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Exploring the dimensions of nomophobia: Development and validation
of a self-reported questionnaire
Caglar Yildirim
a,
⇑
, Ana-Paula Correia
b
a
1620 Howe Hall, Human Computer Interaction Graduate Program, Iowa State University, Ames, IA 50011, USA
b
2633 Lagomarcino Hall, School of Education, Iowa State University, Ames, IA 50011, USA
article info
Article history:
Available online 14 March 2015
Keywords:
Nomophobia
Dimensions of nomophobia
Nomophobia questionnaire
NMP-Q
Situational phobia
abstract
Nomophobia is considered a modern age phobia introduced to our lives as a byproduct of the interaction
between people and mobile information and communication technologies, especially smartphones. This
study sought to contribute to the nomophobia research literature by identifying and describing the
dimensions of nomophobia and developing a questionnaire to measure nomophobia. Consequently, this
study adopted a two-phase, exploratory sequential mixed methods design. The first phase was a qual-
itative exploration of nomophobia through semi-structured interviews conducted with nine undergradu-
ate students at a large Midwestern university in the U.S. As a result of the first phase, four dimensions of
nomophobia were identified: not being able to communicate, losing connectedness, not being able to
access information and giving up convenience. The qualitative findings from this initial exploration were
then developed into a 20-item nomophobia questionnaire (NMP-Q). In the second phase, the NMP-Q was
validated with a sample of 301 undergraduate students. Exploratory factor analysis revealed a four-factor
structure for the NMP-Q, corresponding to the dimensions of nomophobia. The NMP-Q was shown to pro-
duce valid and reliable scores; and thus, can be used to assess the severity of nomophobia.
Ó2015 Elsevier Ltd. All rights reserved.
1. Introduction
Information and communication technologies (ICT) have
become an indispensable part of our lives (Lee, Tam, & Chie,
2013; Salehan & Negahban, 2013). With the proliferation of
inexpensive mobile devices, we are now living in a mobile age in
which mobile ICTs are vigorously and quickly adopted
(Oulasvirta, Rattenbury, Ma, & Raita, 2012). In this mobile age,
smartphones are considered the latest evolution of mobile ICTs
(Oulasvirta et al., 2012).
The advances in mobile ICTs have paved the way for the world-
wide adoption of mobile phones. Mobile phones have become so
pervasive that the number of mobile-cellular subscriptions is
expected to reach almost 7 billion by the end of 2014, approaching
the world population with a penetration rate of 96% (International
Telecommunications Union, 2014).
According to Pew Research Center’s Mobile Technology Fact
Sheet (2014), as of January 2014, 90% of the American adult pop-
ulation have some kind of a cell phone and 58% of American adults
own a smartphone. Among adults who own a smartphone, 83% are
aged 18–29, 74% are aged 30–49, 49% are aged 50–64, and 19% are
aged 65 or older. Thus, smartphones are particularly popular
among young adults. In fact, college students are regarded as the
early adopters of smartphones (Lee, 2014).
The popularity of smartphones among college students is
ascribable to the affordances they provide. Smartphones make it
possible to perform a variety of daily tasks in one device, including,
but not limited to, calling and texting people, checking and sending
email messages, scheduling appointments, surfing the Internet,
shopping, social networking, searching for information on the
Internet, gaming, entertainment, etc. (Park, Kim, Shon, & Shim,
2013). Because smartphones are ubiquitous and provide numerous
capabilities, Kang and Jung (2014) propose that smartphones go
beyond serving communication, information and entertainment
purposes. They state that smartphones enable people to ‘‘fulfill
needs such as learning, individual capability, safety, and human
relationships’’ (Kang & Jung, 2014, p. 377), which is attributed to
the mobility of smartphones.
While the mobility of smartphones provides apparent benefits
and enable individuals to satisfy their basic needs (Kang & Jung,
2014), it may also induce some problems associated with smart-
phone use. Previous studies have shown that smartphones may
cause compulsive checking habits (Oulasvirta et al., 2012), that
smartphones may lead to compulsive usage and increased distress
http://dx.doi.org/10.1016/j.chb.2015.02.059
0747-5632/Ó2015 Elsevier Ltd. All rights reserved.
⇑
Corresponding author. Tel.: +1 515 212 0110.
E-mail addresses: caglar@iastate.edu (C. Yildirim), acorreia@iastate.edu
(A.-P. Correia).
Computers in Human Behavior 49 (2015) 130–137
Contents lists available at ScienceDirect
Computers in Human Behavior
journal homepage: www.elsevier.com/locate/comphumbeh
(Lee, Chang, Lin, & Cheng, 2014; Matusik & Mickel, 2011), and that
smartphones can be addictive (Chiu, 2014; Lee et al., 2014; Salehan
& Negahban, 2013).
Another problem exacerbated by smartphones is nomophobia.
Nomophobia, or no mobile phone phobia, is ‘‘the fear of being out
of mobile phone contact’’ (SecurEnvoy, 2012, para. 1). The term,
nomophobia, is an abbreviation for no-mobile-phone phobia, and
it was first coined during a study conducted in 2008 by the UK
Post Office to investigate anxieties mobile phone users suffer
(SecurEnvoy, 2012). The 2008 study in the UK, conducted with over
2100 people, demonstrated that some 53% of mobile phone users
suffered from nomophobia (Mail Online, 2008). The study also
revealed that men were more prone to nomophobia than were
women, with 58% of male participants and 48% of female partici-
pants indicating feelings of anxiety when unable to use their phone.
