Content uploaded by Kelly Whaling
Author content
All content in this area was uploaded by Kelly Whaling on May 25, 2020
Content may be subject to copyright.
The Media and Technology Usage and Attitudes Scale: An empirical
investigation
L.D. Rosen
⇑
, K. Whaling, L.M. Carrier, N.A. Cheever, J. Rokkum
California State University, Dominguez Hills, CA 90747, United States
article info
Article history:
Available online 29 June 2013
Keywords:
Technology and media usage
Anxiety
Attitudes toward technology
Smartphone
Video gaming
Facebook
abstract
Current approaches to measuring people’s everyday usage of technology-based media and other com-
puter-related activities have proved to be problematic as they use varied outcome measures, fail to mea-
sure behavior in a broad range of technology-related domains and do not take into account recently
developed types of technology including smartphones. In the present study, a wide variety of items, cov-
ering a range of up-to-date technology and media usage behaviors. Sixty-six items concerning technology
and media usage, along with 18 additional items assessing attitudes toward technology, were adminis-
tered to two independent samples of individuals, comprising 942 participants. Factor analyses were used
to create 11 usage subscales representing smartphone usage, general social media usage, Internet search-
ing, e-mailing, media sharing, text messaging, video gaming, online friendships, Facebook friendships,
phone calling, and watching television in addition to four attitude-based subscales: positive attitudes,
negative attitudes, technological anxiety/dependence, and attitudes toward task-switching. All subscales
showed strong reliabilities and relationships between the subscales and pre-existing measures of daily
media usage and Internet addiction were as predicted. Given the reliability and validity results, the
new Media and Technology Usage and Attitudes Scale was suggested as a method of measuring media
and technology involvement across a variety of types of research studies either as a single 60-item scale
or any subset of the 15 subscales.
Ó2013 Elsevier Ltd. All rights reserved.
1. Introduction
Until recently, before mobile computer technologies became
the norm, measuring media and technology use most often in-
volved monitoring hours and minutes spent doing various com-
puter activities (Kraut et al., 1998; Stanger & Gridina, 1999;
Subrahmanyam, Kraut, Greenfield, & Gross, 2000), watching televi-
sion (Stanger, 1998), playing video games (Phillips, Rolls, Rouse, &
Griffiths, 1995) or some combination of those activities (Media Me-
trix, 1999; Nielsen Media Research, 1999). In the pioneering Home-
Net Study, for example, Kraut et al. (1998) reported Internet use in
hours per week. Similarly, in a widely quoted study, the Kaiser
Family Foundation (Rideout, Foehr, Roberts, & Brodie, 1999) re-
ported a national sample of children ages 2- to 18-year-old chil-
dren’s daily television, movies, computers, music, video games,
and radio use in hours and minutes.
Those measurements were only possible because technology
interaction—particularly computer use and online activities—was
primarily accomplished on stationary devices including desktop
and laptop computers or video game consoles. The advent of por-
table technology—including MP3 players, smartphones and other
wireless mobile devices—changed the landscape so that nearly
any activity that can be performed on a desktop or laptop machine
can also be performed on a small, pocket size device. With a Wi-Fi
enabled mobile device, people can access the Internet, e-mail, text,
and use applications that can do most traditional computing activ-
ities anywhere and at any time of the day or night and research
shows that people are doing just that. A recent national study of
7446 18- to 44-year-old smartphone users (IDC, 2013) found that
nearly eight in 10 adults and nine in ten young adults reach for
their phone within 15 min of waking. Other research (Oulasvirta,
Rattenbury, Ma, & Raita, 2012) has demonstrated that adults typi-
cally access their smartphones for an average of 34 daily short
durations (less than 30 s) while another national study (Mobile
Mindset, 2012) showed that 58% of US smartphone users check
their phones at least every hour, and 73% feel panicked if they mis-
place their phone. In a study on Japanese students’ cell phone and
text message use, Kamibeppu and Sugiura (2005) found that al-
most half of the respondents experienced a feeling of insecurity
when their text messages went unanswered. The students devel-
oped insecurity and a perception of being ignored, which the
authors concluded could cause great anxiety among children.
0747-5632/$ - see front matter Ó2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.chb.2013.06.006
⇑
Corresponding author. Address: Department of Psychology, California State
University, Dominguez Hills, Carson, CA 90747, United States. Tel.: +1 310 243
3427; fax: +1 619 342 1699.
E-mail address: lrosen@csudh.edu (L.D. Rosen).
Computers in Human Behavior 29 (2013) 2501–2511
Contents lists available at SciVerse ScienceDirect
Computers in Human Behavior
journal homepage: www.elsevier.com/locate/comphumbeh
1.1. Methods for assessing technology usage
A survey of recent research indicates that there are four current
methods for assessing general technology usage including: (1) time
measured in hours or minutes per day or per usage (Becker, Alza-
habi, & Hopwood, 2012; Carrier, Cheever, Rosen, Benitez, & Chang,
2009; Junco, 2013; Kimbrough, Guadagno, Muscanell, & Dill, 2012;
Padilla-Walker & Coyne, 2011; Pea et al., 2012; Reich, Subrahman-
yam, & Espinoza, 2012; Rideout, Foehr, & Roberts, 2010; Rosen,
Carrier, & Cheever, 2013; Rosen, Chang, Erwin, Carrier, & Cheever,
2010; Rosen, Whaling, Rab, Carrier, & Cheever, 2013; Turner &
Croucher, 2013); (2) frequency measured in the number uses in a
particular time period (Burak, 2012; Johnson, 2010; Thompson,
2013); (3) attitudinal Likert-type scales measured on a continuum
from strongly agree to strongly disagree (Jenkins-Guarnieri,
Wright, & Johnson, 2013a, 2013b; Venkatesh, Thong, & Xu,
2012); and (4) experience sampling, querying use at a particular
prompted point in time (Moreno, Jelenchick, Koff, & Eikoff,
2012a; Moreno, Jelenchick, Koff, Eikoff, Diermyer, & Christakis,
2012b; Wang & Tchernev, 2012).
Although it is appealing to measure actual time of usage, this
has proven problematic. For example, Junco (2013) compared ac-
tual versus self-reported time by first having 45 university stu-
dents report how many hours and minutes they felt that they
accessed Facebook, Twitter, and their e-mail in addition to how
much time they searched for information online on a typical day.
Following this self-reported time, monitoring software was in-
stalled on their computers and their actual use of these websites
was evaluated over a one-month period. Although the correlations
between self-reports and actual time were significant and reason-
ably high (e.g., the correlation between self-reported and actual
Facebook use was .587 and for e-mail it was .628), the estimates
were drastically different. For example, while users self-reported
spending an average of 149 min per day accessing Facebook on
their computer, the actual average time, according to the monitor-
ing software, was 26 min per day. Similar results were found for all
time estimates suggesting that users are not accurate at estimating
time they spend on the computer.
1.2. Assessing social media usage
Since the emergence of social media—particularly Facebook—
special efforts have been performed to measure its usage. An early
attempt saw the creation of the Facebook Intensity Scale (Ellison,
Steinfield, & Lampe, 2007), which presented six attitudinal state-
ments (e.g., ‘‘Facebook is part of my everyday activity’’ or ‘‘I would
be sorry if Facebook shut down’’) initially as open-ended questions
(Ellison et al., 2007) plus an assessment of daily hours and minutes
spent on the site and an accounting of the number of Facebook
friends on a 10-point numerical scale. Later, the attitudinal ques-
tions were modified to be closed-ended requiring Likert scale re-
sponses (Steinfield, Ellison, & Lampe, 2008) and several studies
have used that scale to assess Facebook activities (Clayton, Os-
borne, Miller, & Oberle, 2013; Ellison, Steinfield, & Lampe, 2011;
Glynn, Huge, & Hoffman, 2012; Jenkins-Guarnieri et al., 2013a,
2013b; Kalpidou, Costin, & Morris, 2011; Kapidzic, 2013; Lampe,
Wohn, Vitak, Ellison, & Wash, 2011; Lou, Yan, Nickerson, & McMor-
ris, 2012; Ross et al., 2009; Tazghini & Siedlecki, 2013).
