The Role of Personality Traits, Social Anxiety, Depressive ...
Transcript of The Role of Personality Traits, Social Anxiety, Depressive ...
The Role of Personality Traits, Social Anxiety, Depressive Symptoms, and Other
Psychosocial Factors in the Motivation for Social Internet Use
by
C. Kyle Schindler, M.A.
A Dissertation in
Counseling Psychology
Submitted to the Graduate Faculty
of Texas Tech University in
Partial Fulfillment of
the Requirements for
the Degree of
Doctor of Philosophy
Approved
C. Steven Richards, Ph.D.
Chair of Committee
Andrew K. Littlefield, Ph.D.
Co-chair of Committee
Stephen W. Cook, Ph.D.
Robert A. Morgan, Ph.D.
Mark Sheridan
Dean of the Graduate School
August, 2016
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Copyright 2016, Charles Kyle Schindler
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Acknowledgements
“Silent gratitude isn't much use to anyone.” -G.B. Stern
The dissertation process can be quite difficult at times, and I have been blessed
with support from many amazing people who I should acknowledge. First and foremost,
I would like to sincerely thank my advisor, Dr. Steven Richards, for his support
throughout the last 6 years. Without the aid of your guidance, or the relief of your
patience, experience, and advice, I do not believe I would have been able to make it
through this process. It has truly been a privilege to work with you. I would like to
express my sincerest gratitude to my committee co-chair, Dr. Andrew Littlefield, for his
enthusiasm and his patience as I navigated a sometimes daunting statistical procedure.
Your organization, knowledge, and your time were all highly appreciated. I would like to
thank my committee members, Drs. Bob Morgan and Stephen Cook, for their suggestions
for this dissertation, as well as their valuable insight into graduate school in general.
To my father, my mother, and my younger brothers, Cody and Scott: I love you
more than words can express. You have inspired me to achieve in a way that only a
family could. I simply could not have done this without all of you. To Dr. Jennifer
Vencill, Dr. Andrew Friedman, and Blakely Low: You have served as friends, role
models, and mentors at times when I greatly needed them, motivated me to continue
pushing forward when I felt discouraged, provided a direction when I felt lost, and
offered support and help in innumerable ways. To Adam Cann, Klaudia Pereira, Mike
Crites, Dr. James Cazares, John Schumacher, Dr. Curtis Craig and others too
innumerable to list completely: You have enriched my life in many ways, and have made
my graduate school experience one of the best periods of my life.
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Table of Contents
Acknowledgements………………………………………………………………...…….iii
Abstract……..………………………………………………………………..…………...vi
List of Tables……………………………………………………………………………viii
List of Figures……………………………………………………………………….……ix
List of Abbreviations…………………………………………………………………...…x
Chapter One: Introduction …...………………..………………….…………..…………..1
Rationale for the Present Study……………………………….….……………….9
Hypotheses ………………………………………………………….…...………11
Chapter Two: Methods …………………………………………………….……………15
Sample and Procedures..…………………………………………………………15
Instruments …………………………………………………………….…….......16
Chapter Three: Results ...……………………………………………………..................24
Measurement Model………………………………………………………..……28
Structural Model……………………………………………………………..…..33
Chapter Four: Discussion………………..…………………………………………….…36
Limitations…………………………………………………………………….…39
References ………………………………………………………………………...……..46
Appendices ……………………………………………………………………….…..….65
Appendix A: Extended Literature Review…………………………………....….65
Appendix B: Tables and Figures……………………………………………..…..93
Appendix C: Models…………………………………………………….….…....98
Appendix D: Demographic Form……………………………………………....103
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Appendix E: CES-D……………………………………………………….……105
Appendix F: Big-Five Inventory……………………………………………......106
Appendix G: Perceived Stress Scale – 10 ……………………………………...107
Appendix H: Liebowitz Social Anxiety Scale……………………………….....108
Appendix I: COPE emotional social support items....……………………….....109
Appendix J: Internet Motivation Scale……………………………………..….110
Appendix K: General Problematic Internet Use Scale – ...…………...…….….112
Appendix L: Modified Social Connectedness Scale…………………………....114
Appendix M: Marlow-Crowne Social Desirability Scale………………………116
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Abstract
The use of the Internet for emotional and social support has received increasing
attention from researchers. Although early perspectives suggested this activity was
problematic (Caplan, 2002; Davis, 2001; Young, 1998), a growing body of research has
challenged this perspective (Byun et al., 2009), and found that social Internet use may
sometimes serve as an effective coping mechanism (Kraut et al., 2002; Leung, 2007). In
particular, Internet users who are high on certain personality traits (e.g., neuroticism,
introversion), or who are experiencing certain psychosocial factors (e.g. depressive
symptoms, social anxiety), may obtain some benefit from social Internet use, possibly
due to the unique communicative aspects of the Internet (Butt & Phillips, 2008;
Schouten, Valkenburg, & Peter, 2007; Zywica & Danowski, 2008), including facilitating
coping processes (Barak, Boniel-Nissim, & Suler, 2008; Griffiths, Calear, & Banfield,
2009).
Previous research has focused on the negative aspects of this behavior, or if
focusing on the positive aspects, has only assessed the relationships among a few
empirically identified variables. The current study proposed and assessed a more
complex structural equation model, in which the motivation for social Internet use is
predicted by the personality traits (e.g. neuroticism) and psychosocial factors (e.g.
depressive symptoms, social anxiety, perceived stress) which are most strongly
associated with this behavior. This study is the first known to assess the multivariate
relationships among these variables, including preliminary assessment of both negative
(e.g., obsessive thoughts about the Internet) and positive (e.g., increased social
connectedness) outcomes of Internet use in relation to these variables.
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College students in a large Southern university (N = 293) were assessed on each
of these factors via self-report measures. Structural equation modeling (SEM) was
utilized to determine the multivariate relationships among these variables and assess the
goodness of fit of the proposed model. Using the saturated model to aid in diagnostic
approaches, multicollinearity was discovered between motives and negative outcomes,
such that it impacted fit of the hypothesized model and prevented interpretability. An
alternative model was proposed which was theoretically sound, was interpretable, and
which was ultimately retained. Although the directionality of the relations among these
variables cannot be fully determined through SEM, there appears to be some comorbidity
between depressive symptoms, social anxiety, and outcomes of SIU. Clinicians should
assess the presence of online relationships when determining social support and
interpersonal functioning for socially anxious and depressed clients, as these relationships
may likely be present and influential. Strengths and limitations of the current study and
methodology are discussed.
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List of Tables
1. Factor Loadings and Composite Reliabilities for each Latent Factor………….41
2. Descriptive Statistics for Full Measures………………………………………..102
3. Correlations between Measures………………………………………………..103
4. Fit of Initial Measurement Model and Final Measurement Model with Items
Retained………………………………………………………..………………104
5. Path Loadings and Correlated Disturbances for the Saturated Model………..105
6. Participants’ Reported Internet Use……………………………………………106
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List of Figures
1. Measurement Model………………………………………………………..107
2. Hypothesized Structural Model…………………………………………….108
3. Path Loadings for Initial Hypothesized Model…………………………….109
4. Path Loadings for Alternative Model………………………………………110
5. Path Loadings for Saturated Model………………………………………..111
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List of Abbreviations
BFI: Big Five Inventory
CES-D: Center for Epidemiological Studies – Depression Scale
CFI: Comparative Fit Index
GPIUS-2: General Problematic Internet Use Scale – 2
IMS: Internet Motivation Scale
ISG: Internet Support Group
LSAS: Leibowitz Social Anxiety Scale
MCSDS: Marlowe-Crowne Social Desirability Scale
PANAS: Positive and Negative Affect Schedule
PIU: Problematic Internet Use
PSS-10: Perceived Stress Scale - 10
RMSEA: Root Mean-Square Error of Approximation
SCS-R: Social Connectedness Scale - Revised
SEM: Structural Equation Modeling
SIU: Social Internet Use
ULI: Unit Loading Identification
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Why do people use the Internet to seek relationships?
Human beings are a highly social species, as they have a natural desire to connect
to others (see Baumeister & Leary, 1995, for a review) and seek close relationships for
comfort and support (Buhrmester, 1996). This human need to connect has been
profoundly modified by the advent of the Internet. Social Internet use (SIU), defined as
use of the Internet to communicate with others, has increased throughout the years. This
includes any electronic media through which a minimum of two individuals communicate
at a distance (e.g. cell phones, computers), and can involve immediate (e.g. Skype) or
delayed communication (e.g. email, messaging). Early research found that up to 14% of
U.S. adolescents reported maintaining an online friendship (Wolak, Mitchell, &
Finkelhor, 2003). More recent surveys have found that 65% of adolescents participate in
social networking sites, 49% read the blogs of others, and 68% use Instant Messaging
software (Jones & Fox, 2009). In a two-year period, Facebook experienced a twofold
increase in membership, going from 500 million users to 1 billion users (Facebook Data
Team, 2010; Vance, 2012) and websites such as Instagram report over 400 million active
monthly users in 2016 (www.instagram.com/press). Internet users have become
increasingly more social in their online behaviors, and researchers have increasingly
questioned the potential consequences, both beneficial and problematic, of SIU. In
particular, SIU is seen as both a valid alternative to traditional face-to-face
communication (Amichai-Hamburger, Wainapel, & Fox, 2002; McKenna, Green, &
Gleason, 2002) and a harmful activity which impacts face-to-face communication (Davis,
2001; Caplan, 2002).
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Does SIU Impact Mental Health and Social Support?
Early studies on social Internet use tended to focus on the theoretical limitations
of this medium in the forming and maintaining of relationships. Online relationships
were thought to be lacking in both emotional and physical closeness, making them poor
social support buffers when an individual experienced distress (Kraut et al., 1998). It was
also believed that these relationships would take away some time from face-to-face
relationships, leading to further negative consequences and reducing social support.
Indeed, Kraut and colleagues assessed new Internet users over a period of 12-24 months
and found that SIU led to increased self-reported depressive symptoms and feelings of
loneliness.
Those experiencing mood disruptions, anxiety, or stress were also believed to be
more motivated to engage in SIU (Davis, 2001; Caplan, 2002). This was considered
problematic, as these individuals might maintain maladaptive cognitions and beliefs (e.g.
“I am worthless offline, but online I am someone”), which could cause them to use the
Internet excessively and neglect other areas of functioning (e.g. their jobs, schoolwork).
Some evidence supports this assertion. Excessively engaging in SIU has been
found to be comorbid with mood disorders, anxiety disorders, and ADHD in certain
populations (Tokunaga & Rains, 2016; Weinstein & Lejoyeux, 2010). Individuals high
in loneliness have indicated that they prefer SIU over face-to-face interactions, and that
their Internet use has caused disturbances in functioning (de Ayala López., Gutierrez, &
Jiménez, 2015; Morahan-Martin & Schumacher, 2003). Time spent online has been
shown to have a positive association with feelings of loneliness (Matsuba, 2006;
Stepanikova, Nie, & He, 2010; Tokunaga & Rains, 2016), poorer coping strategies
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(Milani, Osualdella, & Di Blasio, 2009), and decreased social integration (de Ayala
López., Gutierrez, & Jiménez, 2015; Weiser, 2001).
However, several studies have found that there are possibly benefits to SIU as
well. The negative consequences discovered in the original Kraut study (Kraut et al.,
1998) were found to have largely dissipated when the researchers followed up with this
sample (Kraut et al., 2002). Consistent with this, a study with Serbian adolescents found
no relation between depression and level of SIU (Banjanin, Banjanin, Dimitrijevic &
Pantic, 2015). Furthermore, SIU has been associated with decreased depressive
symptoms (Morgan & Cotten, 2003; Shaw & Gant, 2002), as well as decreased
loneliness, improved self-esteem and improved perceptions of social support (Shaw &
Gant, 2002). Moreover, SIU has been associated with improved connection with family,
friends, and individuals with shared interests (Amichai-Hamburger & Hayat, 2011).
Finally, internet users who engage in SIU report specifically perceiving that this kind of
internet use can be psychologically beneficial to them (Campbell, Cumming, & Hughes,
2006).
Internet Support Groups
Studies on the effects of Internet support groups (ISGs; Also known as Online
Support Groups) provide additional support for the potential benefits of this behavior.
ISGs are online message boards or forums where members may receive emotional and
social support, discuss problems, or share information (Barak, Boniel-Nassim, & Suler,
2008). Individuals managing a variety of concerns and significant health conditions have
reported ISG use to be beneficial for them, including hysterectomies (Bunde, Suls,
Martin, & Barnett, 2006), visual impairment (Smedema & McKenzie, 2010), cancer
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(Beaudoin & Tao, 2007; Han et al., 2008; Seckin, 2013; Shim, Cappella, & Han, 2011),
suicidal ideation (Gilat & Shahar, 2009), HIV/AIDS (Mo & Coulson, 2010), and
Parkinson’s Disease (Attard & Coulson, 2012).
In contrast to perspectives that seeking others out online can be harmful (Caplan,
2002; Davis, 2001), the social and emotional connections formed with others on these
ISGs are frequently found to be the most important predictor of the psychological
benefits (e.g. greater perceived well-being) derived from participating (Han et al., 2008;
Shim et al., 2011). For users in a Parkinson’s ISG, those who did not form these
connections appeared to have a more difficult time using it as a resource for coping and
information (Attard & Coulson, 2012). One study assessed users of diabetes and cancer
ISGs, and found that users’ preference for these ISGs was related to dissatisfaction with
their current face-to-face cancer and diabetes support groups (Chung, 2013), suggesting
that ISG use may be perceived by some as analogous to traditional support groups.
Consistent with the qualitative and empirical research on ISGs, one study (Leung,
2007) has found some support for the contention that specifically using the internet for
mood management and social compensation may be beneficial. Leung assessed 717
children and adolescents for internet use motives, stressful life events, and perceived
social support. It was found that higher levels of stress were related to higher degrees of
mood management and social compensation motives for using the internet. Leung also
found that higher perceived social support, both online and offline, was negatively
correlated with stressful life events. Based on the results found, Leung proposed that SIU
could serve as a buffer against stress by altering mood and providing social support, and
that some Internet users seemed to engage in SIU for this particular purpose. Although
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cross-sectional in nature, this study, in addition to previously discussed research on ISGs,
suggests that those who might benefit from SIU are those who are highly distressed and
also willing to use the internet for social or emotional support. This research is also
backed by studies with Italian samples which have determined that assessment of
particular motives may predict negative and positive outcomes of SIU (Mazzoni,
Baiocco, Cannata & Dimas, 2016).
Several mental health factors related to distress have been shown to relate to SIU,
specifically use of the internet for social support and emotional coping. However, studies
have found conflicting results about the exact relationships among these factors, likely
due to methodological concerns: In a review of Internet Addiction studies, Byun et al.
(2009) noted that definitions and conceptualizations in this area are inconsistent. For
example, some Internet Addiction perspectives have adopted a framework based off of
gambling addictions (Young, 1998), while other perspectives have based the
conceptualization off of substance abuse disorders (Kaltiala-Heino, Lintonen, & Rimpela,
2004). This inconsistency applies to scale development, as well: Several popular
measures have been developed which do not assess similar hypothesized antecedents of
Internet Addiction (Morahan-Martin & Schumacher, 2000; Young & Rogers, 1998).
Approaches to model building are often hindered by low sample sizes and the
interpretation of poorly-fitting models (Byun et al., 2009). ISG studies are often largely
qualitative in nature (Barak et al., 2008), and there are a relative lack of studies on ISGs
for mental health concerns compared to ISGs for medical conditions (Griffiths et al.,
2008). Prevelance rates also vary highly between Western and Eastern samples,
depending on the measure used (Quinones & Kakabadse, 2015). Thus, this body of
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research strongly suggests that more comprehensive studies should be conducted on SIU,
with a focus on stronger measures and methodology.
Which variables are most associated with social support seeking online?
Depressive Symptoms. Research on ISGs for individuals with depression have
demonstrated that depressed individuals specifically seek out emotional support when
they are online: A meta-analysis of thirteen studies on ISGs, conducted by Griffiths et al.
(2009), determined that the content of the posts for depression ISGs contained a
significantly greater degree of emotionally supportive content, compared to other kinds of
ISGs (e.g., Anxiety ISGs). Griffiths and colleagues found that many who participated in
ISGs for depression distinctly reported that an attractive feature of the groups was a
tendency to feel emotionally supported and to feel a reduction of their loneliness. Posts
on depression ISGs also frequently contained content related to social companionship
(Muncer, Burrows, Pleace, Loader & Nettleton, 2000). In general, members of
depression ISGs report significant benefit from participating (Houston, Cooper, & Ford,
2002).
