The Relationship Between Social Media Addiction Anxiety, The

THE RELATIONSHIP BETWEEN SOCIAL MEDIA ADDICTION ANXIETY, THE

FEAR OF MISSING OUT, AND INTERPERSONAL PROBLEMS

A Dissertation

Presented to

The Graduate Faculty of The University of Akron

In Partial Fulfillment

of the Requirements for the Degree

Doctor of Philosophy

Marisa Cargill

May, 2019

THE RELATIONSHIP BETWEEN SOCIAL MEDIA ADDICTION, ANXIETY, THE

FEAR OF MISSING OUT, AND INTERPERSONAL PROBLEMS

Marisa Cargill

Dissertation

Approved: Accepted:

______Advisor Interim School Director Dr. Robert C. Schwartz Dr. Varunee Faii Sangganjanavanich

______Committee Member Interim Dean of the College Dr. Rikki Patton Dr. Elizabeth A. Kennedy

______Committee Member Executive Dean of the Graduate School Dr. Varunee Faii Sangganjanavanich Dr. Chand Midha

______Committee Member Date Dr. Delila Owens

______Committee Member Dr. Seungbum Lee

ABSTRACT

The purpose of this study was to examine the relationship between social media addiction and anxiety, the fear of missing out (FOMO), and interpersonal problems among adult social media users. A national sample of 224 adults completed an online survey consisting of five measurements (the Internet Addiction Test modified for social media, the State Trait Anxiety Inventory T scale, the Fear of Missing Out scale, and the

Inventory of Interpersonal Problems) and a demographic questionnaire. A hierarchical multiple regression analysis controlling for age and time spent using social media

(significantly correlated with the measure of social media addiction) revealed a significant positive association with anxiety, FOMO, and interpersonal problems accounting for 26.1% of the variance in social media addition. These results showed that increased use of social media beyond a certain threshold (qualifying for social media addiction) relates to statistically significantly higher trait anxiety, FOMO, and interpersonal problems among adults. Limitations related to the present study are presented, as are implications for counselor education and supervision and for counseling practitioners. Recommendations for future research related to social media addiction, mental health, and the counseling field are provided.

iii

ACKNOWLEDGMENTS

The list of the people who have encouraged me, supported me, and pushed me to reach this goal is quite long. I am very fortunate for the guidance received within the counselor education program in both coursework and the dissertation process. From classmates to faculty, I am extraordinarily grateful for the positivity provided. Words may not fully express the gratitude in my heart for this support. However, I want to ensure these individuals are acknowledged.

First, I would like to share my sincerest gratitude to my advisor and dissertation committee chairperson, Dr. Schwartz. Thank you for helping me become the woman who can take initiative to accomplish goals without needing permission. I am so grateful for the freedom you provided me in this process, while also walking along side of me to ensure my success. Your guidance helped me remain focused and realistic about my goals. Additionally, I would like to thank my dissertation committee members, Drs. Faii,

Owens, Patton, and Lee, for your feedback provided throughout this process to improve my work. The feedback, your kind words, and presence will not be forgotten. I treasure the support and wisdom provided and shared as I progressed through this doctoral program.

Lastly, I would like to thank my loved ones for being my constants throughout this process as well. Doctoral programs are not experienced only by the individual entering them, but all of those in their supporting cast. I am certain I made it this far by the love, understanding, and compassion I received from these individuals and their belief in my abilities, especially when I doubted or questioned myself. There were quite a few

people, especially my mom and my sister, making sure I did not give up, and for that I am eternally thankful. I love you so much. Thank you for it all.

TABLE OF CONTENTS

Page

LIST OF TABLES………………………………………………………………………...x

LIST OF FIGURES……………………………………………………………………....xi

CHAPTER

I. INTRODUCTION ……………………………………………………………………..1

Introduction to Internet Use and Mental Health Issues …………………………..1

Introduction to Literature on Internet Addictions and Mental Health Issues……..3

Summary of the Literature ………………………………………………..4

Statement of the Problem………………………………………………………….5

Technology and Mental Health Issues ……………………………………5

Social Media Use and Anxiety …………………………………………...6

Social Media Use and Social Anxiety ……………………………………8

Social Media Use and FOMO …………………………………………….9

Social Media and Interpersonal Problems ………………………………11

Summary of the Statement of the Problem ……………………………...14

Purpose of the Study …………………………………………………………….15

Research Question …………………………………………………...... 16

Definition of Terms …………………………………………………………...... 16

II. REVIEW OF LITERATURE ………………………………………………………..18

Review of General Literature ……………………………………………………18

Social Media and Mental Health Concerns ……………………………..21

Review of Research Related to Internet Use, General Anxiety, and Related Psychological Constructs………………………………………...... 29

Review of Research Related to Internet or Social Media Use, Social Anxiety, and Related Psychological Constructs …………………………35

Review of Research Related to Internet and Social Media Use and Fear of Missing Out (FoMO) ……………………………………………….44

Review of Research Related to Internet and Social Media Use and Interpersonal Problems ……………………………………………………...49

Critique of Related Research Studies ………………………………………...... 53

Rationale for the Present Study ………………………………………………….61

Summary ………………………………………………………………………...67

III. METHODOLOGY …………………………………………………………………68

Research Question ………………………………………………………...... 69

Null and Directional Hypotheses ………………………………………………..69

Sub-Hypotheses ……………………………………………………...... 69

Research Design …………………………………………………………………71

Participants and Delimitations …………………………………………………..72

Instruments ………………………………………………………………………74

Informed Consent Form …………………………………………………74

Measurement of Demographic Characteristics ………………………….75

The Fear of Missing Out Scale (FoMOS) ……………………………….75

State Trait Anxiety Inventory (STAI) Trait Anxiety (T-Anxiety) Scale ………………………………………………………..79

Inventory of Interpersonal Problems 32 (IIP-32) ………………………..82

Internet Addiction Test (IAT) Modified for Social Media ……………...83

vii

Procedures ……………………………………………………………………….84

Description of Dependent and Independent Variables ………………………….87

Data Analyses …………………………………………………………………...88

Demographic Correlates of Social Media Use ……………………………….…90

Summary ………………………………………………………………………...93

IV. RESULTS ……………………………………………………………………….....94

Pre-Analysis and Data Screening …………………………………………….....94

Testing of Assumptions ………………………………………………………...95

Descriptive Statistics ……………………………………………………………99

Inferential Results ……………………………………………………………..100

Anxiety Regression Results …………………………………………………...102

FOMO Regression Results ……………………………………………………103

Interpersonal Problems Regression Results …………………………………...103

Summary of Results …………………………………………………………...104

V. DISCUSSION ……………………………………………………………………..105

Comparison of Results to Previous Research …………………………………107

Comparison of Results to Related Theory …………………………………….111

Implications of Results ………………………………………….……………..113

Implications for Counselor Education and Training …………………..114

Implications for Counseling Practice ………………………………….117

Implications for Future Research ……………………………………...119

Limitations and Recommendations ……………………………………………120

Summary of Discussion and Implications …………………………………….123

viii

REFERENCES ………………………………………………………………………...125

APPENDICES …………………………………………………………………………134

APPENDIX A. INFORMED CONSENT …………………………………….135

APPENDIX B. DEMOGRAPHIC QUESTIONNAIRE ……………………...137

APPENDIX C. THE FEAR OF MISSING OUT SCALE (FoMOS) …………139

APPENDIX D. STATE TRAIT ANXIETY INVENTORY – T SCALE ...... 141

APPENDIX E. INVENTORY OF INTERPERSONAL PROBLEMS (IIP-32) ...... 141

APPENDIX F. INTERNET ADDICTION TEST (MODIFIED FOR SOCIAL MEDIA) ...………………………...……………………………145

LIST OF TABLES

Table Page

1. Frequency Distributions for Demographic Variables ……………………...... 73

2. Descriptive Statistics for Continuous Demographic Variables ………………...74

3. Bivariate Correlations of Demographic Factors with Dependent Variable ………………………………………………….……...... 96

4. Descriptive Statistics of Study Variables ……………………………………...100

5. Regression Coefficients for All Variables Associated with Social Media Addiction ……….……………………………………………………………...102

6. Regression Coefficients for Anxiety Model – Associations with Social Media Addiction ………………………………………………………………………102

7. Regression Coefficients for FOMO Model – Associations with Social Media Addiction ……………………………………………………………………....103

8. Regression Coefficients for Interpersonal Problems Model – Associations with Social Media Addiction ……………………………………………...... 104

LIST OF FIGURES

Figure Page

1. Normality plot for Social Media Addiction………….……………………...... 97

2. Residuals plot for multivariate normality and homoscedasticity assumption of errors.…...... 97

3 Scatterplot for anxiety and social media addiction ……………………………..98

4 Scatterplot for FOMO and social media addiction …………………………...... 98

5 Scatterplot for interpersonal problems and social media addiction …………...... 98

xi CHAPTER I

INTRODUCTION

Introduction to Internet Use and Mental Health Issues

Weiss and Schneider (2014) defined and discussed the phenomenon we know as social media as a complex social system created by technology used to influence interpersonal interactions.

Social media blends technology and social interaction, thereby producing interactive online content that is both generated and consumed by the same population. We create it, we share it, and we read and/or look at it. The professional/industrial media sometimes refers to social media as CGM, or consumer-generated media. Social media allows us to meet, interact, and develop intimate personal and sometimes even sexual relationships with virtually anyone else, anywhere, anytime. The most prominent social media sites in the United States are Facebook and Twitter (Weiss & Schneider, 2014, p. 188).

According to the Perrin (2015), social media use has climbed significantly since

2005 when they first began recording this information. Of all adult internet users, only ten percent used social networking sites in 2005. By 2015, that number had grown to

76% (Perrin). Greenwood, Perrin, and Duggan (2016) found that 79% of online adults now use Facebook, which is roughly 68% of all Americans. More than half of online adults used more than one social media platform including Facebook, Twitter, Instagram,

Pinterest, and LinkedIn. Of those that used other social media platforms, many also still used Facebook. They found that 93% of Twitter users, 95% of Instagram users, and 92% of Pinterest users use Facebook as well. In addition to these platforms, other messaging platforms that are social in nature have become popular, especially with younger adults.

1 It was estimated that 56% of smartphone users ages 18 to 29 use what are known as auto- delete apps, such as Snapchat (Greenwood et al.). By these statistics, it is evident that social media platforms have become more prevalent and popular in American society.

With the majority of people using both social media and the Internet, it has become imperative that counselors understand how it affects those in our society, specifically in regard to mental health. Recent research indicated that about 46% of

American adults will suffer from a mental disorder in their lifetime (National Institute of

Mental Health, 2012). In 2016, it was estimated that over 44 million adults aged 18 or older were living with a mental illness in the past year, representing over 18% of all U.S. adults (Substance Abuse and Mental Health Services Administration, 2017). Some mental disorders, for example Impulse Control Disorder, can be directly linked to social media use and others such as depressive and anxiety disorders can evolve secondarily from these online activities. The ACA Code of Ethics (2014) requires that counselors are familiar with trends in society and culture that may contribute to clients’ presenting concerns in order to best work with their clients in promoting growth and empowerment.

Although it has become easy to see the prevalence of social media in the lives of

Americans, it has become more difficult to determine the psychological and social effects on those who now use social networking sites (SNS). By connecting the prevalence and popularity of social media sites and our knowledge of mental illness among American adults, a logical next step was to better understand how the two may be correlated. Like any mental health issue, we are unable to accurately diagnose or identify a problem at first glance. Instead, we must examine and assess how a behavior, like engaging in social media, has ties to other issues in an individual’s life.

2 Introduction to Literature on Internet Addictions and Mental Health Issues

Conceptualizing mental health issues and addictions is one of the prime roles of a counselor meeting with a client for the first time. The rationale for this is because it can then assist in the entire treatment process starting at the intake process and moving through treatment and then the assessment of treatment outcomes (Meyer & Melchert,

2011). As such, this approach has been considered in better understanding the research related to the behavioral addictions, like with the internet and/or social media, and their ties to other mental health concerns. Frances (2014) argued that the biopsychosocial approach in counseling keeps the focus on the client or patient and the only way to form a rounded and comprehensive view of the mental disorder as well as the person struggling with it. The conceptualization of these problems associated with social media is best viewed by looking at each problem and the biological, psychological, and sociocultural factors that may have contributed to the problem. Each problem may have many factors contributing and may also be largely attributed to only a specific factor alone. This study examined previous literature to assist in a better integration and understanding of the phenomenon of social media use and addiction. In order to do this, the review of literature begins broadly and continued to narrow down to specific issues related to social media addiction.

Ko, Yen, Yen, Chen, and Chen (2012) conducted a literature review to find whether or not internet addiction has relationships with many mental health issues. The disorders related to internet addictions included substance use disorder, attention deficit

3 hyperactivity disorder, depressive disorder, social phobia, and hostility. Ko et el. argued for the need for interventions to address and assist in prevention of internet addictions in addition to the argument that mental health issues should be treated concurrently with the internet addiction.

Alavi et al. (2012) explored the influence of internet addiction on psychiatric symptoms with a university student population. They discovered that students who met criteria for an internet addiction had higher scores related to all psychiatric symptom measures including somatization, Obsessive Compulsive Disorder, interpersonal sensitivity, depression, anxiety, aggression, phobia, paranoia, psychosis compared with those students who were not categorized as addicted. Upon reviewing these strong relationships, Alavi et al. argued that their results can contribute to the prevention, diagnosis, and treatment of internet addictions among students.

Summary of the Literature

The pitfalls of internet use turning into an internet addiction are vast. The associations with a multitude of mental health concerns promote the need for learning more about how to prevent, diagnose, and treat these problems. It is also helpful to understand what specific aspect of the internet has become addictive for the user. For this study, a social media addiction was the focus. To operationalize the concept in this study, social media addiction, referred to the score on the modified Internet Addiction

Test (IAT; Young, 1998). The measure used in this study put “social media” in place of

“internet” to focus only on addictive behaviors with social media, rather than the internet as a whole. Scores on the measure produced results in the normal, mild, moderate, and severe categories. The items from the IAT addressed the salience of the problem,

4 excessive use, neglecting work, anticipation of use, lack of control, and the neglect of social life.

Tao et al. (2010) reported results from a major survey that was conducted in order to determine diagnostic criteria for internet addiction disorder (IAD). The criteria included clinical symptoms, functional and psychosocial impairments, duration/time spent daily, and exclusion criterion. Specifically, the criteria suggested that the duration of the addiction lasted at least 3 months with at least 6 hours of nonessential internet use per day. Although IAD was not added to the DSM-5, these specific time period of symptomatology assisted in operationalizing IAD (Rosenberg & Feder, 2014).