Facebook usage has been measured in other ways including dai-
ly time spent on the site (Hunt, Atkin, & Krishnan, 2012; Jelenchick,
Eichoff, & Moreno, 2012; Junco, 2012a, 2012b; Karpinski, Kirsch-
ner, Ozer, Mellott, & Ochwo, 2013; McAndrew & Jeong, 2012;
Moore & McElroy, 2012; Rosen et al., 2013), number of times log-
ging onto Facebook on a typical day (Hunt et al., 2012; Junco,
2012a, 2012b; Kittinger, Correia, & Irons, 2012; Locatelli, Kluwe,
& Bryant, 2012; McAndrew & Jeong, 2012; McKinney, Kelly, & Dur-
an, 2012; Moore & McElroy, 2012; Oldmeadow, Quinn, & Kowert,
2012; Rosen et al., 2013; Skues, Williams, & Wise, 2012; Tosun,
2012; Trepte & Reinecke, 2013), and a raw count or assessment
of Facebook activities and friends (Clayton et al., 2013; Deters &
Mehl, 2013; Kittinger et al., 2012; Moore & McElroy, 2012; Ong ,
Ang, Ho, Lim, Goh, Lee, & Chua 2011; Pempek, Yermolayeva, & Cal-
vert, 2009).
1.3. Multitasking and technology usage
Numerous research studies have shown a relationship between
preference for multitasking or task switching as it is often labeled
and the use of various technologies (Media Metrix, 1999; Pea et al.,
2012; Rideout et al., 1999; Rideout et al., 2010). For example, Rosen
et al. (2013) replicated studies by Gonzalez and Mark (2004), Dab-
bish, Mark, and Gonzalez (2011) and Judd and Kennedy (2011) in
demonstrating that students and office workers switched tasks of-
ten and the impetus was most often technological in nature such as
an incoming text message or e-mail message or a perceived need to
check in with a social network site. A recent study by Moreno et al.
(2012a, 2012b) reported that when they sent daily text messages
to university students to assess their multitasking activities at ran-
dom times during the day and evening, more than half the time
they were using the Internet they were multitasking. In addition,
Carrier et al. (2009) showed that younger people in the Net Gener-
ation believe that they can perform more tasks simultaneously,
particularly those that are technological, than older members of
Generation X or Baby Boomers. Based on these results the pro-
posed measurement tool will include a measure of one’s prefer-
ence for task switching or multitasking.
1.4. The Current Study: developing a comprehensive method for
assessment
With such a variety of methods for evaluating media and tech-
nology usage and attitudes, it is often difficult to make compari-
sons across different research studies as each uses its own
measurement tools and most often assesses activities and attitudes
in a limited domain. In addition, many of the current measurement
tools were developed far enough in the past that new technologies
have been developed and their usage needs to be assessed. The cur-
rent study examined a new, comprehensive measurement tool that
incorporates prior models for assessing self-reported frequency of
media and technology use as well as attitudes toward technology
use, rather than relying on inaccurate self-reports of time spent
using a variety of technologies.
Nearly all studies measuring time spent using technology ask
about computer usage in general or do not differentiate between
using the same functions through a variety of devices, including
computers and mobile phones. The current measure was created
with several precepts: (1) it must measure self-reported frequency
of use rather than self-reported time of use; (2) it must include
activities performed on computers as well as those on mobile
phones and those on dedicated devices such as televisions, music
players, and video game players; (3) it must include attitudinal
scales to capture beliefs about the use of technology and (4) it must
be validated by traditional measures such as self-reported time of
use and Internet addiction.
Through a literature search and pilot studies performed by the
researchers, a wide variety of constructs were gathered about the
use of technology which was, through focus groups, streamlined
to include 50 items that spanned usage of all major technologies
on a variety of standard devices. Eighteen additional items that
measured attitudes toward technology and toward task switching
were culled from previous work (Rosen et al., 2013) to form an ini-
tial 68-item measurement tool. This tool was evaluated using data
2502 L.D. Rosen et al. / Computers in Human Behavior 29 (2013) 2501–2511
from two separate studies with separate samples to allow an
assessment of the validity of the new scales compared to more tra-
ditional measures of self-reported time of use, technological anxi-
ety and Internet addiction. Two independent studies using online,
anonymous survey methodology—one examining the impact of
technology use on magical thinking and the other examining the
impact of technology use on sleep—used sets of items for possible
inclusion into the Media and Technology Usage and Attitudes
Scale. Each of those studies also used identical demographic items
as well as items to be used for validity assessment. They will be re-
ferred to as the ‘‘magical thinking study’’ and ‘‘sleep study’’ for clar-
ity. Factor analyses were applied to the results from the combined
sample to refine a series of subscales based on the data.
2. Methods
2.1. Participants
In both studies participants were required to be at least
18 years of age. Both studies allowed students in an upper division
course to participate and/or to solicit participants from the general
community. For the magical thinking study 397 participants com-
pleted the entire online survey without any incomplete or missing
data. For the sleep study 545 participants completed an online sur-
vey without any incomplete or missing data. Participants from the
two studies were combined to form a sample of 942 participants of
which 62% were female, ranging in age from 18 to 73 (M= 29.96;
Mdn = 25; SD = 12.48), and including the following ethnic or cul-
tural backgrounds: 9% Asian, 15% Black/African-American, 14%
Caucasian, 55% Hispanic and 7% other. The sample included mainly
participants with some college (51%), or a college degree (32%),
29% of which were employed part-time and 33% employed full-
time. Overall 49% were single, never married and living with family
or relatives while 31% were married or living with someone in a
romantic relationship; 40% of the sample participants had a mean
of 2.68 children while 60% had no children. Participants supplied
additional demographic information including residence ZIP code,
which was transformed into estimated median income based on
U.S. Census figures (U.S. Census Bureau, 2007–2011). Overall med-
ian income averaged $41,004 (SD = 15,007). These figures match
the census figures for the Los Angeles area (U.S. Census Bureau,
2006).
2.2. Materials
2.2.1. Media and Technology Usage and Attitudes Scale (MTUAS)
The proposed media and technology usage portion of the MTU-
AS, used in both studies, included 50 items. These items were
developed by generating a set of possible technology uses includ-
ing activities performed specifically on a mobile phone (searching
for information, browsing the web, using apps, listening to music,
taking photos, recording video, reading e-mail, getting directions
or using a GPS, checking text messages, sending and receiving text
messages, using a mobile phone during class or work time, check-
ing voice calls, making and receiving voice calls, checking the
phone in the middle of the night, getting news, use while driving),
activities performed specifically on a computer (downloading
media files, watching video clips, watching television shows or
movies, sharing media files), activities performed specifically using
a television set (watching TV shows or movies, watching video
clips), device-free (non-mobile phone) technological activities
(searching the Internet for information, images, videos, or news;
sending and receiving e-mail; checking personal, work or school
e-mail; sending or receiving files via e-mail; playing games with
other people in the same room, playing games alone, playing
games with other people online; listening to music; video chat;
texting or instant messaging; shopping), and social networking
activities, which were only answered by those indicating that they
had a Facebook page (checking Facebook and other social net-
works, checking from a smartphone, checking from work or school,
posting status updates, posting photos, browsing profiles, reading
posts, commenting on posts, clicking like). A 10-item frequency re-
sponse scale was used for these items including: never, once a
month, several times a month, once a week, several times a week,
once a day, several times a day, once an hour, several times an hour
and all the time. Five additional questions queried Facebook users
on the number of friends on Facebook, the number of Facebook
friends known in person, the number of people met online but
never met in person, the number of people regularly interacting
with online but never met in person and the number of close
friends online never met in person. Each of these was answered
on a 9-point numerical scale including 0, 1–5, 51–100 101–175,
176–250, 251–375, 376–500, 501–750 and 751 or more.
Eighteen items were included to assess attitudes toward tech-
nology with responses on a five-point Likert scale (strongly agree,
agree, neither agree nor disagree, disagree, strongly disagree).