Social Anxiety. SIU is argued to be an attractive mode of communication to
socially anxious individuals, particularly due to the relative anonymity afforded by the
Internet, the lack of self-presentational cues, and the ease of controlling the pace and tone
of conversations with others (Caplan, 2007; McKenna & Bargh, 2000). Research has
supported this contention: Studies have shown that individuals high in social anxiety are
rated more positively (e.g. conversation satisfaction, level of anxiety) when conversing
online compared to conversing face-to-face (High & Caplan, 2009). It is also
hypothesized that socially anxious individuals may converse easier online due to an
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increased inability to react to negative or inhibitory feedback cues from others (Schouten
et al., 2007; Stritzke, Nguyen, & Durkin, 2004). Finally, it is argued that socially anxious
individuals may benefit from SIU as a low-risk approach to practicing social interaction
skills, in order to improve on their subsequent face-to-face interactions (Campbell et al.,
2006). However, one drawback for socially anxious individuals online may be the
possibility of experiencing poorer well-being, as online interactions do not fully
supplement the face-to-face interactions they may still desire (Weidman et al., 2012). In
general, socially anxious individuals appear to be motivated to engage in SIU: Increases
in social anxiety were found to be related to increased desire to use the Internet for
coping purposes (Gordon, Juang, & Syed, 2007).
Perceived Stress. Researchers have found a relationship between the presence of
specific stressful life events and motivation to use the Internet socially (Leung, 2007).
One study to date has assessed the role of general perceived stress specifically on the use
of the Internet to cope emotionally (Deatherage, Servaty-Seib, & Aksoz, 2014).
Deatherage and colleagues assessed 267 college seniors on their degree of perceived
stress, dispositional coping styles, motivations for internet use, and problematic internet
use. Motives involving management of negative affect (i.e. “to cheer up when I am in a
bad mood”) were strongly positively associated with perceived stress, while other
motives (e.g. because it is fun, to celebrate a special occasion with friends) were not
associated. Additionally, problematic internet use was not associated with perceived
stress. Although this study was correlational in nature, and thus did not provide support
for a directional relationship between perceived stress and SIU motivation, this study
provides some tentative support for the possibility of this relationship.
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Neuroticism. Neuroticism, a trait defined by emotional instability and negative
affect (McCrae & Costa, 1987), is the personality trait most often related to increased
SIU. Studies have determined that a higher level of neuroticism is related to increased
use of social Internet sites like Facebook (Hughes, Rowe, Batey, & Lee, 2012; Seidman,
2013; Wolfradt & Doll, 2001) and to higher SIU in general (Kalmus, Realo, & Siibak,
2011). Individuals high in neuroticism also tend to be more motivated to engage in this
activity for social support and emotional expression: Highly neurotic individuals report
specific motivations to increase companionship and reduce loneliness through their
Internet use (Amiel & Sargent, 2004), to use blogs for self-expression (Guadagno, Okdie,
& Eno, 2008), and to express their “true selves” to others (Amichai-Hamburger,
Wainapel, & Fox, 2002; Tosun & Lajunen, 2010). It has been argued that highly neurotic
individuals prefer the Internet due to the greater control they have over their presentation
and their statements, compared to face-to-face interactions (Butt & Phillips, 2008;
Nadkarni & Hofmann, 2012). This suggests that an online format is specifically
attractive to neurotic individuals who wish to form new relationships.
Extraversion. Both extraversion, a trait defined by higher sociability, liveliness,
and assertiveness, and introversion, the inverse trait (McCrae & Costa, 1987), have been
demonstrated by research to increase motivations for SIU. Researchers argue that the
relations found for both dimensions of this trait are due to the types of social Internet
activities being assessed: Extroverts prefer to use the Internet to build off of existing
relationships, while introverts prefer to use the Internet to make new friends (Amichai-
Hamburger et al., 2002; Bargh, McKenna, & Fitzsimons, 2002; Orchard & Fullwood,
2010; Tosun & Lajunen, 2010). For example, extroverted individuals tend to report more
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participation on Facebook in general (Jenkins-Guarnieri, Wright, & Hudiburgh, 2012),
and tend to be more involved on other sites that focus primarily on previously-established
friendships (Amichai-Hamburger, Kaplan, & Dorpatcheon, 2008). Introverted
individuals, on the other hand, tend to value more anonymous Internet services such as
ICQ and Instant Messaging (Amichai-Hamburger et al., 2008; Amiel & Sargent, 2004),
and appear to prefer SIU as a means of expressing their “true selves” (Amichai-
Hamburger et al., 2002; Zywica & Danowski, 2008). This research suggests that more
introverted individuals are motivated to seek out and build new relationships online.
Rationale for the Present Study
Although there are varying levels of empirical support for each of the factors that
were previously discussed, no overarching model has attempted to show the multivariate
relations between all of these factors. This conclusion holds for these factors’
relationship with healthy, non-excessive degrees of social and emotional coping on the
Internet. Most prominent research on models of Internet use in general focuses primarily
on this behavior as inherently aberrant and harmful, especially when considering the
social components (Caplan, 2002, 2010; Davis, 2001). These models persist, despite a
strong, growing body of literature demonstrating that there are numerous potential
benefits to engaging with others online (Kraut et al., 2002). Moreover, these models are
somewhat limited in scope, are frequently exploratory in nature, and are sometimes
interpreted even when there is poor fit (Byun et al., 2009; Tokunaga & Rains, 2010).
When assessing the possibility of positive outcomes, research tends to be qualitative
(Barak et al., 2008; Griffiths et al., 2009), and studies that have empirically assessed the
relations between factors in a positive manner tend to only look at two or three factors at
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a time (Leung, 2007). Thus, the relations among factors involved with SIU is still a
nascent research area. There remains a need for a conceptual SIU model which does not
treat this behavior as inherently harmful.
This study will contribute to the literature in this area by being the first of its kind
to create a more comprehensive, empirical model of the personality traits and
psychosocial factors which have been shown to predispose individuals to SIU. This
study will also explore through a confirmatory approach the multivariate relations
between these variables, some of which have not been assessed yet in the literature.
Finally, this study will assess SIU as a possible positive coping mechanism, in contrast to
other major models of Internet use which assert that it is inherently harmful.
The Internet is becoming more social in nature, and individuals are increasingly
starting and maintaining relationships online. This is the first study, to our knowledge, to
assess the roles of, and relations between, multiple empirically-supported factors (i.e.,
depressive symptoms, neuroticism, etc.) in SIU as a positive social and emotional coping
mechanism. In determining the relationship between these factors, this study hopes to
further increase understanding of what might influence the attractiveness of engaging in
SIU for support, and who might find it most appealing and helpful. A strong relationship
between these factors would demonstrate that individuals have multifaceted emotional
and social motivations for using the Internet to meet others. Consistent with some
therapeutic orientations (Palmer-Olsen, Gold, & Woolley, 2011; Rogers, 1961),
researchers have argued that the ability to express one’s “true self”, even over the
Internet, could be helpful for neurotic, introverted, or socially anxious individuals, as
these individuals often have difficulty expressing themselves in face-to-face
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communication (Amichai-Hamburger et al., 2002; Bargh et al., 2002; Ebeling-Witte,
Frank, & Lester, 2007). Some research has found initial support for this contention,
finding that even the use of blogs for self-expression leads to positive outcomes
therapeutically (Hillan, 2003), possibly due to an increase in perceived social support
(Baker & Moore, 2008).
Although there is not yet a complete consensus on the actual positive or negative
consequences of SIU (Huang, 2010), a greater understanding of the factors behind these
behaviors may still benefit Internet users. SIU could potentially allow many individuals
to seek social and emotional support when it is not available through other means.
Additionally, a more comprehensive understanding of these variables may benefit
therapists and other mental health practitioners. By having a greater understanding of
these psychosocial factors as they relate to SIU, therapists may become better equipped to
work with clients who are seeking and maintaining relationships with others online. This
will become increasingly relevant in psychotherapy, as this population is only expected to
grow in the future. Understanding these behaviors as potential coping strategies and
methods of self-expression for those experiencing stress, depression, social difficulties, or
emotional instability may allow for more accurate conceptualizations and interventions.
Future application of SIU as both a social and emotional support mechanism, for certain
types of clients, is also a possibility.
Hypotheses
The proposed structural model (See Figure 1, pg. 100) will be utilized to assess
the hypothesized relations between these variables at a multivariate level. It is proposed
that the relationship between personality characteristics (neuroticism and introversion)
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and SIU motivation is mediated by certain psychosocial factors (social anxiety,
depressive symptoms, perceived stress and the tendency to seek out emotional social
support to cope).
Thus, this study hypothesizes:
H1: SIU motives will mediate the relationship between positive and negative outcomes
and psychosocial factors.
H2: SIU motives will have positive relations with both negative and positive outcomes.
H3: Psychosocial factors will have positive relations with SIU motives.
H4: Psychosocial factors will relate positively with neuroticism and negatively with
extraversion, apart from emotional social support coping, which will relate positively to
both.
H5: The relationship between SIU motives and personality characteristics will be
mediated by psychosocial factors.
Research supports the direct relations between latent factors in this model. Meta-
analyses of personality and clinical disorders conclude that neuroticism is frequently one
of the strongest predictors of anxiety and mood disorders (Kotov, Gamez, Schmidt, &
Watson, 2010; Malouff, Thorsteinsson, & Schutte, 2005). Neuroticism is highly
correlated with negative affect in general (Gutierrez, Jimenez, Hernandez, & Puente,
2005), as well as the recurrence of depressive symptoms (Steunenberg, Braam, Beekman,
Deeg, & Kerkhof, 2009). The Vulnerability model, which states that certain personality
factors precede depression, is also one of the more strongly supported models in the
literature (Bagby, Quilty, & Ryder, 2008). Thus, neuroticism can be hypothesized to
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precede the development of psychosocial difficulties, and the model has included an arc
between neuroticism and social anxiety, and neuroticism and depressive symptoms.
Neurotic individuals also display a tendency to become distressed more easily than their
non-neurotic counterparts (McCrae & Costa, 1987), and to cope in a different manner
than their non-neurotic counterparts (Gunthert, Cohen, & Armeli, 1999). Thus, paths
between neuroticism and perceived stress and neuroticism emotional social support
coping have also been included in the model.
Introversion, a trait defined by less sociability and liveliness in social situations, is
strongly correlated with social phobia, unipolar depression, and dysthymic disorder
(Kotov et al., 2010), and is found to some degree among a wide variety of clinical
symptoms (Malouff et al., 2005). Introversion is also associated with lower levels of life
satisfaction (Lounsbury, Saudargas, Gibson, & Leong, 2005). Consistent with this
research and the Vulnerability model (Bagby et al., 2008), paths between introversion and
depressive symptoms, and introversion and social anxiety, are included in the model.
Although the relationship between introversion and emotional social support coping is
less clear, there does appear to be a relationship (Watson & Hubbard, 1996), thus a path
was also included between these two variables as well.
Coping is inherently a strategy used to manage the demands of situations that are
perceived as stressful, and is typically initiated when experiencing intense negative
emotions or distressing events (Folkman & Moskowitz, 2004). Thus, the model includes
covariances between emotional social support coping and perceived stress, depressive
symptoms, and social anxiety. Although anxiety disorders tend to largely occur before a
comorbid mood disorder (Kessler, Stang, Wittchen, Stein, & Walters, 1999; Mineka,
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14
Watson, & Clark, 1998), the causal relationship between sub-clinical social anxiety and
depressive symptoms is less clear. Thus, these factors are hypothesized to covary in the
model. The perception of stress is also hypothesized to covary with depressive symptoms
and social anxiety.
It is predicted that a tendency to use emotional social support coping will
inherently lead to more motivation to engage in this activity online. Some research
supports this relationship: Seepersad (2004) found that online and offline coping
strategies shared a significant degree of overlap. Specifically, it was found that
adolescents who considered communication to be an important aspect of Internet use
were also likely to cope offline with loneliness through emotional expression and social
coping. Thus, a path is included from emotional social support seeking to SIU
motivation.
Prior research discussed has shown strong support for the contention that
particular individuals will be more motivated to use the Internet socially. Internet
Support Group studies demonstrate that depressed individuals maintain specific
motivations to use the Internet for social and emotional support (Griffiths et al., 2009).
Individuals with social anxiety appear to benefit from the conduciveness of the Internet to
self-presentational concerns (Caplan, 2007; McKenna & Bargh, 2000). Additionally,
they appear to specifically desire SIU as a coping mechanism (Gordon et al., 2007).
Stressful life events, or even the perception of stress, may also lead to an increased
motivation to seek others out online (Deatherage et al., 2014; Leung, 2007). In contrast
to this research, Davis (2001) and Caplan (2002, 2010) argue and attempt to demonstrate
that SIU and motivation to engage in it is an inherently harmful activity, which leads to
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15
negative outcomes in work and school. Thus, there is no current consensus on the relative
benefits and drawbacks of this behavior. As causality will not be assessed in this study, a
research question will be posed:
R1: Do motivations for SIU have a stronger relationship with positive outcomes
or with negative outcomes?
Methods
Sample
Participants were 293 college students (212 identified as female, 80 identified as
male, 0 identified as non-binary) from introductory psychology classes at a Southwestern
university. Participants ranged in age from 17 to 33 years old (M= 19.4, SD=1.7). To
avoid limiting the accuracy of demographic information, a general “multi-racial”
category was eschewed and participants were allowed to choose any racial identities they
wished to report. Thirty participants identified with more than one race (10.2%).
Including multi-racial individuals, 193 participants identified as White/Caucasian
(65.8%), 31 identified as Black/African-American (10.5%), 19 identified as Asian
American/Asian/Pacific Islander (6.4%), 74 identified as Hispanic/Latino (25.2%), 2
identified as Indian or Middle-Eastern (.6%), and 6 identified as Native American (2%).
The majority of the sample were college freshman (186; 63.4%) or sophomores (62;
21.1%). Forty-six percent of the sample reported currently being in a romantic
relationship.
Procedure
Participants were recruited through SONA, an online study sign-up program.
They received course credit for their participation. Participants were not penalized for
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16
failing to complete the study once they have agreed to participate. The current study
received exempt status from the Institutional Review Board (IRB), indicating that
participants did not experience any risks outside of those related to normal life.
Instruments were adapted to online surveys on Qualtrics. Once the participants
signed up for the study on SONA, they were then directed to Qualtrics to complete the
study. They read a webpage containing an informed consent regarding the purpose of the
study (“To gain a greater understanding of why people might seek out new friends and
relationships online”), and were required to indicate their consent to participate via a
checkbox before they were able to continue. Participants first completed a demographics
questionnaire which included various Internet use behaviors they engaged in on a daily
basis (see Appendix C). They then completed the remaining seven measures
(Appendices D - H) in a random order, to account for any order effects. These
questionnaires took approximately 35 to 45 minutes to complete. Upon completion,
participants were directed to a separate webpage which asked them for their contact
information, thanked them for their participation, and provided contact information for
the researcher. Through this method, participants were not matched with their responses
and could remain anonymous.
Instruments
Demographic questionnaire. A short demographic questionnaire was used and
consisted of items about gender, age, ethnicity, and number of years in college. Various
popular online communities and online social behaviors were assessed to provide
qualitative impressions of the degree of SIU in the sample. Examples of behaviors and
communities measured include Facebook use, microblog use (i.e. Tumblr), use of
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message boards, use of blogging software (either reading or writing on blogs), and use of
messaging software. The amount of time spent per day on each activity, on average, was
also assessed. Assessing amount of use at this level is beneficial, in that it allows for
some general comparison between samples. However, Internet use can vary widely and
the reliability of this estimate in the sample is anticipated to be low.
Center for Epidemiological Studies – Depression Scale (CES-D) (Radloff,
1977). The CES-D is a widely used assessment of depressive symptoms (Nezu, Nezu,
Friedman, & Lee, 2009). The CES-D asks participants 20 questions about the intensity
of depressive symptoms over the previous week (e.g., I thought my life had been a
failure). Items are rated on a 4-point Likert scale (1 = Rarely or none of the time, 4 =
Most or all of the time). Rarely or none of the time responses are scored as a 0 and Most
or all of the time responses are scored as a 3, for total score range between 0 and 60, with
higher scores indicate more severe symptomology.
The CES-D scale scores demonstrated high internal consistency with White
populations (α = .84-.85) and clinical samples (α = .90) in the original study (Radloff,
1977). Of particular interest to the current study, the CES-D has good predictive validity
and scale discriminability in English and French-Canadian college samples (Santor,
Zuroff, Cervantes, & Palacios, 1995; Shean & Baldwin, 2008). Additionally, online
versions of the English CES-D appear to have similar psychometric properties to the
paper-and-pencil version (Ogles, France, Lunnen, Bell, & Goldfarb, 1998). Scale scores
have also been shown to have good factorial validity (Orme, Reis & Herz, 1986), as well
as convergent validity with other popular measures of depression, such as the Beck
Depression Inventory (Santor et al., 1995). The English scale has been determined to
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18
demonstrate unidimensionality when the reverse-scored items are removed (Stansbury,
Ried, & Velozo, 2006). Due to its good psychometric properties among many different
populations, the CES-D is recommended for the assessment of depressive symptoms
(Nezu et al., 2009) and was used to measure this construct in the current study.
Internet Motivation Scale (Wolfradt & Doll, 2001). SIU motivations were
assessed with the Internet Motivation Scale (IMS). The Internet Motivation Scale is a
measure intended to assess various motivations participants might have for using the
Internet. This scale includes 20 items which assess 3 different underlying motivations for
using the Internet: information seeking motives (e.g., The Internet updates me on new
trends), entertainment motives (e.g., The Internet stimulates my curiosity), and
interpersonal communication motives (e.g., The Internet is to me a substitute for other
social contacts). Higher scores on this scale signify more motivation to engage in the
Internet in this manner, e.g. a high score on the interpersonal communication motives
subscale indicates greater motivation to use the Internet for this purpose.