Statement of the Problem

Technology and Mental Health Issues

Rosen (2012) explored how technology is associated with mental health issues, ranging from personality disorders to depression to anxiety. When looking at these issues, Rosen connected the symptoms and how technology has either increased symptoms or revealed them in new ways. The author suggested that our attachments to technological devices provided evidence to support the experience of anxiety-related problems. Rosen also explained that technology use can reveal addictive behaviors in individuals like overuse, withdrawal, tolerance, interpersonal problems, health problems, and time management concerns. Important to this topic, Rosen also discussed the importance of managing one’s technology use to support positive mental health outcomes.

Jantz and McMurray (2012) reviewed the dangers of media, technology, and social networking. Their review discussed how the constant use of the three leads to

5 individuals feeling more tired, more stressed, with more time and energy to complete tasks as a result of constant engagement in using media, technology, and social networking. The constant engagement was also cautioned as it posed a threat for addiction, as users may engage in order to escape real life, disconnect from others as a result of use, experience withdrawal, loss of identity, and boredom when they were unable to use. Like others, Jantz and McMurray also supported the idea of finding ways to incorporate media, technology, and social networking in healthy ways for users, so that well-being and control over the behavior is maintained.

Social Media Use and Anxiety

Current literature examining the relationships between social media and mental health concerns suggested that fear and/or anxiety have been revealed in new ways through online platforms. In fact, a new construct has been developed out of fears directly tied to social media and its use. Przybylski, Murayama, DeHaan, and Gladwell

(2013) stated the fear of missing out, often called FOMO, could be defined as the

“pervasive apprehension that others might be having rewarding experiences from which one is absent” (p. 1841).

The acronym FOMO includes the word fear. When referencing fear clinically, anxiety may come to mind. If FOMO can be associated with the use of social media, there is a possibility that anxiety, in general, is associated with social media use as well.

The Substance Abuse and Mental Health Services Administration (SAMHSA, 2014) identifies anxiety disorders as the most common type of mental disorders and indicates that they can last a lifetime. Linking anxiety to a lifetime of thoughts, feelings, and/or actions associated with fear lends itself to the concept of trait anxiety. According to the

6 State of New South Wales’ Department of Education and Communities and Charles Sturt

University (2015):

Trait anxiety refers to a general level of stress that is characteristic of an individual, that is, a trait related to personality. Trait anxiety varies according to how individuals have conditioned themselves to respond to and manage the stress. What may cause anxiety and stress in one person may not generate any emotion in another. People with high levels of trait anxiety are often quite easily stressed and anxious. (Trait and State Anxiety, para. 2).

Given the literature discussed above, linking social media use to individuals who experience this type of anxiety would be useful. Spielberger (2005) referred to trait anxiety as a more long-standing quality of anxiety as opposed to a temporary condition or state. Some of these qualities related to trait anxiety include feelings of apprehension, tension, nervousness, and worry (Spielberger). Clayton, Osborne, Miller, and Oberle

(2013) found that anxiousness was a significant predictor of emotional connectedness to

Facebook and that anxiousness also significantly predicted strategies for connection with others on Facebook further providing evidence linking social media sites to anxiousness or anxiety.

Ho et al. (2014) conducted a meta-analysis in efforts to understand the relationship between internet addiction and psychiatric comorbidities. The results determined that anxiety was proportionally higher for those individuals with an internet addiction. Prevalence of anxiety was highest among adults from ages 19-39. Relatedly,

Kaess et al. (2014) examined the relationship between pathological internet use, psychopathology, and self-destructive behavior in European adolescents. Their findings showed that suicidal behaviors, depression, and anxiety were the strongest predictors of pathological internet use. Anxiety and depression were also reported as proportionally higher in those considered to fall into maladaptive internet use and pathological internet

use (PIU). Kaess et al. argued that recognizing the symptoms of PIU while it may be considered in a maladaptive stage could lead to better psychological well-being and a decrease in suicidal behaviors.

Jenaro, Flores, Gómez-Vela, González-Gil, and Caballo (2007) measured pathological internet use and then examined relationships between the use with psychological health and behavioral measurements. Not only did results show significant relationships between heavy internet use and insomnia, social dysfunction, and depression; they also indicated a significant relationship between heavy internet use and clinical scores on the Beck Anxiety Inventory (BAI). Jenaro et al. also found that the scores on the BAI were the only significant predictor of the likelihood of being a light or heavy internet user.

Social Media Use and Social Anxiety

Because social media and social networking sites are created for the purpose of socializing, reviewing their use in relation to social anxiety has become worthy of attention. Caplan (2007) explored the associations between loneliness, social anxiety, and problematic internet use. Findings showed positive correlations between social anxiety and loneliness. Both social anxiety and loneliness were reported to have positive relationships with a preference for online social interactions (POSI), and also were significant predictors of POSI. Caplan also reported that POSI was the strongest direct predictor of negative outcomes of internet use for participants. Results supported the hypothesis that proposed an indirect effect of social anxiety on negative outcomes of internet use, being mediated by POSI.

Becker, Alzahabi, and Hopwood (2013) focused on multitasking with media with the intent of determining if was a unique predictor of symptoms of depression and social anxiety. Results revealed significant positive correlations between media multitasking and overall media use, depression, and social phobia. Additionally, media multitasking was determined to be a predictor of self-reported symptoms of depression and anxiety, when controlling for neuroticism, extraversion, and overall media use (Becker et al.).

Lee and Stapinksi (2012) also explored the relationship between social anxiety and problematic internet use. Results of their study indicated a significant relationship between social anxiety and problematic internet use. Social anxiety was found as a significant predictor of problematic internet use, when controlling for anxiety, stress, and depression. Participants’ preference for online social interactions was significantly related to social avoidance and the fear of negative evaluation.

Social Media Use and FOMO

When considering all aspects of fear, it is important to better understand the fear reported to stem from social media – fear of missing out or FOMO (Przybylski et al.,

2013). FOMO is defined as the “pervasive apprehension that others might be having rewarding experiences from which one is absent” (p. 1841). Although FOMO is a newer construct, considerable attention has been focused on better understanding the construct and its impact on those coping with the said fear.

Singleton, Abeles, and Smith (2016) explored this relationship and found that their adolescent participants frequently checked their newsfeed to make sure that peers were not talking about them on social media despite that this could negatively impact their mood. Many of those participants reported that they tried to delete or leave SNS

completely, but often returned to satisfy their curiosity, indicating how indispensable

SNS have become for young people. Additionally, they refrained from posting or deleted posts to avoid judgment from their peers as well. It is important to note that participants also spoke of the benefits of being able to use SNS to connect with others, which was a driving force in the continued use of the sites, even while knowing there were negatives to logging in. Their use was considered a gamble of balancing the pros and cons of their experiences on the SNS, which ultimately felt unavoidable. Singleton et al. stated that these findings indicated the significance of routinely gathering information about a client’s social networking use in better understanding their psychological distress.

As new media like Facebook and other SNS continue to be introduced and updated, individuals will continue to have a constant stream of information to access about their social contacts. Individual users also have the capability of sharing their own information around the clock from any given location. Turkle (2011) noted the commonality of being in a coffee shop where almost everyone is on a computer or smartphone, viewing parents check their phones as they push strollers, and witnessing children and their parents text during family meals. The author shared that a colleague who directed a study abroad program with American students traveling to Spain shared that students were no longer “experiencing Spain” due to using their free time on social media and checking in with what was going on at home. Although this may be a common occurrence, the constant connection brings forth a sense of panic -- new anxieties of disconnection (Turkle). We are tuned in because we fear the consequences of tuning out. Arguably, it is possible that FOMO has always existed, but with the increased awareness and exposure of what everyone else is up to at any given moment or

stage of life through social media, it has become a lot easier to start making those comparisons and fear that we might be missing out on something (J. Walter Thompson

Intelligence, 2012).

Alt (2015) examined undergraduate students in Northern Galilee to better understand the associations between social media engagement, FOMO, and academic motivation. FOMO was reported to have likely mediated extrinsic motivation and amotivation for learning. Both extrinsic motivation and amotivation for learning were positively linked to social media engagement. Possible explanations suggested that students used social media for non-academic purposes while in the classroom rather than to engage in or promote their own learning processes.

Although many studies have not navigated into specific platforms, some literature has focused on only specific SNS. Fox and Moreland (2015) reviewed relational and psychological stressors and affordances associated with Facebook use. It was found that the participants felt pressure as a result of being tethered to Facebook. While many participants reported negative feelings toward Facebook, they also expressed the felt that it was necessary to have and feared the consequences of not doing so. Simply put, there was a FOMO on what the site may offer. Social comparison was also reported to be taking place in many ways through the site, which was reported to often result in feelings of jealousy and dissatisfaction. Another theme revealed through this examination was conflict with others; in relationships, with family members, and even publicly on the site

(Fox & Moreland).

Social Media and Interpersonal Problems

This conflict discussed by Fox and Moreland (2015) is tied to social relationships.

Social media has its own meaning, but one of the root words “social” gives an idea of the purpose or opportunity this type of media presents to individuals. It provides those participating the chance to connect with others. Given this information, it is important to understand the impact engaging in social media use may have on interpersonal relationships. Like most situations, there can be benefits and costs for making the choice to participate. Simmons (2014) presented some of these pros and cons. First, more opportunities to connect equates to more opportunities to be excluded. Next, the choice to include or exclude is now more passive. Lastly, if one is excluded, it paradoxically, requires more action to call someone out on the exclusion (Simmons).

Research conducted by Tokunaga (2011) addressed interpersonal conflict in a study that described three factors of SNS that encourage negative events. First, social norms on SNS may differ from offline social norms. Should a breach of the SNS social norms occur, it can be interpreted negatively as carelessness or rudeness, hence affecting the relationship of the individual breaching the norm and the individual interpreting said breach. The next factor is that the notion of ‘friends’ on SNS are not always aligned between users. One person may not accept a friend request from another user if they are not, in fact, friends offline. Another may accept all requests unconditionally regardless of how close they feel to the user requesting friendship through a SNS. Lastly, the lack of or reduced role of social presence is listed as a factor fostering negative events. When negativity is unintentional, SNS make it difficult to receive the nonverbal feedback or cues one would receive in a face-to-face interaction. When negativity is intentional, SNS may make it easier because of the psychological distance (Tokunaga).

When considering the positive and negative effects of excessive internet use on undergraduate students, Suhail and Bargees (2006) created their own instrument, the

Internet Effect Scale (IES). The IES utilized seven subscales, six of which attempt to understand negative effects of internet use, and one to focus on the positive effects.

Behavioral, interpersonal, physical, psychological, and educational problems were found to have positive correlations with the negative effects of internet use. While findings suggested that participants were satisfied with their online friends and received pleasure and satisfaction, it was also found that many used the internet to avoid real life and stress and that family members have complained about less quality time as a result of internet use (Suhail & Bargees).

Nitzburg and Farber (2013) explored how emerging adults’ attachment styles related to their experiences on SNS. Findings revealed that age, specifically lower age, was a significant predictor of the amount of time spent on SNS. Higher age was a significant predictor of feeling more sincere when using the SNS. Disorganized and anxious attachment styles revealed that these individuals were more inclined to use SNS to avoid face-to-face interactions with other people. Avoidant attachment styles resulted in participants avoiding SNS entirely. Those were found to be more likely to feel more intimacy on SNS identified with the anxious attachment style (Nitzburg & Farber).

Tonioni et al. (2012) examined the psychological symptoms, behaviors, and amount of time online spent by persons who may have shown symptoms of addiction.

Results indicated positive relationships between the amount of time spent online weekly and symptoms of anxiety and depression. In addition to that, another item on their survey suggested that participants used the internet as a means to avoid interpersonal

relationships. Tonioni et al. noted that this behavior should be a vital part of the clinical interview for IAD.

Social media can potentially promote the capability of making connections.

However, it can be argued that social media is simultaneously promoting a disconnection between people. Andreassen and Pallesen (2014) discussed how those who are addicted to SNS face the potential for family or interpersonal conflicts, stating that key elements of an addiction include ‘down-prioritizing’ family or friends, meaning users valued their time spent engaging in activities on SNS more than interacting face to face with people in their life. Additionally, the authors reported that the excessive use of SNS may affect romantic relationships revealing an association with jealousy, relationship dissatisfaction, cyber-stalking, and surveillance of the other partner. Lastly, it was suggested that those who excessively use SNS may be replacing real relationships with online relationships that may cause offline communication skills to suffer.

Aside from consequences related to personal communication and disconnection,

Andreassen and Pallesen found other negative consequences of addictions to SNS, including decreased productivity at work, economic or job loss, sleep impairment. While positive effects like stress relief and inspiration were reported, Andreassen and Pallesen still pushed for further research in this field with specific attention to treatment, instruments, and longitudinal designs for those coping with SNS addictions.

Summary of the Statement of the Problem

Social media may not be the sole genesis of FOMO, anxiety, or interpersonal problems. However, as many of the previously mentioned articles suggested, it may have revealed these issues in new ways if not exacerbated symptoms and related psychosocial

impairments. As the literature frequently reported, in order to further understand and explore these relationships, continued research is necessary. It is important that empirical research focus more narrowly on social media-related psychosocial consequences in order to better understand how professional counselors can promote positive mental health outcomes. It is also vital that an integrative approach to understanding and conceptualizing clients is taken when this research is conducted. A biopsychosocial approach is necessary to have a comprehensive and systemic view on human development and functioning of our clients. This approach allows counselors and researchers alike to integrate the biological, psychological, and sociocultural factors that will assist in understanding the problems associated with social media use.

Purpose of the Study

The purpose of this study was to examine the relationships between trait anxiety,

FOMO, interpersonal problems, and social media addiction. By understanding these associations, counselors can better understand their clients and how social media, especially any level of addiction to it, may be associated with the problems or challenges their clients are facing. One anticipated outcome of this study, through descriptive statistics, was to help to explain the constructs of social media addiction, trait anxiety,

FOMO, and interpersonal problems through a participant demographic survey. A second outcome is that this study contributed to research focused on the effect of social media use on consequent anxiety, FOMO, and interpersonal problems so that both clinicians and

users have further evidence to support the impact of social media use on psychosocial well-being.