These items included attitudes toward the importance of finding
any information online, the importance of being able to access
the Internet any time, the importance of keeping up with technol-
ogy, getting anxious without availability of a cell phone, getting
anxious without availability of the Internet, feeling dependent on
technology, believing that technology will provide solutions to
our problems, believing that with technology anything is possible,
believing that more gets accomplished due to technology, believ-
ing that technology is easy to use, enjoying using technology as
soon as it hits the market, believing that technology makes people
waste time, believing that technology makes life more complicated
and believing that technology makes people more isolated. Finally,
this scale included four items taken from the Multitasking Prefer-
ence Inventory (Poposki & Oswald, 2010) such as ‘‘I prefer to work
on several projects in a day rather than completing one project and
then switching to another.’’ Items were selected from the original
14-question inventory (
a
= .88) by using those with the top four
loadings in a factor analysis (Poposki & Oswald, 2010).
2.2.2. Validity scales
Additional validity items were collected in the sleep study that
allowed for the assessment of the validity of the MTUAS. These in-
cluded the following:
Daily media usage hours: Participants were asked nine questions
concerning the amount of time they spent ‘‘on a typical day’’
using 10 forms of media and technology (going online, using a
computer for other than being online, e-mailing, instant mes-
saging/chatting, phone calling, social networking, texting, video
gaming, listening to music, and watching television) and one
additional question on reading books or magazines for pleasure
on a daily use scale including: not at all, 1–30 min, 31 min to
1 h, 1–2 h, 3 h, 4–5 h, 6–8 h, more than 8 h. Responses were
transformed into hours of use by converting each response into
hours including not at all (0), 1–31 min (.25), 31 min to 1 h
(.75), 1–2 h (1.5), 4–5 h (4.5), 6–8 h (7), more than 8 h (9).
Technology-related anxiety: A set of six items were included that
asked, ‘‘If you can’t check in with the following technologies as
often as you’d like, how anxious do you feel?’’ The list of tech-
nologies included: text messages, cell phone calls, Facebook
and other social networks, personal e-mail, work e-mail and
voice mail and each were assessed on a four-point scale (not
anxious at all, a little anxious, moderately anxious, and highly
anxious).
L.D. Rosen et al. / Computers in Human Behavior 29 (2013) 2501–2511 2503
Internet Addiction Test:Young’s (1998) short 8-item Internet
Addiction Test (IAT) was used. This measure includes eight
yes/no items taken from the DSM diagnostic criteria for addic-
tion disorders with a higher score indicating more Internet
addiction. Inadvertently, Item 2 (‘‘Do you feel the need to use
the Internet with increasing amounts of time in order to achieve
satisfaction?’’) and Item 3 (‘‘Have you repeatedly made unsuc-
cessful efforts to control, cut back, or stop Internet use?’’) were
displayed together with only the option to say ‘‘yes’’ or ‘‘no’’ for
both. A ‘‘yes’’ on that item was scored as indicating two diag-
nostic criteria met and a ‘‘yes’’ on any other item was scored
as indicating one diagnostic criterion met. IAT scores were trea-
ted as a bivariate variable with a score of ‘‘5’’ or more indicating
an Internet addiction disorder as noted by Young (1998).
3. Results
3.1. Factor structure of the Media and Technology Usage and Attitudes
Scale (MTUAS)
The 50 media usage items were subjected to a varimax-rotated
factor analysis using the assumption that the factors would and
should be intercorrelated as they all represent uses of similar tech-
nologies. Using a factor loading cutoff of .55 and an eigenvalue of
1.0, the analysis yielded 11 usable factors, which included 44 of
the items. These are displayed in Tables 1 and 2 and in the Appen-
dix. These 11 factors, which accounted for 68% of the variance,
were easily identifiable as representing 11 daily media uses includ-
ing smartphone usage (9 items accounting for 11.94% of the vari-
ance), general social media usage (9 items; 11.61%), Internet
searching (4 items; 7.15%), e-mailing (4 items; 6.94%), media shar-
ing (4 items; 5.81%), text messaging (3 items; 5.56%), video gaming
(3 items; 4.69%), online friendships (2 items; 4.23%), Facebook
friendships, (2 items; 3.69%), phone calling (2 items; 3.35%), and
watching television (2 items; 3.07%). Each factor was computed
using the mean score as all items were scaled on the same fre-
quency scale. Overall, 669 participants (71%) indicated that they
had a Facebook page. Those who did not participate in social net-
working were removed from three scales: general social media
usage, online friendships and/or Facebook friendships.
The 18 attitudinal items, when subjected to an orthogonal fac-
tor analysis with a varimax rotation, resulted in four factors
accounting for 66.13% of the variance. Two items failed to meet
the .55 threshold and were not included in any factor. With this
criterion, the first factor included six items related to positive atti-
tudes toward technology including the importance of finding infor-
mation online on demand, the importance of access the Internet on
demand, the importance of keeping up with technology trends, the
assertion that with technology anything is possible, getting more
accomplished with technology, and the belief that technology will
provide solutions to many of our problems. Items were reversed
scored so that higher scores indicated more positive attitudes to-
ward technology. The second factor included three items reflecting
anxiety related to being without a phone or the Internet and tech-
nological dependence, while the third factor included the four task
switching items after reverse scoring one item (‘‘I like to finish one
task completely before focusing on anything else’’) and then calcu-
lating the mean score with higher scores indicating a stronger pref-
erence to task switch. Finally the fourth factor included three items
reflecting negative attitudes toward technology including technol-
ogy wasting time, technology making people more isolated, and
technology being too complicated. Items for two subscales were
reversed scored so that higher scores indicated more technology
anxiety and dependence and more negative attitudes toward tech-
nology, respectively. Each factor was computed using the mean
score as all items were measured on the same scale.
Table 1
Factor loadings for first five daily media usage factors (minimum factor loading .55).
Media usage items Media usage factors
12 3 45
Smartphoneusage General social media usage Internet searching E-mailing Media sharing
Search for information with a mobile phone .80
Browse the web on a mobile phone .79
Use apps (for any purpose) on a mobile phone .74
Listen to music on a mobile phone .72
Check the news on a mobile phone .69
Take pictures using a mobile phone .66
Record video on a mobile phone .63
Read e-mail on a mobile phone .63
Get directions or use GPS on a mobile phone .62
Read social media postings .85
Comment on social media postings, status updates, photos, etc. .82
Click ‘‘Like’’ to a social media posting, photo, etc. .81
Check Facebook page or other social networks .80
Browse social media profiles and photos .76
Check Facebook at work or school .72
Post social media status updates .66
Check Facebook page from smartphone .65
Post social media photos .60
Search the Internet for informationon any device .81
Search the Internet for images or photos on any device .73
Search the Internet for news on any Device .72
Search the Internet for videos on any device .72
Send, receive and read e-mails(not including spam or junk mail) .87
Check your personal e-mail .86
Check your work or school e-mail .81
Send or receive files via e-mail .81
Download media files from other people on a computer .78
Watch video clips on a computer .76
Watch TV shows, movies, etc. on a computer .62
Share your own media files on a computer .61
2504 L.D. Rosen et al. / Computers in Human Behavior 29 (2013) 2501–2511
Table 3 displays the means, standard deviations, skewness
scores and Cronbach’s alpha coefficient of all 15 subscales. All sub-
scales had acceptable to excellent reliabilities. Only two subscales
had suspect skewness scores: video gaming (1.13) and online
friendships (2.45). In each case the positive skewness was due to
a larger percentage of nonusers or infrequent users and a few par-
ticipants who played video games very often or who reported a
large number of online friendships. Both variables were examined
as the skewed raw averages and also after splitting the averages
into approximate thirds; all analyses of these two scales were per-
formed with the raw scores as well as the tertile splits. Note that
based on the mean scores across all participants the most com-
monly used technologies were text messaging, phone calling, e-
mailing and Internet searching, respectively.
3.2. Demographic differences
Comparisons were made between each demographic—gender,
age, ethnic background, education, employment, living situation
and median income—and the 15 subscales of the Media and Tech-
nology Usage and Attitude Scale.
3.2.1. Gender
Across all these demographics only four significant two-tailed
differences were apparent with males (M= 3.63; SD = 2.63) playing
video games more often than females (M= 3.06; SD = 2.35;
t(940) = 3.44, p< .001); males (M= 2.06; SD = 1.25) having signifi-
cantly more online friends than females (M= 1.79, SD = 1.11;
t(940) = 2.91, p= .004); males (M= 4.14; SD = 2.38) doing signifi-
cantly more media sharing than females (M= 3.52, SD = 2.19;
t(543) = 2.55, p= .011); and females (M= 3.25; SD = 1.09) having
significantly less technological anxiety and dependency than males
(M= 3.00, SD = 1.08; t(940) = 2.55, p= .011). Using the third split
variables indicated that a higher percentage of males were in
the top third of video game playing frequency [
v
2
(2, N=942) =
15.51, p< .001] as well as in the top third of online friendships
[
v
2
(2, N=669) = 8.04, p< .018].