Wolfradt and Doll (2001) found high internal consistency for scores of all three
scales (α = .76 - .84) and discriminate validity to personal factors (e.g. self-efficacy) and
social factors (e.g. expectations of others). Other studies have supported the IMS’s high
internal consistency (α = .89) (Gordon et al., 2007) and its three-factor structure in
English-speaking samples (Matsuba, 2006). Items from the interpersonal communication
factor were used in this study, but all items were assessed in order to preserve the
psychometric properties of the scale and provide additional qualitative data.
Leibowitz Social Anxiety Scale (LSAS) (Liebowitz, 1987). The Leibowitz
Social Anxiety Scale is a widely used, clinician-administered assessment of social
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19
anxiety. The LSAS contains 24 items which assess a range of situations that individuals
with social anxiety may have feared or avoided in the last week (e.g. Eating in public
places, or Looking at people you don’t know very well in the eyes). These items are
divided into two subscales which address Social interaction situations and Performance
situations, and measures participant’s Avoidance and Fear of each type of situation. Items
are rated on a 4-point Likert-scale (e.g. For Fear, 0 = None and 3 = Severe. For
Avoidance, 0 = Never and 3 = Usually), for a total score range between 0 and 144. An
overall total score can be calculated by summing all items, with higher scores indicating
more severe symptomology.
The English LSAS has been shown to have excellent internal consistency (α =
.96), as well as convergent validity with other measures of social anxiety, and
discriminate validity to measures of general anxiety and depression (Heimberg et al.,
1999). The self-report version of the English LSAS was used in the current study, as the
self-report LSAS has comparable psychometric properties to the original, clinician-
administered version (Fresco et al., 2001).
Perceived Stress Scale (PSS-10) (Cohen, Kamarck, & Mermelstein, 1983).
The Perceived Stress Scale is a widely used assessment for nonspecific perceived stress.
The PSS-10 is a version of the Perceived Stress Scale which contains 10 items that assess
participant’s perceptions of how unpredictable, uncontrollable, and overloading they find
their lives (e.g. In the last month, how often have you found that you could not cope with
all the things that you had to do?). Items are rated on a 5-point Likert-scale (e.g. 0 =
Never, 4 = Very often), for a total score range between 0 and 40.
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The PSS-10 has been shown to have better psychometric properties than other
versions of the PSS, and is recommended as a result (Lee, 2012). In general, the PSS-10
has good internal consistency (α = .78-.91), test-retest reliability (α =.85), and convergent
validity with other measures of stress in English-speaking populations (Cohen et al.,
1983; Cohen & Williamson, 1988). The reliability of the PSS-10 extends to nationally
representative samples (Cohen & Janicki-Deverts, 2012). Reviews of the PSS-10 have
demonstrated some variation between studies with regard to the strongest factor solution,
alternating between a one-factor and a two-factor solution (Lee, 2012). Thus, both one-
factor and two-factor solutions were assessed in the current study.
The Big Five Inventory (BFI) (John, Donahue & Kentle, 1991) Neuroticism
and introversion were assessed with The Big Five Inventory. The Big Five Inventory is a
widely used assessment of personality traits, based on the Big Five model. The BFI
contains 44 items which assess participants’ perceptions of themselves (e.g. I am
someone who is reserved, or I am someone who can be moody). Items are rated on a 5-
point Likert-type scale (e.g., 1 = Strongly Disagree, 5= Strongly Agree), and items
corresponding with each Big Five trait are averaged into five scale scores.
The English BFI has been shown to have high internal consistency (α = .83-.85),
convergent validity with other, highly validated Big Five measures such as the NEO,
strong discriminant validity, and substantial self-peer agreement (John, Naumann, &
Soto, 2008; John & Srivastava, 1999; Soto & John, 2009). Domain scales are also
reliable (α =.81-.88), and a five-factor structure is supported (John, Naumann, & Soto,
2008; Soto & John, 2009). Although only items for the neuroticism and introversion
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21
scales were assessed, the entire scale was given to preserve its psychometric properties
and provide further descriptive data for this sample.
COPE (Carver, Scheier, & Weintraub, 1989). The COPE is a widely used
assessment of coping. The full version of the COPE has 15 distinct scales which assess
various ways one might cope with stressful situations (i.e., Humor, Denial, Behavioral
Disengagement, etc.). The COPE can be modified to assess coping behaviors during a
particular time point, or to assess coping as a more dispositional “trait”. Emotional social
support seeking will be assessed as a dispositional trait through the 4 items on the Use of
emotional social support scale on the COPE (e.g. I discuss my feelings with someone, or I
talk to someone about how I feel). Items are measured on a 4-point Likert scale (e.g., 1 =
I usually don’t do this at all, 4 = I usually do this a lot).
In the dispositional form, the English versions of these items have demonstrated
good internal consistency (α =.85 - .90) (Carver et al., 1989; Cook & Heppner, 1997), and
have consistent support for their unidimensionality (Litman, 2006). When assessed as a
three-factor solution, three of the four items (one was removed due to perceived overlap
with another item) loaded moderately (=.56 - .67) onto one factor (Lyne & Roger, 2000),
and all four items were found to load onto one factor (=.71-.83) in a seven-factor solution
(Eisengart et al., 2006).
Generalized Problematic Internet Use Scale – 2 (GPIUS2) (Caplan, 2010).
Negative outcomes of Internet use were assessed with the Generalized Problematic
Internet Use Scale - 2. The GPIUS2 is an updated version of a widely-used assessment
of characteristics associated with excessive Internet use. The GPIUS2 comprises 5
subscales with 3 items each: Preference for online social interaction (e.g., I prefer online
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social interaction over face-to-face communication), mood regulation (e.g., I have used
the Internet to make myself feel better when I was down), cognitive preoccupation (e.g., I
would feel lost if I was unable to go online), compulsive Internet use (e.g., I find it
difficult to control my Internet use), and negative outcomes (e.g., My Internet use has
created problems for me in my life). Items are rated on an 8-point Likert scale
(1=Definitely Disagree, 8=Definitely Agree). The GPIUS-2 was developed to update the
previous measure (GPIUS) and incorporate more recent research findings, and can be
used as a set of sub-scales or as a general composite score (Caplan, 2010). The GPIUS-2
has demonstrated reasonable psychometric properties in some previous research: It has
been shown to have high internal consistency in American samples (α = .91), as well as
adequate construct validity (Caplan, 2010). Currently, the psychometric properties of the
full GPIUS-2 have not yet been replicated in American sample, so these properties are
less established. It has been translated into Spanish (α =.90) (Gamez-Guadix, Orue,
Smith, & Calvete, 2013) and Italian (α =.89) (Chittaro & Vianello, 2013). The GPIUS-2
has also been shown to have adequate construct and convergent validity in Mexican
adolescent samples (Gamez-Guadix, Villa-George, & Calvete, 2012).
Social Connectedness Scale - Revised (SCS-R) (Lee, Draper, & Lee, 2001).
Positive life outcomes were assessed through a modified version of the SCS-R. The
scale contains 20 items, rated on a 6-point Likert scale (1 = Strongly Disagree, 6 =
Strongly Agree), which purport to measure cognitions regarding general, enduring
interpersonal closeness in the social world (e.g., I feel distant from people). The SCS-R
has good internal consistency (α =.92), as well as appropriate convergent and
discriminant validity (Lee et al., 2001). Although no known replication of the scale has
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23
been done in American college populations, the translated SCS-R has good internal
consistency (α =.91) and high test-retest reliability in Taiwanese college student
populations (Chen & Chung, 2007). The SCS-R will be modified to assess social
connectivity with regard to general SIU (e.g., I feel close to people will be modified to I
feel close to people online). The SCS-R has previously been adapted to assess social
connectivity regarding Facebook use specifically (Greive et al., 2013), and demonstrated
good internal consistency in this particular study (α =.89 - .92). However, this measure
has less established psychometric properties and the modified version will be examined
before use in analyses.
Marlowe-Crowne Social Desirability Scale (MCSDS) (Marlowe & Crowne,
1960). Social desirability was measured with the Marlowe-Crowne Social Desirability
Scale. The MCSDS is a 33-item scale which purports to measure the desire to respond to
items in a manner which is appropriate and acceptable in that individual’s culture. It is
the most widely used instrument in this area, and is often utilized to assess undergraduate
populations (Beretvas, Meyers, & Leite, 2002). Stigma related to certain behaviors, such
as SIU, as well as psychosocial characteristics, such as depression and neuroticism, has
been shown to impact the responses of participants in studies. Thus, this scale was
utilized to determine the impact of this construct on responding, in particular related to
Internet use behaviors. It was also used as an additional validation measure to screen
participants who are possibly under-reporting based on social desirability. The original
study by Marlowe and Crowne (1960) determined good internal consistency (α = .88),
though this was based on a relatively small sample size. The scale has been shown to
have adequate reliability for adult women (α = .797) and adult men (α =.704), based on a
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meta-analysis of studies which have reported sample-specific internal consistency
statistics (Beretvas et al., 2002). This study also determined a wide range of test-retest
reliabilities for this measure, ranging from poor (α = .38 for a study with a 2-4 week test-
retest interval) to good (α = .86 for a study with a test-retest interval of more than one
month). The full scale is recommended for use, as it has the best psychometric properties
(Barger, 2002).
Some studies have questioned the factor structure and construct validity of this
measure with undergraduate populations (Barger, 2002; Leite & Beretvas, 2005), and
many studies have not reported sample-specific internal consistency estimates (Beretvas
et al., 2002). Although the dubious properties of this measure have emerged in some
samples, these items appear to have adequate internal consistency for the current study (α
= .73). Thus, a high cutoff score ( > 3 SD) will be utilized to avoid some of the concerns
demonstrated in the literature, but to also allow for determination of those cases most
likely to be impacted by response bias.
Results
Descriptive Statistics
Descriptive statistics and other qualitative information related to this sample’s
reported SIU activities can be found in Table 5. Descriptive statistics related to each
instrument used in the current study may be found in Table 2, and correlations between
each instrument may be found in Table 3.
Structural Equation Modeling
Structural equation modeling (SEM), with weighted least squares (WLSMV)
estimation, was utilized to determine the relations between SIU motivation, negative and
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positive outcomes of this behavior, psychosocial factors (depression, social anxiety,
perceived stress, emotional social support seeking), and personality factors (neuroticism
and introversion). SEM has a number of strengths which make it the most appropriate
technique to assess these relationships. Of primary interest to the current study, SEM
allows for a direct assessment of the hypothesized model and the overall relations
between multiple variables, while linear regression would be relegated to the assessment
of several individual correlations (Tomarken & Waller, 2005). SEM also uses multiple
indicators for each construct, which increases the reliability of factor measurement over
path analysis (Kline, 2011).
SEM with weighted least squares estimation cannot be successfully utilized unless
important assumptions are achieved or considered. The first assumption is that of
multivariate normality, or each indicator being distributed normally at each value of each
other indicator (Kline, 2011). The second assumption is that outliers have not impacted
data and altered model fit as a result. Data screening suggested adequate univariate
normality, but determined the presence of two multivariate outliers, which were removed
from analyses.
The influence of social desirability on participant responses was considered in
data screening. As social desirability may impact the willingness of some subjects to
respond in a forthright manner about subjects such as Internet use, this variable was
assessed to determine potential univariate outliers that would otherwise not be present in
analyses. However, only high scores (Greater than 3 SD) were considered, as this
measure does not appear to have strong psychometric properties (Barger, 2002; Beretvas
et al., 2002; Leite & Beretvas, 2005). One score on this measure was 3 standard
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26
deviations above the mean (M = 16.05, SD = 4.86) and this case was removed from
analyses.
SEM analyses were conducted with Mplus (Muthén & Muthén, 1998-2010). Data
analysis was performed in three steps. First, item parcels were created for each latent
factor in the model. Then, the measurement model was examined to determine if item
parcels adequately measured latent factors. Third, the structural model was examined to
analyze the relations among the latent factors.
Measures of fit. Overall fit of the model was assessed partially through Root
Mean Square Error of Approximation (RMSEA) and the Comparative Fit Index (CFI).
These fit indices take into account sample size and complexity of the model, and are thus
recommended for large models (Kline, 2011). However, Kline also notes that fit indices
are only able to provide an overall picture of the model, and may neglect particularly
poor fit in some areas of the model if other areas fit well. Thus, fit indices may report
that a model fits the data adequately, when in reality it does not. Critics of the perceived
overuse of fit indices have demonstrated that perfectly fitting models may actually
account for very little variance (less than 1%) in the endogenous variables (Tomarken &
Waller, 2003). Finally, common cutoffs for these indices are primarily “rules of thumb”,
and are not necessarily accurate. However, these fit indices may still provide valuable
qualitative information about the fit of the model. Thus, these fit incidences are
interpreted and reported with caution, and overall model fit is assessed primarily through
the researcher’s judgment (Kline, 2011). For the structural model, in addition to
researcher judgment, “lower-order” components of the model are taken into greater
account in assessing model fit, which is a recommended approach (Kline, 2011;
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27
Tomarken & Waller, 2005). Thus, lower-order model components were assessed, such as
the model chi-square, path coefficients, the effect sizes of the direct and indirect arcs
between factors, and the total variance accounted for by the model.
As some constructs of interest did not have multiple, validated instruments
available for inclusion in this model (e.g. positive and negative outcomes of SIU), each
latent factor was assessed through one instrument, which necessitated the use of item
parcels to provide the necessary number of predictors. Unidimensionality is an important
assumption in the creation of item parcels (Little et al., 2002). Thus, each instrument
was assessed at item-level before items were parceled, to determine undimensionality.
Groups of items with factor loadings all above .5 were considered unidimensional. For
CES-D items, the four reverse-scored items were removed apriori, consistent with
recommendations made by previous research (Stansbury, Ried, & Velozo, 2006) that the
CES-D demonstrates unidimensionality afterward. This was supported by the
determination that all items did not load consistently on a single factor, while the
remaining confirmative items did. Consistent with literature (Lee, 2012), PSS-10 items
were assessed as a 1-factor solution with reverse-scored items removed, a 1 factor-
solution with all items retained, and a 2-factor solution. The 2-factor solution appeared to
fit the data best, and was retained.
The chi-square statistic was significant for this model (<.05). This indicates that
the model does not perfectly fit the data and suggests that the model should be rejected.
However, the chi square test statistic is highly sensitive to minor changes in model fit
with large sample sizes, higher correlations between observed variables, and models with
high degrees of freedom (Kline, 2011). Thus, there is some debate regarding the role of
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28
this statistic in interpreting models (Kline, 2011). As this current study has a somewhat
large sample size (n = 293), some high correlations, and a model with high degrees of
freedom (df = 442), this statistic should be interpreted with caution, particularly if lower-
order components of the model seem to support its fit to the data.
The CFI test statistic is recommended to have a cutoff of .90 for “adequate fit”
and a cutoff of .95 for “good fit” (Kline, 2011), and these cutoffs will be utilized for the
current study. The RMSEA test statistic is recommended to have a value of .08 or below
to exhibit adequate fit. This statistic also has a 90% confidence interval which indicates
good generalizability when the absolute value of the intervals is smaller.
Measurement model. Assessment of the hypothesized model in SEM first
requires analysis of the measurement model (Figure 1). This model assesses the
covariances between the indicators and the latent factors. MPlus utilizes the Weighted
Least Squares approach by default for models with both categorical and continuous
variables, and this approach was retained for the purpose of this study (Muthén &
Muthén, 1998-2010). The hypothesized model was theoretically identified. This model
also met the “necessary but not sufficient” conditions (Wang & Wang, 2012, p. 12) for
empirical identification: Each factor had at least three indicators (through item parceling,
discussed below), and the number of free parameters in the model (45) was exceeded by
the number of observations (136). The scale of each latent factor was set using unit
loading identification (ULI; Kline, 2011), such that the scale of one indicator per latent
factor was set to 1. Each latent factor was assumed to be correlated with each other latent
factor, and errors were assumed to be uncorrelated.
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Item parcels. Given the demonstration of adequate unidimensionality, item
parceling was used to create predictors in the measurement model. Item parceling is a
technique in which scale items are combined and used as predictors, instead of full scale
scores. This is a recommended technique over the use of individual items because it
allows for more continuous predictors, reduces the impact of sampling error, has greater
reliability, and increases parsimony of the measurement model (Little, Cunningham,
Shahar, & Widaman, 2002). Items were removed if intercorrelations between that item
and other items differed noticeably, if the item appeared to have redundancy with another
item, if the item loaded poorly in a 1-factor solution, and if researcher judgment
otherwise determined that the item was inadequate for parceling purposes. After
determining all items that met required criteria for inclusion, these items were placed into
parcels sequentially (e.g. 1,2,3,1,2,3…), unless otherwise noted. These predictors were
then placed into the measurement model sequentially, in order to determine potential
specification errors or problematic variables within the model. Composite reliabilities
were calculated rather than alpha coefficients, as these represent the correlations among
latent variables within the measurement model and more adequately describe the
psychometric properties of the item parcels. See Table 4 in Appendix B for initial fit and
final fit of chosen items, as well as list of items retained.
