4
, Javier Llorca
4,5
,
Miguel Delgado-Rodríguez
4,6
and Vicente Martín
1,2,4
Abstract
Background: The wide use of the Internet in the workplace, academic or social field, can have an impact on daily
life. One of the most used questionnaires worldwide to analyse these problems is the Internet Addiction Test (IAT).
Our aim was to validate a Spanish version of the IAT and analyse its psychometric properties.
Methods: Population of study were college students participating in the uniHcos project (Universities of Granada,
Huelva, Jaén, León, Salamanca, and Vigo). The questionnaire was translated and back-translated by two native
English speakers. Reliability of scores was analysed using Pearson’s correlation coefficient and agreement was
analysed using the Bland-Altman and Kappa techniques. Test dimensions were analysed by exploratory and
confirmatory factor analysis.
Results: The reliability of scores was good (r=0.899,Kappa=0.650 and meandifferenceusingBland-Altman=−3.5).
The psychometric assessment identified two factors (Emotional Investment; Performance and Time Management)
which explained 55 % of the variance (total internal consistency of 0.91) and only 19 items. The confirmatory analysis
showed an acceptable goodness of fit, especially when items 6 and 8 were related (RMSEA= 0.07 90%IC = 0.06 - 0.08;
WRMR = 1.01, CFI = 0.96; TLI = 0.95). The two dimensions were negatively correlated with age and positively correlated
with time spent online, especially for the purposes of leisure and entertainment.
Discussion: The results show good reliability and psychometric properties of the Spanish version of IAT with a two-
dimensional solution. This result is partially in concordance with previous validations of the IAT in other languages that
have found uni- and multi-dimensional solutions using different methodologies. Moreover, we want to highlight the
possibility that some item of this questionnaire is outdated due to the technological and lifestyles changes and should
be not taken into account.
Conclusion: The reliability and psychometric properties obtained in this study support the conclusion that this Spanish
short version of the IAT represents a useful tool for the analysis of problems arising from misuse of the Internet.
Keywords: Internet addiction, Internet addiction test, Factorial validity, College students, Spain
* Correspondence: tferv@unileon.es
1
The Research Group in Gene - Environment and Health Interactions,
University of León, León, Spain
2
Faculty of Health Sciences, Department of Biomedical Sciences, Area of
Preventive Medicine and Public Health, University of León, Vegazana Campus
s/n, 24071 León, Spain
Full list of author information is available at the end of the article
© 2015 Fernández-Villa et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Fernández-Villa et al. BMC Public Health (2015) 15:953
DOI 10.1186/s12889-015-2281-5
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Background
The Internet has become an everyday tool for finding infor-
mation and communicating in both academic and profes-
sional contexts, as well as being a medium for socialising
and entertainment, especially among young people [1, 2].
According to the latest Survey on Equipment and Use of
Information and Communication Technologies in House-
holds in Spain, 74 % of households have Internet access,
representing 22 % more than five years ago. The 71 % of
the population aged between 16 and 74 uses the Internet
frequently (at least once a week in the last three months)
and 60 % has heavy use (every day) of this medium [2].
However, despite being a useful tool in our daily lives, it
can also have negative consequences, especially when
users exhibit a lack of self-control regarding the length of
time they spend online, which can be related to states of
Internet use, abuse or dependence, being the most vulner-
able groups the adolescents and young adult [3–6].
Several terms have been employed to refer to this prob-
lem, such as “Internet Addiction Disorder”,“Compulsive
Internet Use”,“Pathological Internet Use”,orthemore
common “Internet Addiction”[7–10]. While this latter
term does not appear as such in any diagnostic manual
(ICD-10 / DSM-IV) [11, 12], due to its relative novelty,
several studies have likened the attraction of the Internet
to the reinforcing properties of addictive substances
[7, 13], describing symptoms or psychological profiles
derived from excessive Internet use which are similar to
those produced in addiction to substances (such as stress
or distress due to the impossibility of connecting). In the
fifth edition of the Diagnostic and Statistical Manual of
Mental Disorders (DSM-V), the criteria for this condition
are limited to “Internet Gaming Disorder”and do not
include general use of the Internet, online gambling, or
social media [14–16].
In spite of this, a recent study conducted in European
adolescents found an association of this problem with
other addictive behaviors, such as alcohol, tobacco, and
other illegal drugs use [17]. Moreover, some authors
warn of the possible association with both physical and
psychological problems (lack of sleep, headaches, back-
aches, depression, or anxiety) [18–21].
In 1998, Kimberly Young developed the Internet
Addiction Test (IAT) to assess possible dependence or
addiction to the Internet [7]. This questionnaire initially
consisted of 8 items based on DSM-IV diagnostic criteria
for pathological gambling, but has undergone subsequent
modifications in order to adapt it even further to the
possible problems or behaviours related to Internet use
[22, 23].
Kimberly Young’s definitive questionnaire contains 20
items and it assesses the extent to which Internet use affects
daily life, social life, productivity, sleep, and the individual’s
feelings. However, despite its widespread use in several
languages (English, Chinese, French, Finnish, German,
Italian, Greek, Arabic, and Portuguese) [23–31], some au-
thors have questioned the quality of its psychometric proper-
ties and its capacity to distinguish between “addicts”and
“non-addicts”based on the established cut-off points [32].