Another study conducted in the UK (SecurEnvoy, 2012) sur-
veyed 1000 employees and showed that the number of people suf-
fering from nomophobia increased from 53% to 66%. Unlike the
2008 study, the 2012 study found out that women were more sus-
ceptible to nomophobia, with 70% of the women compared to 61%
of the men expressing feelings of anxiety about losing their phone
or not being able to use their phone (SecurEnvoy, 2012). In terms of
the relationship between age and nomophobia, the study found
that young adults, aged 18–24 were most prone to nomophobia
with 77% of them identified as nomophobic, followed by users aged
25–34 at 68%. Moreover, mobile phone users in the 55 and over
group were found to be the third most nomophobic users.
In one of the very first research studies into nomophobia (King,
Valença, & Nardi, 2010), nomophobia is considered a 21st century
disorder resulting from new technologies. In this definition, nomo-
phobia ‘‘denotes discomfort or anxiety when out of mobile phone
(MP) or computer contact. It is the fear of becoming technologically
incommunicable, distant from the MP or not connected to the
Web’’ (King et al., 2010, p. 52). Thus, this definition seems to
encompass not only mobile phones but computers, as well. In
another study (King et al., 2013), nomophobia is defined as ‘‘a dis-
order of the modern world [that] has only recently been used to
describe the discomfort or anxiety caused by the non-availability
of an MP, PC or any other virtual communication device in
individuals who use them habitually’’ (p. 141). Although their def-
inition includes the unavailability of computers, they argue that
computers are replaced by mobile phones, which presumably have
smartphone capabilities, and tablets. Therefore, they state that
their research focus is less on computers and more on virtual com-
munication environments, including mobile phones (King et al.,
2013, p. 142). Their definition implies a dependency on virtual
environments for communication. In a recent study (King et al.,
2014), nomophobia is defined as follows:
Nomophobia is the modern fear of being unable to communi-
cate through a mobile phone (MP) or the Internet. ...
Nomophobia is a term that refers to a collection of behaviors or
symptoms related to MP use. Nomophobia is a situational phobia
related to agoraphobia and includes the fear of becoming ill and
not receiving immediate assistance (p. 28).
In this definition, King et al. (2014) seem to emphasize the
inability to communicate through a mobile phone. Another point
that is worth mentioning is the description of nomophobia as a
situational phobia related to agoraphobia. While the previous def-
initions appear to embrace the feelings of anxiety resulting from
the unavailability of such devices as computers or virtual commu-
nication devices, this recent definition is more related to mobile
phones and denotes nomophobia as a situational phobia.
The present study discusses nomophobia in relation to smart-
phones. As King et al. (2010) propose, nomophobia is considered
a modern age phobia and a byproduct of the interaction between
individuals and new technologies. Over the last five years,
smartphones have taken over the mobile phone market and have
almost replaced the phrase ‘‘mobile/cell phone’’ With their numer-
ous capabilities, smartphones facilitate instant communication,
help people stay connected anywhere anytime, and provide people
with constant access to information. Thus, people have become
dependent on their mobile phones more than ever (Park et al.,
2013), which, in turn, supposedly exacerbates the feelings of anxi-
ety caused by being out of mobile phone contact. That connection
is why nomophobia should be considered in relation to smart-
phones, which have the standard capabilities of a cell phone,
(e.g., phone calls, texting, etc.) and have more advanced capabili-
ties like internet access, applications, or sensors (Park et al., 2013).
Although there has been an increasing academic interest in
investigating the problems emanating from smartphone use,
research into nomophobia has been scarce (King et al., 2013,
2014). Thus, the purpose of this two-phase, exploratory mixed
methods study was to explore the dimensions of nomophobia with
the intent of using these findings to develop and validate a self-re-
ported questionnaire to measure nomophobia among U.S. college
students. To our best knowledge, this study is the first to devise
a self-reported measure to assess the severity of nomophobia
among college students.
2. Methods
This study adopted a mixed methods research design because it
encompassed the collection, analysis and combination of both
qualitative and quantitative data (Creswell & Plano Clark, 2011).
Of all the various mixed methods research designs, this study uti-
lized the two-phase, exploratory sequential design. The basic pre-
mise of this design is that the findings of the first, qualitative phase
inform the development of the second, quantitative phase
(Creswell & Plano Clark, 2011). This design is especially useful
when developing and testing an instrument that helps explore a
phenomenon about which little is known or there is no instrument
available (Creswell & Plano Clark, 2011).
In this study, the first phase began with the qualitative explo-
ration of nomophobia through focused interviews. Then the find-
ings from this qualitative phase guided the development of the
items to be used in the Nomophobia Questionnaire, hereinafter
referred to as the NMP-Q. The NMP-Q was psychometrically vali-
dated in the second, quantitative phase. All the steps taken in each
phase are explained in detail in the next sections.
2.1. Phase I: Qualitative exploration
The first, qualitative phase of the study was aimed at exploring
the dimensions of nomophobia as described by college students. To
this end, a phenomenological approach to qualitative exploration
was undertaken. Phenomenology, as a qualitative inquiry
approach, involves the exploration of a phenomenon through
individuals’ narrative descriptions of their own lived experience
pertaining to that specific phenomenon (Moustakas, 1994;
Sokolowski, 2000). Hence, semi-structured interviews were con-
ducted with a sample from the population to gain a thorough
understanding of the dimensions of nomophobia based on the
lived experiences of the interviewees.
2.1.1. Participants
Participants for the interviews were purposively selected with
the aim of identifying the students who had heavily depended on
their smartphone. For this purpose, using snowballing strategies,
a screening questionnaire was distributed through email messages.