COPE. As there were only four items from the COPE used for emotional social
support coping, each item was retained and was modeled as a categorical predictor
instead of a continuous predictor. Due to the categorical nature of the predictors, this
measure was assessed first, in order to make sure that specification of the model was
correct. These items demonstrated unidimensionality, consistent with previous research
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30
(Litman, 2006). The measurement model demonstrated somewhat poor fit to the data:
Chi-square = 11.615 (df = 2, p=.003), RMSEA = .13 (.064 - .204), CFI = 1.0. The CFI
statistic indicates perfect fit for the initial stages of the model, likely due to a combination
of low degrees of freedom and the steps taken in item parceling to ensure that only high
validity items are included in the model.
CES-D. Consistent with previous research (Stansbury, Ried, & Velozo, 2006),
negatively worded items were removed. Then, redundant items were removed, using
differences in intercorrelations and face validity as criteria (e.g. “I felt sad” and “I felt
depressed”). Three parcels were created out of remaining items. This latent factor and
its predictors were then added it in to the model with COPE items, leading to a
measurement model with excellent fit, Chi-square = 18.84 (df = 13, p=.13), RMSEA =
.039 (.00 - .08), CFI = 1.0.
IMS. For IMS items, none of the 7 items were removed, as these items appeared
to demonstrate unidimensionality. One parcel with 3 items was created, and 2 parcels
with 2 items were created. The IMS latent factor was added into the model, leading to a
model with excellent fit: Chi-square = 30.55 (df = 32, p=.54), RMSEA = .000 (.000 -
.040), CFI = 1.0. RMSEA being set to 0 is a product of calculating this statistic, as this is
the only step in the model building at which the degrees of freedom are greater than the
Chi-square statistic.
BFI. For BFI neuroticism items, 2 items were also removed, both of which were
reverse-scored. The remaining reverse-scored item, and five positively-scored items,
were determined to have consistent intercorrelations with each other, were retained, and
were paired to create three parcels. This latent factor was added into the model, leading
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31
to a model with adequate fit: Chi-square = 96.29 (df = 59, p=.00), RMSEA = .046 (.029 -
.063), CFI = .99.
For BFI extraversion items, 2 items were removed, one of which was reverse-
scored and one of which was positively-scored. The remaining six items were split into
three parcels, with one reverse-scored item paired with one positively-scored item. This
latent factor was then added into the model, leading to a model with adequate fit: Chi-
square = 186.01 (df = 94, p=.00), RMSEA = .058 (.046 - .070), CFI = .98.
PSS-10. Five item parcels were created by pairing each of the 4 reverse-scored
items with a positively-worded item, then pairing up the two remaining positively-
worded items. This latent factor was then added into the model, leading to a model with
adequate fit: Chi-square = 346.61 (df = 174, p=.00), RMSEA = .058 (.049 - .067), CFI =
.96.
LSAS. For LSAS items, the subscale which assessed Fear related to Social
Interaction was utilized for item parcel creation, as it was the most subscale most
conceptually similar to socially anxious individuals’ theorized motivation to engage in
SIU. Nine items were retained, and were distributed into three parcels. This latent factor
was then added into the model, leading to a model with adequate fit: Chi-square = 426.75
(df = 231, p=.00), RMSEA = .054 (.046 - .062), CFI = .95.
SCS-R. For modified SCS-R items, six items with adequate factor loadings and
similar intercorrelations were retained out of the original 20 items, and were distributed
into 3 parcels. This latent factor was then added in to the model, leading to continued
adequate fit: Chi-square = 517.68 (df = 296, p=.00), RMSEA = .051 (.043 - .058), CFI =
.95.
Texas Tech University, C. Kyle Schindler, August 2016
32
GPIUS. For GPIUS items, each subscale’s items were combined into a
subsequent parcel, for five parcels total. This final latent factor was added in to the
model, leading to a final measurement model: Chi-square = 703.33 (df = 428, p=.00),
RMSEA = .047 (.041 - .053), CFI = .93.
Measurement model fit. As demonstrated above, this model demonstrated
adequate fit to the data. The model chi-square was significant, indicating that the model
did not perfectly fit the data. However, factor loadings were uniformly high and in the
predicted direction: All factor loadings were above .6 apart from one loading on
Extraversion, with no cross-loadings detected (See Table 1 for factor loadings and
composite reliabilities of parcels).
Table 1: Factor Loadings and Composite Reliabilities for each Latent Factor
Latent Factor Factor Loadings Composite Reliability
Depression .90, .84, .79 .88
SIU Motivation .89, .80, .64 .82
Emotion Social Support Coping .92, .89, .80, .95 .94
Social Anxiety .86, .79, .80 .86
Perceived Stress .65, .70, .60, .80, .71 .78
Neuroticism .85, .68, .69 .78
Extraversion .75, .59, .72 .73
Positive Outcomes .69, .83, .73 .80
Negative Outcomes .72, .75, .78, .79, .80 .85
Contrary to expectations, Emotional Social Support Coping was not significantly
correlated with Depression (.00), Perceived Stress (.04), or Social Anxiety (.01). Model
results were retained without further specification, due to strong and expected path
loadings, no cross-loadings, no correlation of errors, and adequate overall fit of the
model.
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33
Structural model
As the measurement model was retained, the final step of analysis involved
assessment of the hypothesized structural paths between latent factors in the
measurement model, including disturbance terms. The hypothesized structural model
(Figure 2) described the relationship between Positive and Negative Outcomes and
psychosocial factors (Depression, Social Anxiety, Emotional Social Support Coping, and
Perceived Stress) as mediated by SIU Motivation, and the relationship between SIU
Motivation and personality factors (Neuroticism and Extraversion) as mediated by these
psychosocial factors.
The initial hypothesized model (Figure 3) converged upon a solution and fit
reasonably well to the data: Chi-square = 801. 067 (df=442, p=.000), CFI = .908,
RMSEA = .053 (.047 - .058). Relations between Extraversion and Depression (-.103)
and Extraversion and Perceived Stress (.075) were not significant, contrary to
expectations. Relations between SIU Motives and Perceived Stress (.078) and Emotional
Social Support Coping (.056) were also not significant, contrary to expectations. Finally,
depression was correlated with Perceived Stress, but no other psychosocial factors were
correlated with each other, contrary to expectations. All other paths were in the expected
direction and level of significance. Negative Outcomes and Positive Outcomes were not
correlated (-.112). The correlation between Negative Outcomes and Motives approached
a value of 1 (.97), indicating possible multicollinearity.
The saturated model was assessed to determine potential relations unaccounted
for within the hypothesized model and determine possible reasons for the
multicollinearity which occurred during model building. Paths between social anxiety
Texas Tech University, C. Kyle Schindler, August 2016
34
and motives (.098), and depression and motives (.149) lost significance in the saturated
model, compared to the hypothesized model. Significant paths between social anxiety
and negative outcomes (.201), and depression and negative outcomes (.17) emerged in
the saturated model. Trimming insignificant paths from the saturated model led to a final
model with these two paths retained, with the path between social anxiety and negative
outcomes (.38) and depression and negative outcomes (.30) remaining significant. A
significant path also emerged between positive outcomes and perceived stress (-.29), and
motives and depression (.26). Paths between motives and positive (.87) and negative
outcomes (.81) remained high.
The fluctuation of path loadings when non-significant paths were removed
indicated the presence of multicollinearity. Latent variables involved in the
multicollinearity were sequentially removed from the model, and alternative models were
assessed, to determine the possible source. A Haywood case occurred between motives
and negative outcomes (-1.703) when positive outcomes was removed from the model,
suggesting that these remaining variables were collinear. Possible reasons for
multicollinearity between these variables was investigated further, specifically the
possibility that the SIU Motivation items are redundant with the GPIUS-2 items.
Thematically, some items from the GPIUS-2 measure preference for SIU (e.g. “I prefer
online social interaction over face-to-face interaction”) and perception of social support
online (e.g. “I have used the Internet to make myself feel better when I was down”), in
addition to measuring negative outcomes. These items may overlap with SIU Motivation
items (e.g. “The Internet is to me a substitute for other social contacts”, “The Internet
Texas Tech University, C. Kyle Schindler, August 2016
35
helps me coping with personal problems”), to the extent that endorsement of one item
necessarily leads to similar endorsement of the other item.
The hypothesized model was thus not retained, due to the multicollinearity between these
variables disrupting path loadings and decreasing interpretation of the model. As the
Negative Outcomes factor appeared to have stronger and more theoretically consistent
relations with other factors in the model, and SIU Motivation items had weak or non-
significant relations with psychosocial factors, it appears that Motivation items are
redundant to Outcomes items and should not be retained.
An alternative model was specified (See Figure 4), in which the Motivation
variable was removed from the model and each Outcomes factors was directly predicted
by each of the psychosocial factors. Model fit appeared to remain similar to the original
hypothesized model: Chi-square = 596.203 (df=353, p=.000), CFI = .938, RMSEA =
.048 (.042 - .055). While the Chi-square statistic decreased, it remained significant and
did not appear to approach non-significance. The CFI statistic increased somewhat and
the RMSEA statistic decreased slightly, though both continued to indicate that the model
remained in the “adequate” fit range. Positive Outcomes and Depression had a
significant positive relationship (.23), while Negative Outcomes had a significant positive
relationship with both Depression (.26) and with Social Anxiety (.24). Positive and
Negative Outcomes had a now-significant positive relationship (.54) in the modified
model, suggesting that Motives may have suppressed this relationship through its
redundancy in the original hypothesized model. All other paths remained consistent and
in the same direction as previously hypothesized. As the revised model appears to have
Texas Tech University, C. Kyle Schindler, August 2016
36
similar fit to the hypothesized model, while also being more parsimonious, this model
will be retained.
Discussion
Previous research has determined relations between SIU, including Internet use
outcomes, and variables such as depressive symptoms, social anxiety, neuroticism,
introversion, and stress. This is the first study to date to assess the multivariate relations
between these particular variables. This study is also the first known to evaluate relations
among the positive and negative outcomes of Internet use and various factors which may
increase or decrease motivation to engage in the behavior. The current study sought to
understand these relations better in order to inform research on Internet use as a coping
mechanism, and to provide an initial framework for more consistent future research on
the various motives and outcomes of this behavior.
Participants appeared to engage in SIU to an extent that was similar to that in
other studies. Microblog usage was most frequent, with 28% of participants endorsing
daily use of Pinterest and 52% of participants endorsing daily use of Instagram. Around
9% of participants endorsed daily use of dating websites, including popular dating apps
such as Tinder (6%). Participants also endorsed popular online activities which usually
involve some anonymous interactions, such as 55% endorsing daily Twitter use, 6%
endorsing Reddit, and 6% endorsing engagement in MMORPGs. Many used Facebook
(75%) and Facetime/Skype (30%), suggesting that a good deal of SIU involves
interactions with previously-established friends and family.
As SIU motives were not retained for the final model due to difficulties with
multicollinearity impeding interpretation, the majority of original hypotheses and
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37
questions (H1, H2, H3, and H5) could not be adequately confirmed or disconfirmed for
either model. The research question posed could not be addressed adequately either. The
fourth hypothesis (H4), that psychosocial factors will relate positively to neuroticism and
negatively with extraversion (except coping, which relates positively to both), was mostly
confirmed in the respecified model. All relations occurred in the expected direction,
apart from a positive, non-significant relationship between extraversion and perceived
stress.
Multicollinearity between SIU Motives and Negative Outcomes suggests some
complications which could be addressed in future studies. SIU Motives appears to assess
very general social motivations and beliefs related to using the Internet, likely
contributing to the overlap these items had with Preference items on the GPIUS-2.
Assessment of particular motives may be beneficial, consistent with recent research on
this area (Mazzoni, Baiocco, Cannata & Dimas, 2016), and may prevent
multicollinearity. Combination of SIU Motives with coping items may effectively
address this area, and could include emotional social support motives (e.g., “My online
friends help me feel better when I’m upset”), general social support motives (e.g.,
“People I meet online are generally caring and willing to listen to me”), and distraction
motives (e.g., “It is enjoyable to play games or chat with people online to pass the time”).
SIU Motives may also be more accurately assessed through a variable measuring
actual or perceived face-to-face social support. That is, motivation to engage in SIU
could be thought of as the inverse of perceived or actual face-to-face social support, such
that individuals with very little social support are more motivated to engage in SIU. A
Texas Tech University, C. Kyle Schindler, August 2016
38
variable of this nature would be theoretically consistent with the GPIUS-2 and would
likely not have similar items, leading to less possibility of multicollinearity.
The respecified model appears to fit the data well, especially given its relative
complexity, and has been retained for the purposes of this study. Of particular interest is
a positive relationship between depressive symptoms and positive outcomes. This
finding appears to support the results found in a meta-analysis conducted by Griffiths et
al. (2009) and Campbell et al. (2006), as highly depressed individuals may be more
willing to seek out social support online and report experiencing a particular benefit to
these relationships. However, this may also support perspectives which argue that those
who prefer online social support may experience increased depressed mood from their
relatively greater interactions online. The relationship between social anxiety and
positive outcomes is also noteworthy, given the lack of significance. Contrary to the
findings of this study, previous research has determined that certain elements of online
social interaction are particularly useful for socially anxious individuals, such as the
ability to control self-presentational concerns and timing of conversations (Schouten et
al., 2007; Stritzke, Nguyen, & Durkin, 2004). The non-significant relationship between
emotional social support coping and both outcomes suggests that this form of coping may
not play a large role in participants’ experiences online. This provides some tentative
support for the cognitive-behavioral model put forth by Davis (2001) and Caplan (2002),
which states that use of the Internet for social purposes inherently leads to difficulties
once face-to-face interactions are replaced.
Positive relations between negative outcomes and depressive symptoms (.26) and
social anxiety (.24) are also consistent with the cognitive-behavioral model. Though
Texas Tech University, C. Kyle Schindler, August 2016
39
items assessing SIU motives were no longer in the model, the GPIUS-2’s scale measuring
SIU preferences appears to also measure these motivations to an extent. Thus, the
relationship between these variables appear to provide some tentative support for this
hypothesis.
Perceived stress did not relate to either outcome in the respecified model, though
it related to positive outcomes in the saturated model and has a significant, positive
relationship with depressive symptoms. Due to these relations, depressive symptoms
may account for a significant portion of variance between perceived stress and each
outcome, with social anxiety also accounting for a significant portion of variance in
relation to negative outcomes. The relation between these two variables and each
outcome may warrant further exploration.
Though correlational in nature, the results of the study appear to provide support
for each outcome, suggesting that other, unknown components of SIU might determine
the relative positive or negative impact of this behavior on the individual more
accurately. The cognitive-behavioral model posed by Caplan (2002) and Davis (2001)
appears to have somewhat greater support, suggesting that the possibility of negative
outcomes may be more likely than positive outcomes. However, this may due primarily
to the use of a relatively more established measure. Given some of the limitations of the
current model and study, interpretation of this model is tentative and primarily to indicate
areas of future research and consideration.
Limitations
Although many instruments used in this study have shown strong psychometric
properties (e.g. CES-D, BFI), the use of some instruments without established
Texas Tech University, C. Kyle Schindler, August 2016
40
psychometrics likely reduced interpretability of the model. SIU motives items appeared
to share significant variance with GPIUS-2 items, to the extent that they appeared
multicollinear. This was confirmed through assessment of the face validity of these items
and assessment of the saturated model. Though removal of items which assess motives
allowed for a more parsimonious model without reduced fit, measures such as the
GPIUS-2 are not widely established yet. Other measures, such as the modified SCS-R,
were created specifically for the current study and do not have established psychometric
qualities outside of this study, though internal consistency was adequate (α = .82).
Though these instruments appeared to have acceptable psychometric properties, the
possibility of poor construct validity or decreased power may still hinder interpretation of
these results.
In particular, the modified SCS-R was utilized as a measure of positive outcomes
of social Internet use, as it appeared to approximate the perception of good social support
online. Individuals who feel connected to others online should ideally have experienced
positive interactions beforehand. However, there are alternative ways in which this
construct could be assessed which might relate more closely to positive outcomes,
conceptually. Specifically, the modified SCS-R primarily appears to assess satisfaction
with online relationships. Thus, it may not adequately assess general positive
consequences experienced through these relationships, as a relationship between this
satisfaction and actual benefits experienced is only implicitly assumed. Additionally, this
measure does not differentiate between maladaptive and adaptive SIU, making it difficult
to draw conclusions. Use of a measure which focuses on general outcomes from
behaviors, such as positive affect or well-being scales, should be considered in future
Texas Tech University, C. Kyle Schindler, August 2016
41
studies, e.g. the Positive and Negative Affect Schedule (PANAS). In general, further
development and validation of the measures for positive and negative outcomes, and SIU
motives in particular, will improve on some of the limitations in this area.
A further limitation of this study is a non-diverse sample, which consisted entirely
of college-going students. These students were also largely White (66% of sample) and
were college freshman or sophomores (84% of sample). Thus, the results of this study
may not generalize to other populations or even other groups of college-going students
(e.g. non-traditional students), and should be carefully interpreted.
Finally, interpretation of the results was hindered by the measurement model.
Though the measurement model had high path loadings and adequate enough fit to assess
some of the hypotheses in the current study, the fit was not ideal. In particular, the chi-
square statistic remained significant and the CFI was lower than expected, indicating that
the model is not an exact fit. Replication of the model will be necessary to make more
definite conclusions about the relations among each of the variables.