The IAT was developed as a unidimensional instru-
ment to evaluate Internet Addiction but different valida-
tions have showed a factor structure with two [27, 29,
32, 33], three [24, 30, 34, 35], or six dimensions [23].
In Spain, this questionnaire has been translated by
Estevezetal.[36]withouthavingcarriedoutavalid-
ation process with psychometric analysis. Puerta -
Cortés et al. [34] evaluated its factor structure in a
sample of Colombian Internet users, finding three
dimensions (Consequence of Internet use, cognitive
and emotional factors, and time control).
The methodological differences in all these validations
(sample size, type of population sampled, the statistical
method used, etc.) make it difficult to establish a com-
parison or know what is the best factor solution.
Moreover it should be noted that this questionnaire
was developed in 1998, so some items may be outdated,
given the evolution of new technologies in recent years.
Authors like Pawlikowsky et al. [33], Chang & Law [24],
or Lai et al. [37] established short versions of the IAT
with good properties of 12 and 18 items respectively.
Therefore, the aim of this study was to validate a
Spanish version of the IAT and analyse its psychometric
properties using factor analysis techniques. The specific
goals were: 1) to adapt to Spanish one version of the
IAT using translation-back-translation method with a
sample of college students, 2) to assess its reliability; and
3) to analyse its dimensional structure using exploratory,
and confirmatory factor analysis.
Methods
Design and study population
Our study was performed in three different phases, in
which we used different samples of college students. In
the first phase, the adaptation and translation of IAT
into Spanish, was carried out using one sample of 50
volunteer students from University of León in order to
detect semantic problems. In the second phase, test-
retest reliability was analysed using one sample of 80
student volunteers from the University of León who
used the social networks Facebook and Tuenti. Eighteen
participants were eventually excluded from the analysis
due to lack of responses on some of the items. Finally, in
the third phase, we performed a process of cross valid-
ation with data collected between October 2012 and
January 2013 from 851 first year students participating
in the uniHcos project (dynamic cohort of college stu-
dents for the study of drug and other addictions),
enrolled for the first time at the universities of Granada,
Fernández-Villa et al. BMC Public Health (2015) 15:953 Page 2 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Huelva, Jaén, León, Salamanca, and Vigo [38]. A split
half –sample approach was used to evaluate structural
factor test [39]. One random half was used as “develop-
ment”sample (424 participants with 8 % of Problematic
Internet use, 33 % males with 21 ± 6 years and 67 %
females with 20 ± 4 years). The other half-sample was
used as a “validity”sample (427 participants with 7 % of
Problematic Internet use, 28 % males with 21 ± 5 years
and 72 % females with 20 ± 4 years).
Measurements
The IAT consists of 20 items which are scored using a
five-point Likert scale (see Additional file 1), and it is
designed to measure the frequency with that problematic
situations arise as a result of Internet use. The test iden-
tifies two main groups of users according to the score
obtained: 1) Normal users or users without problems
(<40 points), and 2) problematic internet users (≥40
points) [32].
Questionnaires were administered and completed online
and data were collected concerning socio demographic
variables and Internet use profiles (IAT questionnaire,
total time spent online per week, for leisure, or work/
study. The time was measured in hours a week).
Ethical issues
Previously, each participant completed online a written
informed consent accepting the conditions of the study.
The SphinxOnline® platform used in this study allows
keep the confidentiality of data and thus comply with
the regulations of Data Protection Act 15/1999 [40], cre-
ating two independent databases codified (one with per-
sonal data and the other one with the questionnaire
data), so that no researcher can know who corresponds
each questionnaire.
All Ethics Committees of the collaborating universities
evaluated and accepted this procedure (Ethic Committee
of University of Granada, Ethic Committee of University
of Huelva, Ethic Committee of University of Jaén, Ethic
Committee of University of León, Ethic Committee of
University of Salamanca and Ethic Committee of Univer-
sity of Vigo) and the participants collaborated voluntarily
without any compensation for this.
Procedure
The questionnaire was translated into Spanish and then
back-translated with the help of two native English
speakers. One of them translated the test from English
into Spanish, and then the other one translated the
Spanish version back into English (back-translation).
This new English version was independently assessed
by both individuals, comparing it with the original Eng-
lish version of the questionnaire in order to detect errors
in reading or understanding. The Spanish version was
tested on the sample of students from the University of
León, in order to detect possible problems. The Inter-
national Test Commission Guidelines for test adaptation
were followed in all process [41].
Reliability of test-retest scores was analysed, using a
between-test interval of approximately a week in
order to avoid response recall [42]. The structural di-
mension of the IAT was analysed with two different
samples of college students using exploratory and
confirmatory factor analysis. As an initial analysis/es-
timation step, the model was fit to a “development”
half-sample, with multiple model specifications. Then,
the most interpretable model was chosen from among
the best –fitting models, and this model’s specifica-
tions were fit to the “validation”half-sample, with
modification indices used in a post-estimation exam-
ination of these model specifications [39].
Statistical analyses
In the phase of test –retest, correlation between the two
tests was analysed using Pearson’s linear correlation co-
efficient, whilst agreement was analysed using the Bland
& Altman technique [43], employing the IAT score
quantitatively, and the Kappa technique [44], comparing
users without problems (<40 points) and users with
problems (≥40 points) according to the score obtained
in the IAT. For these analyses, we used the statistical
package MedCalc v.12.3.0.