3.2.2. Age
Table 4 displays the correlations between the subscales and age.
As is apparent, older people showed significantly lower daily use of
all media/technology items with the exception of online friend-
ships and general Facebook usage. When treated as a tertile split,
there was no significant age difference among the top, middle
and bottom thirds of online friendships [F(2, 666) = .64, p> .05]
but there was a significant age difference between tertiles of video
gaming [F(2, 939) = 30.93, p< .001] with lower third (mean
age = 34.28) significantly older than those in the middle third
(M=27.96) and the top third (M=27.54). In addition, older people
showed less positive attitudes toward technology and were less
anxious about not checking in with technology but age was not
correlated with preference for task switching or negative attitudes
toward technology.
3.2.3. Ethnic background
Oneway ANOVAs were used to assess ethnic background differ-
ences on the subscales. Only three subscales demonstrated signif-
icant differences with both the omnibus F-test and a posthoc
Scheffe Test: online friendships [F(3, 614) = 5.92, p< .001; Black
(M= 2.33) significantly higher than Asian (M= 1.64) and Hispanic
(M= 1.83)]; voice calls [F(3, 868) = 3.19, p= .023; Black (M= 6.89)
significantly higher than Asian (M= 6.10)]; and negative attitudes
toward technology [F(3, 491) = 3.44, p= .017; Caucasian (M=3.60)
significantly higher than Black (M= 3.11)].
3.2.4. Education
Education level was correlated with several subscales includ-
ing: smartphone usage (r= .14, p< .001), Internet searching
(r= .20, p< .001), e-mailing (r= .25, p< .001), media sharing
(r= .08, p= .011), text messaging (r= .19, p< .001), voice calls
(r= .13, p< .001), positive attitudes (r= .21, p< .001), and techno-
logical anxiety (r= .19, p< .001). In all cases, more educated partic-
ipants showed higher scores.
3.2.5. Employment
Part-time employees showed significantly higher scores than
either full-time employees or unemployed (mostly students) on
the following subscales: smartphone usage, general Facebook
use, Internet searching, media sharing, e-mailing, texting, Face-
book friends, voice calling and anxiety.
3.2.6. Living situation
Single/unmarried participants showed significantly higher
scores than either married participants or separated/divorced/wid-
owed participants on the following subscales’’ smartphone usage,
Table 2
Factor loadings for second five daily media usage factors (minimum factor loading .55).
Media usage factors Media usage items
6 7 8 9 10 11
Text
messaging
Video
gaming
Online
friendships
Social media
friendships
Phone
calling
Television
viewing
Check for text messages on a mobilephone .72
Send and receive text messages on a mobile phone .69
Use Your Mobile phone during class or work time .59
Play games on a computer, video game console or smartphone WITH OTHER
PEOPLE IN THE SAME ROOM
.82
Play games on a computer, video game console or smartphone BY YOURSELF .79
Play games on a computer, video game console or smartphone WITH OTHER
PEOPLE ONLINE
.78
Number of people you regularly interact with online that you have never
met in person
.80
People have you met online that you have never met in person .74
Facebook friends you know in person .89
Friends you have on Facebook .86
Check for voice calls on a mobile phone .69
Make and receive mobile phone calls .56
Watch TV shows, movies, etc. on a TV set .83
Watch video clips on a TV set .72
L.D. Rosen et al. / Computers in Human Behavior 29 (2013) 2501–2511 2505
Internet searching, e-mailing, media sharing, text messaging, video
game playing, Facebook friendships and anxiety.
3.2.7. Median income
Using a one-tailed test, median income was significantly corre-
lated with only general social media use (r= .07, p= .039) indicat-
ing that those who had a higher median income used social media
more often.
3.3. Facebook users vs. nonusers
Table 5 displays the comparisons between Facebook users and
nonusers on the 12 relevant subscales completed by all partici-
pants and, thus, did not include the Facebook usage, online friend-
ships and Facebook friends subscales. As is evident, Facebook users
showed significantly more use of nearly all technologies except for
television viewing. When examining the tertile split in video gam-
ing, Facebook users were more likely to be in the top and middle
third while nonusers were more likely to be in the bottom third
[
v
2
(2, N=942) = 58.27, p< .001]. In addition, Facebook users
showed significantly more positive attitudes and less negative atti-
tudes toward technology but also significantly higher anxiety
about not checking in often enough with technology. There was
no difference in multitasking preferences between Facebook users
and nonusers. A discriminant function analyses was performed
using the eight relevant media usage factors (not including the
three that relate to social media use, Facebook friendships or on-
line friendships) as potential discriminators between Facebook
users and nonusers. Results indicated a significant discriminant
function [
v
2
(8, N=942) = 132.59, p< .001] with the three highest
canonical discriminant function coefficients (beta weights) attrib-
uted to: text messaging (.632), Internet searching (.338) and
e-mailing (.308). No other coefficient exceeded .175. When the
attitudes subscales were included (which were only collected in
the sleep study) the discriminant function analysis showed similar
results with the reduced sample [
v
2
(12, N=545) = 150.64,
p< .001] with the top beta weights belonging to text messaging
(.530) followed by Internet searching (.245), and media sharing
(.211). All other beta weights were below .180.
3.4. Multitasking and technology usage
Research has demonstrated a positive relationship between
technology use and multitasking. Correlations were computed be-
tween the preference for task switching subscale and the 11 usage
scales of which eight were significant in the predicted direction.
Those participants who preferred to task switch showed more
usage of smartphones (r= .10, p< .05), more general Facebook
usage (r= .14, p< .01), more Internet searching (r= .14, p< .001),
more e-mail use (r= .14, p< .001), more media sharing (r= .11,
p< .05), more text messaging (r= .10, p< .05), more video gaming
(r= .10, p< .05) and more phone calling (r= .09, p< .05). Those
who preferred to task switch more often also showed more anxiety
about not checking in often enough with technology (r= .21,
p< .001) and less positive attitudes (r= .19, p< .001).
3.5. Validity assessment
3.5.1. Daily media usage hours
Several measures were collected in the sleep study that allowed
an examination of the validity of the 15 subscales. A set of ques-
tions queried the hours per day that the participant typically used
a variety of media and technologies. The top two correlations be-
tween these measures of media and technology usage and the
new subscales are presented in Table 6. As can be seen, nearly all
the top two correlations are the ones that would have been pre-
dicted. For example, those who self-reported watching television
for more daily hours had a higher frequency of watching television
in the MTUAS. This result was also evident for other activities
including texting, video game playing, e-mailing, social network-
ing, phone calling and media sharing. One noteworthy result is that
those who used smartphones more often spent more hours texting
and social networking, which are the two most common smart-
phone activities.
3.5.2. Technology-related anxiety
Table 7 displays the correlations between each of the subscales
and anxiety about not checking in often enough with six different
communication technologies. As is evident, with the exception of
television viewing, the subscales were all correlated with at lest
two areas of anxiety and most correlated with four or five of the
six anxiety items. The fact, for example, that those who used
smartphones more showed more anxiety about missing out on text
Table 3
Mean, standard deviation, and skewness of subscales.
Subscale Mean SD Skewness Alpha
Usage subscales
Smartphone usage
a
5.00 2.61 .01 .93
General Facebook usage
a
4.82 2.21 .08 .97
Internet searching
a
5.64 2.73 .01 .91
E-mailing
a
5.89 2.37 .23 .91
Media sharing
a
3.76 2.29 .97 .84
Text messaging
a
7.21 2.41 .85 .84
Video gaming
a
3.28 2.33 1.13 .83
Online friendships
b
1.89 1.17 2.45 .83
Facebook friendships
b
4.92 1.94 .24 .96
Phone calling
a
6.47 2.06 .28 .71
Television viewing
a
5.33 2.42 .42 .61
Attitudes subscales
Positive
c
3.66 .84 .70 .87
Anxiety and dependence
d
3.15 1.09 .23 .83
Negative
e
3.35 .92 .23 .80
Multitasking preference
f
3.25 .92 .05 .85
a
Scale ranges from 1 to 10 with higher numbers indicating more daily usage.
b
Scale ranges from 1 to 10 with higher numbers indicating more friendships.
c
Scale ranges from 1 to 5 with higher scores indicating more positive attitudes
toward technology.
d
Scale ranges from 1 to 5 with higher scores indicating more technological
anxiety and dependence.
e
Scale ranges from 1 to 5 with higher scores indicating more negative attitudes
toward technology.
f
Scores range from 1 to 5 with lower scores indicating increased preference for
task switching.