Further limitations of the current study are related to the use of structural equation
modeling. Although it allows directionality to be specified, SEM is not able to establish
causality among variables. Thus, there remains the possibility that the relations among
these variables have different directionality than hypothesized. The possibility of bi-
directionality or curvilinear relation should also be considered. Additionally, despite the
assessment of a plausible alternative model assessing a different causal pathway, there
remains the possibility that there are several other equivalent and non-equivalent models
not assessed in this study which fit the data equally or better. Thus, the results found
should be interpreted with caution, and the model considered only a plausible explanation
Texas Tech University, C. Kyle Schindler, August 2016
42
(Tomarken & Waller, 2003). In particular, more accurate and interpretable assessment of
the role of increased or decreased motivation to engage in SIU, and subsequent negative
or positive outcomes, would be better determined through a longitudinal SEM approach
such as a panel study.
Finally, a common criticism of SEM analysis is that there is always a possibility
that some variables which are not assessed in this study may still impact the relations
among latent factors in the model. For this study in particular, certain demographic
characteristics, such as gender and cultural factors, and other psychosocial factors, such
as loneliness, have some support in regards to their relationship with SIU motivation as a
coping mechanism. As the model has demonstrated adequate but not perfect fit to the
data, determination of other empirically supported variables and inclusion of these
variables into the model may improve interpretability.
This study was the first to assess the relationship among SIU, positive and
negative outcomes of this behavior, and all empirically associated variables. Though
difficulties with multicollinearity required significant respecification of the model and
limited confirmation or disconfirmation of apriori hypotheses, the model provided some
insight into multivariate relations between these variables that had not been determined
previously in the literature. Additional research will be needed to provide more definite
conclusions regarding the nature of these relations, but this study contributes unique
initial findings which may benefit this research through determination of particular
directions this research might take. Continued research in this area will be necessary, as
communication in all forms is becoming increasingly more reliant on electronic
mediums. Determining the benefits and pitfalls of this communication medium on
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43
interpersonal relationships will be important, especially with populations that could
utilize this technology most (e.g. individuals in rural areas, individuals with medical
concerns that prevent mobility). Though there continues to be a relative lack of clarity
regarding the directional relationship between various mental health outcomes and SIU,
this comorbidity is demonstrably present in this study and the literature reviewed within.
Clinicians should potentially consider the presence of online communication
when assessing interpersonal relationships and social activities, as these behaviors may
serve as an indicator of possible mental health difficulties, particularly social anxiety or
depressive symptoms. It appears that individuals who engage in heavy Internet use,
when there are face-to-face relationships available to them, are potentially at risk for
mental health difficulties, based on some results of this study. Frequency and severity of
use may be beneficial areas to explore further with these clients, particularly in relation to
their satisfaction with face-to-face relationships. In contrast to problematic forms of SIU,
it is possible that individuals who supplement their face-to-face relationships with online
relationships, rather than replacing them, will experience particular benefits that may be
worthwhile. In conclusion, though the potential benefits and consequences of SIU
remain to be seen, this study provides some support for the possibility that both can occur
under particular circumstances and in relation to particular factors. Further determination
of these areas will be beneficial at providing a new potential area of social and emotional
support that individuals around the world can utilize.
Texas Tech University, C. Kyle Schindler, August 2016
44
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Communication, 14(1), 1-+. doi: 10.1111/j.1083-6101.2008.01429.x
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Appendix A
Overview
The current study observes the relationships between SIU motivation, depressive
symptoms, social anxiety, perceived stress, emotional social support seeking,
neuroticism, and introversion. The hypothesized relationship between these variables
will be assessed through the use of structural equation modeling. The two major
perspectives on the benefits of SIU will be examined through the assessment of alternate
models, and the inclusion of negative and positive outcomes of internet use in the model.
The purpose of this review is to define the constructs in this study, and review previous
literature which has elucidated the relationships between these constructs. This review
will provide a brief examination of the increasing sociality of the internet, and define key
terms. The two major perspectives on the potential benefits and consequences of SIU
will be critically examined. Finally, the constructs most frequently associated with SIU
motivation will be reviewed. The significance of this research area, specifically in the
clinical realm, will be highlighted throughout this review.
Definitions
Problematic Internet Use (PIU): Problematic Internet Use is defined by overuse
of the Internet, such that the user experiences negative occupational, interpersonal, and
academic consequences. Early research on this construct did not have unanimously
agreed upon terminology, and many early studies used disparate terms such as Internet
addiction (Young, 1996) and Pathological Internet Use (Davis, 2001) to describe the
same general phenomenon defined above. Other terms which have been used include
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cyberspace addiction, Internet addiction disorder, online addiction, and high Internet
dependency (Byun et al., 2009). These terms will be used in this review as they appear in
the relevant studies, in order to maintain continuity with the literature. Problematic
Internet Use appears to be the most preferred and most frequently used term in the
literature currently (Tokunaga & Rains, 2010), as it does not pathologize the behavior to
an excessive degree (LaRose et al., 2003), and as such, will be used throughout the
remainder of the review to describe this construct.
SIU: SIU is generally defined as use of the Internet to communicate with other
people. This construct encompasses use of Instant Messaging clients, discussion on
public or private Internet forums, social networking sites, online multiplayer games,
microblogs, or any other online activity which includes direct communication with other
persons. These online behaviors may be done to maintain existing relationships or to
initiate novel relationships. Other terms for this construct include computer-mediated
communication (CMC). SIU was chosen for this study due to its greater focus on the
social motivations for this behavior, rather than a focus on the behavior itself.
Sociality of the Internet
Human beings are a highly social species, as they have a natural desire to connect
to others (Baumeister & Leary, 1995) and seek close relationships for comfort and
support (Buhrmester, 1996). It is thus not surprising that the advent and rapid expansion
of the Internet throughout the population in the 1990s coincided with a great deal of
debate among scholars and researchers as to the social implications of this burgeoning
technology (Shotton, 1991; Sproull & Kiesler, 1991; Young, 1998). At the turn of the
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century, over half of all Americans had Internet access (Wellman, Quan-Haase, Witte, &
Hampton, 2001). Although early Internet technology was considerably less sophisticated
than current platforms, early Internet users frequently engaged in SIU; Instant messaging
clients, such as IRC and ICQ, and online communities, such as self-help groups
(Galegher, Sproull, & Kiesler, 1998), were found to be popular. Many early Internet
users reported that communication with others on the Internet could be a viable
alternative to traditional face-to-face communication (McKenna, Green, & Gleason,
2002).
SIU has increased dramatically with the continued advancement of the Internet.
Early research found that up to 14% of U.S. adolescents reported maintaining an online
friendship (Wolak, Mitchell, & Finkelhor, 2003). More recent surveys have found that
65% of adolescents participate in social networking sites, 49% read the blogs of others,
and 68% use Instant Messaging software (Jones & Fox, 2009). One of the largest social
networking sites, Facebook, claimed half of a billion users in 2010 (Facebook Data
Team, 2010), claimed around one billion users total in 2012 (Vance, 2012), and as of
early 2013, claimed 1.3 billion active monthly users
(http://www.statisticbrain.com/facebook-statistics). Many popular online dating sites
claim hundreds of thousands of members (PEW Research Center, 2006). Internet forums
and message boards, where users express themselves and discuss topics and hobbies of
interests, have also increased in membership, with some of the largest message boards
numbering hundreds of thousands of users and billions of posts
(www.thebiggestboards.com). As the Internet has become more social in nature, and
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Internet users have become more social in their online interactions, researchers have
increasingly questioned the potential benefits and consequences of SIU.
Is SIU Helpful?
Young (1996) conducted one of the first empirical studies to assess the possible
consequences of Internet use. Based on reports of Internet overuse and negative
consequences associated with it, Young hypothesized that Internet use could become an
addictive behavior, and sought to determine possible criteria which could define Internet
addiction. Young determined that criteria for pathological gambling most adequately fit
the nature of Internet addiction, and defined it as “an impulse-control disorder that does
not involve an intoxicant”. Young developed a questionnaire which modified items for
pathological gambling into questions about pathological internet use. She gave this
measure to 496 individuals who had answered flyers or advertisements for the study, or
who had typed in “Internet addiction” into search engines, or who were members of
Internet addiction support groups.
Young reported considerable differences between those who were determined to
be Dependent, i.e. those who responded Yes to five or more criteria, and Non-Dependent,
i.e. those who responded Yes to less than five criteria. Young found that a majority of
Dependents (83%) had been on the Internet for less than a year at the time of the study,
and suggested that Internet addiction could happen relatively quickly. Controlling for
occupational uses and other necessary uses of the Internet, Dependents were found to
report using the internet 33.6 hours more, on average, than Non-Dependents. Dependents
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spent the majority of their time engaging in SIU: They used chat rooms significantly
more often than Non-Dependents (35% and 7%, respectively), as well as MUDs (Multi-
User Dungeons), an online multi-user fantasy roleplaying game which was popular
during the early period of the Internet (28% and 5%, respectively).
Dependents reported that they experiencing a wide variety of impairment in their
functioning due to this higher degree of internet use: Academic impairment (40%
indicated Moderate, 58% indicated Severe), Relationship impairment (45% Moderate,
53% Severe), Financial impairment (38% Moderate, 52% Severe), and Occupational
impairment (34% Moderate, 51% Severe) were all reported by a majority of the
Dependents. Young argued that the results of this study provided promising initial
support for the inclusion of Internet addiction as a diagnostic category and a distinct
clinical disorder.
Although Young found significant support for internet use as an addictive
behavior, the results found have several notable flaws. Despite being acknowledged in
the original study, Young’s use of a largely self-selected population is problematic, as
many of these individuals apparently suspected or believed that they were addicted to the
Internet already. Confirmation bias in this sample could have possibly inflated some of
the differences found between Dependents and Non-Dependents. This may have also
inflated the Dependent’s self-reported impairments that they believed were due to their
internet use. Additionally, the relative inexperience with the Internet found in the sample
may have inflated the results, as has been found in one longitudinal study conducted later
(Kraut et al., 2002). Finally, LaRose et al. (2003) have pointed out that Young may have
a potential conflict-of-interest in establishing Internet Addiction as a psychological
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disorder, as she currently maintains a website which purports to offer therapy for Internet
Addiction (www.netaddiction.com).
In an early, seminal study on SIU, Kraut et al. (1998) argued that use of the
Internet to form interpersonal relationships would have a negative impact on Internet
users, as these bonds would be largely superficial in nature. They argued that online
communication lacks the physical closeness necessary for sufficient social interaction, as
the other person typically cannot be seen while interacting. Related to this, emotional
intimacy would also be difficult to achieve, as the expression and perception of emotions
would be difficult through a chat medium. Due to their lack of emotional and physical
closeness, online relationships would not be able to provide a social support buffer
against life stressors, and the individuals who engaged in these relationships over
traditional face-to-face relationships would experience negative consequences during
stressful times as a result. Kraut and colleagues performed one of the few longitudinal
studies to-date to provide support for their hypothesis, and assessed the psychological
well-being of 231 new Internet users over a period of 12-24 months. It was found that,
controlling for initial differences in well-being, engaging in SIU led to greater depression
and feelings of loneliness in new Internet users. Kraut and colleagues coined the term
Internet Paradox to describe this effect, and concluded that the Internet, despite being a
largely social technology, has ironic and counterproductive effects on users’ social
involvement and subsequent psychological well-being.
Consistent with the argument put forth by Kraut et al., Davis (2001) proposed a
cognitive-behavioral model of Pathological Internet Use, or internet use that leads to
negative consequences in user’s careers or academic functioning and negative impacts in
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psychosocial well-being. Davis argued that Pathological Internet Use may take on a
generalized form, i.e. a pathological attitude towards Internet use in general, not a
reliance on any particular activity or website. Davis argued that individuals who use the
Internet broadly and excessively are doing so because of preexisting mental health
problems and social stress, which lead to a preference for SIU as a coping mechanism.
Davis argued that maladaptive cognitions about Internet use (e.g.,“I am worthless offline,
but online I am someone’’), which can often co-occur with disorders such as depression
and social anxiety, cause susceptible individuals to use the Internet excessively and
subsequently experience negative life outcomes.
Caplan (2002) built off of Davis’s theory by creating and operationalizing a
measure for generalized Problematic Internet Use (PIU). Caplan found a seven-factor
model of generalized PIU: 1.) Mood alteration, or use of the Internet to alter negative
mood states, 2.) Social Benefits, or the level of perceived social benefits of Internet use,
3.) Negative Outcomes, 4.) Compulsivity, 5.) Excessive Time, 6.) Withdrawal, and 7.)
Interpersonal Control, or the degree of perception that there is increased social control
when interacting with others on the Internet. Caplan determined that these findings were
consistent with the cognitive-behavioral model developed by Davis (2001), as the factors
found related to problematic cognitions (e.g. Social Benefits), subsequent problematic
behaviors (e.g. Excessive Time), or negative outcomes experienced afterward. In
particular, Caplan found that perceived benefits had the strongest correlation with
measures of psychosocial health, such as depression, loneliness, and self-esteem. Based
on these results, Caplan argued that those who were motivated to use the Internet in a
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social manner were inherently more predisposed to PIU, as the underlying cognitions for
this behavior were maladaptive.
Caplan (2003) built off of Caplan (2002) and found further support for this
contention: Scores on loneliness and depression measures significantly predicted
participant’s preference for SIU, compared to face-to-face interaction. It was also found
that preference for SIU predicted a significant amount of variance in symptoms of PIU,
and preference also mediated the relationship between psychosocial health factors and
negative outcomes of SIU. Caplan (2005) expanded upon these results and found further
support for the idea that a preference for SIU leads to PIU and greater negative outcomes.
Caplan found that a perceived lack of self-presentational skill led to greater PIU,
providing support for the hypothesis that the unique format of the Internet causes some
individuals to prefer it, and also leads to negative outcomes.
In contrast to the previous perspectives, LaRose et al. (2003) expressed
disagreement with Young (1996) and what they viewed as an overly-pathologized
conceptualization of internet use as an addiction, and attempted to expand upon the
underlying behavioral mechanisms of PIU. LaRose and colleagues believed that
deficient self-regulation in particular, defined as the inability to self-monitor an activity
appropriately, was an important factor in the development of PIU. In line with classical
conditioning approaches, they hypothesized that individuals who use the Internet to cope
with depression or loneliness become conditioned to engage in this behavior,
experiencing increasing incentives to behave in this manner. The dysphoric mood states
that lead to internet use also prevent the ability to self-regulate the degree of internet use
that is undergone, thus leading to increasingly deficient self-regulation and PIU. Thus,
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this perspective argues that psychosocial problems hinder behavior, rather than precede
behavior, in the development of PIU. LaRose and colleagues conducted a study on 465
college students, and found that there was a significant positive relationship between
deficient self-regulation and Internet use in general. This relationship held when only
those who met criteria for Internet addiction were included, based on proposed criteria
from Kimberly S. Young and Rogers (1998). Finally, depressive symptoms were found
to significantly relate to deficient self-regulation of internet use. LaRose and colleagues
concluded that deficient self-regulation played a large role in the development of PIU.
Caplan (2010) attempted to update the cognitive-behavioral model presented in
Caplan (2002) by integrating the model with findings in LaRose et al. (2003) that using
the internet to alleviate depressed mood led to deficient self-regulation. Caplan proposed
a model wherein preference for SIU predicts desire to use the internet for mood
regulation, and both of these factors then led to deficient self regulation. This inability to
self-monitor then leads to negative outcomes associated with PIU. Caplan found strong
evidence for the model and argued that this study provided further support for the link
between using the internet for mood regulation and negative outcomes.
Consistent with the results found in Caplan’s studies (Caplan, 2002, 2003, 2005,
2010), several studies have found support for the idea that seeking others out online may
be harmful for mental health. In a review of internet addiction studies, Weinstein and
Lejoyeux (2010) found that excessive use of the internet is often highly comorbid with
mood disorders, anxiety disorders, and ADHD in certain populations. Morahan-Martin
and Schumacher (2003) found that lonely individuals preferred SIU more than their non-
lonely counterparts, tended to make more friends online, reported heightented satisfaction
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with their online friends, used the internet more often for emotional support, and believed
that their Internet use caused disturbances in their functioning. Consistent with these
findings, time spent online has been shown to have a positive association with feelings of
loneliness (Matsuba, 2006; Stepanikova et al., 2010), and has been shown to potentially
increase feelings of loneliness (Kim et al., 2009). Building off of these studies, Yao and
Zhong (2014) performed a longitudinal study in order to assess the possible casual
relationships between Internet Addiction, loneliness, and depressive symptoms in Hong
Kong university students, and found that Internet Addiction did lead to increased feelings
of loneliness. Although this does not support the cognitive-behavioral perspective put
forth by Davis (2001), which argues that loneliness precedes excessive Internet use, it
does provide support for the relationship between excessive Internet use and negative
psychosocial outcomes. Increased time spent online has been found to associate with
poorer coping strategies and interpersonal relationships in general (Milani et al., 2009),
and increased time spent online with social motivations has been shown to relate to
decreased social integration (Weiser, 2001).