The screening questionnaire included questions about smartphone
ownership, duration of ownership, and smartphone use. Moreover,
C. Yildirim, A.-P. Correia / Computers in Human Behavior 49 (2015) 130–137 131
the questionnaire adapted eight items from a previously validated
questionnaire, Test of Mobile Phone Dependence (TMD), developed
by Chóliz (2012). This questionnaire was used to identify the
respondents who heavily depended on their smartphones by
calculating a dependence score for all respondents using their
responses to the TMD items. The selection of the respondents
was based upon the following criteria: (a) the respondent owned
a smartphone for a year or more; (b) the respondent had a mobile
data plan providing access to the Internet via the smartphone; (c)
the respondent spent more than an hour using his or her smart-
phone and (d) the respondent had a dependence score, calculated
using the responses to the TMD items, greater than the mean of
the scores of all respondents. Respondents who met these criteria
were contacted through email and were invited for an interview.
As a result, nine undergraduate students (four males, five females),
aged 19–24, were recruited as participants for the interviews.
2.1.2. Procedures
Semi-structured interviews were conducted with the partici-
pants. All the interviews took place in a university office on cam-
pus. When the interviewees arrived at the designated location,
they were introduced to the study and then asked to read and sign
the Informed Consent Form if they agreed to be interviewed and
audio-recorded. After the interviews’ permission was granted, all
the interviews were audio-recorded and the interviewees were
assured that their identity would be kept confidential and that
no associations between their identity and audio recording would
be made.
During the interviews, an interview guide was followed to make
sure that all the interviewees were given the same information
about the study and were asked the same questions. The interview
guide consisted of questions varying from general questions about
college students’ smartphone use habits (e.g., for what purposes do
you usually use your smartphone?) to their feelings when out of
reach of their smartphones (e.g., how would you feel if you left your
smartphone at home and had to spend your day without it?, and
would you feel anxious if you could not use your smartphone for some
reason when you wanted to do so?).
2.1.3. Data analysis
All the interview recordings were transcribed verbatim. These
transcriptions were analyzed following the phenomenological data
analysis steps as described by Moustakas (1994). Having thor-
oughly and repeatedly read all the transcriptions, significant state-
ments about the interviewees’ experience, or horizons, were
extracted from each interviewee’s transcription. Through thematic
clustering, these horizons were grouped into meaning units
(Creswell, 2012). Consequently, the textural description of the
interviewees’ experience was produced. Next, a structural descrip-
tion of the interviewees’ experience was written and it was used as
a basis to construct the essence of the phenomenon of nomophobia
through the interviewees’ narrative descriptions of their experi-
ence (Miles & Huberman, 1994). As a result of the phenomenologi-
cal analysis of the qualitative data gathered in this phase, four
dimensions of nomophobia were identified, which are elaborated
in the Findings and Results section.
2.2. Interim phase: Questionnaire development
The interim phase of the study was devoted to the development
of the NMP-Q. Specifically, this phase of the study was aimed at
building on the findings from the first, qualitative stage to design
and develop the NMP-Q. Hence, it was the interim phase in which
the first, qualitative phase was connected to the second, quantita-
tive phase.
The questionnaire was devised following the scale development
guidelines proposed by DeVellis (2003). The findings of the first,
qualitative phase of the study were invaluable in that they pro-
vided an in-depth description of the dimensions of nomophobia
in the words of the interviewees. Within each dimension, there
were several recurrent components mentioned by the intervie-
wees. Taking into consideration the importance of each compo-
nent, for each dimension, several items were carefully
constructed using the statements of the interviewees from the
transcriptions. This resulted in a list of 23 items, covering the four
dimensions of nomophobia. Three items were paraphrased ver-
sions of other items.
A 7-point Likert scale, with 1 being ‘‘Strongly Disagree’’ and 7
‘‘Strongly Agree’’, was chosen as the rating scale for the question-
naire because the items were presented as declarative statements
and the intent was to have respondents indicate the degree of their
agreement or disagreement with each statement.
The questionnaire was reviewed by two experts for content
validity. These experts evaluated the items for their clarity, impor-
tance, and relevance. The results of the expert review indicated
that all 23 items were relevant to nomophobia and important for
the questionnaire. Based on the experts’ comments and feedback,
three paraphrased items were removed from the questionnaire
because the experts indicated that they were overlapping with
the original items. Also, some minor changes were made in word
choices and sentence structure to improve the clarity of the items.
After the expert review, the questionnaire was reviewed by an
English language editor to make sure that there were no structural
errors in the items and the wording of the items was appropriate.
Based on the editor’s feedback, some wording changes were made
to improve the consistency among and clarity of the items. For
instance, the language expert recommended the use of ‘‘email mes-
sages’’ instead of ‘‘emails.’’
Finally, representatives of the population (two undergraduate
students who were naïve to the study) piloted the 20-item ques-
tionnaire to ensure that all the items were comprehensible. The
two students indicated that the items were meaningful to them,
and that they had no difficulty in reading and understanding the
items. As a result of this step, a penultimate questionnaire with
20 items was created.
DeVellis (2003) recommends that other relevant measures be
administered to check for construct validity. For this purpose, the
8-item Mobile Phone Involvement Questionnaire (MPIQ) devel-
oped by Walsh, White, and Young (2010) was administered
together with the NMP-Q. The MPIQ was just used for purposes
of analysis and is not part of the NMP-Q.