To investigate the factor structure, an Exploratory
Factor Analysis (EFA) was carried out in the “develop-
ment”sample. The suitability of the data for factor ana-
lysis was tested using the Kaiser-Meyer-Olkin (KMO)
measure of sampling adequacy and Bartlett’s test of
sphericity. The relationships between the observed vari-
ables were evaluated with the matrix of polychoric cor-
relations due to the ordinal scale of the IAT [45]. The
multivariate normality was tested and violated in the
data, for this reason, we decided used a robust method
to estimate the factor analysis, the Weighted Least
Square Mean and Variance Adjusted (WLSMV) [46].
The factors were extracted through visual examination
of a scree plot in combination with the conventional
cut-off of eigenvalues greater than one or Kaiser criter-
ion. In order to distinguish underlying constructs,
Geomin oblique rotation was employed to determine
factor loadings. Items were assigned to the factor that
produced the highest factor loading (cut off ≥0.300)
[47]. Goodness-of-fit was tested with chi-square (a non-
significant value corresponds to an acceptable fit). How-
ever, the chi-square increases with sample size and
model complexity and therefore this test was comple-
mented by other indices that depend on a conventional
cut-off: the Root Mean Squared Error of Approximation
Fernández-Villa et al. BMC Public Health (2015) 15:953 Page 3 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

(RMSEA) and the Standardised Root Mean Square
Residual (SRMR). The combination of these indices is
valuable because the RMSEA is sensitive to the misspe-
cification of the factor “loadings”and the SRMR is sensi-
tive to the misspecification of the “co-variances”.An
RMSEA between 0 and 0.05 indicates a good fit while
one between 0.05 and 0.08 indicates an acceptable fit.
An SRMR between 0 and 0.05 indicates a good fit and
one between 0.05 and 0.10 an acceptable one. Also, the
Comparative Fit Index (CFI) and Tucker Lewis Index
(TLI) were used. Values in these fit indices higher than
0.90 are generally interpreted as an acceptable fit [48].
The internal consistency of each factor was confirmed
by calculating Standardized Cronbach’s alphas. For each
items, the corrected item-total correlation and Alpha
without item were calculated [49].
Confirmatory Factor Analysis (CFA) was conducted
to test the fit of our factor structure in the other
half-sample, using WLSMV estimator and the same
indices explained before. Also, the Weighted Root
Mean Square Residual (WRMR) was calculated to
compare other models.
Lastly, in order to test relationships between the estab-
lished dimensions as well as with risk variables such as
age and time spent online, Pearson’s correlation coeffi-
cients were calculated [46].
For all this, SPSS 20, STATA 13 and MPLUS 7.3. stat-
istical packages were used.
Results
Following administration of the Spanish translation of
the IAT to student volunteers from the University of
León, some minor modifications of a semantic nature
were made to the questionnaire in order to obtain the
final version (see Additional file 2). The two main
changes were in item 10, “¿Con qué frecuencia enmas-
caras tus problemas de la vida real con pensamientos
relajantes sobre Internet?”(How often do you block out
disturbing thoughts about your life with soothing
thoughts of the Internet?) using the word “relajantes”in-
stead of “tranquilizantes”, and in item 20,“¿Con qué
frecuencia te sientes deprimido, de mal humor o nervioso
cuando no estás conectado y se te pasa todo en cuanto
vuelves a conectarte?”(How often do you feel depressed,
moody, or nervous when you are off-line, which goes
away once you are back on-line?), using the term “de
mal humor”instead of the initial word “temperamental”.
In the reliability test-retest scores analysis, the retest
was conducted after an average of 8.3 ± 3.1 days (range:
5–17 days; Median = 7.0 days). The total mean score
obtained in the first test was 17.8 ± 11.5 points (range = 0–
52, median = 15.5), and in the second test the mean score
was 14.2 ± 11.1 points (range = 0–48, median = 12.0).
The correlation obtained between the total scores of
both tests was good (r= 0.899), as was agreement ac-
cording to the Bland-Altman technique (Fig. 1), with a
mean difference of 3.5 and a 95 % CI between −6.6 and
13.6 when a quantitative analysis was conducted of the
IAT score in the two tests. A good agreement was also
obtained for the detection of users with problems
(0.650) according to the Kappa technique.
In the preliminary analysis of “development”half-
sample of 424 college students, the Kaiser-Meyer-Olkin
index was 0.924, and the Bartlett test of sphericity was
significant (χ2 = 3440.21; df = 190; p< 0.001), showing
the adequacy of the data for use in the EFA.
One first analysis showed that three factors could be
extracted. However, in the third factor only there was
one variable with load higher than 0.300 and the Item_7
did not load in any factor. After that, we decided remove
this last variable and repeat the EFA. Then, two factors
were extracted (correlation 0.654; p< 0.001) that ex-
plained 55 % of the total variance (Table 1), yielding a
Cronbach’s alpha for the questionnaire as a whole of
0.91 and showing acceptable fit indices: RMSEA = 0.063
(90 % IC 0.055 –0.071); CFI = 0.921; TLI = 0.957;
SMRM = 0.049.