Limitations of PIU Perspectives. However, some researchers have questioned
the validity of results found in studies of PIU. Byun et al. (2009) reviewed 39
quantitative studies on Internet Addiction which were published between 1996 to 2006,
in order to provide suggestions for the direction of future research in this area. Byun and
colleagues determined that there were several significant methodological concerns which
impacted the interpretability of results found in these studies. First, it was argued that the
definition for this phenomenon is highly inconsistent between researchers (see Definition
section). In addition to difficulties with consistency in definitions, Byun and colleagues
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noted that the conceptualization of Internet Addiction also tended to change considerably
between researchers. Some Internet Addiction perspectives adopted a framework based
off of gambling addictions (Young, 1998) while other perspectives based their
conceptualization off of substance abuse disorders (Kaltiala-Heino, Lintonen, & Rimpela,
2004). Byun and colleagues also stated that researchers frequently use inconsistent
criteria for measurement of the disorder as well, noting that several popular measures had
been developed which did not assess similar hypothesized antecedents of Internet
Addiction (Morahan-Martin & Schumacher, 2000; Young & Rogers, 1998). Based on
the inconsistent conceptualizations and criteria used in Internet Addiction research, it was
argued that it is meaningless to try to compare results across these studies (Kaltiala-Heino
et al., 2004). Byun and colleagues also criticized Internet Addiction researchers for
frequently using convenience samples of adolescents and college students, which may
inflate reported rates of Internet Addiction in these studies, as well as frequently having
small sample sizes. Finally, Byun and colleagues noted that Internet Addiction
researchers frequently use exploratory, rather than confirmatory, approaches in studies.
Tokunaga and Rains (2010) performed a meta-analysis of 100 studies on PIU
conducted between the years of 2000 and 2009, in order to assess the evidence for and
against the cognitive-behavioral model put forth by Caplan (2002) and Davis (2001), and
the deficit self-regulation model proposed by LaRose et al. (2003). Tokunaga and Rains
assessed the overall relationships between PIU, time spent using the Internet, social
anxiety, loneliness, and depression. They conducted a path analyses and determined that
the deficient self-regulation model (LaRose et al., 2003) fit the data well: Psychosocial
factors (loneliness, depression, and social anxiety) lead to PIU (i.e. deficit self-
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regulation), which then leads to increased time spent online. However, the pathology
model (Caplan, 2002; Davis, 2001), describing a path from psychosocial factors to time
spent online, which then leads to PIU, did not fit well to the data. Tokunaga and Rains
(2010) also described several limitations of research in this area: PIU studies have almost
largely been cross-sectional in nature, are frequently limited in scope, and are only able to
provide a ‘snapshot’ of a potentially cyclical process between psychosocial factors and
internet use behaviors. Thus, there remains the possibility that psychosocial difficulties
and SIU share a more complex relationship than those hypothesized by PIU perspectives.
For example, curvilinear relationships between variables such as the number of friends on
social networks and psychological well-being have been found (LaRose et al., 2014):
Well-being has been shown to increase with number of friends up to a certain point, at
which point well-being begins to drop off.
In contrast to research on PIU, several studies have found that there are possibly
more benefits to SIU than negative consequences. After describing the original Internet
Paradox (Kraut et al., 1998), Kraut et al. (2002) revisited the sample of their previous
study three years later in order to determine the long-term effects of internet use. Kraut
and colleagues found that the effects found in the previous study had largely diminished:
The association between depression and internet use had significantly declined within a
three year period, the association between loneliness and internet use was now non-
significant. In addition, participants in the study reported that there was no longer a
negative impact of internet use on their level of familial communication and the size and
strength of their interpersonal circles. However, increased internet use was associated
with an increase in daily life stress over this three year period. Kraut and colleagues
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performed a second longitudinal study with a different sample to assess the
generalizability of the results found in the first study and in Kraut et al. (1998). The
results of Study 2 closely resembled those of Study 1: Heavier internet users found
significant increases in their social circles and face-to-face communication with friends
and family, their involvement in community activities increased, and most psychological
well-being factors were positive. However, internet use was again associated with
increased daily life stress, as well as less commitment to the local area.
Kraut and colleagues hypothesized that the diminishment of negative outcomes
between Kraut et al. (1998) and Kraut et al. (2002) could have been due to several
possible reasons. One possibility put forth was that the sample in Kraut et al. (1998) was
comprised primarily of new Internet users, and that the novelty of the Internet led to
frequent unrewarding use at first. As participants matured in their Internet use, the
Internet itself matured, more people began using the Internet in general, and avenues for
social interaction became more plentiful as a result, participants may have been able to
focus more often on personally rewarding uses and eschew the unnecessary uses.
SIU has been associated with decreased depression (Morgan & Cotten, 2003;
Shaw & Gant, 2002), decreased loneliness, improved self-esteem and perceived social
support (Shaw & Gant, 2002), and improved connection with family, friends, and
individuals with shared interests (Amichai-Hamburger & Hayat, 2011). In addition,
internet users who engage in SIU report specifically perceiving that this kind of internet
use can be psychologically beneficial to them, although they also report believing that the
Internet can be addictive (Campbell et al., 2006). Facebook use in particular has been
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shown to relate with decreased depression and anxiety scores, as well as an increase in
perceived well-being (Grieve, Indian, Witteveen, Tolan, & Marrington, 2013).
Internet Support Groups
Although research on the benefits and consequences of SIU is conflicted, studies
on the effects of internet support groups (ISGs), or online support groups, show strong
support for the potential benefits of this behavior. ISGs are online message boards,
forums, or instant messaging clients where members may talk to each other and discuss
problems or share information. Use of ISGs is reported to be beneficial for individuals
who are managing a variety of concerns and significant health conditions, including
hysterectomies (Bunde et al., 2006), visual impairment (Smedema & McKenzie, 2010),
cancer (Beaudoin & Tao, 2007; Han et al., 2008; Seckin, 2013; Shim et al., 2011),
suicidal ideation (Gilat & Shahar, 2009), HIV/AIDS (Mo & Coulson, 2010), and
Parkinson’s Disease (Attard & Coulson, 2012).
Barak et al. (2008) reviewed quantitative literature on ISGs and concluded that
these groups provided multifaceted components which increased participants’ self-
empowerment and led to improved well-being. Barak and colleagues noted that
communication on the Internet allowed for decreased inhibition and a subsequently
increased willingness to share information with others, dubbed the disinhibition effect
(Suler, 2004). Consistent with Joinson (2001) and Suler (2004), Barak and colleagues
determined that the Internet provides a sense of anonymity which allows for less feelings
of vulnerability and an increased comfort in sharing with others. Consistent with findings
in Schouten et al. (2007) and Stritzke et al. (2004), it was found that a perception of
invisibility is also beneficial to participants, in that their self-presentational concerns are
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reduced and others’ negative reactions are not visible. Finally, Barak and colleagues
determined that the tendency of delayed communication in online settings (i.e.
communication with several minutes or hours in between) provides a benefit, in that it
allows participants the opportunity to process their thoughts and feelings longer at a
deeper level. Finally, the invisibility and anonymity provided by the Internet allow for a
secondary effect of neutralizing the statuses of participants in the group. Barak and
colleagues argued that the inability to assess factors related to power and status, namely
physical appearance, job status, etc., allowed for a decreased inhibition and a greater
willingness to speak up without fear of authority. Despite the disinhibition effect often
leading to beneficial outcomes that are typically considered crucial in successful support
groups, such as an increased willingness to share deeper emotions and be honest, it was
noted that disinhibition may also lead to negative outcomes, in the form of rude language,
harsh criticism, anger, or hatred (Tanis, 2007).
Barak and colleagues also reviewed studies which showed that the act of
participating in an online support group provided benefits above and beyond those which
led to disinhibition. One primary benefit to online support groups is the catharasis that
can be gained from writing about one’s experiences. In one study reported by Barak et
al. (2008) in this area, Scandinavian participants who told stories about their experiences
with breast cancer experienced an increased sense of self-empowerment and control
(Hoybye, Johansen, & Tjornhoj-Thomsen, 2005). Hoybye and colleagues reported that
the shift from being acted upon (i.e. breast cancer) to being active in the storytelling
process led to this shift. Barak and colleagues also reviewed studies which show that the
benefits derived from face-to-face support groups translated to online support groups as
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well; Online support groups fostered an increased connection to emotions, the ability to
gain information and knowledge about one’s presenting concern, the opportunity to gain
social support and increase social networks, and the ability to reexamine one’s decision-
making.
In addition to reviewing quantitative studies on the benefits of online support
groups, Barak et al. (2008) assessed the qualitative experiences of participants who
participate in these support groups, and found that there were some cons to this
experience which may be maladaptive or harmful. Participants reported that one aspect
of online support groups they considered problematic was the sheer number of groups
available. Choosing a “good” group was often difficult, due to the wide variety of groups
to investigate. Some groups were found to provide incomplete or inaccurate descriptions
in listings, and group size could range from overwhelmingly large to small and inactive.
Information provided by these groups could sometimes be outdated, biased, or outright
wrong, which could be particularly harmful with more serious presenting concerns.
Additionally, new participants in groups oftentimes had to quietly observe the support
group for a period of time to get a sense of the temperament and norms in the group.
However, participants reported several positive aspects of their experiences, as well.
Participants reported that the therapeutic factor of these groups were surprising, and
indicated particular surprise at how strong their emotional reactions were to discussions.
In addition, many participants reported making strong one-on-one relationships with
other group members, with these members often providing support and coaching on how
to share more effectively in the group.
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Consistent with findings in Barak et al. (2008), and in contrast to perspectives on
PIU which argue that seeking out others online can be harmful (Caplan, 2002; Davis,
2001), the social and emotional connections formed with others on these ISGs are
frequently found to be the most important predictor of the psychological benefits derived
from participating (Han et al., 2008; Shim et al., 2011), and users who do not form these
connections appear to have their online experience compromised significantly (Attard &
Coulson, 2012). Consistent with studies reviewed by Barak et al. (2008), preference for
ISGs has also been shown to increase when individuals are dissatisfied with current face-
to-face support groups (Chung, 2013), suggesting that ISG use may be perceived by some
as analogous to traditional support groups.
Consistent with research on ISGs, one study (Leung, 2007) has found strong
support for the contention that specifically using the internet for mood management and
social compensation may be beneficial. Leung assessed 717 children and adolescents for
internet use motives, stressful life events, and perceived social support. Lueng found that
higher levels of stress were related to higher degrees of mood management and social
compensation motives for the internet. Leung also found that higher social support,
whether online or offline, provided a buffer against many stressful live events. Although
cross-sectional in nature, this study, in addition to previously discussed research on ISGs,
suggests that those who might benefit from SIU are those who are highly distressed and
also willing to use the internet for social or emotional support. Several mental health
factors related to distress have been shown to relate to SIU, specifically use of the
internet for social support seeking and emotional coping.
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Who might benefit from support on the Internet?
Studies on Problematic Internet Use are rife with methodological concerns which
jeopardize the validity and generalizability of the results (Byun et al., 2009), and
ultimately hinder the strength behind the argument that use of the Internet for social
reasons is inherently harmful. In addition, the most empirically supported approach in
the realm of PIU argues that excessive Internet use is not addictive but largely a result of
poor self-regulation skills, a behavior which can consciously be reversed by the
individual if desired (LaRose et al., 2003; Tokunaga & Rains, 2010). In contrast to PIU
research, qualitative and quantitative reviews of studies on ISGs (Barak et al., 2008) and
research on childhood and adolescent relationships (Leung, 2007) demonstrate that there
are considerable benefits to engaging in emotional support and social support seeking on
the Internet. However, data in this area is frequently qualitative and cross-sectional in
nature. This body of research strongly suggests that it is not a matter of whether or not
the Internet can be beneficial for support, but for whom and when will it be most
effective.
Depressive Symptoms. Research specifically on depression ISGs has
demonstrated that depressed individuals specifically seek out emotional support when
they are online: A meta-analysis of thirteen studies on depression ISGs, conducted by
Griffiths et al. (2009), determined that the content of the posts for depression ISGs
contained a significantly greater degree of emotionally supportive content, compared to
other kinds of ISGs. Griffiths and colleagues found that many who participated in ISGs
for depression distinctly reported that an attractive feature of the groups was a tendency
to feel emotionally supported and to feel a reduction of their loneliness. Posts on
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depression ISGs also frequently contained content related to social companionship
(Muncer, Burrows, Pleace, Loader & Nettleton, 2000).
Houston et al. (2002) followed 103 users of a depression ISG for one year to
determine the impact of the support group on their interpersonal relationships, well-
being, and their level of depressive symptoms. A large majority of participants had been
diagnosed with a depressive disorder by a health-care professional (N=101) and were
currently in treatment for depression at baseline (N=96). Around one quarter of the
participants reported little to no social support (18%-25%), and perceptions of social
support did not significantly deviate throughout the study. However, participants at the
1-year mark reported receiving an average of 26-50% of their social support through the
Internet. Around one-third of participants (33.8%) reported a decrease in depressive
symptoms, with this decrease happening more often in participants who frequently
participated in the ISG than those who participated less frequently. Additionally, the
majority of participants reported perceiving that these support groups were helpful for
managing their symptoms.
Loneliness. Loneliness has been a widely assessed construct in the motivations
for SIU (Caplan, 2003), as well as the possible negative outcomes of SIU (Kim et al.,
2009). This construct may be assessed often due to the oft held belief in the population
that heavy Internet users are frequently lonely (Campbell et al., 2006). Studies have
found that higher SIU is associated with increased loneliness (Brandtzaeg, 2012; Weiser,
2001), and longitudinal studies have found that loneliness both precedes excessive
internet use (Yao & Zhong, 2014) and is a consequence of it (Kim et al., 2009). Studies
have shown that individuals who report high loneliness and avoidant coping styles will
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not attempt to cope differently when they are online, suggesting that they will not use the
Internet adaptively (Seepersad, 2004). In contrast, some studies have found that lonely
individuals do use the Internet adaptively, and report positive outcomes as a result.
Valkenburg and Peter (2008) surveyed 1,158 Dutch adolescents and found that
participants who reported more loneliness were more likely to engage in identity
experimentation, and led to a greater sense of social competence. Valkenburg and
colleagues argued that the Internet could serve as a platform for lonely individuals to
practice social skills due to the relative anonymity it affords, which is consistent with
previous research on the effects of perceived anonymity in online communication
(Joinson, 2001). Other studies have found that individuals who report more loneliness
actually have more Facebook friends, suggesting that they are trying to compensate for
face-to-face interactions through online interactions (Skues, Williams, & Wise, 2012).
The exact role of loneliness in SIU has not been consistently found. Some
research has suggested that the inconsistency found is possibly due to loneliness being
more appropriate as an indirect predictor, or that other conceptually-similar factors are
possibly better predictors than loneliness. Caplan (2003) found that loneliness and
depression together predicted 19% of the variance in preference for online interaction.
However, loneliness by itself only accounted for 1% of the variance, and was also
mediated by several other factors in predicting negative outcomes of internet use. Caplan
(2007) argued that the inconsistent findings for loneliness as a predictor is due to the
assumption of previous research that all lonely individuals would be drawn to the
Internet. Caplan argued that there is frequently a conflagration that individuals
experiencing situational loneliness (e.g., lack of time for social activities, recent move to
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a new city) will be similar to those with dispositional loneliness, and that many lonely
individuals would not necessarily be drawn to the Internet to interact socially. Caplan
argued that social anxiety would serve as a more conceptually accurate predictor, as
socially anxious individuals would have a specific motivation to take advantage of the
anonymity afforded by the Internet in their social interactions (Joinson, 2001). Caplan
assessed 343 undergraduate students on preference for online social interaction, social
anxiety, loneliness, and negative outcomes of internet use. Caplan found that social
anxiety explained a significant, unique amount of variance in the preference for online
social interaction. Caplan also found that when loneliness was added to the model after
social anxiety was entered, it did not produce a significant increase in variance explained.
Caplan concluded that social anxiety appeared to be a good predictor of preference for
online social interaction, and that the relationship between loneliness and preference for
online social interaction is spurious. Consistent with the arguments made by Caplan
(2007), Lee (2013) acknowledged the inconsistent findings of loneliness and sought to
determine more clearly the relationship between loneliness and negative outcomes. Lee
assessed 265 South Korean students on their loneliness, their degree of self-disclosure on
social networks, their perception of their social support, and their well-being. Lee found
that social support mediated the relationship between self-disclosure and well-being for
lonely individuals. The results of this study suggest that the presence of strong ties online
may have a more direct role in the benefits and consequences of SIU than loneliness
itself.
Social Anxiety. SIU is argued to be an attractive mode of communication to
socially anxious individuals, particularly due to the relative anonymity afforded by the
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Internet, the lack of self-presentational cues, and the ease of controlling the pace and tone
of conversations with others (Caplan, 2007; Joinson, 2001; McKenna & Bargh, 2000).