The intention in reviewing these relationships together was to use the biopsychosocial approach for a better understanding of social media addiction. Using this approach is supported in the counseling field because it aids in supporting the understanding multiple dimensions of a client in regard to their biological, psychological, and social functioning. CACREP (2016) promotes this approach with the hope that clinicians are better able to conceptualize and assess their clients as well as their clients’ concerns. Trait anxiety was the type of anxiety measured in this study. Trait anxiety is commonly defined as a longstanding and stable form of anxiety over time and across many situations (Spielberger, 2015). Because this type of anxiety is tied to its length and stability, it could explain or be considered a biological tie to social media addiction. In further conceptualizing the ties to the independent variables in this study, FOMO lends itself to a psychological association to this pervasive fear that one may be absent from others’ rewarding experiences (Pryzbylski et al., 2013). FOMO then assists in linking social media addiction to the social component of the biopsychosocial approach as this fear may lead to problems with those whom have shared their rewarding experiences on social media. As such, it was fitting to measure interpersonal problems to address this social component of the biopsychosocial approach.

Implications of this study addressed the need for the development of counseling interventions related to social media use and alleviating psychosocial symptoms. This study also highlighted the necessity of counselors in assessing how social media may be impacting their clients. Finally, this study made suggestions related to what points may

be helpful and necessary in developing interventions to address problematic social media use and ways to assess clients’ social media behaviors.

Research Question

Research Question 1: Is the degree of trait anxiety, fear of missing out (FOMO), or interpersonal problems significantly associated with the degree of social media addiction among adult social media users?

Definition of Terms

FOMO or the fear of missing out is described as ““pervasive apprehension that others might be having rewarding experiences from which one is absent,” (Przybylski,

Murayama, DeHaan, and Gladwell, 2013, p. 1841).

Interpersonal problems refer to interpersonal difficulties as defined by the

Inventory of Interpersonal Problems (IIP-32) which identifies areas that may difficult for a person as well as areas where a person may do too much. The IIP-SC has shown to be useful in finding clinically relevant aspects of an individual’s interpersonal functioning

(Soldz, Budman, Demby, & Merry, 1995).

Social media addiction refers to the score on the modified Internet Addiction Test

(IAT; Young, 1998). The measure will put “social media” in place of “internet” to focus only on addictive behaviors with social media, rather than the internet as a whole. Scores on the measure will produce results in the normal, mild, moderate, and severe categories.

The items from the IAT address the salience of the problem, excessive use, neglecting work, anticipation of use, lack of control, and the neglect of social life.

Trait anxiety refers to a more long-standing quality of anxiety as opposed to a temporary condition or state. Some of these qualities related to trait anxiety include feelings of apprehension, tension, nervousness, and worry (Spielberger, 2005).

CHAPTER II

LITERATURE REVIEW

Review of General Literature

Current expert predictions revealed that 80% of the world’s population was expected to own a smartphone, tablet, or laptop in 2015 (Fuchs, 2014). Therefore, people around the world have become hyper-connected and rarely ever disconnected from technology and the internet. However, not only are there a variety of forms of technology, there are a variety of ways people connect through technology interpersonally. Having a social connection with others through specific technologies has been termed social media. It is important to note that social media is more than just a form of internet use. Fuchs stated that social media allows people to connect, network, communicate, collaborate, create content, play games, and share information all of which are various forms of sociality. Weiss and Schneider (2014) defined social media as a complex social system created by technology used to influence interpersonal interactions.

Simply put, social media sites or social networking sites provide the opportunity for individuals to be social with others virtually. While many social media sites have similar features, there are several different sites that aim to have unique purposes that set each one apart from other sites and often target different types of users.

For example, Facebook has become one of the largest and most well recognized social media sites. The platform was launched in February 2004 and now has over one billion members. Users sign up, create a personal profile that provides personal information about oneself in as much or as little detail as an individual provides. Often,

Facebook profiles contain photos, lists of personal interest topics or activities, contact information, and various other personal data. Once an account has been created, users

“friend” other users (which means they’re asking them to “follow” their posts and vice versa); exchange messages; post pictures, videos, and hyperlinks; “like” the posts of other people; and generally, keep tabs on each other. To address privacy concerns, Facebook allows users to control their own privacy settings, limiting those who can see parts of their profile – though a member’s name and profile picture are almost always accessible to all Facebook users unless that member specifically blocks someone (Weiss &

Schneider, 2014).

Twitter is another social media platform that has become well known. Early 2016 statistics revealed that Twitter has an average of 310 million monthly active users

(Statista, 2016). Weiss and Schneider refer to Twitter as the most popular form of blogging, which is “microblogging.” Users write messages or microblogs up to 140 characters long, which is usually about the length of a normal sentence. Users can post through their computers, smartphones, and other mobile devices. The messages posted are referred to as “tweets” and are read by “followers” on the platform. Twitter has over

100 million active users who collectively tweet more than 175 million times per day

(Weiss & Schneider, 2014).

Like the others, Instagram has continued to grow in its popularity. The application boasts over 500 million users who upload at least 80 million photos per day

(Instagram Today, 2016). Instagram is a photo-sharing and social networking application that allows users to take pictures and share them, either manually or automatically, on other social media sites like Facebook and Twitter. A unique feature on Instagram is that

the photos are distinctly square, much like older Polaroid images, which is different than the usual four-to-three ratio of digital photos (Weiss & Schneider, 2014).

Another specific way to connect on social media is through LinkedIn, a career- focused social media site. LinkedIn enables users to “link” up with colleagues, look for career-oriented, business, and professional relationships in addition to seek answers to industry-related questions. LinkedIn users can invite their contacts to become “linked in” to them. The business connections of the invited users are in turn linked as well. It is important to note, however, that in order to reach connections down the line, a request for an introduction must be made (PC Magazine Encyclopedia, 2015).

Similar to Instagram, another visually driven social media site is Pinterest, which has also gained popularity in the last several years. Pinterest reports 100 million monthly active users (Marketing Land, 2015). Pinterest is a photo-sharing site where users create one or multiple online pin boards that are shared with their followers. Members post images, referred to as “pins,” onto their boards for multiple purposes, including planning a wedding, saving recipes, and redecorating a home. The site was co-founded by Ben

Silbermann. Pinterest’s tag line is “Organize and share the things you love.” (PC

Magazine Encyclopedia, 2015).

Snapchat is another social media site focusing on more visual content. Snapchat statistics show over 200 million monthly active users and more than 400 million snaps per day (Social Times, 2015). Unlike the other sites, Snapchat is more of a mobile messaging service. A message can be written, but the application focuses more on visual messages in a photo or video format. The message that is sent lasts only up to 10 seconds before it disappears. During that time, the recipient can take a screenshot. However, the

sender is then notified a screenshot has taken place. Snapchat also enables users to post to “My Story,” which are photos viewable for 10 seconds to all of their contacts within the application. The posts to “My Story” disappear from the application after 24 hours from when it was originally posted (PC Magazine Encyclopedia, 2015).

Social Media and Mental Health Concerns

Social media has many potentially positive aspects, including but not limited to allowing people to make new acquaintances and permitting people who already know one another to stay connected despite geographic distance. However, technology in general, and social media specifically, may also have negative consequences psychologically and socially. As previously mentioned, the field of counseling focuses on client assessment and case conceptualization from a biopsychosocial approach (CACREP, 2016). As such, understanding these psychological and social consequences is vital. For example, Rosen

(2012) examined the relationship between technology and several mental health issues.

He suggested links between technology and symptoms of narcissism, obsessive- compulsive disorder, addictions, mania, depression, bipolar disorder, attention-deficit hyperactivity disorder, social phobia, communication issues, hypochondriasis, self- esteem issues, eating disorders, delusions, hallucinations, social avoidance, schizophrenia, and social voyeurism. Rosen focused on a specific disorder or its symptoms and showed how technology either exacerbates the symptoms or reveals symptoms in a different way. He shared the information to demonstrate how it influences people, yet proposed ways to keep technology in one’s life without letting it negatively affect one’s wellbeing.

Rosen (2012) suggested that it is not a large stretch for an individual to possess at least five of the nine traits of narcissistic personality disorder (NPD) when reviewing the individual’s social networking behaviors like wanting “friends” to be aware of what you are doing at all times, collecting friends or admirers, wanting to seem greater based off the types of friends one has collected, and the need to look one’s best and only allowing the best photos of one’s self to be shown on a profile. He also posited that anxiety- related problems were evident due to the great attachments to our devices and the need to constantly check in on them with social, work, or personal information. The process of checking these arenas plays a role in reducing the anxiety of missing out on something.

Regarding technological addictions, which can only be diagnosed as an unspecified impulse control disorder through The Diagnostic and Statistical Manual of Mental

Disorders (5th ed.; DSM-5; American Psychiatric Association, 2013) rather than a specific diagnosable addiction, Rosen focused on the concepts of overuse, withdrawal, tolerance, interpersonal problems, health problems, and time management problems due to types of technology addictions.

Rosen (2012) also discussed the association between technology-related uses and behaviors like constantly checking in, excessive messaging, and spending a lot of time on line, getting anxious when unable to check in and a preference for multitasking with mood disorders. Adding to the discussion, Rosen suggested that the issue of multitasking might also be related to symptoms of attention-deficit hyperactivity disorder that are consequences of our use of technology. He provided the examples of attention difficulties, poor decision-making, lack of depth, information overload, internet addiction, poor sleeping habits, and an overuse of caffeine.

Rosen (2012) stated that many mental disorders involve symptoms related to poor communication skills. He posited that because technology has essentially changed the way we communicate, it [technology] might also have more of an effect on those who may already suffer from a lack of social and communication skills, repetitive behaviors, and an inability to express empathy. Rosen also shared how the increased popularity of the Internet has information readily available for any who consume it, which can be a challenge for those seeking medical advice from websites rather than from medical professionals. This phenomenon, according to Rosen, highlighted how media related activities could be triggers toward hypochondriasis. Rosen argued that the increased use of technology has also impacted society’s focus on beauty and focus—making it only stronger. This, he argued, exacerbates body image issues that may contribute to self- esteem problems and eating disorders.

Rosen (2012) went on further to address how technology might be revealing symptoms similar to schizoid and schizotypal personality disorders with symptoms such as social withdrawal, trouble connecting with others, appearing delusional because of following direction from technology (e.g. GPS), the delusions associated with phantom vibration syndrome – being triggered to check messages when the phone has not rung or vibrated, fixation on others because of heightened exposure due to media and technology, and paranoia due to material consumed through technology. He also linked this heightened exposure of others due to technology use to increased surveillance of others, which can be considered voyeuristic behaviors.

While Rosen (2012) linked technology use to so many disorders, he also provided ways to manage technology use in one’s life in efforts to maintain positive mental health.

He suggested using nature or exposure to nature, art, and music as a way to reset and calm the brain. Additionally, Rosen suggested removing distractions from your environment, giving oneself breaks from technology, getting enough sleep, as well as using mindfulness and positive psychology as ways to defend against what he terms

“iDisorders.”

Ko, Yen, Yen, Chen, and Chen (2012) reviewed literature regarding the relationship between internet addiction and psychiatric disorders. The study reviewed 18 articles regarding the associations between internet addiction and specific mental disorders. The general findings showed that internet addiction is related to substance use disorder, ADHD, depressive disorder, social phobia, and hostility. Attention deficit hyperactivity disorder, depressive disorder, social phobia, and hostility were also significant predictors of developing an Internet addiction. The authors suggested that internet addiction and other mental health disorders should be treated concurrently. Ko et al. also highlighted the need for interventions to aid in prevention of Internet addictions.

Hormes, Kearns, and Timko (2014) investigated similar associations regarding mental health and substance use issues with addictions to social networking. A sample of

253 undergraduate students from a large northeastern university in the United States was used in this study. Each participant was measured by completing assessments modified from substance use issues to social networking issues. They were assessed using the

DSM-IV-TR (APA, 2000) criteria for symptoms of dependence to Facebook, instead of a substance; a modified version of the Penn Alcohol Craving Scale to determine their urges to use Facebook; and a modified CAGE (Cut-down, Annoyed, Guilt, Eye-opener) scale to identify problems related to excessive Facebook use. In addition to these modified

instruments, participants completed the following: a survey identifying disordered online social networking use, the Young Internet Addiction Test (YIAT), the Alcohol Use

Disorders Identification Test, the Acceptance and Action Questionnaire – II, the White

Bear Suppression Inventory, and the Difficulties in Emotion Regulation Scale. Results indicated that roughly 10% of the participants had the presence of disordered online social networking. Of those participants, there were significant and positive correlations with the scores on the YIAT, and more difficulties with emotional regulation and problem drinking, suggesting the potentially addictive nature of social networking sites like Facebook.

Jantz and McMurray (2012) explored the pitfalls of media, technology, and social networking. The book started by discussing how technology gives us access to more information and people than ever before and simultaneously gives others more access to us, which threatens our privacy. With the increase in technology use, Jantz and

McMurray suggested that people are constantly on autopilot also known as engaging in continuous partial attention, which increases how often the brain has to switch back and forth from one activity to another. As a result, individuals are more tired, more stressed, and spend more time and energy to complete activities. Another outcome of this pattern of switching between activities is that individuals derive less enjoyment from the activity and fail to appreciate each separate activity as much. Jantz and McMurray posited that technology makes everything in our life more urgent and the trends lead toward a demand for more speed in every new device. These trends lead individuals down a path of always searching for the next best thing.

An additional pitfall mentioned by Jantz and McMurray (2012) was the threat for developing an addiction. They argued that technology use activates the brain pleasure centers. Although there is nothing inherently wrong with feeling the reward, Jantz and

McMurray discussed that there is a line crossed in which activity becomes an addiction.

They also discussed how technology lends itself to impulsivity because of its omnipresence. Related to addiction, using the internet and technology as a means to escape real life was also problematic, according to the authors. Jantz and McMurray suggested that escaping real life might lead to individuals valuing their online experience more than they value their own reality. By doing so, individuals can become more disconnected from aspects of their real life, including the people in it. Jantz and

McMurray further discussed disconnection in terms of living offline or taking time away from the Internet and technology. In reviewing other studies, they discussed that some individuals suffer from symptoms of withdrawal, loss of identity, and boredom when detached from technology. Additionally, they discussed how being tethered to technology provides a sense of safety, self-worth, social connection, and security to those who use it.

According to Jantz and McMurray (2012), another problem that increased media and technology use can cause is a role reversal in the home. They stated that children are now teaching their parents, as they tend to know more about technology and have more uses for it than their parents. The danger suggested by Jantz and McMurray is that parents, in turn, have less control over the access their children have to their friends, to media, and to the world.

Jantz and McMurray (2012) discussed how technology puts an individual in the driver’s seat of their own life. Because of this, they argued the importance of acknowledging what an individual’s technology use says about them as a person and ensuring that is an accurate reflection of their real identity. Like Rosen (2012), finding ways to maintain well-being while living in a world of technology was addressed by

Jantz and McMurray. Jantz and McMurray highlighted the importance of recognizing that every piece of technology has an off switch and the importance over being in control of that rather than allowing technology to be in control of us. Additionally, they added that technology detox could be helpful in order to restore an individual back to optimal health and in helping an individual move forward. Their work provides tips on how to plan a technology detox on an individual and family level.