Factor 1 (called “Emotional investment”) explained
47 % of the total variance and consisted of eleven items
related to the feelings that an individual experiences
when unable to connect to the Internet, or the attitude
an individual adopts when asked what he or she does
while online. Factor 2 (called “Time Management and
Performance”) explained 8 % of the total variance and
consisted of 8 items related to possible impairment of
academic or professional performance and excessive
time spent online.
Fig. 1 Data analysis using the Bland-Altman technique. The terms
used refer to the following: Total 1 = Total score obtained in the first
test; Total 2 =Score obtained in the second test; Mean = Average,
SD = Standard Deviation
Fernández-Villa et al. BMC Public Health (2015) 15:953 Page 4 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

After that, four models were tested using CFA in
the “validation”half-sample of 427 college students
(Table 2). Model 1 with one dimension of 20 original
items of the IAT, the model 2 as one dimensional but
with 19 items (without item 7), the model 3 with the
two dimensions detected in the EFA, and the model 4
incorporating a small modification, co-varying items 6
and 8, given their possible semantic similarity and the
recommendations of modification indices, obtaining a
better fit of the model (Fig. 2).
In relation to the other variables (Table 3), both factors
correlated negatively with age and positively with hours
spent online per week in total and for leisure purposes.
No statistically significant differences were found be-
tween any of the dimensions and time spent online for
academic or professional reasons.
Discussion
The Spanish version of the IAT reported here has
shown good evidence of reliability and acceptable psy-
chometric properties for use in the analysis of prob-
lems related to Internet use and abuse, demonstrating
a positive correlation with time spent online, espe-
cially for leisure purposes.
Table 1 Items corresponding to each factor after the exploratory analysis
Item Mean SD Skewness Kurtosis Corrected
item-total
correlation
Alpha
without
item
Factor loading
Factor 1
a
Factor 2
b
20 0.29 0.724 3.161 14.524 0.608 0.897 0.863 −0.008
10 0.56 1.066 2.332 8.511 0.601 0.896 0.774 0.035
13 0.57 0.912 1.942 7.245 0.598 0.896 0.762 −0.007
11 0.69 0.967 1.682 6.212 0.627 0.896 0.734 0.071
15 0.50 0.845 1.892 6.487 0.664 0.895 0.723 0.132
12 0.80 1.126 1.555 5.063 0.531 0.898 0.688 −0.001
19 0.31 0.717 2.774 11.363 0.396 0.901 0.685 −0.066
3 0.21 0.667 4.275 24.174 0.310 0.902 0.676 −0.127
9 0.63 1.021 1.963 6.953 0.562 0.897 0.541 0.213
14 1.12 1.290 1.161 3.750 0.616 0.895 0.423 0.346
4 1.00 1.112 1.183 4.279 0.340 0.903 0.347 0.093
8 0.86 1.142 1.395 4.375 0.611 0.895 −0.021 0.844
6 0.99 1.137 1.153 3.899 0.660 0.894 0.003 0.841
17 0.96 1.216 1.203 3.677 0.597 0.896 0.124 0.689
1 2.34 1.412 0.172 2.120 0.474 0.900 −0.035 0.629
2 1.43 1.242 0.780 3.102 0.614 0.895 0.139 0.622
16 1.57 1.351 0.547 2.460 0.617 0.895 0.261 0.533
18 0.45 0.903 2.732 11.451 0.636 0.896 0.404 0.469
5 0.92 1.108 1.203 3.928 0.559 0.897 0.331 0.379
Standarized alpha 0.86 0.86
Eigenvalue 9.25 1.57
Total explained variance 46.96 % 7.86 %
The higher of the two-factor loadings are printed in bold. All of these were significant at 5 % level
a
Factor 1 =emotional investment
b
Factor 2 = time management and performance
Table 2 Comparison of goodness of fit indices obtained from the confirmatory factor analysis
Items Factors χ2dfχ
2
/df RMSEA (90 % CI) CFI TLI WRMR
Model 1 20 1 794.128
**
170 4.7 0.093 (0.086 - 0.099) 0.914 0.904 1.451
Model 2 19 1 775.386
**
152 5.1 0.098 (0.091 - 0.105) 0.912 0.901 1.482
Model 3 19 2 594.173
**
151 3.9 0.083 (0.076 - 0.090) 0.938 0.929 1.272
Model 4 19 2 451.741
**
150 3.0 0.069 (0.061 - 0.076) 0.958 0.952 1.081
**
p< 0.001
Fernández-Villa et al. BMC Public Health (2015) 15:953 Page 5 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

A wide range of diagnostic tools have been used in
previous studies examining pathological Internet use
[7, 8, 50–55], the majority of which are closely related
to the criteria established in the DSM –IV [12] and
the ICD-10 [11] for pathological gambling or sub-
stance addiction. However, most of the questionnaires
used to detect potentially problematic or addictive be-
haviours related to Internet use have been based on
national surveys or ad hoc questionnaires that have
not been previously validated [56].
According to Chang, 2008 [24], regardless of the type
of instrument used, when the dimensionality of prob-
lematic Internet use is analysed, it is possible to find
common aspects that can be grouped into more or fewer
dimensions in the factorial solutions, as: 1) Compulsive
Internet use and excessive time spent online, 2) with-
drawal symptoms when being restricted from Internet
use, 3) Using online social interaction to replace Real-life
interpersonal activities, and 4) Negative consequences re-
lated to Internet use (as social, academic or work
problems).