2.2.1. Pilot study
Before the main study, a pilot study of the penultimate NMP-Q
was conducted with a convenience sample of 86 undergraduate
students from the population, who were not included in the sam-
ple for the main study. The sample consisted of 11 male students
(12.8%) and 75 female students (87.2%) aged 18–24 with a mean
age of 19. The NMP-Q was administered in a large undergraduate
class. Rather than identifying the factor structure of the question-
naire, the purpose of the pilot study was to see whether the NMP-Q
produced reliable scores because the sample size was relatively
small to perform exploratory factor analysis; and thus, to make
informed decisions about the factor structure of the questionnaire.
The pilot study demonstrated that the NMP-Q held good inter-
nal consistency, with a Cronbach’s alpha value of .918. Therefore, it
was concluded that the questionnaire was appropriate for use in
the main study; and thus, the second phase of the study was
initiated.
132 C. Yildirim, A.-P. Correia / Computers in Human Behavior 49 (2015) 130–137
2.3. Phase II: Quantitative validation
The purpose of the second, quantitative phase of the study was
to psychometrically validate the penultimate NMP-Q with a large
sample and investigate the extent to which the NMP-Q generated
valid and reliable scores. For this purpose, the NMP-Q was admi-
nistered to a sample representative of the undergraduate students
at a large Midwestern university in the U.S.
2.3.1. Sample
To improve the representativeness of the results, a stratified
sample was selected for the main study. In particular, proportion-
ate stratification was used and college was chosen as the strata.
Since the university where this study was conducted had six col-
leges offering different undergraduate programs, the population
was divided into six strata. The proportionate stratification was
calculated on the basis of the enrollment statistics of the university
for the last 4 years (2010–2014). This ascertained that the number
of students selected for the sample from each stratum (i.e., college)
was proportionate to the number of students in each college at the
university level or in the population.
A sample size of 300 students was chosen for statistical analysis
purposes because it is commonly accepted as a sufficiently large
sample to perform exploratory factor analysis (Comrey & Lee,
1992; DeVellis, 2003; Tabachnick & Fidell, 2013). Hence, the sam-
ple in the main study consisted of 301 undergraduate students,
135 males and 166 females, with a mean age of 20. 15.3% of the
students (n= 46) were from the College of Agriculture and Life
Sciences, 13.6% (n= 41) were from the College of Business, 7%
(n= 21) were from the College of Design, 24.6% (n= 74) were from
the College of Engineering, 15% (n= 45) were from the College of
Human Sciences, and 24.6% (n= 74) were from the College of
Liberal Arts and Sciences. Of 301 undergraduate students, 14.6%
(n= 44) reported checking their smartphone every five minutes,
25.2% (n= 76) every ten minutes, 23.9% (n= 72) every twenty min-
utes, 18.9% (n= 57) every thirty minutes, 12.0% (n= 36) every hour
time and 5% (n= 15) every two hours or more.
2.3.2. Data collection
Students were invited to participate in the study on a voluntary
basis through a brief in-class announcement, explaining the pur-
pose of the study and procedures for data collection. Having been
introduced to the study, the students were asked to complete the
NMP-Q provided that they voluntarily agreed to take part in the
study.
2.3.3. Instruments
The Nomophobia Questionnaire and Mobile Phone Involvement
Questionnaire (Walsh et al., 2010) were employed in the present
study.
Nomophobia Questionnaire (NMP-Q): The questionnaire was
composed of three main sections: demographics, smartphone
use, and nomophobia questionnaire. The Demographics section
was specifically designed for undergraduate students and included
age, sex, year of study, major, and college. The Smartphone Use
section was constructed based on the findings from the interviews.
It included duration of smartphone ownership, data plan owner-
ship, average time spent daily using the smartphone, frequency
of checking, number of phone calls made/received per day, number
of text messages sent/received per day, number of email messages
sent/received per day, number of applications on the smartphone,
purposes for which the smartphone is used, and contexts in which
the smartphone is used. The nomophobia questionnaire section
included the 20 items developed as a result of the first, qualitative
phase, as seen in Table 1. All 20 items in NMP-Q are rated using a
7-point Likert scale, with 1 being ‘‘Strongly Disagree’’ and 7 being
‘‘Strongly Agree’’ only at the extremes. Total scores are calculated
by summing up responses to each item, resulting in a nomophobia
score ranging from 20 to 140, with higher scores corresponding to
greater nomophobia severity. NMP-Q scores are interpreted as fol-
lows: an NMP-Q score of 20 indicating the absence of nomophobia;
an NMP-Q score greater than 20 and less than 60 corresponding to
a mild level of nomophobia; an NMP-Q score greater than or equal
to 60 and less than 100 corresponding to a moderate level of
nomophobia; and an NMP-Q score greater than or equal to 100
corresponding to a severe nomophobia.
Mobile Phone Involvement Questionnaire (MPIQ): In addition to
the NMP-Q, the 8-item MPIQ (Walsh et al., 2010) was adminis-
tered. MPIQ was also rated using a 7-point Likert scale ranging
from 1 (strongly disagree) to 7 (strongly agree). MPIQ was used
to check for construct validity of the NMP-Q.
2.3.4. Data analysis
Statistical analyses were performed using IBM Statistical
Package for the Social Sciences (SPSS) 20. To explore the underlying
factor structure of the NMP-Q, exploratory factor analysis (EFA)
was performed on the dataset. A principal component analysis
with varimax rotation was performed on the 20 items in the ques-
tionnaire. Varimax rotation, which is the most commonly used
orthogonal technique, minimizes factor complexity with maxi-
mized variance of factor loadings (Tabachnick & Fidell, 2013).