Andreassen and Pallesen (2014) reviewed the consequences of addictions to social networking sites (SNS). In doing so, they found that use of SNS at work can lead to decreased productivity, economic loss, job loss, a contagious effect of use among coworkers, and overall inefficiency. Alternatively, they discovered that the use of SNS at work might also refresh and inspire the worker. In examining domestic problems related to SNS, Andreassen and Pallesen found that family and interpersonal conflicts might be more common. The addictive behavior was characterized by down-prioritizing hobbies, leisure activities, exercise and family members or friends. Additionally, they found that addiction to SNS might be related to problems in romantic relationships including jealousy, relationship dissatisfaction, cyber-stalking, and surveillance from the partner. Excessive use of SNS was also related to a negative impact on communication skills.

In their review, Andreassen and Pallesen (2014) also discovered that sleep impairment is a major potential consequence of SNS addictions. In regard to this problem, they found associations between excessive SNS use and delayed bedtimes and rising times as well as psychological, social, and somatic outcomes as a result of poor sleep. They went on further to add that it has been suggested that SNS addiction has a negative impact on self-esteem and well-being. Although many of these factors associated with SNS addiction are considered negative, Andreassen and Pallesen pointed out that those suffering from this addiction might find positivity from their SNS use by relieving stress, enhancing self-esteem, strengthening interpersonal relationships, escaping negative feelings, and building social identity.

Andreassen and Pallesen (2014) provided suggestions for the future of social network site addictions as well. They argued that multilevel counseling may be the best approach in regard to the treatment of SNS addictions, including the potential of pharmacological, school-oriented, and organizational interventions. Because SNS are still relatively new, Andreassen and Pallesen argued for research to continue in this field.

Implications for future studies suggested that treatment of SNS addictions and instruments that would measure for SNS addictions should be included as well as more longitudinal designs.

When taking these findings into consideration, links to the biological, psychological, and social aspects of mental health concerns associated with social media use are evident. The associated concerns included social media addiction, addictive behaviors, communication issues, anxiety symptomology, depressive symptomology, relationship problems, impaired physical health, decreased productivity, identity issues,

and even links to symptoms of personality disorders. With so many links, it is important to better understand social media use and its related mental health concerns, including the addiction to social media platforms.

Review of Research Related to Internet Use,

General Anxiety, and Related Psychological Constructs

As can be seen, there is a large amount of literature linking internet and social media addiction to a wide array of mental health concerns. Through further exploration, it was noted that there is a growing body of research showing the relationships between internet and social media addiction with specific mental health concerns, like anxiety.

Kim and Davis (2009) conducted a study to evaluate how self-esteem, anxiety, flow, and self-rated importance of internet activities related to problematic internet use (PIU). In this study, flow was represented as the tendency to become totally absorbed in the activity at hand. The researchers examined undergraduate students 315 students (39% men, 61% women) at a large southeastern university. The participants were asked to identify the importance of positive functions of using the internet, specifically for social networking sites, as compared to other students of the same gender at the same college.

Of those activities, seven items were combined into an importance of 7-positive activities measure (I7PA). The seven items were retained because Kim and Davis anticipated the activities to be linked to the possibility of problematic use. The participants were also asked to look at 10 potentially negative events that could be the outcome of using social networking sites and compare the likelihood of these events happening to them versus other students (of the same gender, at the same college.) In addition to positive activities

and negative outcomes, participants completed the 10-item Rosenberg Self-Esteem Scale

(1965), an 11-item sensation-seeking scale, and a blended problematic internet use assessment combining items from Young’s (1998) Internet Addiction Test and Caplan’s

(2002) Generalized Problematic Internet Use Scale. The results indicated negative correlations between problematic internet use and self-esteem. Also, positive correlations were found with problematic internet use and the amount of time spent online, the longest time spent online, the time spent on social networking sites, the likelihood of negative outcomes, and the I7PA.

Kim and Davis (2009) conducted a second study to construct what they thought would be a better constellation of PIU characteristics and their possible causes. This study included 279 college students. In addition to the instruments used in the first study, the participants in this round were given surveys to assess their anxiety and flow. Results indicated positive relationships between sensation seeking with the I7PA and between flow and the I7PA. A negative relationship was found between self-esteem and anxiety.

Global PIU was linked to lower self-esteem, higher anxiety, and positively related to the

I7PA.

Ho et al. (2014) conducted a meta-analysis examining the relationship between internet addiction (IA) and psychiatric comorbidities. The meta-analysis included eight separate studies, with a total of 1641 participants with an internet addiction. In order to include the studies in the meta-analysis, they were required to use a formal definition of internet addiction based off of Young’s Internet Addiction Test, Chen Internet Addiction

Scale, or other well-defined IA criteria. The psychiatric co-morbidity was measured by standard questionnaires and included an addicted group and a control group. Results for

anxiety indicated that symptoms of anxiety were proportionally higher among those that have with internet addiction. The prevalence rater for IA patients was 23.3%, and only

10.3% for the control group. Among those studied, anxiety was the most prevalent among adults from ages 19-39 years old.

Kaess et al. (2014) investigated the relationship between pathological internet use, psychopathology, and self-destructive behaviors amount European adolescents. The sample consisted of 11,356 adolescents. Pathological internet use was evaluated by using

Young’s Diagnostic Questionnaire. Other psychopathologies were measured by using the Beck Depression Inventory-II, Zung Self-Rating Anxiety Scale, and the Strengths and

Difficulties Questionnaire. Lastly, self-destructive behaviors were assessed using the

Deliberate Self-Harm Inventory and Paykel Suicide Scale. Results indicated that suicidal behaviors, depression, and anxiety were the strongest predictors of pathological internet use. Symptoms of depression, hyperactivity/inattention, conduct problems, and suicidal behaviors were all listed as significant and independent predictors of pathological

Internet use. Results of the Young’s Diagnostic Questionnaire were rank ordered as adaptive internet use (AIU), maladaptive internet use (MIU), and pathological internet use (PIU). Anxiety and depression were proportionally higher in the MIU and PIU groups. Kaess et al. suggested that catching the symptoms of pathological internet use while it still may be in the maladaptive stage could lead to better psychological well- being and a decrease in suicidal behaviors.

Ni, Yan, Chen, and Liu (2009) studied influential factors of internet addiction among college freshmen in China. Participants were assessed using Young’s 20-item

Internet Addiction Test, a Self-Rating Depression scale (SDS), a Self-Rating Anxiety

scale (SAS), and a basic information questionnaire. The study included the results from

3,557 first year students from a university in northwest China. Results indicated that the students who met the criteria for internet addiction had significantly higher SAS and SDS scores. There was a positive correlation between SAS scores and internet addiction, as well as SDS scores and internet addiction.

Romano, Osborne, Truzoli, and Reed (2013) examined 60 participants living on or around Swansea University in Wales to determine the psychological impact of internet exposure on internet addicts. Participants completed the Internet Addiction Test (IAT), the Positive and Negative Affect Schedule (PANAS), Spielberger Trait-State Anxiety

Inventory (STAI-T/S), Beck’s Depression Inventory (BDI), the Oxford Liverpool

Inventory of Feelings and Experiences – Brief Version (O-LIFE (B)), and the Autistic

Spectrum Quotient Questionnaire (AQ). All measures were given to the participants at once. The PANAS and STAI-T/S, were given a second time as well. After the first set of measures was completed, the participants were permitted to access the internet for 15 minutes. Once the 15 minutes had passed, the participants completed the PANAS and

STAI-T/S again. Results revealed strong relationships between internet addiction and depression (BDI), schizotypal impulsive nonconformity (O-LIFE (B)), and with autism- traits (AQ). Still significant, but weaker than those aforementioned, associations also existed between negative mood (PANAS) and trait or long-standing anxiety (STAI-T).

The sample was divided into two groups based off of IAT scores. The groups were identified as lower and higher problematic Internet use. In examining the effects of the exposure to the internet, it was determined that there was significantly greater increase in state anxiety for the lower-problem group compared to the higher-problem group, a

significantly greater decrease in positive mood for the higher-problem group than the lower-problem group, and no impact on negative mood for either group.

Alavi et al. (2012) studied university students looking specifically at the influence of internet addiction on psychiatric symptoms. Participants were surveyed using the

Young Diagnostic Questionnaire, the Internet Addiction Test, the Symptom Checklist-

90-Revisions (SCL-90-R) and were asked to complete a demographic questionnaire. A total of 250 students from universities in Isfahan, Iran participated. The findings of this study showed that the students who met criteria for an internet addiction had higher scores related to all psychiatric symptoms [somatization, OCD, interpersonal sensitivity, depression, anxiety, aggression, phobia, paranoia, psychosis, Global Severity Index,

Positive Symptom Total, and Positive Symptom Distress Index] than those students who were not considered addicted. The results indicated that the presence of an internet addiction might lead to serious mental health issues in youths. Alavi et al. argued that understanding the results of their study can contribute to the prevention, diagnosis, and treatment of internet addiction among students.

Jenaro, Flores, Gómez-Vela, González-Gil, and Caballo (2007) measured pathological internet use and identified correlations between those and psychological, health, and behavioral measurements. A total of 337 college students from the

Universidad de Salamanca, Spain were surveyed. Two scales developed for the study assessed internet and cell-phone use: the Internet Over-Use Scale and the Cell-Phone

Over-Use Scale. Both scales were determined to have internal consistency and construct validity. Participants were also given Beck’s Depression Inventory (BDI), Beck’s

Anxiety Inventory (BAI), and the General Health Questionnaire. Jenaro et al. found a

significant association between heavy internet use and insomnia, social dysfunction, and depression. Results also indicated a significant association between heavy internet use and nonclinical versus clinical scores on the BAI. No significant association was found for the association between internet use and the nonclinical versus clinical scores on the

BDI. When examining their results, Jenaro et al. performed two logistic regressions with internet use and cell-phone use as their dependent variables. The BAI scores were the only significant predictor on the likelihood of being a light or heavy internet user.

Mehroof and Griffiths (2010) studied the relationship between online gaming addictions and personality traits including sensation seeking, self-control, aggression, neuroticism, state anxiety, and trait anxiety. Participants were from a sample of students at an East Midlands university in the United Kingdom. A total of 123 participants completed all of the measurements in order to be included in the study’s results. The students were asked to provide demographic information regarding their gender and age.

Additionally, they were given the Game Addiction Scale, the Self-Control Scale (SCS), the Buss Perry Aggression Questionnaire, the Arnett Inventory of Sensation Seeking, the

State-Trait Anxiety Inventory for Adults, and the Eysenck Personality Questionnaire.

Results of this study indicated five significant predictor variables for gaming addiction: neuroticism, sensation seeking, state anxiety, trait anxiety, and aggression. The strongest predictors of gaming addiction were state anxiety and sensation seeking. In regard to anxiety, these findings suggest that online gaming addictions may be a coping mechanism in dealing with state and trait anxiety.

Clayton, Osborne, Miller, and Oberle (2013) looked at the associations between loneliness, anxiousness, alcohol, and marijuana use in predicting Facebook use and

emotional connectedness to the social networking site. A total of 229 undergraduate students living in dorms at a mid-sized public university in the southwest United States were surveyed to explore these relationships. To assess these constructs, researchers used the UCLA Loneliness scale, the self-consciousness scale developed by Feingstein,

Scheier, and Buss (1975), the Alcohol Use Disorders Identification Test: Interview

Version with an additional four questions written by the researchers, the UNCOPE screening questionnaire addressing marijuana use, the Facebook Intensity Scale, and the

Facebook Connections Strategies scale. Results revealed that there were significant correlations between alcohol and marijuana use; and between anxiousness and loneliness.

In examining the constructs as predictor variables, the conclusions drawn were that anxiousness, alcohol use, and marijuana use significantly predict emotional connectedness to Facebook. In addition to that, anxiousness and loneliness were significant predictors for strategies for connecting with people on Facebook.

The research studies summarized above describe the negative impacts that internet and social media use can have in regard to anxiety. The collective results included negative outcomes of lower self-esteem, increased anxiety, addiction, and other psychiatric symptoms as a result of increased internet and/or social media use. Anxiety, in several instances, was a predictor of heavy or problematic internet use. Additionally, anxiousness was reported as a predictor of emotional connectedness to Facebook. Given all of this, it is imperative to examine the use of social media, not just Internet use, in understanding its impact on or relationship with anxiety.

Review of Research Related to Internet or Social Media Use,

Social Anxiety, and Related Psychological Constructs

The previous research reviewed internet and social media use links to anxiety, in general. Especially when examining social media use, social anxiety, rather than general anxiety, has also been explored and a topic of conversation in mental health research.

McCord, Rodebaugh, and Levinson (2014) studied the associations between social anxiety, anxiety on Facebook, and social Facebook use. A total of 216 Facebook users were surveyed in this study. Participants were given the Facebook Questionnaire (FBQ) to assess how often an individual used the socially interactive features on Facebook as opposed to features considered to be non-social. They were also given the Social

Interaction Anxiety scale and the Social Phobia Scale (SIAS-SPS-12), as well as the

Facebook-Social Interaction Anxiety Scale (F-SIAS). Results indicated a significant positive correlation between the SIAS-SPS-12 and the F-SIAS. However, there was no significance between the SIAS-SPS-12 and the FBQ. Anxiety on Facebook in addition to the interaction between anxiety on Facebook and social anxiety significantly predicted social Facebook use. They also found that anxiety on Facebook and social Facebook use were significant predictors of social anxiety.

Caplan (2007) examined the relationships between loneliness, social anxiety, and problematic internet use. A total of 343 undergraduate students participated in this study.

These students completed the UCLA Loneliness Scale, the Social Avoidance and Distress scale, the Preference for Online Social Interaction (POSI) measure, and a Negative

Outcomes of Internet Use survey. In addition to the measurements, participants were measure on exogenous variables including their gender and their self-reported frequency of three content specific problematic internet behaviors that could impact negative outcomes. The researchers found that social anxiety and loneliness were positively

correlated. In addition to that, both social anxiety and loneliness were significantly positively related to POSI. Results also indicated social anxiety and loneliness were significant predictors of POSI. Caplan also found that POSI was the strongest direct predictor of negative outcomes. Finally, the results supported the hypothesis that suggested an indirect effect of social anxiety on negative outcomes, being mediated by

POSI.