Of all the questionnaires found in the current scien-
tific literature to evaluate this problematic, the most
widely used is the one created by Kimberly Young [7],
whose psychometric properties have shown the existence
of both unidimensional and multidimensional solutions
[22–32, 37].
For this reason, we decided to adapt this questionnaire
in Spanish and evaluate its psychometric properties
following a strict methodology according to the latest
recommendations [45–47].
The Spanish version of Puerta-Cortés et al. [34]
showed similarities in the context of the questions with
our results, but the translation of sentences is somewhat
different, given the cultural differences between Spanish
and Colombian population.
As part of our validation process, reliability test-
retest scores was assessed using a mean between-test
interval of 8.3 ± 3.1 days, within the range recom-
mended in the literature [44], obtaining a level of
reliability (r= 0.899) very similar to that reported in
the initial validation of the IAT (r=0.85) [32]. Our
results also showed good agreement using both the
Kappa method (0.650) and Bland-Altman technique
(mean difference 3.5), aspects that cannot be
compared for lack of these parameters in other
validations.
Fig. 2 Confirmatory Factor Analysis of IAT. Items 6 and 8 were covariate due to their semantic similarity. Factor_1 =Emotional Investment;
Factor_2 = Time Management and Performance. All standardized estimations were significant at level 5 %
Table 3 Correlation between IAT factors with other variables
Factor 1 Factor 2
Emotional investment Time management
and performance
Age
a
−0.121
*
−0.170
*
Total Time
b
0.322
**
0.310
**
Leisure Time
b
0.346
**
0.334
**
Work/Study Time
b
0.010 −0.002
a
Age in years
b
Hours per week
*
p< 0.05
**
p< 0.001
Fernández-Villa et al. BMC Public Health (2015) 15:953 Page 6 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

The estimation of internal consistency was analysed
using Cronbach’s alpha, obtaining a value (0.91) which
was similar to that found in other published validations,
in which the value has ranged from 0.89 to 0.93 [25–27].
Moreover, our analysis of the questionnaire’s dimen-
sions revealed the existence of two principal factors
(“Emotional Investment”and “Time management and
Performance”) which explained 55 % of the variance,
consistent with other published psychometric analyses,
independently of the number of factors reported that
varied between one and six depending on the character-
istics of the samples used for the analyses [23–32].
Chinese version conducted in 2008 by Chang and Law
[24], reported the existence of three factors explaining
57 % of the variance. The first, which they called “With-
drawal and Social Problems”, was related primarily to
alterations in behaviour when the subject was not online
and to thoughts of what he or she will do the next time
they connect to the Internet, corresponding to what we
have called “Emotional Investment”. The second dimen-
sion identified by these authors coincides with our sec-
ond factor, “Performance and Time Management”,
which encompasses issues related to time spent online
and how a loss of control in this aspect can impair aca-
demic and professional performance. Lastly, the third
component described in the Chinese study was called
“Reality Substitution”and consisted of items related to
the social relationships established through the Internet
and a preference for these as opposed to relationships in
real life, in turn related to our first factor [24].
On the other hand, an analysis of the IAT’s factor
structure conducted by Widyanto and McMurran [23]
indicated the existence of six factors (salience or pre-
occupation about what will be done during the next ses-
sion online, excessive use, neglecting work, anticipation,
lack of control, and neglecting social life) which ex-
plained a total of 68 % of the variance. However, these
same authors conducted a later psychometric study [35],
in which they found that these six components could be
grouped into three main dimensions that explained 56 %
of the variance. In this later study, they established the
following dimensions: “Psychological emotional conflict”,
“mood modification”(consistent with our first factor),
and “time management”(similar to our second factor).
Another important validation study was that con-
ducted of the French version by Khazaal et al. [25], who
performed a factor analysis comparing the six-factor
model [23] described in the previous paragraph with a
single-factor model. This latter solution explained 45 %
of the variance and resulted in better psychometric
properties compared to the six-factor model.
Lastly, one of the most recent psychometric analysis,
conducted in Germany [27], showed the existence of
two factors (preoccupation and loss of control),
explaining between 63 and 73 % of the variance depend-
ing on whether the questionnaire was administered in
paper or online format.
Among all the factors found in different studies, time
spent online is highlighted as a benchmark, due to it is
an indicator of problems related to Internet use. How-
ever, it is not a single criterion, reason why in different
psychometric analyses other aspects as lack of self-
control, negative consequences in daily life related to
internet use or reality substitute among others, have
been referred to as related to it [24].
Furthermore, our adaptation of the IAT to Spanish
showed that some item of the questionnaire might be
outdated. This problem was found by other authors,
who have validated short versions of the IAT with 12
and 18 items [29, 33]. Costello & Osborne [47] suggest
that problematic items (ones that are low-loading, cross-
loading or Freestanding) must be removed of the factor
analysis. In the first EFA conducted in this paper, the
Item_7 did not have enough loading on any factor (fac-
tor loadings lower than 0.300), reason why was elimi-
nated. We removed this item considering also a
theoretical explanation based in other validations [24,
37], due to the IAT questionnaire was development in
1998 [7], when the email was checked by computer, but
today, we can see the email in our smartphones many
times a day and this behaviour can be considered nor-
mal. For this, we believe that this item was not relevant
to evaluate the Problematic Internet Use and it did not
compromise the validity of the questionnaire.