The reason for the use of an orthogonal rotation technique ema-
nated from the need for orthogonal factors in other analyses
(e.g., correlation test).
To determine how homogeneous the items in the NMP-Q were
(DeVellis, 2003), internal consistency reliability was examined
using Cronbach’s alpha as the internal consistency reliability coef-
ficient. In addition, the relationship between the NMP-Q and MPIQ
was investigated using a parametric correlation coefficient (the
Pearson product-moment correlation). The degree of the correla-
tion between the scores provided evidence of similarity between
the NMP-Q and MPIQ (DeVellis, 2003); and thus, served as a means
of checking for construct validity of the NMP-Q.
3. Findings and results
3.1. Findings of phase I: Understanding the dimensions of nomophobia
Four themes emerged from the interviews as the dimensions of
nomophobia. These dimensions are: (1) not being able to
communicate, (2) losing connectedness, (3) not being able to
access information and (4) giving up convenience.
The first theme that emerged as a dimension of nomophobia,
not being able to communicate, refers to the feelings of losing
instant communication with people and not being able to use the
services that allow for instant communication. The items under
this theme are related to the feelings of not being able to contact
people and to be contacted. In this regard, one of the female par-
ticipants with the pseudonym Tracy, a 22-year-old senior in
Kinesiology, said that she would feel anxious if she was out of con-
tact: ‘‘I just blew through my first 300 min a couple of days ago. I
was like ‘‘Now how are people gonna call me?’’ Even that makes
me have a feeling of anxiety.’’
Losing connectedness was the second dimension of nomophobia.
The items grouped under this theme are related to the feelings of
losing the ubiquitous connectivity smartphones provide, and being
disconnected from one’s online identity, especially on social media.
Participants described how this connectivity is an indispensable
part of their lives. Astrid, a 22-year-old senior in Microbiology, sta-
ted that one of the benefits of her smartphone was that it helped
her stay connected: ‘‘I think it allows me to stay up-to-date with
C. Yildirim, A.-P. Correia / Computers in Human Behavior 49 (2015) 130–137 133
my friends and all of that.’’ She went on to explain how her smart-
phone facilitated her ability to stay connected to her friends.
Moreover, participants described how important it was for them
to make sure that they saw the notifications from their smart-
phones and their desire to check their smartphones for notifica-
tions. They appeared to view notifications as a way of ensuring
connectedness: if they received notifications, it meant they stayed
connected to their online identity and networks. Lily, a 20-year-old
sophomore in Elementary Education, said she would feel the desire
to check her smartphone immediately when receiving a notifica-
tion: ‘‘If I hear it go off then I had that need of ‘‘what is it? what
is the notification?’’ If I could, I would check.’’ Additionally, partici-
pants described the feelings of discomfort when losing this con-
nectedness. Tracy said: ‘‘my smartphone is very important
because of that connectedness.’’ She continued to explain how hard
it was to go backward and live without a smartphone. In the same
vein, Olivia, a 21-year-old junior in Agricultural Education, eluci-
dated how she was used to having her smartphone with herself
all the time. When asked how she would feel if she did not have
her smartphone with her, Olivia said:
‘‘Because you are used to having it in your pocket or in your
hand and it is like you are always touching your pockets, look-
ing for it and like situations like on the bus or if I am sitting out-
side the classroom, waiting for the class to start, I don’t know
what to do with myself ‘cause in that situation I’d be probably
on my phone.’’
The third dimension was labeled not being able to access infor-
mation. The items under this theme reflect the discomfort of losing
pervasive access to information through smartphones, being
unable to retrieve information through smartphones and search
for information on smartphones. Participants’ portrayal of how
they used their smartphones to access information revealed the
importance of having access to information through their smart-
phones in their lives. Since it is a very essential component of their
smartphone use, young adults reported problems when they could
not access information through their smartphones. When dis-
cussing how she used her smartphone to access information,
Olivia mentioned how it enabled her to instantly access informa-
tion and touched upon how she would feel if she did not have that
instant access:
‘‘I like having information at my fingertips like if I don’t know
the answer of something, I wanna know it right away. So I’m
gonna use my smartphone to look it up. [] And if I couldn’t
answer a question right away, without that access to the
Internet I feel like that would make me uncomfortable.’’
The fourth theme identified as a result of the qualitative analy-
sis was giving up convenience. The items grouped under this theme
are related to the feelings of giving up the convenience smart-
phones provide and reflect the desire to utilize the convenience
of having a smartphone. Participants touched upon how they made
sure that they had their smartphone’s battery charged at all times.
John, a sophomore in Supply Chain Management, described his
smartphone as ‘‘a peace of mind.’’ John appeared to associate hav-
ing a charged battery in his smartphone with being free of stress
and anxiety. He explained his desire for having a charged battery
in his smartphone as follows:
‘‘[] If it does go dead, that’s the sort of thing when it is like ‘‘I
need to charge my phone right now’’. Especially, if I’m not at
home and it dies, it is just an uncertainty of like what if I forgot
my keys? [] If it does die, you lose a peace of mind.’’
3.2. Results of phase II
3.2.1. Exploratory factor analysis
As an initial solution, PCA was performed on the 20 items in the
questionnaire before rotating the factors to estimate the factorabil-
ity of the correlation matrix and the likely number of factors.