Becker, Alzahabi, and Hopwood (2013) examined multitasking with media to determine if it was a unique predictor of symptoms of depression and social anxiety. A total of 319 college undergraduates participated in the study. The participants were given the Personal Health Questionnaire to measure symptoms of depression and the Social

Phobia Inventory to measure social anxiety. To measure aspects of their personality, participants completed two scales of the Big Five Inventory: the neuroticism and extraversion scales. The students were also given the Media Multitasking Index

Questionnaire, which indexed their total media usage and media multitasking. Results indicated significant positive relationships between media multitasking and overall media use, depression, and social phobia. When controlling for neuroticism, extraversion, and overall media use, media multitasking was found to be a predictor of self-reported symptoms of depression and social anxiety.

Yen et al. (2012) examined the differences in social anxiety between online and real-life interactions. In addition to this, they investigated the relations between online and real-life interactions with depression, internet addiction, internet activity type, scores on the Behavioral Inhibitions System (BIS) scale, and the Behavioral Activation System

(BAS) scale. A total of 2,282 students from colleges in Taiwan scores were included in

this study. Each participant completed the Brief Version of Fear of Negative Evaluation

Scale to identify symptoms of social anxiety, the Center for Epidemiological Studies

Depression Scale, the Chen Internet Addiction Scale, and the BIS/BAS scales to assess differences in motivation. Results indicated that students who scored higher in social anxiety had more anxiety in real-life and online. However, the key finding of this study was that social anxiety was lower in online interactions than real-life interactions. The differences between social anxiety online and social anxiety in real-life were greater for the students who scored higher on the scales for depression, BIS, and BAS. Internet addiction and activity had no significant effect on social anxiety online or in real-life.

Because social anxiety was evaluated only by self-report from those examined, the assessment was unable to prevent recall bias for social anxiety especially in the online context. This limitation listed in the literature may have impacted the correlation analysis between internet addiction and social anxiety.

Lee and Stapinski (2012) examined the relationship between social anxiety and problematic internet use. The sample size was 338 Australian adults after excluding people who did not complete the online survey and participants who under the age of 18.

Each participant completed an online survey including an internet usage survey, the

Depression, Anxiety and Stress Scale-21-item version, the self-report version of the

Liebowitz Social Anxiety Scale, the Brief Fear of Negative Evaluation Scale II, the

Levels of Development in Online Relationships survey, the Generalized Problematic

Internet Use Scale, the Preference for Online Social Interaction scale, the Subtle

Avoidance Frequency Examination measure to identify common safety behaviors displayed by those who are social anxious, and the Probability and Consequences of

Threat survey to indicate the participants’ perception of social threat of being evaluated negatively by another and their estimate of how bad things would be if this threat occurred. Results indicated that social anxiety was significantly related to problematic internet use. When controlling for anxiety, stress, and depression, social anxiety remained a significant predictor of problematic internet use. The researchers reported that those with higher levels of social anxiety communicated more with people online than offline. However, they did not find that those with higher levels of social anxiety viewed their online relationships as better than those they held offline. In regard to relationships, social anxiety was significantly related to decreased breadth, depth, and predictability of relationships regardless of communication mode. Higher interdependence and predictability in online relationships was positively related to problematic internet use. Lower levels of breadth, depth, predictability, and commitment in face-to-face interactions were associated with more problematic internet use.

Participants with higher levels of social anxiety felt more interpersonal control online and less probability of threat than in offline interactions. Lee and Stapinski also found that the association between social anxiety and preference for online interactions was partially mediated by the tendency to use safety behaviors. Preference for online social interactions had a significant relationship to face-to-face avoidance when controlling for avoidance explained by the fear of negative evaluation. Lastly, results showed that the association between social avoidance and fear of negative evaluation was partially mediated by having a preference for online social interactions.

Campbell, Cumming, and Hughes (2006) examined internet use to determine if it served as a therapy or as a problematic addiction particularly for those considered

socially fearful. A total of 188 participants were included in this global sample. All participants completed the Zung (self-rating) Depression Scale, the Depression, Anxiety, and Stress Scale, the Eysenck Personality Questionnaire-Revised Short Scale, the Fear of

Negative Evaluation Scale, Internet Use Questionnaire (IUQ), and an Internet Effects

Questionnaire (IEQ). The researchers for the current study created the IUQ and IEQ.

The battery of tests was given to the sample through an online survey. Results indicated there was no relationship between time spent online and anxiety or depression. The researchers also found that people who chat online more often are less socially fearful and less likely to try to manipulate the impression they make toward others than those who do not chat online. Chat users believed that the internet was beneficial to their psychological well-being. However, they also believed that the Internet could be addictive and that people who used the Internet frequently were lonely.

Landoll, Greca, and Lai (2013) studied aversive peer experiences on social networking sites. This study also included the development of the Social Networking-

Peer Experiences Questionnaire (SN-PEQ). In this project, the researchers conducted two studies. Study one included 216 adolescents and young adults. Of those students,

108 were selected to be part of a larger study on peer relationships. In a second study by

Landroll et al., a total of 214 adolescent participants were included. Participants from both studies provided their demographic information as well as information regarding the frequency of their Internet use. They also completed the Social Networking-Peer

Experiences Questionnaire (SN-PEQ), the Revised Peer Experiences Questionnaire, the

Social Anxiety Scale for Adolescents, and the Center for Epidemiological Studies –

Depression Scale. Results indicated that when controlling for all other variables, the

participants’ cyber experiences on the SN-PEQ had significant associations with greater symptoms of social anxiety and depression. Also, the participants’ reports of relational peer victimization were significantly positively related to social anxiety and symptoms of depression.

Oldmeadow, Quinn, and Kowert (2013) examined attachment style, social skills, and Facebook use in adults. A total of 617 participants completed the measures for this study. The participants were given the Experiences in Close Relationships Scale (ECR), the Social Skills Inventory (SSI), and questions regarding their Facebook usage and experience developed by the researchers. The findings show a positive association between higher attachment anxiety and increased Facebook use. Positive relationships were also found between Facebook usage and social sensitivity, social expressivity, and social control. Those with higher scores in those social skills were using Facebook more often than those with lower social skills. Results also indicated positive relationships between attachment anxiety and Facebook comfort seeking, as well as between attachment anxiety and Facebook evaluation concern or worrying about how others perceive one’s Facebook. The attachment avoidance style was negatively related to

Facebook attachment, Facebook openness, and Facebook positivity. In other words, those who scored higher in attachment avoidance were more likely to think about deleting their profile, not as happy for significant others to look at their profile, and less likely to like or be happy with their own profile. Social sensitivity as well as emotional control had significant correlations with Facebook evaluation concern, comfort seeking, and attachment to Facebook. The researchers reported that this can be interpreted by viewing those higher in social sensitivity and/or emotional control were more concerned

with other’s perceptions of them on Facebook and were more inclined to use Facebook when they were feeling anxious, sad, lonely, or stressed. The association between emotional expressivity and both comfort seeking, and evaluation concerns were negatively correlated. In other words, participants with higher emotional expressivity were less likely to be concerned with how others perceive them on Facebook and were less likely to use Facebook to deal with negative emotions. Regression analysis results indicated that attachment anxiety was a significant predictor of Facebook usage, comfort seeking, evaluation concern and positivity independent of the social skills. Additionally, attachment avoidance was a significant predictor of evaluation concern, openness, and positivity independent of social skills.

Liu and Kuo (2007) examined internet addiction using interpersonal theory. A total of 555 students from institutions in Taiwan were included in the final sample.

Participants completed the Parent-Child Relationship scale, the Interpersonal

Relationships scale, the Fear of Negative Evaluation (FNE) scale, and Young’s Internet addiction scale. Results indicated significant relationships between interpersonal relationships and the parent-child relationship as well as interpersonal relationships and social anxiety. The parent-child relationship, interpersonal relationships, and social anxiety were also found to have significant influences on internet addiction. Statistically significant differences were found in the degree of social anxiety, the quality of interpersonal relationships, and the quality of parent-child relationships when compared to the level of internet addiction suggested by Young. Each hypothesis Liu and Kuo made was supported by their findings. The parent-child relationship was positively correlated with the child’s interpersonal relationships with peers. In addition to that, the

parent-child relationship and interpersonal relationships were both negatively correlated to a person’s internet addiction. They also found that the child’s interpersonal relationships with peers are negatively correlated with the level of the child’s social anxiety. Additionally, the more a participant experienced social anxiety, the more they were addicted to the internet.

Lee (2015) examined the use of smartphones and Facebook among African

American young adults. Participants were all recruited from one of the largest

Historically Black College and Universities (HBCUs) in Texas. A total of 304 African

American college students made up the study’s sample. All students provided demographic information, information regarding their smartphone ownership and usage, as well as their study time outside the classroom and grade point average. In addition to this information, the participants completed the Smartphone Addiction Scale (SAS-Short

Version), the Facebook Addiction Scale (BFAS), the Mini-International Personality Item pool (IPIP) excluding the openness subscale, and four items from the Social Interaction

Anxiety Scale (SIAS) in place of the IPIP openness subscale. Results showed that social interaction anxiety, Facebook addiction, and multitasking were all significantly positively associated to smartphone addiction. Roughly 33.5% of the variance in smartphone addiction was explained by demographic factors, Facebook addiction, and multitasking behaviors. Additionally, age, social interaction anxiety, and multitasking were significantly positively associated to Facebook addiction. Among the sample, it was found that none of the personality traits were significantly associated with Facebook addiction. Similarly, there were no significant differences between genders on Facebook addiction scores. However, age was found to be a significant predictor of Facebook

addiction with older students showing higher usage of Facebook. Smartphone addiction and Facebook addiction scores were significantly and positively related. In regard to grade point average, the only significant predictor found was the number of hours studying outside of the classroom. Facebook use and multitasking were not found to negatively impact the participants’ grade point averages.

The research summarized in this section pointed out several ways that the internet and social media use are linked to social anxiety. When examining these results collectively, it can be noted that social anxiety can be linked to internet and social media use or addiction and may also predict it. In a reciprocal manner, the use of the internet or social media may also predict social anxiety. It was also found that those who experience more social anxiety also invest more time in online communication likely because of perceived control and social avoidance. The link to behavioral addictions revealed positive relationships meaning the more social anxiety experienced by an individual, the more addicted that person was to the internet and/or social media. Understanding the social implications and purposes the internet and social media are pertinent in understanding how people experience social anxiety. By knowing how clients may be experiencing social anxiety in their daily lives, counselors will be better equipped to treat and address the concerns of their clients.

Review of Research Related to Internet and Social Media Use

and Fear of Missing Out (FoMO)

Anxiety in relation to internet and social media use has been well established given the previously reviewed research. Synonymous with the word “anxiety” is the

word “fear.” The use of social media has been linked to a new and specific type of fear.

Przybylski, Murayama, DeHaan, and Gladwell (2013) examined the relationships between motivations, emotions, and behaviors with the fear of missing out. The fear of missing out was defined by “pervasive apprehension that others might be having rewarding experiences from which one is absent, FoMO is characterized by the desire to stay continually connected with what others are doing” (Przybylski et al., 2013, p. 1841).

The investigation included three separate studies. Study one was used to develop a scale to measure the fear of missing out (FoMO). The scale started with 32 items and through analysis, the authors were able to identify 10 items that accurately measured the construct. Study two included a total of 2,079 adults from ages 22 to 65 participated in the study. The sample respondents were a nationally representative group from Great

Britain. The participants completed the 10-item Fear of Missing Out scale (FOMOs), a survey providing information about their social media engagement, the Need Satisfaction

Scale, an overall life satisfaction assessment, and the Emmons Mood Indicator. Study two results indicated a negative relationship between FoMO and age, meaning younger participants reported higher levels of FoMO. Gender did not affect FoMO scores in older participants, but in younger participants, young men reported the highest levels. The older participants reported higher levels in need satisfaction and life satisfaction as well as lower scores regarding their social media engagement. Those who reported less satisfaction of needs for competence, autonomy, and relatedness reported higher levels of

FoMO as well. Additionally, Przybylski et al. found that the participants who reported high FoMO also reported lower levels of general mood as well as lower overall levels of

life satisfaction. FoMO was found to be a linking factor in the explanation of how individual need satisfaction and well-being were related to social media use.

In a third study Przybylski et al. (2013) examined a total of 87 first year undergraduate students. The participants completed the FOMOs, five items regarding their Facebook engagement, the Positive Affect Negative Affect Scale brief version

(PANAS-X), and items regarding distracted learning as well as distracted driving.

Results of study 3 found that FoMO was associated with greater Facebook engagement.

In regard to the results of PANAS-X in relation to FoMO, the authors found that those with higher FoMO scores were more inclined to experience mixed emotions when using social media. Lastly, the results of this study indicated that the students with higher

FoMO scores were more likely to engage in social media during class as well as more likely to engage in distracted driving.

Alt (2015) examined 296 undergraduate students from a major college in

Northern Galilee. Each student completed a demographic questionnaire, the Social

Media Engagement (SME) questionnaire and the Fear of Missing Out Scale (FoMOs).

Additionally, academic motivation of each student was measured using three constructs from the Academic Motivation Scale – College version. The SME was created specifically for this study to measure the extent to which students used or engaged in social media in the classroom. Resulted indicated positive links between social media engagement and two of the motivational factors, extrinsic and amotivation for learning.

Extrinsic motivation denotes behaviors that are driven by external rewards. Amotivation refers to an inability or unwillingness to participate in what are considered to be normal social situations. In this study, these two motivational factors were more likely mediated

by the fear of missing out. This can be interpreted as the students using social media for non-academic purposes while in the classroom rather than to promote their learning processes.

Fox and Moreland (2015) studied stressors, both relational and psychological, associated with the use of Facebook and its affordances. A total of 44 participants were involved in the study, all from a large Midwestern university, ranging from ages 19 to 52.

The study used focus groups to identify themes regarding participants’ experience using

Facebook. The researchers determined five themes: managing inappropriate or annoying content, being tethered to Facebook, perceived lack of privacy and control, social comparison and jealousy, and relationship tension. The major findings regarding managing inappropriate or annoying content were that participants were annoyed with misuse of Facebook or automatic features of the site, shocked from learning important news via Facebook rather than in person, and disgust from viewing inappropriate content posted by other Facebook friends. Participants appeared to feel the pressure of being tethered to Facebook. Even though they had negative feelings toward Facebook, many of the participants expressed that they felt like they had to have it and feared the consequences of not having an account. In other words, they expressed a fear of missing out. When the participants discussed the perceived lack of privacy and control, they generally focused on the inability to hide things from their own network and the inability to hide things from the world. The researchers found that social comparison was taking place in many different ways through Facebook. Of the reasons given, comparing number of friends, comparing one’s own life to their Facebook friends, and comparing self to romantic interests or their former mates, were the most prevalent. Often these

comparisons resulted in jealousy and dissatisfaction. Lastly, when they examined relationship tension, the authors found issues between romantic partners often became bigger because of interactions or lack thereof on Facebook and their expectations regarding the interactions. It was also found that not accepting friend requests or de- friending relatives on Facebook was a source of conflict for many of the participants.