In addition, the covariation between item_6 and
item_8 increased the fit model in the confirmatory factor
analysis. This covariation was done according to the
modification fit indices values, to other previous valida-
tions of the IAT and to the content similarity semantic
of the items [31, 37, 46].
Although pathological use or addiction to the Internet
is not recognised as such in any diagnostic manual [11,
12], some authors have underscored excessive time spent
online and age as risk variables [24, 35]. For this reason,
we analysed the possible relationship with these vari-
ables, obtaining results similar to those reported by
Widyanto et al. for the English version of the IAT [23],
indicating a negative relationship with age and a positive
one with time spent online, especially for leisure and
entertainment purposes.
With respect to the main limitations of this study, we
emphasize that the sample used is voluntary and non-
randomized, as well as being college population, which
hinders the generalizability of the results. Moreover, the
study presents selection and information biases due to
the online methodology used in the development of
questionnaires. Finally, methodological differences with
other published studies published (type of population,
Fernández-Villa et al. BMC Public Health (2015) 15:953 Page 7 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

statistic method used, sample size, etc.) make difficult to
conduct a thorough data comparison.
On the other hand, our main strength is the strict
methodological analysis used. We have evaluated the
association between variables using polychoric corre-
lations (given the ordinal nature of the IAT scale) in-
stead of Pearson correlations [45]. Moreover, as the
sample size was large enough we used a split-half
sample approach, using a subsample to EFA and the
other one to CFA [39]. And finally, given the viola-
tionofnormalityinourdata,weusedrobuststatis-
tical methods to the analyses [46].
For all these reasons, we believe that the psychometric
properties of our short version of IAT presents accept-
able characteristics to use it as a diagnostic or screening
tool of Internet related problems in daily life, consider-
ing as future lines of research the influence of gender in
these problems or the evaluation of psychological dis-
eases associated, as well as the analyse in general popu-
lation to improve the external validity.
Conclusions
The two main dimensions were founded in our valid-
ation of the IAT: “emotional investment”and “Time
Management and Performance”which correlated nega-
tively with age and positively with hours spent online
per week. This Spanish short version of the IAT showed
good properties to evaluate problematic Internet use in
college students.
Additional files
Additional file 1: English version of IAT. (DOC 39 kb)
Additional file 2: Spanish version of IAT. (PDF 7 kb)
Abbreviations
CFA: Confirmatory Factor Analysis; CFI: Comparative Fit Index; DSM-
V: Diagnostic and Statistical Manual of Mental Disorders 5
th
edition;
EFA: Exploratory Factor Analysis; IAT: Internet Addiction Test; KMO: Kaiser-
Meyer-Olkin; RMSEA: Root Mean Squared Error of Approximation;
SRMR: Standardised Root Mean Square Residual; TLI: Tucker Lewis Index;
WLSMV: Weighted Least Square Mean and Variance Adjusted.
Competing interests
The authors declare that they have no competing interests.
Authors’contributions
AJM and MGM directed the study; analyzed the data and wrote the
manuscript: TFV, AJM; Conceived, performed and designed the study: TFV
AJM MGM JLD MDR VMS and contributed to the supervision of the study
and critical analysis of the article: AJM MGM JLD MDR VMS. All authors read
and approved the final manuscript.
Authors’information
Not applicable.
Availability of data and materials
Not applicable.
Acknowledgements
The authors acknowledge the help from the Department of Epidemiology
and Biostatistics of Michigan State University, especially from James C.
Anthony, as well as the collaboration of the students who participated in the
study, Rodrigo Martín-Rojas. from University of León and other members of
the uniHcos project.
Funding
This study was funded by the National Drug Plan, Ministry of Health, Social
Services and Equality of Spain (Codes: 2010|145 and 2013|034).
Author details
1
The Research Group in Gene - Environment and Health Interactions,
University of León, León, Spain.
2
Faculty of Health Sciences, Department of
Biomedical Sciences, Area of Preventive Medicine and Public Health,
University of León, Vegazana Campus s/n, 24071 León, Spain.
3
Department
of Preventive Medicine and Public Health, University of Granada, Granada,
Spain.
4
The Biomedical Research Centre Network for Epidemiology and
Public Health (CIBERESP), Madrid, Spain.
5
Area of Preventive Medicine and
Public Health, University of Cantabria, Cantabria, Spain.
6
Area of Preventive
Medicine and Public Health, University of Jaén, Jaén, Spain.
Received: 29 April 2015 Accepted: 15 September 2015
References
1. Valkenburg PM, Peter J. Online communication among adolescents: an
integrated model of its attraction, opportunities, and risks. J Adolesc Health.
2011;48:121–7.
2. Instituto Nacional de Estadística. Encuesta sobre Equipamiento y Uso de
Tecnologías de Información y Comunicación (TIC) en Hogares. 2014.
http://www.ine.es/prensa/np864.pdf. Accessed 10 Feb 2015.