Table 4
Correlations between all subscales and participant age.
Subscale rp-value
Usage subscales
Smartphone usage .37 <.001
General Facebook usage .07 .083
Internet searching .32 <.001
E-mailing .25 <.001
Media sharing .27 <.001
Text messaging .45 <.001
Video gaming .21 <.001
Online friendships .04 .355
Facebook friendships .19 <.001
Phone calling .12 <.001
Television viewing .10 <.005
Attitudes subscales
Positive .21 <.001
Anxiety and dependence .29 <.001
Negative .03 .482
Multitasking preference .03 .465
2506 L.D. Rosen et al. / Computers in Human Behavior 29 (2013) 2501–2511
messages and social networks shows validity for this subscale as
those are the two main activities that are performed with smart-
phones. Similarly, the Facebook usage scale was most highly corre-
lated with anxiety about not checking in often enough with social
networks and similar results were seen for each subscale with the
most anxiety reported by people who used that communication
function the most.
3.5.3. Internet Addiction Test
The Internet Addiction Test yielded a bivariate variable where
those participants with five or more signs of Internet addiction
(n= 64; 22%) could be compared to those with fewer than five
signs (n=230; 78%). From the MTUAS, an independent t-test
indicated that those who were more likely to be addicted to the
Internet were those who: used Internet searching more often
[t(292) = 1.96, p< .05]; shared media more often [t(292) =
3.41, p< .001]; and played video games more often [t(292) =
2.21, p< .05] all three activities that have been linked to Internet
addictive behaviors. The only other variable showing a significant
difference between those addicted and those not addicted was
anxiety about being without technology and dependence on tech-
nology [t(292) = 3.83, p< .001]. This latter result shows strong
validity as these anxieties and dependencies are reflective of items
in the IAT.
4. Discussion
Attempts to measure media and technology usage have been
widespread and no single measurement tool has been adopted
by more than a handful of studies. This makes it difficult to com-
pare results across studies. The current study was designed to de-
velop a tool that could fill that gap and be used across research
paradigms in different fields. The initial tool included two parts:
a pool of items assessing frequency of usage of various technolo-
gies and media and a smaller pool of items assessing attitudes
toward technology and toward task switching. The latter items—
assessing one’s attitude toward either completing one task before
moving to another or working on one task and then switching to
another before its completion—have been shown in previous re-
search to relate to technology usage (Rosen et al., 2013). The resul-
tant 60-item measurement tool—the Media and Technology Usage
and Attitudes Scale—includes 15 subscales, 11 measuring usage
and four assessing attitudes. The subscales can be used together
or separately as they are internally reliable and externally valid.
The 11 usage subscales of the new measure provide a solid mix-
ture combining the use of older technologies such as television
with newer technologies such as smartphones as well as separat-
ing device-based assessments (e.g., smartphone usage subscale,
television viewing subscale) from device-free assessments (e.g.,
Internet searching subscale, e-mailing subscale). They are also
phrased in such a manner as to make them available for new items
as new technologies emerge.
Three of the 11 usage subscales also involve social networking
with two subscales relating directly to Facebook usage and one
to generic online friendships. This is of extreme importance given
the nearly ubiquitous use of Facebook as the current social net-
work (Smith 2012). When Facebook users and nonusers were com-
pared they showed strong differences on individual subscales of
the MTUAS, painting a picture of social media users as consumers
of other media and technology—with the exception of television—
and possessing both positive attitudes, but also anxieties about
missing out on technology as well as feeling dependent on technol-
ogy. Although two subscales directly relate to Facebook, the indi-
vidual items can be modified to fit any social networking site or
application that may arise in the future.
The MTUAS also offers the inclusion of four attitude-based sub-
scales including both positive and negative attitudes toward tech-
nology in general rather than toward any specific technologies as
well as attitudes that reflect anxiety and dependence on technol-
ogy and preferences for task switching over task completion. The
addition of these four subscales makes the MTUAS a robust mea-
surement tool as it includes both frequency of usage and attitudes
toward that usage where the attitudes expressed are independent
of the specific form of technology being used. Again, the MTUAS
can be used with or without the attitudinal items.
The 15 subscales of the MTUAS showed strong reliability and
validity. In every case when assessing the validity of individual
subscales there was a stronger correlation with the predicted sub-
scale and daily media usage, anxiety about not checking in often
enough and Internet addiction. This supports the power and stabil-
ity of the MTUAS. In addition to the direct validity and reliability
Table 5
Comparison between Facebook users (n= 669) and nonusers (n= 273) on all relevant
subscales.
Subscale Users mean
(SD)
Nonusers mean
(SD)
t-score
Usage subscales
a
Smartphone usage 5.42 (2.44) 3.95 (2.72) 8.10
***
Internet searching 6.13 (2.50) 4.46 (2.92) 8.82
***
E-mailing 6.29 (2.07) 4.90 (2.75) 8.49
***
Media sharing 4.02 (2.25) 3.11 (2.25) 5.68
***
Text messaging 7.71 (2.00) 6.00 (2.86) 10.38
***
Video gaming 3.51 (2.49) 2.70 (2.35) 4.61
***
Phone calling 6.65 (1.88) 6.05 (2.41) 4.07
***
Television viewing 5.35 (2.40) 5.27 (2.48) 0.48
Attitudes subscales
Positive
b
3.85 (.70) 3.26 (.95) 8.15
***
Anxiety and
Dependence
c
3.39 (.97) 2.67 (1.16) 7.63
***
Negative
d
3.23 (.90) 3.58 (.92) 4.26
***
Multitasking
Preference
e
3.22 (.91) 3.31 (.93) 1.11
***
p< .001.
a
Scale ranges from 1 to 10 with higher numbers indicating more daily usage.
b
Scale ranges from 1 to 5 with higher scores indicating more positive attitudes
toward technology.
c
Scale ranges from 1 to 5 with higher scores indicating more technological
anxiety and dependence.
d
Scale ranges from 1 to 5 with higher scores indicating more negative attitudes
toward technology.
e
Scores range from 1 to 5 with lower scores indicating increased preference for
task switching.
Table 6
Top two correlations between MTUAS subscales and daily hours using media and
technology (all correlations are significant at p< .001 unless otherwise noted).
Subscale Top Second
Usage subscales
Smartphone usage Texting (.46) Social network (.45)
General Facebook usage Social network (.51) Online (.37)
Internet searching Online (.48) Social Network (.45)
E-mailing E-mail (.48) Computer (.40)
Media sharing Games (.36) Online (.36)
Text messaging Texting (.61) Social network (.43)
Video gaming Video games (.57) Online (.41)
Online friendships Video games (.31) Social network (.19)
Facebook friendships Texting (.31) Social network (.19)
Phone calling Phone calling (.27) E-mail (.22)
Television viewing TV (.41) IM/Chat (.20)
Attitudes subscales
Positive Online (.29) Social network (.27)
Anxiety and dependence Social network (.35) Online (.30)
Negative IM/Chat (.16) E-mail (.16)
Multitasking preference Online (.16) E-mail (.12
a
)
a
p= .004.
L.D. Rosen et al. / Computers in Human Behavior 29 (2013) 2501–2511 2507
assessment, the 15 MTUAS subscales were also examined as a
function of the sample demographics. As expected, the subscale
differences were exactly those that one would expect from past re-
search. For example, males were more active in video gaming and
media sharing, older people used less technology than younger
people, and more highly educated people used more technology
than less highly educated people.
One interesting side note is the lack of correlations between
median income, as measured by residence ZIP code, and 14 of
the 15 subscales with only social media showing a small significant
correlation. This result suggests that the once prevalent ‘‘digital di-
vide’’ may no longer be as strong (Zickuhr & Smith, 2012).