Lastly, private conflicts or arguments becoming public on Facebook were another form of tension participants noted in regard to their Facebook experiences.

Hetz, Dawson, and Cullen (2015) examined social media use and the fear of missing out of students while they were studying abroad. Specifically, they used mixed methods to determine how social media affected the students’ experience abroad and whether the students experienced FoMO. Results showed that the students used social media primarily for communication amongst themselves and connecting with others at home. FoMO was present in the study, however, not as the researchers expected. Rather than experiencing FoMO, the students tried to create FoMO in others.

Elhai, Levine, Dvorak, and Hall (2016) studied the fear of missing out, need for touch, anxiety and depression in relation to problematic smartphone use. They gathered data from 308 participants from Amazon’s Mechanical Turk labor market, which is an online labor market often used in social science. All participants recruited were English- speaking North American adults. The participants completed a smartphone usage survey created by the researchers and the Smartphone Addiction Scale (SAS) to measure problematic smartphone use. The participants were also given a six-item survey to assess need for touch. The six items were used to tap into the needs and desires to touch consumer products while shopping and were taken from Peck and Childers (2003) larger

list of need for touch items. Additionally, they completed the 10-item FoMO scale created by Przybylski et al. (2013), a 21 item Depression Anxiety Stress Scale (DASS), and the Behavioral Activation Scale for Depression-Short Form (BADS) to measure behavioral activity and engagement, and the Emotional Regulation Questionnaire (ERQ).

Results showed that problematic smartphone use was most highly related to anxiety, need for touch, and FoMO. Frequency of smartphone use was most correlated (inversely) with depression. Regression models indicated that problematic smartphone use was associated with FoMO, depression (inverse relationship), anxiety, and need for touch. Elhai et. al argued that their results highlighted the importance of social and tactile fulfillment variables such as FoMO and need for touch as critical mechanisms that can explain problematic smartphone use and its relation to mental health disorders like anxiety and depression.

Because FoMO is a newer construct, there is little research to date. Interestingly, it seems to be a topic that is gaining momentum and popular interest. In 2016, Merriam-

Webster announced FoMO as one their newest additions to their dictionary (Steinmetz,

2016). FoMO has also received mainstream news coverage as social media use and technology has expanded. CNN aired a documentary hosted by Anderson Cooper titled

#Being13, which sparked conversation about social media use in the lives of American adolescents. CNN’s Cooper partnered with Underwood and Faris (2015) who studied

216 8th graders from six different states. The students agreed to complete a survey and then enroll in Smarsh, an online archive that securely stored all of their social media communication from Instagram, Twitter, and Facebook between September 2014 and

April 2015. The purpose of the study was to determine how teens use social media in

order to assist parents in understanding how deeply teens are invested in social media and also to help parents to encourage using social media for good. FoMO emerged as significant theme in the study’s results. Through analysis of the archives and surveys,

Underwood and Faris found that FoMO might be a driving force in the teens’ intense social media use and feelings of anxiety when disconnected from it. Over half of the participants reported being cut off from social media due to travel or parental restrictions during the study. Of those, 43% stated the cutoff did not affect them, 47% stated they experienced some anxiety during their cutoff, and 10% reported feeling relieved from the separation. Even when not cutoff, 60% of the 13-year olds worried they were missing out on what their friends were doing online at least once a week. That figured dropped to

45% by the time of the exit interview, yet this still means close to half of the students were suffering from FoMO.

Review of Research Related to Internet and Social Media Use

and Interpersonal Problems

The research on FOMO is not as extensive as is the body of research on other mental health concerns as it is a newer concept. Recall that this concept of FOMO is defined as the “pervasive apprehension that others might be having rewarding experiences from which one is absent, FoMO is characterized by the desire to stay continually connected with what others are doing” (Przybylski et al., 2013, p. 1841).

Linking this idea of wanting to be connected to others then makes it fitting to better understand internet and social media use’s associate with an individual’s problems with other people. Hence, this section examines how interpersonal problems are related to internet and social media use.

Kim and Ahn (2013) examined the presentation of self on Facebook, specifically during times of interpersonal conflict. The researchers conducted semi-structured interviews with 16 participants; 6 undergraduate and 10 graduate students at east coast universities. The goal of the interviews was to understand the participants’ perceptions and behaviors associated with interpersonal conflicts on Facebook. Results indicated that users coordinated performances on Facebook in order to achieve a goal in regard to a specific interpersonal relationship. When conflict was an issue, users were found to consider their behavior on Facebook and how that would impact their interactions with others before they would respond or interact with others. Closer relationships were more likely to provoke conflict among users as opposed to non-close relationships.

Jenkins-Guarnieri, Wright, and Johnson (2013) investigated the correlations among attachment style, personality traits, interpersonal competency, and Facebook use.

A total of 617 active Facebook users from a medium sized Rocky Mountain region university participated in this study. Participants completed an 8-item scale regarding their use of and engagement with Facebook, the Experiences in Close Relationships-

Revised Scale (ECR-R) Attachment Anxiety and Attachment Avoidance subscales, the

Big Five Index (BFI), and the Interpersonal Competence Scale (ICS). Results indicated that insecure attachment had negative effects on both extraversion and interpersonal competency. Alternatively, insecure attachment had a positive effect on neuroticism.

Extraversion was found to have a positive effect on both interpersonal competency and on Facebook use. Neuroticism was not significantly associated with interpersonal competency or Facebook use.

Tokunaga (2011) examined negative events that occur over social networking sites (SNSs). The sample consisted of 197 undergraduate students from large Pacific and

Southwest universities. The students were asked to share (in writing) a situation in which they experienced interpersonal strain while using SNSs. In addition to responding to this open-ended question, participants were asked to identify the nature relationship with the person mentioned in the situation, how long they had known this person, and whether or not they knew this person offline. A content analysis revealed ten types of negative events that occur over SNSs. The ten events were listed in order of most common to least common in the sample:

A friend request was declined or ignored, a public message or an identification tag was deleted, a person did not appear or was ranked lower than expected on a Top Friends application, the surveillance of profiles, a posted question or comment that was ignored, disparaging public remarks on message boards, gossip appearing on third parties’ message boards, limited access to friends’ profiles, removal as a friend, and not being allowed to join a group or having and undesirable group created in reference to the respondent. (Tokunaga, 2011, p. 428)

Suhail and Bargees (2006) investigated both the positive and negative effects of excessive Internet use on undergraduate students. A total of 200 undergraduate students

(181 men, 19 women) from Government College University in Pakistan participated.

The participants completed the Internet Effect Scale (IES) developed by the authors to examine effects of internet use on human functioning. Six of the seven subscales were created to understand the negative effects related to internet use. The seventh subscale focused on the positive effects. Results indicated a negative association between internet abuse and the positive effects subscale suggesting that participants who used the internet for negativity did not receive much benefit from doing so. Positive relationships were found between behavioral, interpersonal, physical, psychological, and educational

problems with the negative effects of internet use. The results from the participants’ scores on the interpersonal subscale revealed that most of the students felt more comfortable and satisfied with their online friends and that they used the internet to communicate with others when they felt isolated. Approximately half of the respondents shared that their families have complained about spending less time with them due to internet use. Psychologically, over half of the participants reported internet use as a means to forget problems in their real life or to avoid stress. Additionally, about half of them also expressed pleasure and satisfaction from their internet use. Roughly a quarter of the sample shared that they have experienced restlessness, irritability, increased anxiety and low mood in the instance they were unable to use the internet. Lastly, results indicated that the majority of the students reported that the internet was positive in helping them with worldwide communication, improving their grades, and improving their reading, writing, and information processing skills.

Nitzburg and Farber (2013) examined attachment status in relation to the experiences of emerging adults with social networking sites (SNS). A total of 339 emerging adults participated by completing the Technology and Communications Survey

(TACS) in order to provide information regarding their feelings, beliefs, attitudes, and disclosure tendencies about SNS. Participants also responded to the Relationship

Questionnaire (RQ) to identify their adult attachment style. Descriptively, lower age was found to be a significant predictor of more time spent on SNS. Higher age, conversely, was found to be significant predictor of feeling more insincere when using SNS. Results also indicated that those who identified more with disorganized and anxious attachment styles were more inclined to use SNS to avoid face-to-face interactions with others while

those with more avoidant attachment styles were more inclined to avoid SNS completely.

Finally, those who identified with the anxious attachment style were likely to feel more intimacy on SNS.

Tonioni et al. (2012) studied the psychological symptoms, behaviors, and hours spent online of patients with Internet addiction disorder (IAD). After screening for IAD,

21 patients were included in the clinical groups study’s results, with 65 participants in the control group. The participants in the clinical group were given an IAD diagnosis before completing the survey portion of the study. All participants were measured using the

Internet Addiction Test (IAT) and the Symptom Checklist-90-Revised (SCL-90-R).

Results indicated that IAD patients had significantly higher scores on the IAT compared to the participants from the control group. However, those in the control group had higher scores on item 7 of the IAT: How often do you check your email before something else that you need to do? The authors suggested this was because the IAD patients were not using the internet to check email. Positive relationships were found among the number of hours spent online weekly with the SCL-90-R anxiety and depression subscale scores as well as item 19: How often do you choose to spend more time online over going out with others? The authors suggested that this internet use can be a means to avoiding interpersonal relationships and that identifying this should be a key part of the clinical interview for diagnosis of IAD.

Collectively, the research summarized above sheds insight into how the Internet and social media use affects the user’s relationships. Like the impact seen with social anxiety, the internet and social media can be used as a way to avoid interpersonal relationships. Additionally, the use of either can often cause strain in relationships and

negative effects for the user, especially for closer relationships, much like other addictions or problematic behaviors. In understanding how meaningful quality relationships can be toward an individual’s wellness, it is necessary to examine how these relationships can be impacted by something that has become so common.

Critique of Related Research Studies

When reviewing research related to internet and social media use or addiction with mental health concerns like anxiety, social anxiety, FOMO, and interpersonal problems, various strengths and limitations were noted. This section will highlight the primary methodological qualities found in the related literature in order to critique the research reviewed. For example, Ko et al. (2012) and Hormes et al. (2014) both noted the cross-sectional design as a limitation to their work as it did not assist in proving a direction to their findings. Ko et al. stated their limitations made it difficult to determine the direction of the interaction between internet addiction and psychiatric symptoms.

Their explanation was that heavy internet use could be used as a way to cope with symptoms or that maladaptive use could also further amplify symptomology.

Similarly, Hormes et al. could not determine the direction of their results to prove that disordered online social networking use’s relationship with substance use or emotional regulation deficits are a result or cause of disordered online social networking use. Hormes et al. also suggested that their study was limited by their measures being administered via an online survey, which may have raised the possibility of a high-risk group self-selecting themselves to participate as frequent internet users. In addition to all of this, Hormes et al. called for future research to carefully evaluate which diagnostic criteria can be translated to provide evidence of dependence to online social networking.

Andreassen and Pallesen (2014) also suggested that their topic is in its infancy. Due to that, the topic of addiction to social networking sites needs more extensive conceptual and empirical exploration. Andreassen and Pallesen stated that further exploration should include longitudinal designs and studies, which included objective measures of both behavior and health based on broad representative samples.

When reviewing the literature related to anxiety, further limitations emerged. The cross-sectional designs were addressed as limitation in several studies (e.g., Alavi et al.,

2012; Ho et al., 2014; Kaess et al., 2014; Kim & Davis, 2009). This is generally considered a limitation as this style of design limits the possibility of being able to draw conclusions in regard to causal relationships within the studies (Alavi et al., 2012).

Much like the general literature, the literature related to internet use, general anxiety and related psychological constructs, many studies mentioned gathering further research being necessary. Kim and Davis suggested gathering data over time in order to better understand plausibility of causal directions and the long-term effects of problematic internet use. Alavi et al. also stated longitudinal studies on the long-term effects of excessive internet use would be helpful in discovering direction(s) in relationships between addiction and depression or other symptoms.

A new theme that emerged with the literature related to general anxiety were limitations related to the samples. Kim and Davis shared that although their samples were moderately large, their study was limited because the samples were made up entirely of young college students at one large state university. Ho et al. stated their study was limited due to the majority of the subjects included being young Asians from

China and Korea. They suggested further studies to investigate other ethnic and age

groups’ patterns of internet use. Alavi et al. shared that their results were limited because they were unable to use the sample to generalize the results to a non-college population.

Jenaro et al. also shared limitations about being unable to generalize their results, which also prevented them from estimating prevalence of the disorders related to pathological internet and cell phone use being studied. Lastly, Mehroof and Griffiths (2010) shared that because their sample was made up of all university students, it was also a limitation because it could not be representative of all gamers.

Other data collection methods in this section were also identified as limitations.

Ho et al. shared that the scales and structured interviews administered in their meta- analysis focused on internet addiction rather than specific behaviors related to it like gaming, shopping, and social media. As a result, there was no way to indicate the psychiatric co-morbidity with said behaviors. Similarly, Romano et al. (2013) did not monitor the content of the websites that were visited by participants in their study.

Because of this, it is unknown if they visited sites similar to those when not being observed. Additionally, because they were being observed, visiting pornographic or gambling websites may have been much less likely. This study also shared that the time spent being exposed to the internet was only 15 minutes. It is not known how longer exposure to the internet sites would do in terms of altering mood and/or anxiety (Romano et al., 2013).

Alavi et al. addressed a similar limitation stating they were unable to control for or measure the length of time their participants had been using the internet excessively.

Due to this, understanding how internet usage affected an individual’s psychological and physical well-being over an extended period of time was unable to be assessed. Clayton

et al. (2013) shared their limitation was in regard to the reactivity of their participants, as they may have altered responses due to the awareness of being observed. Participants knew they would be asked about their alcohol and marijuana use, in addition to their relationship with Facebook. If they altered their true responses, it is possible the data could have been skewed.

Cross-sectional research was noted as a limitation in a number of the articles regarding social anxiety in relation to the internet and social media because it did not allow for an assessment of causal relationships or associations (Caplan, 2007; Lee &

Stapinski, 2012; Yen et al., 2012). Further research was also a common theme in reviewing the literature related to social anxiety. Caplan suggested that because the research on problematic internet use is still in its infancy, we must continue to research so we can further develop testable theories related to problematic use. Lee and Stapinski added that further research was specifically needed to support the construct validity of the

Generalized Problematic Internet Use Scale (GPIUS), which they believed would be able to take place as internet access and problematic use became more prevalent. Lee (2015) called for further research to examine specific smartphone and social networking behaviors or activities young adults engage in because of the variability and evolution in said behaviors.