3. Young K. Internet addiction: a new clinical phenomenon and its
consequences. Am Behav Sci. 2004;9:402–15.
4. Chen YF, Peng S. University students’internet use and its relationships with
academic performance, interpersonal relationships, psychosocial adjustment,
and self-evaluation. Cyberpsychol Behav. 2008;11:467–9.
5. Durkee T, Kaess M, Carli V, Parzer P, Despalins R, Floderus B, et al. Prevalence
of pathological internet use among adolescents in Europe: demographic
and social factors. Addiction. 2012;107:2010–22.
6. Sasmaz T, Oner S, Kurt AÖ, Yapici G, Yazici AE, Bugdayci R, et al. Prevalence
and risk factors of internet addiction in high school students. Eur J Public
Health. 2014;24:15–20.
7. Young K. Internet addiction: the emergence of a new clinical disorder.
Cyberpsychol Behav. 1998;1:237–44.
8. Young K. Internet addiction over the decade: a personal look back. World
Psychiatry. 2010;9:91.
9. Luengo A. Adicción a Internet: conceptualización y propuesta de
intervención. Revista Profesional Española de Terapia Cognitivo-Conductual.
2004;2:22–52.
10. Douglas AC, Mills JE, Niang M, Stepchenkova S, Byun S, Ruffini C, et al.
Internet addiction: meta-synthesis of qualitative research for the decade
1996–2006. Comput Human Behav. 2008;24:3027–44.
11. World Health Organization. The ICD-10 classification of mental and
behavioural disorders. Clinical descriptions and diagnostic guidelines.
1993. http://www.who.int/classifications/icd/en/bluebook.pdf. Accessed 3
May 2014.
12. American Psychiatric Association. Diagnostic and Statistical Manual of
Mental Disorders (DSM-IV). 4th ed. Barcelona: Masson; 2003.
13. Lam-Figueroa N, Contreras-Pulache H, Mori-Quispe E, Nizama-Valladolid M,
Gutiérrez C, Hinostroza-Camposano W, et al. Adicción a Internet: Desarrollo
y Validación de un instrumento en escolares adolescentes de Lima, Perú.
Rev Peru Med Exp Salud Publica. 2011;28:462–9.
14. American Psychiatric Association. DSM-5 development. Internet gaming
disorder (Section III). 2013. http://www.dsm5.org/Documents/
Internet%20Gaming%20Disorder%20Fact%20Sheet.pdf. Accessed 7 Jun 2014.
15. Carbonell X. The internet gaming disorder in the DSM-5. Adicciones.
2014;26:91–5.
16. Petry NM, O’Brien CP. Internet gaming disorder and the DSM-5. Addiction.
2013;108:1186–7.
Fernández-Villa et al. BMC Public Health (2015) 15:953 Page 8 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

17. Secades-Villa R, Calafat A, Fernández-Hermida JR, Juan M, Duch M,
Skärstrand E, et al. Duration of Internet use and adverse psychosocial effects
among European adolescents. Adicciones. 2014;26:247–53.
18. Diddia J, Dorphinghaus A, Maggi C, Haro G. Adicciones a Internet: Una
posible inclusión en la nosografía. Revista de Psiquiatría del Uruguay.
2009;73:73–82.
19. Pezoa-Jares R, Espinoza-Luna IL, Vasquez-Medina JA. Internet addiction: a
review. J Addict Res Ther. 2012;S6:004.
20. Kaess M, Durkee T, Brunner R, Carli V, Parzer P, Wasserman C, et al. Pathological
Internet use among European adolescents: psychopathology and self-
destructive behaviours. Eur Child Adolesc Psychiatry. 2014;23:1093–102.
21. Li W, O’Brien JE, Snyder SM, Howard MO. Characteristics of internet
addiction/pathological internet use in U.S. University students: a qualitative-
method investigation. PLoS One. 2015;10, e0117372.
22. Young K. Caught in the net: how to recognize the signs of internet
addiction and a winning strategy for recovery. Young KS, editor: John Wiley
& Sons, Inc., 605 Third Avenue, New York, NY 10158–0012. 248; 1998.
23. Widyanto L, McMurran M. The psychometric properties of the internet
addiction test. Cyberpsychol Behav. 2004;7:443–50.
24. Chang M, Law S. Factor structure for Young’s internet addiction test: a
confirmatory study. Comput Human Behav. 2008;24:2597–619.
25. Khazaal Y, Billieux J, Thorens G, Khan R, Louati Y, Scarlatti E, et al.
French validation of the internet addiction test. Cyberpsychol Behav.
2008;11:703–6.
26. Korkelia J, Kaarlas S, Jaaskelainen M, Vahlberg T, Taiminen T. Attached
to the web - harmful use of Internet and its correlates. Eur Psychiatry.
2010;25:236–41.
27. Barke A, Nyenhuis N, Kröner-Herwig B. The German version of the internet
addiction test: a validation study. Cyberpsychol Behav Soc Netw.
2012;15:534–42.
28. Hawi NS. Arabic validation of the internet addiction test. Cyberpsychol
Behav Soc Netw. 2013;16:200–4.
29. Faraci P, Craparo G, Messina R, Severino S. Internet Addiction Test (IAT):
which is the best factorial solution? J Med Internet Res. 2013;9, e225.