Initially, the correlation matrix was examined for correlations
among the items. Since there were numerous correlations among
the items exceeding .30, it was concluded that the use of PCA
was appropriate for the matrix (Tabachnick & Fidell, 2013). To fur-
ther investigate the factorability of the matrix, Bartlett’s test of
sphericity was used to examine partial correlations in addition to
bivariate correlations. Moreover, the Kaiser–Meyer–Olkin (KMO)
measure of sampling adequacy was examined to assess the sam-
pling adequacy during the analysis. Barlett’s test of sphericity
was significant (
v
2
(190) = 4266.807, p< .01), which rejected the
null hypothesis that the correlations in the correlation matrix were
zero and that the matrix was an identity matrix. As for the ade-
quacy of sampling, the KMO index was .941, which is greater than
the minimum acceptable value of .60 (Tabachnick & Fidell, 2013).
Thus, the results of these tests indicated that the factor analysis
was appropriate.
Table 1
The 20 items in the NMP-Q.
1. I would feel uncomfortable without constant access to information through my smartphone
2. I would be annoyed if I could not look information up on my smartphone when I wanted to do so
3. Being unable to get the news (e.g., happenings, weather, etc.) on my smartphone would make me nervous
4. I would be annoyed if I could not use my smartphone and/or its capabilities when I wanted to do so
5. Running out of battery in my smartphone would scare me
6. If I were to run out of credits or hit my monthly data limit, I would panic
7. If I did not have a data signal or could not connect to Wi-Fi, then I would constantly check to see if I had a signal or could find a Wi-Fi network
8. If I could not use my smartphone, I would be afraid of getting stranded somewhere
9. If I could not check my smartphone for a while, I would feel a desire to check it
If I did not have my smartphone with me,
10. I would feel anxious because I could not instantly communicate with my family and/or friends
11. I would be worried because my family and/or friends could not reach me
12. I would feel nervous because I would not be able to receive text messages and calls
13. I would be anxious because I could not keep in touch with my family and/or friends
14. I would be nervous because I could not know if someone had tried to get a hold of me
15. I would feel anxious because my constant connection to my family and friends would be broken
16. I would be nervous because I would be disconnected from my online identity
17. I would be uncomfortable because I could not stay up-to-date with social media and online networks
18. I would feel awkward because I could not check my notifications for updates from my connections and online networks
19. I would feel anxious because I could not check my email messages
20. I would feel weird because I would not know what to do
134 C. Yildirim, A.-P. Correia / Computers in Human Behavior 49 (2015) 130–137
As a result of the initial solution, four factors explaining 69.6%
of variance were extracted with initial eigenvalues larger than 1,
as can be seen in Table 2. Eigenvalues can be used to determine
the likely number of factors to be extracted. Factors with eigen-
values greater than 1 are considered important and therefore
retained because they account for a significant amount of vari-
ance (Field, 2009; Tabachnick & Fidell, 2013). Moreover, the
scree plot of the eigenvalues and factors supported that a four-
factor structure was a reasonable estimate because the eigenval-
ues started descending below 1 after that point.
Having determined the estimated number of factors, a second
run of PCA with varimax rotation was performed to enhance the
interpretability of the factors. As can be seen from Table 2, after
rotation Factor I – not being able to communicate – accounted for
22.9% of item variance, Factor II – losing connectedness – accounts
for 18.5% of item variance, Factor III – not being able to access infor-
mation – accounts for 14.3% of item variance, and Factor IV – giving
up convenience – accounts for 13.9% of item variance. Considering
the substantial proportion of variance accounted for by each factor,
it was concluded that the factors were important for the question-
naire (Tabachnick & Fidell, 2013).
Loadings of all items on each factor are shown in Table 3.To
facilitate interpretation of the table, items are ordered and grouped
by factor loadings. A factor loading of .45 was used as a cutoff
value. Table 4 provides a summary of the results of the exploratory
factor analysis and reliability analysis of all items.
As can be seen in Table 3, the results of the second run of PCA
showed that each item loaded on a single factor, and that the load-
ings on other factors were generally very low, except for Item 7 and
Item 15. Item 7 had a loading value of .669 on Factor IV – giving up
convenience – and of .421 on Factor II – losing connectedness.
Similarly, Item 15 had a loading value of .646 on Factor I – not
being able to communicate – and of .425 on Factor II – losing con-
nectedness. Due to the fact that their loadings on the primary fac-
tors were more salient and thus explained more variance, and that
with a cutoff value of .45, their loadings on the secondary factors
would not be considered, these items were considered to load on
their primary factors.
Table 4 lists the communality values for each item. With PCA,
the initial communality for all items is 1, and the decision as to
whether the variance is predictable by the underlying factor is
made by examining the communalities after factor extraction.
As seen in Table 4, the extract communality values are reason-
ably high for all items, suggesting that the items loading on each
factor can be well predicted by the respective factors.
When Table 4 is closely examined, it can be seen that the
majority of the items had excellent or very good loadings on a
single factor and some had good loadings. This factor structure,
which has several variables correlating with each factor and only
one factor correlating highly with each variable, is referred to as
‘‘simple structure’’ (Thurstone, 1947). The presence of simple
structure supports the adequacy of rotation (Tabachnick & Fidell,
2013).
3.2.2. Reliability analysis
As can be seen from Table 4, Cronbach’s alpha reliability coeffi-
cient for internal consistency of the questionnaire is .945, indicat-
ing that the questionnaire has good internal consistency (DeVellis,
2003; Field, 2009; Nunnally, 1978). In fact, an alpha value of .945 is
considered excellent (George & Mallery, 2011). In order to assess
the internal consistency of the items under each factor,
Cronbach’s alpha was computed separately for each factor. The
alpha coefficients of Factor I – not being able to communicate –
(6 items), Factor II – losing connectedness – (5 items), Factor III –
not being able to access information – (4 items) and Factor IV – giv-
ing up convenience – (5 items) were .939, .874, .827, and .814,
respectively (see Table 4). They were all above the commonly
accepted minimum value of .7 (Nunnally, 1978), suggesting that
they demonstrate good internal consistency.