Much like the literature referencing general anxiety and other psychological problems related to internet and social media use, the literature related to social anxiety was limited by issues with samples as well. McCord et al. (2014) stated their sample was small and underrepresented by ethnic minorities and men. Further, in this study, the recruitment of the sample was suggested to be a reason why the demographics were

biased as some of the potential participants were collected from primary investigator’s email contact history. Caplan reported that his sample was limited as it comprised of only college students, which is common practice that may only serve as a starting point for research representing the general population.

Lee and Stapinski also suggested sampling and recruitment limitations due to the pool of participants primarily being pulled from listservs from the Macquarie University

Department of Psychology and that the participants were young. Because of this, they stated the sample may not have been representative of the broad population of internet users or older individuals. Landoll et al. (2013) were another that were limited by a young sample in academic and school settings may have affected the generalizability of their results in understanding how all age groups experience social networking sites. Lee reported the same limitation due to convenience sampling. He suggested that further research be conducted in different geographic locations in the country on and off campuses in order to allow for generalization of the results.

Another theme in the literature representing social anxiety in relation to internet and social media use were limitations in measurement. Caplan shared that his study was limited because there was no full measurement of generalized problematic internet use behaviors believed to be associated with a preference for online social interaction.

Becker et al. (2013) shared that although their findings imply a need to understand the association between media use and mental health, and an understanding of how people engage in media is something that needs to be reviewed and/or measured. Similarly, Yen et al. (2012) shared that online activities, like gaming and chatting, are often done simultaneously and therefore are more difficult to measure or isolate.

Both Yen et al. (2012) and Lee (2014) reported that their measures were further limited because the evaluations relied on self-report, which puts the data at risk for bias in reporting their behavior(s). Lee also stated the use of shorter version scales in his data were used to assist in decreasing survey time, but that the use of full length scales may have helped to determine the full impact of personality traits and psychological variables on smartphone use. Oldmeadow et al. (2013) stated their measure was limited due to their survey being specific to Facebook users. Therefore, their data may not be capable of being generalizable to other social networking site use.

Like the other sections, cross-sectional research was a limitation discussed in the literature reviewing FoMO. Alt (2015) shared that cross-sectional design prevented her from being able to make and definitive statements about the causality of social media engagement in the study. Alt also suggested further research in order to consider other variables associated learning motivations, like self-efficacy to help expand the model tested in the study examining students’ social media engagement and motivations for engagement. Fox and Moreland (2015) shared that more quantitative research would support more nationally representative and cross-cultural samples of Facebook users, especially as Facebook use continues to grow. Further research was also a theme for

Przybylski et al. (2013) as they stated examining FoMO in experimental settings could lead toward causal models to be evaluated.

Przybylski et al. reported that FoMO, like other stable constructs like self-esteem, is often influenced by situational and relational factors that could contribute to variability in FoMO. They stated their current research did not account for this type of variability over time. Another external variable listed as a limitation in the FoMO literature was

unreliable WiFi connections in and around town in students studying abroad (Hetz et al.,

2015). Hetz et al. also reported that the length of their study also limited their data because a one-month time period may not have been long enough for some to begin missing home or feel as though they were missing out.

The last set of limitations amongst the literature examining FoMO was in regard to the measures and samples. Alt stated the sample was a limitation because data was collected from a single, regional college and therefore, not necessarily generalizable to all. Alt also discussed limitations due to all of the measures using self-report methods, which she identified as an aspect that may decrease the confidence level of the conclusions due to bias from the participants. Fox and Moreland reported their sample as a limitation due to collecting data from primarily younger adults. Additionally, their data collection was limited potentially due to the influence of all women moderators.

Much like the other sections in this review, the literature reviewing interpersonal problems related to internet and social media use revealed limitations related to the samples used in each study. Kim and Ahn (2013) stated their sample was demographically very similar due to age and geographic location of the participants, which limits the generalizability of their results related to Facebook and interpersonal conflict. Jenkins-Guarnieri et al. (2013) had a similar problem because their sample consisted primarily of first time, first year undergraduate students from a single university, had few minority students, and more women participating in the study.

Tokunaga (2011) reported the same limitation as using only university students, limiting the generalizability of the data discovered about negative events on social networking sites. Suhail and Bargees (2006) stated that their sample was limited because

it was self-selected of student who volunteered to participate. Hence, the sample consisted of those who were either negatively or positively affected by the events.

Additionally, Suhail and Bargees noted self-report measures as a limitation because they did not supplement their findings with other measures, like parents or teachers’ reports or assessments of the students participating. Lastly, Tonioni et al. (2012) shared that their sample’s small size limited their findings related to the problems associated with Internet addiction disorder (IAD).

Further research being necessary was a common limitation listed in the literature addressing interpersonal problems. Kim and Ahn (2013) stated that further studies would address contexts that could influence Facebook user perception and behavior. In the same vein, Jenkins-Guarnieri et al. (2012) suggested that further research would provide more insight from the psychological constructs related to the nature and extent of

Facebook use and its possible impact on other psychological variables and individual functioning. Tonioni et al. (2012) also reported that further research would help establish a more representative control group representing issues associated with IAD.

Extraneous factors were another theme among the limitations in the literature related to interpersonal problems. Jenkins-Guarnieri et al. (2012) discussed that participation may have been influenced by personality traits of the participants that voluntarily chose to participate discussing their experiences with Facebook. These traits potentially may have influenced the results by biasing how participants responded to the survey. Tokunaga (2011) stated that the research results may spread confusion as some of the events taking place on social networking sites are ordinary and therefore, may not be specific to social networking sites, or the internet, in general. For example, an

individual may disengage with another or express resistance with unwanted friendships in similar ways both online and offline. Suhail and Bargees (2006) shared that their results were limited because they did not establish specific types of internet activities, so they were unable to note potential links between different activities and positive or negative behaviors of the subjects engaging in said activities.

The limitations discussed here led to the rationale for the present study as it helped to better comprehend the unique phenomenon of social media addiction and associated mental health concerns. Using the biopsychosocial approach also helped to further address some limitations as anxiety, FOMO, and interpersonal problems had not been studied collectively in past research. In doing so, the ties to a theoretical approach in understanding social media addiction and having a better foundation for conceptualizing and assessing clients experiencing an addiction and/or the associated concerns was provided.

Rationale for the Present Study

Weiss and Schneider (2014) documented the time it took for new communication technologies to enter the homes of 50 million people in the United States. Results revealed the following: radio (38 years), television (13 years), Internet (4 years), social networking (16 months), and smartphone applications (9 months). As media technologies continue to grow quickly, it is vital that mental health professionals adapt and respond. Although the variables mentioned throughout much of the research (i.e. internet addiction, time spent using social media, social anxiety, FoMO, and interpersonal problems) have all been studied previously, there is little research that examined these variables together. Because social media is obviously social in nature, it is important to

examine how its use, especially its excessive use may impact how individuals relate to others or how they may suffer due to it. The literature examined here revealed how the use (or overuse) may be related to social suffering – including anxiety, in general, social anxiety, the fear of missing out on what others are doing, and problems with interpersonal relationships. As previously mentioned problematic internet and social media use can be linked to lower self-esteem, increased anxiety, a higher expression of psychiatric symptoms, loneliness, depression, social avoidance, interpersonal strain, and interpersonal conflicts (Rosen, 2012).

Rosen (2012) shared he does not feel technology, including social media, is not inherently bad, but rather calls for attending to how we interact with each site, specifically what sites we visit, what we post on them, and the interactions we have so we can begin to recognize if they may be causing these symptoms, or at least be related.

Above all, Rosen stated a healthy approach to social media is the goal. Rosen, along with others, advocated for further research into how social media continues to impact our mental health, our relationships, and communities (e.g., Hart & Hart Frejd, 2013; Jantz &

McMurray, 2012; Turkle, 2011; Weiss & Schneider, 2014).

In order to better understand social media’s impact on social anxiety, FoMO, and interpersonal problems together, a descriptive design is appropriate. This research design will afford the opportunity to observe the correlations between variables without influencing participants’ normal behavior. Nitzburg and Farber (2013) suggested that that even experienced clinicians will struggle to understand their clients, especially younger clients, as technology continues to advance how individuals establish connections with others. Their solution to this is through assessing and understanding

how clients attach to others through technology and their motivations in doing so.

Determining the relationships that exist between social media addictions and time spent using social media with social anxiety, FoMO, and interpersonal problems has the potential to identify problems clinicians can address in counseling, information that can lead to interventions to address the problems, and questions that clinicians may want to ask to evaluate their clients’ functioning in regard to their social media use. For example, a clinician may understand that a client is suffering from social anxiety, yet if the client’s social media use is not assessed, the clinician may not have a full conceptualization of the way the client experiences social anxiety. By examining a client’s social media use, the clinician is better able to make informed treatment decisions and determine if the use is contributing to the presenting problem. In doing so, informed practice becomes the norm for clinicians, which is deemed ethically appropriate and essential by counseling accrediting bodies and associations (CACREP, 2016; ACA Code of Ethics, 2014).

The Council for Accreditation of Counseling and Related Educational Programs

(CACREP, 2016) accreditation is indicative of an organization’s commitment to program excellence and status of providing a notable educational quality to its students. CACREP standards require that an institution provide resources that discuss relevant research within the counseling field to its faculty and students. CACREP also promotes that counseling program objectives reflect current knowledge and needs in a multicultural society.

Knowing about society’s trends, which include the use of social media, then becomes essential. It is critical that current counseling-related research be infused into the counseling curriculum by CACREP standards. It is also imperative under CACREP’s

standards that a graduate from a CACREP accredited program be well-versed in foundational knowledge which includes but is not limited to the following: how technology impacts the counseling profession and processes; theories and understanding of addictions and addictive behaviors; systemic and environmental influences that affect human development, functioning, and behavior; strategies for promoting growth and wellness across the lifespan; how to effectively conduct assessments, case conceptualize, diagnose, treatment plan, and use interventions with clients (CACREP). Hence, it would be appropriate for counselors and counselors-to-be to understand how social media is or can be related to their clients’ functioning or presenting problem(s). Counselors should then also be competent in assessing for issues related to social media use and recognize how the existing research implies a need to assess for and develop interventions related to it.

CACREP (2016) also highlights the importance of research in advancing the counseling profession as well as critiquing research to inform our practice. This becomes important as counselor educators and supervisors are key informants of counseling practice to students and supervisees alike. In these roles, it is vital that individuals demonstrate a level of expertise and competency in educating and supervising clinical mental health counselors. Specifically, counselor educators and supervisors are required to be capable of assessing the needs of counselors in training as well as be knowledgeable in techniques that will aid in the development of competent counselors; show an understanding of case conceptualizations and effective interventions for different settings and diverse populations; and understand current and political issues within the field and how said issues impact counselors’ daily work as well as the profession as a

whole. In essence, counselor educators and supervisors must be familiar with current trends like social media’s impact on mental health, so they are competent and capable of educating future counselors and imparting knowledge on treatment and interventions where social media issues may be at work.

In addition to CACREP requirements, the American Counseling Association

(ACA) Code of Ethics (2014) also mandates behaviors related to technology and the role of the counselor. Fulfilling the ACA Code of Ethics requirements also has direct ties to understanding how social media impacts mental health. Section C of the ACA Code of

Ethics address professional responsibility. Counselors are required to monitor their effectiveness, continue education, and contribute to the public good. Expanding knowledge to other populations and situations helps us to maintain our professional responsibility. By understanding the trendiness of social media use and how it may influence clients, professional responsibility is being maintained. In addition to professional responsibility, the ACA Code of Ethics addresses assessment. Results of the current literature imply assessing for problematic Internet or social media use. The main goal of assessment addresses determining if these issues exist and how they can be useful. For example, ethical code E.1 states:

The primary purpose of educational, mental health, psychological, and career assessment is to gather information regarding the client for a variety of purposes, including, but not limited to, client decision making, treatment planning, and forensic proceedings. Assessment may include both qualitative and quantitative methodologies. (American Counseling Association, 2014, p. 11).

The ACA Code of Ethics (2014) includes an entire section with guidelines regarding distance counseling, technology, and social media. In essence, the section details the counselor’s role in understanding the evolving nature of the profession in

relation to technology and social media as well as the concerns they may raise. It also addresses that it is important counselors recognize how technology and social media may be used to better serve their clients. As a result of these guidelines, the code of ethics also indicates that counselors must strive to become more knowledgeable about these resources. In following this code, the case for counselors to understand social media’s impact becomes even stronger. We, as counselors, must be able to comprehend how technology is shaping our world and lives of our clients.

Like CACREP, the ACA Code of Ethics (2014) also addresses the role of counselor educators and supervisors in Section F.7.b. It is vital that those who fulfill the role of counselor educators and/or supervisors are competent in sharing knowledge with their students and supervisees, respectively. Understanding the trends in society and in the field, like the growing popularity and presence of social media in clients’ lives, contribute to the knowledge that counselor educators and supervisors can pass on, which in turn contributes to the effectiveness of clinical mental health counselors working with clients experiencing problems related to social media.

As previously discussed when reviewing the limitations of existing research, social media addiction has yet to be studied specifically with anxiety, FOMO, and interpersonal problems jointly. The rationale behind examining these constructs together is linked to the use of the biopsychosocial approach in counseling, competence requirements set forth through CACREP and the ACA Code of Ethics, and to further contribute to a better understanding of the newer phenomenon of social media and how it impacts its users, especially in regards to these specific mental health concerns. The biopsychosocial approach has been commonly used in the counseling field, especially in

regards to addictive behaviors (Griffiths, 2008). By using this approach, there can be a greater understanding of the complexity of the addictive behavior. Further rationale in using this approach and examining these variables together with social media addiction is that anxiety (biological), FOMO (psychological), and interpersonal problems (social) each take on one of the three angles of the biopsychosocial approach.

Summary

The purpose of this literature review was to identify problems associated with

Internet addictions and social media use. The review included how social media can be defined in a broad sense, as well as specific definitions of popular social networking sites.

In general, there was a wide number of mental health issues that have been linked to IAD or PIU and social media use. While the literature separately addressed anxiety, social anxiety, and interpersonal problems, this study will address these constructs collectively.

In addition to those constructs, the fear of missing out will also be addressed. Very little research examined this newly developed phenomenon often linked to social media use.

Additionally, the chapter provided a methodological review of the existing research presenting the limitations of the studies, which examined types of anxiety and interpersonal problems. It also discussed the need for the current study, how it fulfilled ethical guidelines in counseling, and the rationale for the descriptive design approach.