4.1. Limitations
This study was done with participants comprised a self-selected
sample of convenience from urban Southern California and, as
such, was comprised of a unique mixture of cultural backgrounds
that may not generalize to other settings. However, the fact that
there were very few differences in ethnic backgrounds on the 15
subscales supports the use across any sample. In addition, the res-
idence-based median income assessment showed that the sample
was firmly middle class with a range of incomes spanning the typ-
ical census figures. The current study also has several other obvi-
ous limitations including: (1) combining samples from two
different but similar research projects, (2) using online survey
methodology to collect data and (3) being collected through uni-
versity classroom participation and friends and family of those
same students. Additional studies with different samples, collected
from different parts of the country or the world, should be done to
further validate the MTUAS. Further, although validity was as-
sessed with concurrently collected measures of time spent using
various technologies, technology-related anxiety, and Internet
addiction, future research should consider validating the measure-
ment tool with actual usage measured similar to that done by Jun-
co (2013). In addition to Junco’s software monitoring system,
research should also assess mobile device usage, which is more dif-
ficult to assess as many users simply check in briefly with their so-
cial media, electronic communication, and information apps,
taking in the necessary information in a matter of seconds. Any
smartphone monitoring system must account for both frequency
and time of access to provide validity information for the new
measurement tool.
Acknowledgements
Thanks to the George Marsh Applied Cognition Laboratory
for their work on this project. Sincere appreciation to the Na-
tional Institutes of Health Minority Access to Research Careers
Undergraduate Student Training in Academic Research Program
(MARC U⁄STAR Grant No. GM008683) for supporting Ms. Kelly
Whaling.
Appendix A
Media. and Technology Usage and Attitudes Scale (60 items)
Usage. subscales
This scale includes 44 items which comprise 11 subscales:
Smartphone Usage (9 items), General Social Media Usage (9 items),
Internet Searching (4 items), E-Mailing (4 items), Media Sharing (4
items), Text Messaging (4 items), Video Gaming (3 items), Online
Friendships (2 items), Online Friendships (2 items), Facebook
Friendships (2 items), Phone Calling (2 items) and TV Viewing (2
items)
10-point frequency scale for items 1–40 (with scoring in
parentheses):
Never (1)
Once a month (2)
Several times a month (3)
Once a week (4)
Several times a week (5)
Table 7
Correlations between MTUAS subscales and anxiety about not being able to check in with various technologies.
Subscale Anxiety about not checking in often enough with specific media/technology
Text messages Phone calls Social networks Personal E-mail Work/school E-mail Voice mail
Media usage subscale
a
Smartphone usage .44
***
.19
***
.33
***
.17
***
.17
***
.09
*
General Facebook usage .35
***
.23
***
.44
***
.19
***
.08 .04
Internet searching .32
***
.16
***
.30
***
.30
***
.24
***
.04
E-mailing .24
***
.17
***
.15
***
.36
***
.38
***
.12
**
Media sharing .27
***
.14
**
.28
***
.21
***
.18
***
.07
Text messaging .51
***
.27
***
.29
***
.18
***
.23
***
.07
Video gaming .24
***
.10
*
.27
***
.16
***
.09
*
.06
Online friendships .08 .05 .22
***
.14
**
.08 .10
Facebook friendships .31
***
.08 .17
**
.03 .04 .01
Phone calling .20
***
.30
***
.12
**
.20
***
.14
**
.26
***
Television viewing .06 .05 .09
*
.04 .00 .03
Attitude subscale
Positive
b
.36
***
.29
***
.28
***
.28
***
.21
***
.13
**
Anxiety and dependence
c
.57
***
.46
***
.46
***
.36
***
.28
***
.16
***
Negative
d
.17
***
.17
***
.21
***
.17
***
.08
*
.09
*
Multitasking preference
e
.16
***
.12
**
.13
**
.17
***
.16
***
.07
*
p< .05.
**
p< .01.
***
p< .001.
a
Scale ranges from 1 to 10 with higher numbers indicating more daily usage.
b
Scale ranges from 1 to 5 with higher scores indicating more positive attitudes toward technology.
c
Scale ranges from 1 to 5 with higher scores indicating more technological anxiety and dependence.
d
Scale ranges from 1 to 5 with higher scores indicating more negative attitudes toward technology.
e
Scores range from 1 to 5 with lower scores indicating increased preference for task switching.
2508 L.D. Rosen et al. / Computers in Human Behavior 29 (2013) 2501–2511
Once a day (6)
Several times a day (7)
Once an hour (8)
Several times an hour (9)
All the time (10)
Please indicate how often you do each of the following e-mail
activities on any device (mobile phone, laptop, desktop, etc.)
1. (E-mailing subscale) Send, receive and read e-mails (not
including spam or junk mail).
2. (E-mailing subscale) Check your personal e-mail.
3. (E-mailing subscale) Check your work or school e-mail.
4. (E-mailing subscale) Send or receive files via e-mail.
Please indicate how often you do each of the following activities
on your mobile phone.
5. (Text messaging subscale) Send and receive text messages
on a mobile phone.
6. (Phone calling subscale) Make and receive mobile phone
calls.
7. (Text messaging subscale) Check for text messages on a
mobile phone.
8. (Phone calling subscale) Check for voice calls on a mobile
phone.
9. (Smartphone usage subscale) Read e-mail on a mobile
phone.
10. (Smartphone usage subscale) Get directions or use GPS on
a mobile phone.
11. (Smartphone usage subscale) Browse the web on a mobile
phone.
12. (Smartphone usage subscale) Listen to music on a mobile
phone.
13. (Smartphone usage subscale) Take pictures using a mobile
phone.
14. (Smartphone usage subscale) Check the news on a mobile
phone.
15. (Smartphone usage subscale) Record video on a mobile
phone.
16. (Smartphone usage subscale) Use apps (for any purpose)
on a mobile phone.
17. (Smartphone usage subscale) Search for information with
a mobile phone.
18. (Text messaging subscale) Use your mobile phone during
class or work time.
How often do you do each of the following activities?
19. (TV viewing subscale) Watch TV shows, movies, etc. on a
TV set.
20. (TV viewing subscale) Watch video clips on a TV set.
21. (Media sharing subscale) Watch TV shows, movies, etc. on
a computer.
22. (Media sharing subscale) Watch video clips on a
computer.
23. (Media sharing subscale) Download media files from other
people on a computer.
24. (Media sharing subscale) Share your own media files on a
computer.
25. (Internet searching subscale) Search the Internet for news
on any device.
26. (Internet searching subscale) Search the Internet for
information on any device.
27. (Internet Searching Subscale) Search the Internet for
videos on any device.
28. (Internet searching subscale) Search the Internet for
images or photos on any device.
29. (Video gaming subscale) Play games on a computer, video
game console or smartphone BY YOURSELF.
30. (Video Gaming Subscale) Play games on a computer, video
game console or smartphone WITH OTHER PEOPLE IN THE
SAME ROOM.
31. (Video gaming subscale) Play games on a computer, video
game console or smartphone WITH OTHER PEOPLE ONLINE.
Do you have a Facebook account? If the answer is ‘‘yes,’’ con-
tinue with item 32; if ‘‘no’’, skip to the Attitudes subscales below.
NOTE: The word ‘‘social media’’ may be substituted for Facebook
in the question stem above and in items 32–34.
How often do you do each of the following activities on social
networking sites such as Facebook?
32. (General social media usage subscale) Check your
Facebook page or other social networks.
33. (General social media usage subscale) Check your
Facebook page from your smartphone.
34. (General social media usage subscale) Check Facebook at
work or school.
35. (General social media usage subscale) Post status updates.
36. (General social media usage subscale) Post photos.
37. (General social media usage subscale) Browse profiles and
photos.
38. (General social media usage subscale) Read postings.
39. (General social media usage subscale) Comment on
postings, status updates, photos, etc.
40. (General social media usage subscale) Click ‘‘Like’’ to a
posting, photo, etc.
Please answer the following questions about your Facebook and
other online friends. NOTE: In items 41 and 42 the words ‘‘social
media’’ (or any specific social media site) may be substituted for
Facebook.