To assess the reliability of each item, corrected item-total
correlation and Cronbach’s alpha if item deleted values were taken
into consideration. Corrected item-total correlation is a measure of
the extent to which an item correlates with all the other items in a
questionnaire, excluding the item itself (DeVellis, 2003). As seen in
Table 4, all corrected item-total correlations were greater than .40,
showing that all items correlate with the total. Cronbach’s alpha if
item deleted refers to the Cronbach’s alpha value of the total items
if a given item were to be excluded from the questionnaire. The
comparison of the Cronbach’s alpha if item deleted to Cronbach’s
alpha reliability coefficient for internal consistency of the ques-
tionnaire (.945) reveals that there is no item whose deletion will
result in an increase in the Cronbach’s alpha of all items. Hence,
we concluded that no item needed to be deleted from the ques-
tionnaire, as suggested by Field (2009).
3.2.3. Construct validity
A Pearson product-moment correlation coefficient was com-
puted to assess the relationship between the scores of the partici-
pants on the NMP-Q and MPIQ. NMP-Q scores and MPIQ scores
were strongly and directly correlated, r(299) = .710, p< .01. The
strong correlation between the two scores provided evidence of
similarity between the two questionnaires (DeVellis, 2003), and
thus ensured the construct validity of NMP-Q.
Table 2
Eigenvalues and total variance explained by factors before and after rotation.
Initial eigenvalues Rotation sums of squared
loadings
Total % of variance Cumulative
%
Total % of variance Cumulative
%
Factor I 9.979 49.894 49.894 4.575 22.877 22.877
Factor II 1.653 8.264 58.158 3.695 18.477 41.354
Factor III 1.264 6.318 64.476 2.863 14.317 55.671
Factor
IV
1.022 5.110 69.586 2.783 13.915 69.586
Table 3
Loadings of all items in each factor.
Items Factor
I II III IV
Item 11 .861 .148 .119 .200
Item 13 .836 .258 .262 .172
Item 12 .782 .228 .276 .238
Item 14 .778 .206 .162 .278
Item 10 .753 .197 .331 .234
Item 15 .646 .425 .228 .213
Item 16 .242 .838 .110 .206
Item 17 .235 .835 .169 .220
Item 18 .180 .800 .202 .287
Item 19 .390 .512 .272 .046
Item 20 .214 .523 .326 .295
Item 2 .208 .084 .830 .259
Item 4 .211 .142 .734 .340
Item 1 .254 .342 .668 .088
Item 3 .324 .288 .605 .119
Item 5 .204 .304 .197 .708
Item 8 .294 .027 .200 .672
Item 7 .165 .421 .134 .669
Item 6 .197 .384 .195 .623
Item 9 .375 .272 .284 .473
Factor loadings > .45 are in bold
C. Yildirim, A.-P. Correia / Computers in Human Behavior 49 (2015) 130–137 135
4. Discussion and conclusion
This two-phase, exploratory sequential mixed methods study
sought to explore the dimensions of nomophobia, and to design
and develop a questionnaire to measure nomophobia. In doing
so, this study extended nomophobia research by exploring the
dimensions of nomophobia and devising a validated nomophobia
questionnaire.
The items in the NMP-Q were developed based on the findings
of the first, qualitative phase of the study that revealed the dimen-
sions of nomophobia. The items were written using the statements
that were recurrently made by the interviewees. Then the NMP-Q
was validated with a sample of college students through explora-
tory factor analysis, which revealed a four-factor structure for the
NMP-Q. These factors corresponded to the dimensions of nomo-
phobia identified as a result of the first phase and were named
accordingly; that is, not being able to communicate, losing con-
nectedness, not being able to access information and giving up
convenience.
Based on the results of the reliability analysis, the internal con-
sistency coefficient, Cronbach’s alpha, for all the items in the NMP-
Q was .945. Cronbach’s alpha values for the four dimensions were
.939, .874, .827, and .814, respectively. Thus, this study empirically
supports that the NMP-Q demonstrates good internal consistency,
and that the NMP-Q generates reliable scores.
The four-factor solution (i.e., not being able to communicate,
losing connectedness, not being able to access information and giv-
ing up convenience) obtained as a result of the exploratory factor
analysis corroborates the connection of the four dimensions to
the theoretical construct of nomophobia, and thus ensures the con-
struct validity of the NMP-Q (DeVellis, 2003). The comparison of
the scores obtained from the NMP-Q with those of Mobile Phone
Involvement Questionnaire (MPIQ) indicates that there is a signifi-
cantly strong correlation between the scores, r(299) = .710, p< .01.
The MPIQ has been previously proved to produce valid scores
(Walsh et al., 2010). Also, the strong correlation between the scores
of NMP-Q and MPIQ provides evidence for the similarity between
the questionnaires and suggests that they should behave in similar
ways (DeVellis, 2003). Hence, this suggests that the NMP-Q gener-
ates valid scores.
In line with King et al. (2010), this study purports that nomo-
phobia, or no mobile phone phobia, can be considered a modern
age phobia introduced to our lives with the rapid proliferation
and adoption of smartphones. Within the scope of this study,
nomophobia is defined as the fear of not being able to use a smart-
phone or a mobile phone and/or the services it offers. It refers to
the fear of not being able to communicate, losing the con-
nectedness that smartphones allow, not being able to access infor-
mation through smartphones, and giving up the convenience that
smartphones provide.