Research has supported this contention: Studies have shown that individuals with social
anxiety specifically benefit from an increase in interpersonal potential while online (High
& Caplan, 2009), and possibly benefit from an increased inability to react to negative or
inhibitory feedback cues from others (Schouten et al., 2007; Stritzke et al., 2004). It is
argued that socially anxious individuals may additionally benefit from SIU as a low-risk
approach to practicing social interaction skills, in order to improve on subsequent face-to-
face interactions (Campbell et al., 2006). Consistent with this research, increases in
social anxiety were found to be related to increased desire to use the Internet for coping
purposes (Gordon et al., 2007). As social anxiety increases, individuals have been shown
to display greater motivation to make new friends via blogs and disclose more on blogs,
which in turn is associated with higher quality friendships made (Tian, 2013). However,
while socially anxious individuals do report greater comfort and self-disclosure when
online, they may also experience poorer well-being due to the conduciveness of SIU in
avoiding face-to-face interactions (Weidman et al., 2012).
Neuroticism. Neuroticism, a trait defined by emotional instability and negative
affect (McCrae & Costa, 1987), is the personality trait most often related to increased
SIU. Studies have determined that a higher level of neuroticism is related to increased
use of social Internet sites like Facebook (Hughes et al., 2012; Seidman, 2013; Wolfradt
& Doll, 2001) and to higher SIU in general (Kalmus et al., 2011). The specific social and
emotional motivations of neurotic individuals are supported: Highly neurotic individuals
report specific motivations to increase companionship and reduce loneliness through their
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Internet use (Amiel & Sargent, 2004), to use blogs for self-expression (Guadagno et al.,
2008), and to express their “true selves” to others (Amichai-Hamburger et al., 2002;
Tosun & Lajunen, 2010). It has been argued that highly neurotic individuals prefer the
Internet due to the greater control they have over their presentation and their statements
(Butt & Phillips, 2008; Nadkarni & Hofmann, 2012), suggesting that an online format is
specifically attractive to neurotic individuals who wish to form new relationships.
Extraversion. Both extraversion, a trait defined by higher sociability, liveliness,
and assertiveness, and introversion, the inverse trait (McCrae & Costa, 1987), have been
demonstrated to increase motivations for SIU in the literature. Researchers argue that the
significance found for both dimensions of this trait is due to the types of social Internet
activities being assessed: Extroverts prefer to use the Internet to build off of existing
relationships, while introverts prefer to use the Internet to make new friends (Amichai-
Hamburger et al., 2002; Bargh et al., 2002; Orchard & Fullwood, 2010; Tosun &
Lajunen, 2010). For example, extroverted individuals tend to report more participation
on Facebook in general (Jenkins-Guarnieri et al., 2012), and tend to be more involved on
other sites that focus primarily on previously-established friendships (Amichai-
Hamburger et al., 2008). Introverted individuals, on the other hand, tend to value more
anonymous Internet services such as ICQ and Instant Messaging (Amichai-Hamburger et
al., 2008; Amiel & Sargent, 2004), and appear to prefer SIU as a means of expressing
their true selves (Amichai-Hamburger et al., 2002; Zywica & Danowski, 2008),
suggesting that there are more social and emotional motives present for these individuals.
However, some studies have found the opposite pattern: Introverted individuals have
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also been shown to communicate less and self-disclose less, leading to less friendship
formation online (Peter, Valkenburg, & Schouten, 2005).
Perceived Stress. A relationship between the presence of specific stressful life
events and motivation to use the Internet socially has been found (Leung, 2007). One
study to date has assessed the role of general perceived stress specifically on the use of
the Internet to cope emotionally (Deatherage et al., 2014). Deatherage and colleagues
assessed 267 college seniors on their degree of perceived stress, dispositional coping
styles, motivations for internet use, and problematic internet use. Emotion-focused
coping tendencies were found to be strongly positively associated with perceived stress,
and more specifically, coping-related motives to use the Internet (i.e. “to cheer up when I
am in a bad mood”) were also strongly positively associated with perceived stress.
Problematic internet use was not associated with perceived stress. Although this study
was correlational in nature, and thus did not provide support for a directional relationship
between perceived stress and SIU motivation, this study provides some tentative support
for the possibility of this relationship.
Cross-cultural Factors. More recently, research has begun to specifically focus
on the role cultural factors play in the impact of SIU on psychological well-being.
LaRose et al. (2014) assessed undergraduates in three countries (Ireland, Korea, and the
United States) on their levels of deficit self regulation, psychosocial outcomes, and their
perceived connection demands (i.e. feelings of social obligation to interact with friends
on social media),. LaRose and colleagues found that a strong sense of collectivism
appeared to buffer the inverse relationship between connection demands and negative
affect. They hypothesized that highly collectivist participants might see connection
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demands as a natural extension of their face-to-face social activities, lessening possible
negative affective reactions.
Gender. Some research has specifically assessed the impact of gender on SIU
motivations. Kimbrough, Guadagno, Muscanell, and Dill (2013) assessed 381
undergraduate students to determine gender differences in general SIU motivations. It
was found that women were more likely than men to have a preference for SIU, including
the use of text messaging, social networking in general, and video chatting. Women were
found to also video chat for a significantly greater amount of time than men. The authors
conclude that this provides support for the influence of social roles, with women
behaving more communally online and engaging in more SIU. However, this study was
limited in several respects: In terms of its methodology, this study used a 26-item survey
to assess all constructs of interest, and the psychometric properties of these items were
not reported, nor established in the previous literature. Additionally, the majority of the
sample was White (85%), limiting generalizability considerably.
Some studies have specifically assessed gender differences in social network use.
Muscanell and Guadagno (2012) assessed 238 undergraduate students who reported
being a member of one social networking site, and found that gender and personality
differentially impacted preferences for SIU on social networks. Muscanell and Guadagno
found that men were actually more likely to engage in some aspects of SIU: Namely,
men reported greater use of social network sites to find dates, network for careers, and to
make new friends. Women reported posting more public messages on these sites and
sending more private messages to others. Personality also interacted with gender: Men
who were low in agreeableness tended to make more blog posts than men who were high
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in agreeableness, and women who were low in agreeableness tended to Instant Message
others more often than women who were high in agreeableness.
Although gender has been shown to impact motivation for SIU to an extent, the
data is not entirely consistent. Research seems to show that men and women are not
necessarily different in their online sociability, but that they are different in which kinds
of social online activities they prefer (Kimbrough et al., 2013; Muscanell & Guadagno,
2012). Research on gender differences in SIU as a coping mechanism appear to support
this: Online support groups for depression which report gender makeup have shown an
even split between groups which are predominately female and groups which are
predominately male (Griffiths et al., 2009).
Gaps in the Literature
Although there is varying levels of empirical support for each of these factors, no
overarching model has attempted to show the multivariate relationships between all of
these factors in regards to their involvement with healthy, non-excessive degrees of social
and emotional coping on the Internet. Most prominent research on models of Internet use
in general focus primarily on this behavior as inherently aberrant and harmful, especially
when considering the social components (Caplan, 2002, 2010; Davis, 2001; Young,
1998). These models persist, despite a strong, growing body of literature demonstrating
that there are numerous potential benefits to engaging with others online (Kraut et al.,
2002), as well as considerable methodological and conceptual concerns which threaten
the validity of these models and their results (Byun et al., 2009; Tokunaga & Rains,
2010) and possible conflicts of interest for some researchers (LaRose et al., 2003). Thus,
the possible positive impact of SIU is still a nascent research area. The majority of
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research in this area is focused on the role of Internet Support Groups (Barak et al.,
2008), and is often qualitative in nature rather than empirical. For example, a
considerable portion of research on the relationship between depression and SIU is
comprised of qualitative ISG studies (Griffiths et al., 2009). Studies that have
empirically assessed the relationships between factors in a positive manner tend to only
look at two or three factors at a time (Leung, 2007).
This study will fill a hole in the literature by being the first of its kind to create a
more comprehensive, empirical model of the various psychosocial characteristics which
have been shown to predispose individuals to SIU. This study will also explore the
multivariate relationships between these variables, some of which have not been assessed
yet in the literature. Finally, this study will assess SIU as a possible positive coping
mechanism, in contrast to other major models of Internet use which assert that it is
inherently harmful.
Conclusion
The Internet is becoming more social in nature, and individuals are increasingly
starting and maintaining relationships online. This is the first study known to assess the
roles of, and relationships between, multiple empirically-supported factors in SIU as a
positive social and emotional coping mechanism. In determining the relationship
between these factors, this study hopes to further increase understanding of what might
influence the attractiveness of engaging in SIU for support, and who might find it most
appealing. A strong relationship between these factors would demonstrate that
individuals have multifaceted emotional and social motivations for using the Internet to
meet others. Consistent with some therapeutic orientations (Palmer-Olsen et al., 2011;
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Rogers, 1961), researchers have argued that the ability to express one’s “true self”, even
over the Internet, could be helpful for neurotic, introverted, or socially anxious
individuals, as these individuals often have difficulty expressing themselves in face-to-
face communication (Amichai-Hamburger et al., 2002; Bargh et al., 2002; Ebeling-Witte
et al., 2007). Some research has found initial support for this contention, finding that
even the use of blogs for self-expression leads to positive outcomes therapeutically
(Hillan, 2003), possibly due to an increase in perceived social support (Baker & Moore,
2008). Research on Internet Support Groups strongly supports this contention as well,
finding that users receive several psychosocial benefits from participation (Barak et al.,
2008).
Although there is not yet a complete consensus on the actual positive or negative
consequences of SIU (Huang, 2010), a greater understanding of the factors behind these
behaviors may still benefit therapists and other mental health practitioners. By having a
greater understanding of these psychosocial factors as they relate to SIU, therapists may
become better equipped to work with clients who are seeking and maintaining
relationships with others online. This will become increasingly relevant in
psychotherapy, as the population of Internet users is only expected to grow in the future.
Understanding these behaviors as potential coping strategies and methods of self-
expression for those experiencing stress, depression, social difficulties, or emotional
instability may allow for more accurate conceptualizations and interventions. Future
application of SIU as both a social and emotional support mechanism, for certain types of
clients, is also a possibility.
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Appendix B
Table 2: Descriptive Statistics for Full Measures
α Minimum Maximum Mean Std. Deviation Variance Skewness Kurtosis
SDS .73 4 31 16.05 4.86 23.64 .08 .07
CES-D .90 0 48 17.06 10.48 109.74 .61 -.27
LSAS .96 0 110 44.54 25.24 637.12 .23 -.55
BFI (Neuroticism) .73*
8 38 24.00 5.83 34.02 -.23 -.06
BFI (Extroversion) 13 40 26.61 5.74 32.99 .13 -.53
COPE (Emotional Support) .91 4 16 9.99 3.51 12.31 .07 -.90
IMS (Social) .93* 4 35 19.88 5.68 32.26 -.17 -.01
SCS-R .82 27 113 72.56 12.56 157.88 .18 1.25
GPIUS-2 .94 8 111 44.29 21.35 455.99 .44 -.50
PSS-10 .81 1 34 18.23 6.03 36.32 -.12 .17
Note: *Cronbach’s alpha coefficients reported for full measure. SDS: Social Desirability Scale; CES-D: Center for Epidemiological Studies – Depression Scale; LSAS: Liebowitz Social Anxiety Scale; BFI: Big Five Inventory; SCS-R: Social Connectedness Scale – Revised; GPIUS – 2: Generalized Problematic Internet Use Scale – 2; PSS-10: Perceived Stress Scale – 10.
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Table 3: Correlations between measures
SDS CES-D LSAS BFI-N BFI-E COPE IMS SCS-R GPIUS-2 PSS-10
SDS 1 CES-D -.243** 1 LSAS -.258** .263** 1 BFI-N -.428** .456** .334** 1 BFI-E .064 -.245** -.382** -.154** 1 COPE .093 -.076 .022 .121* -.189** 1 IMS -.158** .202** .173** .163** -.049 .034 1
SCS-R -.021 -.058 -.045 -.044 .113 .122* .515** 1 GPIUS-2 -.182** .346** .343** .243** -.144* .043 .608** .272** 1 PSS-10 -.344** .593** .304** .596** -.084 .031 .207** -.05 .308** 1
*Correlation is significant at .05 level (2-tailed) **Correlation is significant at .01 level (2-tailed)
Note: SDS: Social Desirability Scale; CES-D: Center for Epidemiological Studies – Depression Scale; LSAS: Liebowitz Social Anxiety Scale; BFI-N: Big Five Inventory, Neuroticism subscale; BFI-E: Big Five Inventory, Extraversion subscale; SCS-R: Social Connectedness Scale – Revised; GPIUS – 2: Generalized
Problematic Internet Use Scale – 2; PSS-10: Perceived Stress Scale – 10.
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Table 4: Fit of initial measure and final measure with items retained
Fit of Initial Model Fit of Final Model Items Retained
CES-D χ ² = 409.78 (df = 164, p=.000)
RMSEA = .07 (.063 - .08)
CFI = .89
χ ² = 140.95 (df = 77, p=.000)
RMSEA = .053 (.039 - .067)
CFI = .97
1, 2, 5, 6, 7, 9, 10, 11, 13,
14, 15, 17, 19, 20
IMS χ ² = 18.94 (df = 9, p=.026)
RMSEA = .061 (.021 - .100)
CFI = .98
--- all
COPE χ ² = 11.61 (df = 2, p=.003)
RMSEA = .128 (.064 - .204)
CFI = 1.0
--- all
LSAS χ ² = 748.41 (df = 252, p=.000)
RMSEA = .082 (.076 - .089)
CFI = .93
χ ² = 72.93 (df = 27, p=.000)
RMSEA = .077 (.056 - .098)
CFI = .98
5, 7, 10, 11, 15, 18, 19,
23, 24
PSS-10 χ ² = 8.26 (df = 5, p=.142)
RMSEA = .047 (.000 - .102)
CFI = .99
--- all
BFI-N χ ² = 170.58 (df = 20, p=.000)
RMSEA = .160 (.139 - .183)
CFI = .89
χ ² = 23.37 (df = 9, p=.005)
RMSEA = .074 (.038 - .111)
CFI = .98
4, 9, 14, 19, 29, 39
BFI-E χ ² = 293.25 (df = 20, p=.000)
RMSEA = .216 (.194 - .238)
CFI = .88
χ ² = 51.44 (df = 9, p=.000)
RMSEA = .127 (.094 - .162)
CFI = .97
1, 6, 11, 21, 26, 31
SCS-R χ ² = 3017.65 (df = 170, p=.000)
RMSEA = .240 (.232 - .247)
CFI = .41
χ ² = 36.64 (df = 9, p=.000)
RMSEA = .099 (.065 - .135)
CFI = .98
1, 5, 8, 10, 14, 16
GPIUS χ ² = 18.20 (df = 5, p=.003)
RMSEA = .095 (.051 - .144)
CFI = .98
--- all
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Table 5: Path loadings and correlated disturbances for saturated model
Neuroticism Extraversion Depression Social Anxiety ESS Coping Perceived Stress
Motives Positive Outcomes
Negative Outcomes
Neuroticism 1
Extraversion -.22** 1
Depression .49** -.10 1
Social Anxiety .32** -.33** .01 1
ESS Coping .18** .23** -.06 .04 1
Perceived Stress .76** .08 .45** .10 -.12 1
Motives -.02 .01 .15 .10 .05 .14 1
Positive Outcomes -.04 .01 .13 -.09 .07 -.21 .76** 1
Negative Outcomes -.11 -.01 .17* .20** .01 .12 .59** .12 1
Note: *Significant at .01 level (2-tailed), **Significant at .05 level
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Table 6: Participants’ Reported Internet Use
Website or Activity Percentage endorsed use (daily) Range (daily) M (SD)
Facebook (accessing) 75% 0 – 370 min. 33.8 (46.5)
Facebook (posting) 28% 0 – 4 posts .38 (.66)
Texting 99% 0 – 1000 texts 127.7 (165.3)
Facetime/Skype/etc. 30% 0 – 300 min. 15.5 (38.2)
Reddit 6% 0 – 180 min. 3.6 (19.8)
MMORPGs 6% 0 – 400 min. 10.0 (45.0)
Twitter 55% 0 – 250 min. 30.0 (43.6)
Microblogs
Tumblr 14% 0 – 300 min. 58 (57.7)
Pinterest 28% 0 – 1000 min. 49.1 (110.0)
Instagram 52% 0 – 500 min. 62.9 (67.8)
Message Boards/Forums
1 message board/forum 5%
2+ message boards/forums 1%
Dating Websites
Any 9%
Tinder 6%
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Appendix C
Figure 1: Measurement Model. Predictors for the latent variables Depression, Introversion, Neuroticism, Social Anxiety, Perceived Stress, and SIU
Motivations are shown as parcels. Curved lines indicate estimate correlations of latent variables.
ESS Coping Depression Positive Outcomes
Social Anxiety
Internet Motivations
Perceived Stress
Neuroticism Negative Outcomes
Extroversion
CESD 1
CESD 2
CESD 3
BFI 1
BFI 2
BFI 3
BFI 4
BFI 5
BFI 6
COPE 1
COPE 2
COPE 3
COPE 4
LSAS 1
LSAS 2
LSAS 3
LSAS 4
PSS 1
PSS 2
PSS 3
IMS 1
IMS 2
IMS 3
SCS 1
SCS 2
SCS 3
GPIUS 1
GPIUS 2
GPIUS 3
E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15 E16 E17 E18 E19 E20 E21 E22 E23 E24 E25 E26 E27 E28 E29
Depressive Symptoms
ESS Coping
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Figure 2: Hypothesized Structural Model
Note: (+) relation hypothesized to be positive, (-) relation hypothesized to be negative
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Figure 3: Path Loadings for Initial Hypothesized Model
Note: IC = inconsistent with hypotheses; C = consistent with hypotheses
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Figure 4: Path Loadings for Alternative Model
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Figure 5: Path loadings for Saturated Model
Note: Non-significant and non-hypothesized paths are not shown. See Table 5 for full path loadings for saturated model.