30. Tsimtsiou Z, Haidich AB, Kokkali S, Dardavesis T, Young KS, Arvanitidou M.
Greek version of the internet addiction test: a validation study. Psychiatr Q.
2014;85:187–95.
31. Pontes HM, Patrão IM, Griffiths MD. Portuguese validation of the internet
addiction test: an empirical study. J Behav Addict. 2014;3:107–14.
32. Jelenchick LA, Becker T, Moreno MA. Assessing the psychometric properties
of the Internet Addiction Test (IAT) in US college students. Psychiatry Res.
2012;196:296–301.
33. Pawlikowski M, Altstötter-Gleich C, Brand M. Validation and psychometric
properties of a short version of Young’s internet addiction test. Comput
Human Behav. 2013;29:1212–23.
34. Puerta-Cortés DX, Carbonell X, Chamarro A. Análisis de las propiedades
psicométricas de la versión en español del Internet Addiction Test. Trastor
Adict. 2012;14:99–104.
35. Widyanto L, Griffiths MD, Brunsden V. A psychometric comparison of the
internet addiction test, the internet-related problem scale, and self-
diagnosis. Cyberpsychol Behav Soc Netw. 2010;14:141–9.
36. Estévez L, Bayón C, de la Cruz J, Fernández A. Uso y abuso de Internet en
adolescentes. In: Echeburúa E, Labrador FJ, Becoña E, editors. Adicción a las
nuevas tecnologías. Madrid: Pirámide; 2009. p. 111–30.
37. Lai CM, Mak KK, Watanabe H, Ang RP, Pang JS, Ho RC. Psychometric
properties of the internet addiction test in Chinese adolescents. J Pediatr
Psychol. 2013;38:794–807.
38. Fernández-Villa T, Alguacil J, Ayán C, Bueno-Cavanillas A, Cancela JM, Capelo
R, et al. Proyecto uniHcos. Cohorte dinámica de estudiantes universitarios
para el estudio del consumo de drogas y otras adicciones. Rev Esp Salud
Publica. 2013;87:575–85.
39. Fabrigar LR, Wegener DT, MacCallum RC, Strahan EJ. Evaluating the use of
exploratory factor analysis in psychological research. Psychol Methods.
1999;4:272–99.
40. Boletín Oficial del Estado. Ley Orgánica 15/1999 de Protección de Datos de
Carácter Personal. 1999. http://www.boe.es/boe/dias/1999/12/14/pdfs/
A43088-43099.pdf. Accessed 10 May 2014.
41. Muñiz J, Elosua P, Hambleton RK. Directrices para la traducción y adaptación
de los test: segunda edición. Psicothema. 2013;25:151–7.
42. Santisteban C. Principios de Psicometría. Madrid: Síntesis; 2009.
43. Bland J, Altman D. Statistical methods for assessing agreement between
two methods of clinical measurement. Lancet. 1986;8:307–10.
44. Altman D. Practical statistics for medical research. New York: Chapman and
Hall; 1991.
45. Hoffmann AF, Stover JB, de la Iglesia G, Fernández –Liporace M.
Correlaciones policóricas y tetracóricas en estudios factoriales exploratorios
y confirmatorios. Ciencias Psicológicas. 2013;7:151–64.
46. Muthén LK, Muthén BO. Mplus user’s guide. 7th ed. Los Angeles, CA:
Muthén & Muthén; 1998–2012.
47. Costello AB, Osborne JW. Best practices in exploratory factor analysis: four
recommendations for getting the most from your analysis. Pract Assess Res
Eval. 2005;10.
48. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure
analysis: conventional criteria versus new alternatives. Struct Equ Modeling.
1999;6:1–55.
49. Ferrando PJ, Anguiano-Carrasco C. El análisis factorial como técnica de
investigación en psicología. Papeles del psicólogo. 2010;31:18–33.
50. Brenner V. Psychology of computer use: parameters of internet use, abuse
and addiction: the first 90 days of the internet usage survey. Psychol Rep.
1997;80:879–82.
51. Davis R. A cognitive - behavioral model of pathological Internet use (PIU).
Comput Human Behav. 2001;17:187–95.
52. Echeburúa E, Corral P. Adicción a las nuevas tecnologías y a las redes
sociales en jóvenes: un nuevo reto. Adicciones. 2010;22:91–6.
53. Schermelleh-Engel K, Moosbrugger H. Evaluating the fit of structural
equation models: test of significance and descriptive goodness-of-fit
measures. Methods Psychol Res Online. 2003;8:23–74.
54. Casas JA, Ruiz-Olivares R, Ortega-Ruiz R. Validation of the internet and social
networking experiences questionnaire in Spanish adolescents. Int J Clin
Health Psychol. 2013;13:40–8.
55. Christakis D, Moreno M, Jelenchick L, Myaing M, Zhou C. Problematic
internet usage in US college students: a pilot study. BMC Med. 2011;9:77.
56. Carbonell X, Fúster H, Chamarro A, Oberst U. Adicción a Internet y Móvil:
una revisión de estudios empíricos españoles. Papeles del Psicólogo.
2012;33:82–9.
Submit your next manuscript to BioMed Central
and take full advantage of:
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at
www.biomedcentral.com/submit
Fernández-Villa et al. BMC Public Health (2015) 15:953 Page 9 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com