King et al. (2010) and King et al. (2014) suggest that nomopho-
bia be regarded as a situational phobia. Based on the description of
specific situational phobias (Choy, Fyer, & Lipsitz, 2007), we also
propose that nomophobia can be considered a situational phobia
evoked by the unavailability of a smartphone or the thought of
not having it, not being able to use it and losing it. Choy et al.
(2007) explain that ‘‘specific phobia is characterized by an exces-
sive, irrational fear of a specific object or situation, which is
avoided at all cost or endured with great distress’’ (p. 267).
Situational phobias are experienced when a specific situation
evokes an intense, irrational fear that leads to an intense reaction
that can be both physical and emotional. Thus, people with nomo-
phobia, or nomophobes, would have an irrational fear of being out
of smartphone contact or not being able to use their smartphones,
and would strive to eliminate the chances of not being able to use
their smartphone. Had they been unable to use their smartphones,
they would have intense feelings of anxiety and distress. Moreover,
it has been suggested nomophobia should be included in DSM-5
(Bragazzi & Del Puente). Considering the DSM-5 Criteria for
Specific Phobia (American Psychiatric Association, 2013), it is plau-
sible that nomophobia may be listed as a situational phobia under
specific phobia identified in DSM-5.
The use of mixed methods research, specifically exploratory
sequential design, made it possible to explore qualitatively the
dimensions of nomophobia through the experiences of individuals
from the population. By utilizing both qualitative and quantitative
approaches, this study provided greater insight into nomophobia
as a theoretical construct than could be obtained using either qual-
itative methods or quantitative methods. In that manner, this
study contributes to the nomophobia research literature by reveal-
ing the dimensions of nomophobia, and by devising and validating
the NMP-Q, which was proven to yield valid and reliable scores.
With its novel approach to investigating nomophobia as a theo-
retical construct, this study provides a better understanding of the
dimensions of nomophobia. However, there are certain limitations
that should be addressed. Firstly, although the population of the
present study is undergraduate students in the U.S., selecting the
entire sample from a large Midwestern university may be a lim-
itation to the generalizability of the study’s results because the
convenience sample used in the present study was not representa-
tive of all undergraduate students in the U.S. Therefore, this lim-
itation should be considered when interpreting the results of this
study. Further research should seek to replicate the results of the
present study using more representative samples. Secondly, as
with any other self-reported questionnaire, the self-reported struc-
ture of the NMP-Q may be a limitation because of social desirabil-
ity bias.
In an attempt to address the scarcity of research into nomo-
phobia, this study explored the dimensions of nomophobia,
devised the NMP-Q as self-reported measure to assess the severity
of nomophobia, and provided empirical support for the validity
and reliability of the NMP-Q. Future research should seek to further
Table 4
Exploratory factor analysis and reliability analysis of all items.
Items Factor
loading
Communality
after
extraction
Corrected
item-total
correlation
Cronbach’s
alpha if item
deleted
Alpha
Factor I – Not being able to communicate .939
Item 11 .861 .818 .675 .942
Item 13 .836 .864 .774 .941
Item 12 .782 .797 .764 .941
Item 14 .778 .751 .714 .942
Item 10 .753 .770 .751 .941
Item 15 .646 .694 .756 .941
Factor II – Losing connectedness .874
Item 16 .838 .815 .682 .942
Item 17 .835 .829 .706 .942
Item 18 .800 .795 .703 .942
Item 19 .512 .490 .592 .944
Item 20 .523 .512 .629 .943
Factor III – Not being able to access information .827
Item 2 .830 .807 .600 .943
Item 4 .734 .719 .628 .943
Item 1 .668 .635 .618 .943
Item 3 .605 .569 .619 .943
Factor IV – Giving up convenience .819
Item 5 .708 .674 .643 .943
Item 8 .672 .578 .503 .945
Item 7 .669 .671 .634 .943
Item 6 .623 .612 .641 .943
Item 9 .473 .520 .654 .943
Overall Cronbach’s alpha .945
136 C. Yildirim, A.-P. Correia / Computers in Human Behavior 49 (2015) 130–137
investigate the psychometric properties of the NMP-Q and to
explore the psychological mechanisms underlying nomophobia.
Especially, studies examining the psychological factors comorbid
with nomophobia are imperative. Moreover, further investigation
into the prevalence of nomophobia among different demographic
groups in diverse contexts are needed. For instance, a previous
study revealed that females were more susceptible to nomophobia
when compared to males (SecurEnvoy, 2012). Conversely, in
another study, males were shown to be more likely to demonstrate
nomophobic behaviors than males (Mail Online, 2008). Given these
inconsistent results, further investigation is needed to disentangle
whether males and females differ in their proclivity to nomo-
phobia. In addition, future research should aim to determine which
factors predict nomophobia, which can be useful for identifying the
risk groups and developing prevention strategies to help those
groups cope with nomophobia. Overall, we envisage that further
investigation into the phenomenon of nomophobia is viable, and
that the NMP-Q, as a self-reported measure of nomophobia, can
be useful for future research.
Acknowledgements
We would like to offer our sincere appreciation to the students
who participated in this study, and to those who helped us during
the data collection process. We also would like to thank Dr. Mack
Shelley and Dr. Stephen Gilbert for their suggestions and feedback
during the development of NMP-Q.
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