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Appendix D
1. What best describes your gender? Please choose one.
Male Female Transgender
2. How old are you? __________
3. What is your race? Please circle one or more as appropriate.
Caucasian/White
African American/Black
Hispanic/Latino
Asian American/Asian/Pacific Islander
Indian/Middle Eastern
Native American
My race is not listed here (please specify): ______________________
4. What is your current class year at Texas Tech? Please circle one.
First year
Second year
Third year
Fourth year
Fifth year
Other (please specify): _______________________
5. Are you currently in a romantic relationship? Y N
If yes, circle the type of relationship which most applies.
Married/Engaged Dating Open Other (please specify): _________
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This survey measures the amount of time per day, on average, that you spend on
various online activities. If you do not use a particular service, please write “n/a” into one of the blanks for that service. If you access more than 3 blogs, forums/message boards, or dating websites on a given day, write in the 3 ones you use most.
*Facebook: _____ minutes per day, ____ # posts per day, _______# messages sent per day
*Reddit: _____ minutes per day, ______# posts per day
*Twitter: _____ minutes per day, ______# of tweets per day
*Email: _____ minutes per day, ____# logins per day, ______# of messages sent per day
*Skype, Facetime, other video chat services: _____ minutes per day
*Text Messages: _____ # of texts sent per day
*Instant Messaging (MSN, AIM, etc.): _____ minutes per day
*MMORPGs (World of Warcraft, League of Legends, etc.) : _____ minutes per day
*Last.fm, Spotify, Soundcloud, other social music sites: _____ minutes per day
*Message boards/forums:
1. Name: ____________________, minutes per day: ______, posts per day: _______
2. Name: ____________________, minutes per day: ______, posts per day: _______
3. Name: ____________________, minutes per day: ______, posts per day: _______
*Dating websites (OkCupid, PlentyofFish, Tindr, Grindr, etc.):
1. Name: ________________________, minutes per day: ________
2. Name: ________________________, minutes per day: ________
3. Name: ________________________, minutes per day: ________
*Blogs (Yours or others, Tumblr, Pinterest, Instagram, etc.):
1. Name: ________________________, minutes per day: ________
2. Name: ________________________, minutes per day: ________
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3. Name: ________________________, minutes per day: ________
Appendix E
Using the scale below, indicate the number which best describes
How often you felt or behaved in this way during the past week:
0 Rarely or none of the time (less than 1 day)
1 Some or a little of the time (1-2 days)
2 Occasionally or a moderate amount of time (3-4 days)
3 Most or all of the time (5-7 days)
1. I was bothered by things that usually don’t bother me. 0 1 2 3
2. I did not feel like eating, my appetite was poor. 0 1 2 3
3. I felt that I could not shake the blues, even with help from
my family and friends. 0 1 2 3
4. I felt that I was just as good as other people. 0 1 2 3
5. I had trouble keeping my mind on what I was doing. 0 1 2 3
6. I felt depressed. 0 1 2 3
7. I felt that everything I did was an effort. 0 1 2 3
8. I felt hopeful about the future. 0 1 2 3
9. I thought my life had been a failure. 0 1 2 3
10. I felt fearful. 0 1 2 3
11. My sleep was restless. 0 1 2 3
12. I was happy. 0 1 2 3
13. I talked less than usual. 0 1 2 3
14. I felt lonely. 0 1 2 3
15. People were unfriendly. 0 1 2 3
16. I enjoyed life. 0 1 2 3
17. I had crying spells. 0 1 2 3
18. I felt sad. 0 1 2 3
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19. I felt that people disliked me. 0 1 2 3
20. I could not get “going.” 0 1 2 3
Appendix F
Here are a number of characteristics that may or may not apply to you. For example, do you agree
that you are someone who likes to spend time with others? Please write a number next to each
statement to indicate the extent to which you agree or disagree with that statement.
1
Disagree
Strongly
2
Disagree
a little
3
Neither agree
nor disagree
4 Agree
a little
5
Agree
strongly
I am someone who… 1. _____ Is talkative
2. _____ Tends to find fault with others
3. _____ Does a thorough job
4. _____ Is depressed, blue
5. _____ Is original, comes up with new ideas
6. _____ Is reserved
7. _____ Is helpful and unselfish with others
8. _____ Can be somewhat careless
9. _____ Is relaxed, handles stress well.
10. _____ Is curious about many different things
11. _____ Is full of energy
12. _____ Starts quarrels with others
13. _____ Is a reliable worker
14. _____ Can be tense
15. _____ Is ingenious, a deep thinker
16. _____ Generates a lot of enthusiasm
17. _____ Has a forgiving nature
18. _____ Tends to be disorganized
19. _____ Worries a lot
20. _____ Has an active imagination
21. _____ Tends to be quiet
22. _____ Is generally trusting
23. _____ Tends to be lazy
24. _____ Is emotionally stable, not easily upset
25. _____ Is inventive
26. _____ Has an assertive personality
27. _____ Can be cold and aloof
28. _____ Perseveres until the task is finished
29. _____ Can be moody
30. _____ Values artistic, aesthetic experiences
31. _____ Is sometimes shy, inhibited
32. _____ Is considerate and kind to almost everyone
33. _____ Does things efficiently
34. _____ Remains calm in tense situations
35. _____ Prefers work that is routine
36. _____ Is outgoing, sociable
37. _____ Is sometimes rude to others
38. _____ Makes plans and follows through with
them
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39. _____ Gets nervous easily
40. _____ Likes to reflect, play with ideas
41. _____ Has few artistic interests
42. _____ Likes to cooperate with others
43. _____ Is easily distracted
44. _____ Is sophisticated in art, music, or literature
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Appendix G
Instructions: The questions in this scale ask you about your feelings and thoughts during the last month. In
each case, please indicate with a check how often you felt or thought a certain way.
1. In the last month, how often have you been upset because of something that happened unexpectedly?
0 = never 1 = almost never 2 = sometimes 3 = fairly often 4 = very often
2. In the last month, how often have you felt that you were unable to control the important things in
your life?
0 = never 1 = almost never 2 = sometimes 3 = fairly often 4 = very often
3. In the last month, how often have you felt nervous and "stressed"?
0 = never 1 = almost never 2 = sometimes 3 = fairly often 4 = very often
4. In the last month, how often have you felt confident about your ability to handle your personal
problems?
0 = never 1 = almost never 2 = sometimes 3 = fairly often 4 = very often
5. In the last month, how often have you felt that things were going your way?
0 = never 1 = almost never 2 = sometimes 3 = fairly often 4 = very often
6. In the last month, how often have you found that you could not cope with all the things that you had
to do?
0 = never 1 = almost never 2 = sometimes 3 = fairly often 4 = very often
7. In the last month, how often have you been able to control irritations in your life?
0 = never 1 = almost never 2 = sometimes 3 = fairly often 4 = very often
8. In the last month, how often have you felt that you were on top of things?
0 = never 1 = almost never 2 = sometimes 3 = fairly often 4 = very often
9. In the last month, how often have you been angered because of things that were outside of your
control?
0 = never 1 = almost never 2 = sometimes 3 = fairly often 4 = very often
10. In the last month, how often have you felt difficulties were piling up so high that you could not
overcome them?
0 = never 1 = almost never 2 = sometimes 3 = fairly often 4 = very often
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Appendix H
This measure assesses the way that social phobia plays a role in your life across a variety
of situations. Read each situation carefully and answer two questions about that
situation. The first question asks how anxious or fearful you feel in the situation. The
second question asks how often you avoid the situation. If you come across a situation
that you ordinarily do not experience, please imagine that you were faced with that
situation, and then rate the degree to which you would fear this hypothetical situation and
how often you would tend to avoid it. Please base your ratings on the way that the
situations have affected you in the last week.
Fear or Anxiety Avoidance
0 = None
1 = Mild
2 = Moderate
3 = Severe
0 = Never (0%)
1 = Occasionally (1-33%)
2 = Often (33- 67%)
3 = Usually (67 – 100%)
1. Telephoning in public
2. Participating in small groups
3. Eating in public places
4. Drinking with others in public places
5. Talking to people in authority
6. Acting, performing, or giving a talk in
front of an audience
7. Going to a party
8. Working while being observed
9. Writing while being observed
10. Calling someone you don’t know very
well
11. Talking with people you don’t know
very well
12. Meeting strangers
13. Urinating in a public bathroom
14. Entering a room when others are
already seated
15. Being the center of attention
16. Speaking up at a meeting
17. Taking a test
18. Expressing a disagreement or
disapproval to people you don’t know very
well
19. Looking at people you don’t know very
well in the eyes
20. Giving a report to a group
21. Trying to pick up someone
22. Returning goods to a store
23. Giving a party
24. Resisting a high pressure salesman
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Appendix I
Then respond to each of the following items by blackening one number on your answer sheet for
each, using the response choices listed just below. Please try to respond to each item separately
in your mind from each other item. Choose your answers thoughtfully, and make your answers
as true FOR YOU as you can. Please answer every item. There are no "right" or "wrong"
answers, so choose the most accurate answer for YOU--not what you think "most people" would
say or do. Indicate what YOU usually do when YOU experience a stressful event.
1 = I usually don't do this at all
2 = I usually do this a little bit
3 = I usually do this a medium amount
4 = I usually do this a lot
------------------------------------------------------------------------
1. I discuss my feelings with someone. 1 2 3 4
2. I try to get emotional support from friends or relatives. 1 2 3 4
3. I get sympathy and understanding from someone. 1 2 3 4
4. I talk to someone about how I feel. 1 2 3 4
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Appendix J
The Internet Motivation Scale The following statements describe some motivations for using the internet. For each statement, please indicate whether you agree with the statement, disagree with the statement, or are neutral about the statement. 1 = Completely Disagree 2 =Disagree 3 =Neutral 4 =Agree 5 =Completely Agree 1. The Internet is to me a substitute for other social contacts.
1 2 3 4 5
2. I receive real news through the Internet.
1 2 3 4 5
3. The Internet helps me in passing my time.
1 2 3 4 5
4. The Internet helps me coping with personal problems.
1 2 3 4 5
5. The Internet has a lot to offer: I can talk with friends and acquaintances.
1 2 3 4 5
6. I use the Internet to express myself.
1 2 3 4 5
7. The Internet offers more variation than other media do.
1 2 3 4 5
8. The Internet provides me with many things of interest that I can’t access anywhere else.
1 2 3 4 5
9. I use the Internet to form my own opinion.
1 2 3 4 5
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10. I distract myself from school stress by using the Internet.
1 2 3 4 5
11. I use the Internet because of its current information.
1 2 3 4 5
12. The Internet promotes my way of life.
1 2 3 4 5
13. The Internet helps me to solve practical problems.
1 2 3 4 5
14. The Internet makes me feel like I am close to others.
1 2 3 4 5
15. I consider the Internet as an additional mass medium.
1 2 3 4 5
16. The Internet stimulates my curiosity.
1 2 3 4 5
17. I have found new friends and acquaintances through the Internet.
1 2 3 4 5
18. The Internet updates me on new trends.
1 2 3 4 5
19. Ever since I went on-line, I make less use of other media.
1 2 3 4 5
20. The Internet forces me to make choices between its many offers.
1 2 3 4 5
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Appendix K
Negative Outcomes:
Below are some statements about Internet use. Please rate each statement based on how strongly you agree or disagree with it.
Definitely Disagree
Strongly Disagree
Somewhat Disagree
Slightly Disagree
Slightly Agree
Somewhat Agree
Strongly Agree
Definitely Agree
1. My internet use has made it difficult for me to manage my life.
1
2
3
4
5
6 7 8
2. I have missed social engagements or activities because of my Internet use.
1
2
3
4
5
6 7 8
3. My Internet use has created problems for me in my life.
1
2
3
4
5
6 7 8
4. I prefer online social interaction over face-to-face interaction.
1
2
3
4
5
6 7 8
5. Online social interaction is more comfortable for me than face-to-face interaction.
1
2
3
4
5
6 7 8
6. I have used the Internet to talk with others when I was feeling isolated.
1
2
3
4
5
6 7 8
7. I have used the Internet to make myself feel better when I was down.
1
2
3
4
5
6 7 8
8. I have used the Internet to make myself feel better when I’ve felt upset.
1
2
3
4
5
6 7 8
9. When I haven’t been online for some time, I become preoccupied with the thought of going online.
1
2
3
4
5
6 7 8
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10. I would feel lost if I was unable to go online.
1
2
3
4
5 6 7 8
11. I think obsessively about going online when I am offline.
1
2
3
4
5
6 7 8
12. I prefer communicating with people online rather than face-to-face.
1
2
3
4
5
6 7 8
13. I have difficulty controlling the amount of time I spend online.
1
2
3
4
5
6 7 8
14. I find it difficult to control my Internet use.
1
2
3
4
5 6 7 8
15. When offline, I have a hard time trying to resist the urge to go online.
1
2
3
4
5
6 7 8
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Appendix L Positive Outcomes:
The following are a number of statements that reflect various ways in which we view ourselves. Rate the degree to which you agree or disagree with each statement using the following scale (1 = Strongly Disagree and 6 = Strongly Agree). There are no right or wrong answers. Do not spend too much time with any one statement and do not leave any unanswered. 1 = Strongly Disagree 2 = Somewhat Disagree 3 =Slightly Disagree 4 =Slightly Agree 5 =Somewhat Agree 6 =Strongly Agree
1. I am comfortable in the presence of strangers when I’m online.
1 2 3 4 5 6
2. I am in tune with the online world.
1 2 3 4 5 6
3. Even among my friends online, there is no sense of brother/sisterhood.
1 2 3 4 5 6
4. Online, I fit in well in new situations.
1 2 3 4 5 6
5. I feel close to people online.
1 2 3 4 5 6
6. Online, I feel disconnected from the world around me.
1 2 3 4 5 6
7. Even on websites involving people I know, I don’t feel that I really belong.
1 2 3 4 5 6
8. I see online friends as friendly and approachable.
1 2 3 4 5 6
9. I feel like an outsider when I’m online.
1 2 3 4 5 6
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10. I feel understood by the people I know when I’m online.
1 2 3 4 5 6
11. Online, I feel distant from people.
1 2 3 4 5 6
12. I am able to relate to my online friends.
1 2 3 4 5 6
13. I have little sense of togetherness with my online friends.
1 2 3 4 5 6
14. I find myself actively involved in online friend’s lives.
1 2 3 4 5 6
15. Online, I catch myself losing a sense of connectedness with society.
1 2 3 4 5 6
16. I am able to connect with other people online.
1 2 3 4 5 6
17. I see myself as a loner when I am online.
1 2 3 4 5 6
18. I don’t feel related to most people online.
1 2 3 4 5 6
19. My online friends feel like family.
1 2 3 4 5 6
20. Online, I don’t feel I participate with anyone or any group.
1 2 3 4 5 6
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Appendix M
Social Desirability
Listed below are a number of statements concerning personal attitudes and traits. Read each item and select “T” if the statement is True for you, or select “F” if the statement is False for you.
True False
1. Before voting I thoroughly investigated the qualifications of all the candidates. T F
2. I never hesitate to go out of my way to help someone in trouble. T F
3. It is sometimes hard for me to go on with my work if I am not encouraged. T F
4. I have never intensely disliked anyone. T F
5. On occasion I have had doubts about my ability to succeed in life. T F
6. I sometimes feel resentful when I don’t get my way. T F
7. I am always careful about my manner of dress. T F
8. My table manners at home are as good as when I eat out in a restaurant. T F
9. If I could get into a movie without paying and be sure I was not seen, I probably
would do it. T F
10. On a few occasions, I have given up doing something because I thought too little
of my ability. T F
11. I like to gossip at times. T F
12. There have been times when I felt like rebelling against people in authority
even though I knew they were right. T F
13. No matter who I’m talk to, I’m always a good listener. T F
14. I can remember “playing sick” to get out of something. T F
15. There have been occasions when I took advantage of someone. T F
16. I’m always willing to admit it when I make a mistake. T F
17. I always try to practice what I preach. T F
18. I don’t find it particularly difficult to get along with loud-mouthed, obnoxious people. T F
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19. I sometimes try to get even rather than forgive and forget. T F
20. When I don’t know something I don’t at all mind admitting it. T F
21. I am always courteous, even to people who are disagreeable. T F
22. At times I have really insisted on having things my own way. T F
23. There have been occasions when I felt like smashing things. T F
24. I would never think of letting someone else be punished for my wrongdoings. T F
25. I never resent being asked to return a favor. T F
26. I have never been irked when people expressed ideas very different from my own. T F
27. I never make a long trip without checking the safety of my car. T F
28. There have been times when I was quite jealous of the good fortunes of others. T F
29. I have almost never felt the urge to tell someone off. T F
30. I am sometimes irritated by people who ask favors of me. T F
31. I have never felt that I was punished without cause. T F
32. I sometimes think when people have a misfortune they only got what they deserved. T F
33. I have never deliberately said something that hurt someone’s feelings. T F