9-point scale for items 37–40 (with scoring in parentheses:
0 (1)
1–50 (2)
51–100 (3)
101–175 (4)
176–250 (5)
251–375 (6)
376–500 (7)
501–750 (8)
751 or more (9)
41. Facebook friendships subscale) How many friends do
you have on Facebook?
42. (Facebook friendships subscale) How many of your
Facebook friends do you know in person?
43. (Online friendships subscale) How many people have
you met online that you have never met in person?
44. (Online friendships subscale) How many people do you
regularly interact with online that you have never met in
person?
Attitudes. subscales
These subscales includes 16 items, which comprise four sub-
scales: Positive Attitudes Toward Technology (6 items), Anxiety
L.D. Rosen et al. / Computers in Human Behavior 29 (2013) 2501–2511 2509
About Being Without Technology or Dependence on Technology (3
items), Negative Attitudes Toward Technology (3 items) and Pref-
erence for Task Switching (4 items)
5-point Likert scale for all items (with scoring in parentheses):
Strongly agree (5)
Agree (4)
Neither agree nor disagree (3)
Disagree (2)
Strongly disagree (1)
1. (Positive attitudes) I feel it is important to be able to find any
information whenever I want online.
2. (Positive attitudes) I feel it is important to be able to access
the Internet any time I want.
3. (Positive attitudes) I think it is important to keep up with the
latest trends in technology.
4. (Anxiety/dependence) I get anxious when I don’t have my
cell phone.
5. (Anxiety/dependence) I get anxious when I don’t have the
Internet available to me.
6. (Anxiety/dependence) I am dependent on my technology.
7. (Positive attitudes) Technology will provide solutions to
many of our problems.
8. (Positive attitudes) With technology anything is possible.
9. (Positive attitudes) I feel that I get more accomplished
because of technology.
10. (Negative attitudes) New technology makes people waste
too much time.
11. (Negative attitudes) New technology makes life more
complicated.
12. (Negative attitudes) New technology makes people more
isolated.
13. (Preference for task switching) I prefer to work on several
projects in a day, rather than completing one project and
then switching to another.
14. (Preference for task switching) When doing a number of
assignments, I like to switch back and forth between them
rather than do one at a time.
15.
(Preference for task switching) I like to finish one task com-
pletely before focusing on anything else.
16. (Preference for task switching) When I have a task to com-
plete, I like to break it up by switching to other tasks
intermittently.
Scoring for item 15 is reversed with strongly agree = 1 and
strongly disagree = 5.
References
Becker, M. W., Alzahabi, R., & Hopwood, C. J. (2012). Media multitasking is
associated with symptoms of depression and social anxiety. Cyberpsychology,
Behavior, and Social Networking, 16(2), 132–135.
Burak, L. (2012). Multitasking in the university classroom. International Journal for
the Scholarship of Teaching and Learning, 6(2), 1–12.
Carrier, L. M., Cheever, N. A., Rosen, L. D., Benitez, S., & Chang, J. (2009). Multitasking
across generations: Multitasking choices and difficulty ratings in three
generations of Americans. Computers in Human Behavior, 25, 483–489.
Clayton, R. B., Osborne, R. E., Miller, B. K., & Oberle, C. D. (2013). Loneliness,
anxiousness, and substance use as predictors of Facebook use. Computers in
Human Behavior, 29(3), 687–693.
Dabbish, L., Mark, G., & Gonzalez, V. (2011). Why do I keep interrupting myself?
Environment, habit and self-interruption. In Proceedings of the 2011 annual
conference on human factors in, computing systems (pp. 3127–3130).
Deters, F. G., & Mehl, M. R. (2013). Does posting Facebook status updates increase or
decrease loneliness? An online social networking experiment. Social
Psychological and Personality Science.http://dx.doi.org/10.1177/
1948550612469233.
Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook ‘‘friends:’’
Social capital and college students’ use of online social network sites. Journal of
Computer-Mediated Communication, 12, 1143–1168.
Ellison, N. B., Steinfield, C., & Lampe, C. (2011). Connection strategies: Social capital
implications of Facebook-enabled communication practices. New Media and
Society.http://dx.doi.org/10.1177/1461444810385389.
Glynn, C. J., Huge, M. E., & Hoffman, L. H. (2012). All the news that’s fit to post: A
profile of news use on social networking sites. Computers in Human Behavior, 28,
113–119.
Gonzalez, V. M., & Mark, G. (2004). Constant, constant, multitasking
craziness: Managing multiple working spheres. In Proceedings of CHI ’04 (pp.
113–120).
Hunt, D., Atkin, D., & Krishnan, A. (2012). The influence of computer-mediated
communication apprehension on motives for Facebook use. Journal of
Broadcasting and Electronic Media, 56(2), 187–202.
IDC (2013). Always Connected: How smartphones and social media keep us
engaged. IDC Research. Retrieved <http://www.scribd.com/doc/133393152/IDC-
Facebook-Always-Connected>.
Jelenchick, L., Eichoff, R., & Moreno, M. A. (2012). ‘‘Facebook Depression?’’ Social
networking site use and depression in older adolescents. Journal of Adolescent
Health, 52(1), 128–130.
Jenkins-Guarnieri, M. A., Wright, S. L., & Johnson, B. (2013a). Development and
validation of a social media use integration scale. Psychology of Popular Media
Culture, 2(1), 38–50.
Jenkins-Guarnieri, M. A., Wright, S. L., & Johnson, B. (2013b). The interrelationships
among attachment style, personality traits, interpersonal competency, and
Facebook use. Psychology of Popular Media Culture.http://dx.doi.org/10.1037/
a0030946.
Johnson, G. M. (2010). Internet use and child development: The techno-
microsystem. Australian Journal of Educational and Developmental Psychology,
10, 32–43.
Judd, T., & Kennedy, G. (2011). Measurement and evidence of computer-based task
switching and multitasking by ‘Net Generation’ students. Computers and
Education, 56, 625–631.
Junco, R. (2012a). Too much face and not enough books: The relationship between
multiple indices of Facebook use and academic performance. Computers in
Human Behavior, 28(1), 187–198.
Junco, R. (2012b). The relationship between frequency of Facebook use,
participation in Facebook activities, and student engagement. Computers and
Education, 58(1), 162–171.
Junco, R. (2013). Comparing actual and self-reported measures of Facebook use.
Computers in Human Behavior, 29, 626–631.
Kalpidou, M., Costin, D., & Morris, J. (2011). The relationship between Facebook and
the well-being of undergraduate college students. Cyberpsychology, Behavior,
and Social Networking, 14(4), 183–189.
Kamibeppu, K., & Sugiura, H. (2005). Impact of the mobile phone on junior high-
school students’ friendships in the Tokyo metropolitan area. CyberPsychology
and Behavior, 8(2), 121–130.
Kapidzic, S. (2013). Narcissism as a predictor of motivations behind Facebook
profile picture selection. Cyberpsychology, Behavior, and Social Networking, 16(1),
14–19.
Karpinski, A. C., Kirschner, P. A., Ozer, I., Mellott, J. A., & Ochwo, P. (2013). An
exploration of social networking site use, multitasking, and academic
performance among United States and European university students.
Computers in Human Behavior, 29, 1182–1192.
Kimbrough, A. M., Guadagno, R. E., Muscanell, N. L., & Dill, J. (2012). Gender
differences in mediated communication: Women connect more than do men.
Computers in Human Behavior, 29, 896–900.
Kittinger, R., Correia, C. J., & Irons, J. G. (2012). Relationship between Facebook use
and problematic Internet use among college students. Cyberpsychology,
Behavior, and Social Networking, 15(6), 324–327.
Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukipadhyay, T., & Scherlis,
W. (1998). Internet paradox: A social technology that reduces social
involvement and psychological well-being? American Psychologist, 53(9),
1017–1031.
Lampe, C., Wohn, D. Y., Vitak, J., Ellison, N. B., & Wash, R. (2011). Student use of
Facebook for organizing collaborative classroom activities. Computer-Supported
Collaborative Learning, 6, 329–347.
Locatelli, S. M., Kluwe, K., & Bryant, F. B. (2012). Facebook use and the tendency to
ruminate among college students: Testing meditational hypotheses. Journal of
Educational Computing Research, 46(4), 377–394.
Lou, L. L., Yan, Z., Nickerson, A., & McMorris, R. (2012). An examination of
the reciprocal relationship of Loneliness and Facebook use among first-
year college students. Journal of Educational Computing Research, 46(1),
105–117.
McAndrew, F. T., & Jeong, H. S. (2012). Who does what on Facebook? Age, sex, and
relationship status as predictors of Facebook use. Computers in Human Behavior,
28, 2359–2365.
McKinney, B. C., Kelly, L., & Duran, R. L. (2012). Narcissism or openness? College
students’ use of Facebook and Twitter. Communication Research Reports, 29(2),
108–118.
Media Metrix (1999). Simultaneous use of PC and television growing rapidly. New
York: Media Metrix. <http://www.mediametrix.com/usa/press/releases/
19990712.jsp>.
Mobile Mindset (2012). Mobile Mindset Study. Lookout Mobile Security. Retrieved
<https://www.lookout.com/_downloads/lookout-mobile-mindset-2012.pdf>.
Moore, K., & McElroy, J. C. (2012). The influence of personality on Facebook
usage, wall postings, and regret. Computers in Human Behavior, 28,
267–274.
2510 L.D. Rosen et al. / Computers in Human Behavior 29 (2013) 2501–2511
Moreno, M. A., Jelenchick, L., Koff, R., & Eikoff, J. (2012a). Depression and Internet
use among older adolescents: An experience sampling approach. Psychology, 3,
743–748.
Moreno, M. A., Jelenchick, L., Koff, R., Eikoff, J., Diermyer, C., & Christakis, D. A.
(2012b). Internet use and multitasking among older adolescents: An experience
sampling approach. Computers in Human Behavior, 28, 1097–1102.
Nielsen Media Research (1999). TV viewing in Internet households. New York: Nielsen
Media Research. <http://www.nielsenmedia.com>.
Oldmeadow, J. A., Quinn, S., & Kowert, R. (2012). Attachment style, social skills,
and Facebook use amongst adults. Computers in Human Behavior, 29(3),
1142–1149.
Ong, E., Ang, R., Ho, J., Lim, J., Goh, D., Lee & Chua (2011). Narcissism, extraversion,
and adolescent’s self-presentation on Facebook. Personality and Individual
Differences, 50, 180–185.
Oulasvirta, A., Rattenbury, T., Ma, L., & Raita, E. (2012). Habits make smartphone use
more pervasive. Personal and Ubiquitous Computing, 16(1), 105–114.
Padilla-Walker, L. M., & Coyne, S. M. (2011). ‘‘Turn that thing off!’’ Parent and
adolescent predictors of proactive media monitoring. Journal of Adolescence, 34,
705–715.
Pea, R., Nass, C., Meheula, L., Rance, M., Kumar, A., Bamford, H., et al. (2012).
Media use, face-to-face communication, media multitasking, and social well-
being among 8- to 12-year-old girls. Developmental Psychology, 48(2),
327–336.
Pempek, T. A., Yermolayeva, Y. A., & Calvert, S. L. (2009). College students’ social
networking experiences on Facebook. Journal of Applied Developmental
Psychology, 30(3), 227–238.
Phillips, C. A., Rolls, S., Rouse, A., & Griffiths, M. D. (1995). Home video game playing
in school children: A study of incidence and patterns of play. Journal of
Adolescence, 18(6), 687–691.
Poposki, E. M., & Oswald, F. L. (2010). The multitasking preference inventory:
Toward an improved measure of individual differences in polychronicity.
Human Performance, 23, 247–264.
Reich, S. M., Subrahmanyam, K., & Espinoza, G. (2012). Friending, IMing, and
hanging out face-to-face: Overlap in adolescents’ online and offline social
networks. Developmental Psychology, 48(2), 356–368.
Rideout, V., Foehr, U. G., & Roberts, D. (2010). Generation M
2
: Media in the lives of 8-
to 18-year-olds. Menlo Park, CA: Kaiser Family Foundation.
Rideout, V. J., Foehr, U. G., Roberts, D. F., & Brodie, M. (1999). Kids and media at the
new millennium: A comprehensive national analysis of children’s media use. Palo
Alto, CA: Kaiser Family Foundation.
Rosen, L. D., Carrier, L. M., & Cheever, N. A. (2013). Facebook and texting made me
do it: Media-induced task-switching while studying. Computers in Human
Behavior, 29(3), 948–958.
Rosen, L. D., Chang, J., Erwin, L., Carrier, L. M., & Cheever, N. A. (2010). The
Relationship between ‘‘Textisms’’ and Formal and Informal Writing Among
Young Adults. Communication Research, 37(3), 420–440.
Rosen, L. D., Whaling, K., Rab, S., Carrier, L. M., & Cheever, N. A. (2013). Is Facebook
Creating ‘‘iDisorders’’? The link between clinical symptoms of psychiatric
disorders and technology use, attitudes and anxiety. Computers in Human
Behavior, 29, 1243–1254.
Ross, C., Orr, E. S., Sisic, M., Arseneault, J. M., Simmering, M. G., & Orr, R. R. (2009).
Personality and motivations associated with Facebook use. Computers in Human
Behavior, 25(2), 578–586.
Skues, J. L., Williams, B., & Wise, L. (2012). The effects of personality traits, self-
esteem, loneliness, and narcissism on Facebook use among university students.
Computers in Human Behavior, 28, 2414–2419.
Smith, C. (2012). Facebook S-1 amendment: New stats from Q1 2012 and more.
Huffington Post. Retrieved <http://www.huffingtonpost.com/2012/04/23/
facebook-s-1-amendment_n_1446853.html>.
Stanger, J. D. (1998). Television in the home 1998. Philadelphia: Annenberg Public
Policy Center, University of Pennsylvania.
Stanger, J. D., & Gridina, N. (1999). Media in the home 1999: The fourth annual survey
of parents and children. Philadelphia: Annenberg Public Policy Center, University
of Pennsylvania. 1999.
Steinfield, C., Ellison, N. B., & Lampe, C. (2008). Social capital, self-esteem, and use of
online social network sites: A longitudinal analysis. Journal of Applied
Developmental Psychology, 29, 434–445.
Subrahmanyam, K., Kraut, R. E., Greenfield, P. M., & Gross, E. F. (2000). The impact of
home computer use on children’s activities and development. Children and
Computer Technology, 10(2), 123–144.
Tazghini, S., & Siedlecki, K. L. (2013). A mixed method approach to examining
Facebook use and its relationship to self-esteem. Computers in Human Behavior,
29, 827–832.
Thompson, P. (2013). The digital natives as learners: Technology use patterns and
approaches to learning. Computers and Education, 65, 12–33.
Tosun, L. P. (2012). Motives for Facebook use and expressing ‘‘true self’’ on the
Internet. Computers in Human Behavior, 28, 1510–1517.
Trepte, S., & Reinecke, L. (2013). The reciprocal effects of social network site use and
the disposition for self-disclosure: A longitudinal study. Computers in Human
Behavior, 29, 1102–1112.
Turner, J. S., & Croucher, S. M. (2013). An examination of the relationships among
United States college students’ media use habits, need for cognition, and grade
point average. Learning, Media and Technology.http://dx.doi.org/10.1080/
17439884.2013.777349.
U.S. Census Bureau (2006). American community survey. <http://factfinder.
census.gov/servlet/ACSSAFFFacts?_event=Search&_langx=585en&_sse=on&geo_
id=05000US06037&_ county=Los+Angeles+County>.
U.S. Census Bureau (2007–2011). American Community Survey. Retrieved <http://
www.census.gov/acs/www>.
Venkatesh, V., Thong, J. Y. T., & Xu (2012). Consumer acceptance and use of
information technology: Extending the Unified Theory of Acceptance and Use of
Technology. MIS Quarterly, 36(1), 157–178.
Wang, Z., & Tchernev, J. M. (2012). The ‘‘myth’’ of media multitasking: Reciprocal
dynamics of media multitasking, personal needs, and gratifications. Journal of
Communication, 62, 493–513.
Young, K. S. (1998). Internet addiction: The emergence of a new clinical disorder.
CyberPsychology and Behavior, 1(3), 237–244.
Zickuhr, K., & Smith, A. (2012). Digital differences. Washington, DC: Pew Internet &
American Life Project.
L.D. Rosen et al. / Computers in Human Behavior 29 (2013) 2501–2511 2511