CHAPTER III

METHODOLOGY

The purpose of this study was to examine the relations between participants’ social media addiction and trait anxiety, fear of missing out (FOMO), and interpersonal problems. By understanding these associations, it is hoped that counselors can better assist their clients by learning how social media may be linked with the problems or challenges their clients are facing. Additionally, this study helped to determine other factors that may be related to FOMO, trait anxiety, and interpersonal problems like demographic indicators, such as age, gender, race, and income. The amount of time spent on social media and the participants’ most used platform were also included in this study. Therefore, this study has contributed to the existing research related to the effects of social media on mental disorder-related symptoms and interpersonal problems.

Implications of this study addressed the need for the development of interventions directly related to social media use and alleviating symptoms related to its use. This study also included the necessity of counselors to assess how social media may be impacting their clients. Finally, this study made suggestions related to what points may be necessary to include in the development of interventions addressing problematic social media use and ways to assess clients’ social media behaviors.

Research Question

Null and Directional Hypotheses

Null Hypothesis 1: Self -reported degree of trait anxiety (as measured by the State Trait

Anxiety Inventory – T scale [STAI-T scale]), FOMO (as measured by the Fear of

Missing Out Scale [FoMOs]), and interpersonal problems (as measured by the Inventory of Interpersonal Problems [IIP-32]) will not be statistically significantly associated with social media addiction (as measured with the IAT modified for social media).

Directional Hypothesis 1: Higher scores of social media addiction (as measured with the

IAT modified for social media) will be statistically significantly associated with higher scores of trait anxiety (as measured by the State Trait Anxiety Inventory – T scale [STAI-

T scale]), FOMO (as measured by the Fear of Missing Out Scale [FoMOs]), and interpersonal problems (as measured by the Inventory of Interpersonal Problems [IIP-32])

Sub-Hypotheses

Null Sub-Hypothesis 1: When controlling for the influence of all other independent variables and significantly correlated demographic variables, self -reported degree of anxiety (as measured with State-Trait Anxiety Inventory [STAI] Trait Anxiety [T-

Anxiety] scale) will not be statistically associated with the degree of social media addiction (as measured with the IAT modified for social media).

Directional Sub-hypothesis 1: Higher scores of social media addiction (as measured with the IAT modified for social media) will be statistically significantly associated with higher scores of trait anxiety (as measured with STAI T-Anxiety scale), when controlling for the influence of all other independent variables and significantly correlated demographic variables.

Null Sub-hypothesis 2: When controlling for the influence of all other independent variables and significantly correlated demographic variables, self-reported degree of

FOMO (as measured by the Fear of Missing Out Scale [FoMOs]) will not be statistically associated with the degree of social media addiction (as measured with the IAT modified for social media).

Directional Sub-hypothesis 2: Higher scores of social media addiction (as measured with the IAT modified for social media) will be statistically significantly associated with higher scores of FOMO (as measured by the Fear of Missing Out Scale [FoMOs]), when controlling for the influence of all other independent variables and significantly correlated demographic variables.

Null Sub-hypothesis 3: When controlling for the influence of all other independent variables and significantly correlated demographic variables, self-reported degree of interpersonal problems (as measured by the Inventory of Interpersonal Problems (IIP-32) will not be statistically significantly associated with the degree of social media addiction

(as measured with the IAT modified for social media).

Directional Sub-hypothesis 3: Higher scores of social media addiction (as measured with the IAT modified for social media) will be statistically significantly associated with higher scores of interpersonal problems (measured by the Inventory of Interpersonal

Problems (IIP-32), when controlling for the influence of all other independent variables and significantly correlated demographic variables.

Research Design

To test the null hypothesis, a descriptive research design was used. This design is also referred to as an ex post facto research or casual-comparative design. The purpose of this design style is to examine differences that exist among the participants based on specific variables, like age, gender, race, income, time spent on social media, and the platform most used by the participant. Kerlinger (1973) explained that “ex post facto research is a systematic inquiry in which the scientist does not have direct control of the independent variables because their manifestations have already occurred or because they are inherently not manipulable” (p. 379). Ex post facto research does not determine causation but tests relationships which aim to be beneficial to researchers. Newman and

Newman (1994) explained, “one of the most effective ways of using ex post facto research is to help identify a small set of variables from a large set of variables related to the dependent variable for future experimental manipulation” (p. 124). Therefore, the use of this design in this study is appropriate, as relationships between two or more variables were examined. The ex post facto research design was used to examine group differences among anxiety, the fear of missing out, and interpersonal problems among study participants.

This study also incorporated a descriptive correlational design. The purpose for

using correlational design was to determine if associations existed between the dependent and independent variables. Correlational analysis is "the study of the relationships that exist among random variables, including the identification and summary of such relationships" (Kachigan, 1991, p. 118). The variables in correlational designs cannot be manipulated. In other words, the researcher has no control over the variables used in the study (Kachigan, 1991).

Participants and Delimitations

A convenience sample of social media users was used in order to encourage volunteer participation and have ease in the access to research participants. This method of sampling also offered the advantages quick data gathering and the availability of participants. The sample population in this study had only one delimitation. The study was delimited to participants over the age of 18. There was no maximum age of participants. Participants were not delimited by any other demographic factors. The intent of delimiting this study by age was purposeful, so additional parental consent was not necessary for participation in the study. As the survey was posted and shared on social media, it was expected that participants use a social media platform in their personal and/or professional lives.

Power analyses were conducted to decrease the likelihood that a null hypothesis would not be rejected if it was false (Cohen, 1992). A power analysis showed that with a total of nine independent variables, an alpha level of p < .05, and a hypothesized medium effect size, a power of at least .80 would be achieved with at least 113 participants when conducting a multiple regression analysis with a total of nine independent variables (i.e.,

FOMO, anxiety, interpersonal problems, age, gender, race, SES, amount of time spent

using social media, and social media platform most used) (Cohen, 1992; Soper, 2018).

Therefore, data will be gathered from at least 113 participants to ensure adequate statistical power in this study.

Table 1

Frequency Distributions for Demographic Variables (N = 224)

Variables Frequency Percent1

Gender Man 26 11.6 Woman 185 82.2 Transgender 7 3.1 Other 7 3.1

Race / Ethnicity Native American 3 1.3 Asian / Asian American 8 3.6 Black / African American 19 8.4 Hispanic / Latin American 7 3.1 White / European American 171 76.0 Biracial 11 4.9 Multiracial 5 2.2 Other 1 .4

Income Less than $20,000 96 43.0 $20,000 to $34,999 44 19.7 $35,000 to $49,999 35 15.7 $50,000 to $74,999 32 14.3 $75,000 to $99,999 10 4.5 Over $100,000 6 2.7

Platform Most Used Facebook 177 78.7 Instagram 27 12.0 Twitter 5 2.2 76

SnapChat 8 3.6 Pinterest 2 .8 Other 6 2.7

Note. 1Percentages are based on the participants who reported the information for each demographic variable.

Table 2

Descriptive Statistics for Continuous Demographic Variables (N = 224)

Variable Mean Median Minimum Maximum

Age 33.01 28.50 18.0 81.0 Time Spent Daily Using Social Media 4.92 4.50 0.50 15.0

The participants included 224 adults in the United States. Participation in this study was delimited to adults age 18 and above that used social media. A description of the demographics of the participants is reported in Table 1 and Table 2. The majority of the participants identified as White / European American women. Eighty-two percent of the respondents identified as a woman. Seventy-six percent of the participants identified as White / European American. The average age of participants was 33 years old. Over sixty percent of the respondents earned $34,999 or less when responding in regard to their estimated annual income. The average amount of time spent using social media daily was 4.9 hours.

Instruments

The instruments used in this research study were comprised of an informed consent form, a measure of demographic characteristics, a measure of anxiety, a measure of the fear of missing out related to social media, a measure of interpersonal problems, and a measure of social media addiction.

Informed Consent Form

The informed consent form was the first form provided to participants (see

Appendix A). Its purpose was to deliver written communication regarding participants informed and voluntary choice to participate in this study. The informed consent document described the title of this study, information about the researcher, the purpose of this study, procedures that would be used, risks and benefits of this study, information regarding the right to refuse participation or withdraw from this study, steps taken to ensure confidentiality of the participants and their information, contact information for participants’ questions, and a statement indicating voluntary agreement to participate in this study.

Measurement of Demographic Characteristics

A demographic survey was created by this researcher (see Appendix B) in order to gather information about participants including age, gender, race / ethnicity, income, amount of time spent daily using social media, and the social media platform most used by the participant. These variables were used to gather basic participant information for disclosure purposes and also to test whether a relationship, if any, existed with the dependent variable (in which case they were included as covariates in the model as necessary).

The Fear of Missing Out Scale (FoMOs)

The Fear of Missing Out Scale (FoMOs) (see Appendix C) gathers specific information related to fears and anxieties participants may experience as a result of social media engagement and their everyday experiences, particularly a pervasive apprehension that one may be absent from others’ rewarding experiences. It is a 10-item instrument measured by a five-item Likert-type scale ranging from (1) not at all true of me to (5) extremely true of me. One total score was used for the FoMOs (cumulatively summing all 10 participant ratings).

This scale measured how apprehension about missing out may affect a participant’s desire to stay continually connected with what others are doing. The researchers estimate that the scale takes only a few minutes to complete. Items include statements like “I fear others have more rewarding experiences than me” and “When I have a good time, it is important for me to share the details online (e.g. updating status)”

(Przybylski et al., 2013).

While the scale is relatively new, Przybylski et al. (2013) conducted three studies to operationalize the construct of FOMO and measure its impact. The first study used a latent-theory guided method with a latent trait theory analysis to create an assessment to measure FOMO. The initial scale included 32 items reflecting fears, worries, and anxieties dealing with being in or out of touch “with events, experiences, and conversations happening across their extended social circles” (p. 1842). A large sample

(N = 1013) then completed a self-report instrument online using the 32 candidate items.

Using item response theory resulted in the final ten item scale which is brief and able to identify those who experience low, moderate, and high levels of the fear of missing out.

The latent trait (i.e., FOMO) spectrum was scaled with a mean of 0 and SD = 1.0. The

maximum information was observed at a slightly positive level ( = .51), indicating that the final scale was most sensitive to assessing participants with a moderate to high level of FOMO. Overall, the curve was well distributed showing that the scale can also reliably assess a broad range of FOMO. Additionally, Przybylski et al. computed the participants’ latent trait scores using the graded response model and associated them with scale scores calculated by averaging the row rating scores of the 10-item scale. The resulting correlation (r = .95) showed that overall FOMO scores for individuals could be calculated by averaging across the raw rating scores (M = 2.56, SD = 0.82). The final scale items showed good consistency ( = .87) and acceptable distributions in skewness

(0.27) and kurtosis (-0.48) when pared down from 32 items to the final ten.

The second study was used to explore how FOMO related to demographics, individual differences, and engagement in social media across the general population

(Przybylski et al., 2013). Additionally, the second study aimed to better understand how individual differences in well-being and need satisfaction were associated with social media engagement. They hypothesized that participants whose basic needs (e.g. competence, autonomy, and relatedness) were met daily would be lower in FOMO. Also, they anticipated that FOMO would be negatively correlated with indicators of psychological well-being.

A subset of panelist use from a 150,000-person panel of Great Britain was invited by email to participate resulting in a nationally representative sample of 2,079 working age adults. Each participant completed the FoMOs developed in the first study, a series of social media engagement questions, the Need Satisfaction Scale (LaGuardia, Ryan,

Couchman, & Deci, 2000), an overall life satisfaction assessment, and an adapted nine-

item version of the Emmons Mood Indicator (Diener & Emmons, 1984) to assess for general mood. Results indicated that younger participants and younger men, specifically, tended to report the highest levels of FOMO. They also confirmed their hypothesis that those who reported less satisfaction in need satisfaction reported higher levels of FOMO

( = -.25, p < .001, when holding variability in participant age and gender constant).

Using regression to assess the expectation that FOMO would be negatively associated with general mood scores showed that those high in FOMO also reported lower levels of general mood ( = -.20, p < .001, when holding variability in demographic factors constant). When regressing FOMO scores on life satisfaction while controlling for demographic variables, results indicated those who experience higher levels of FOMO tended to report lower levels of life satisfaction ( = -.17, p < .001) (Przybylski et al.,

2013).

To assess the hypothesis that that individual differences in need satisfaction and well-being related to social media engagement, three mediation models were used by using the bootstrapping approach described by Preacher and Hayes (2008). In continuing to control for age and gender, results revealed three total effects associated with need satisfaction ( = -.12, p < .001), general mood ( = -.09, p < .00), and life satisfaction (

= -.06, p < .01) to social media engagement were in the evidence. Additionally, levels of

FOMO were predicted by participants’ results on need satisfaction, mood, and life satisfaction. All three models were robustly correlated with social media engagement. In considering the three mediation models, the results showed that FOMO served as a mediating factor explaining the associations that linked individual differences in need satisfaction and well-being to social media use (Przybylski et al., 2013).

The third study shifted to a smaller sample to better understand how FOMO related to emotion and behavior. The goal was to better understand how those who reported higher levels of FOMO felt about their social media use, how frequently they used, and to what degree FOMO enables social media as a distraction from other everyday responsibilities. A sample of young adult university students (N = 87) was recruited for this study in exchange for course credit. Participants were assessed by using the FoMOs, a five-item Facebook engagement scale, a brief 10-item version of the

PANAS-X (Watson & Clark, 1994) used to measure ambivalent emotional experiences when using Facebook, a single item question to identify distracted learning, and a series of questions assessing for distracted driving (Przybylski et al., 2013).

Results of the third study indicated that FOMO was related to more engagement with Facebook at key times in the day. To test that hypothesis, Facebook engagement was regressed onto FOMO scores yielding  = .41, p < .001. Regressions were also used to test the hypothesis that higher levels of ambivalent emotions when using Facebook.

With positive affect on FOMO scores, results showed  = .31, p < .001. The negative affect on FOMO scores regression resulted with  = .40, p < .001. These results indicated that those high in FOMO were more likely to experience mixed feelings when using social media (Przybylski et al., 2013). Further regressions were used to assess the hypotheses related to FOMO with distracted learning and distracted driving. The regression with FOMO and distracted learning,  = .27, p = .013, showed that students high in FOMO were more liable to use Facebook during university lectures. The regression used to test FOMO and distracted driving,  = .28, p = .029. showed that those

higher in FOMO paid more attention to emails, text messages, and their mobile phones when driving compared to those lower in FOMO.

Although not mentioned in their literature specifically, face and content validity appeared apparent through the continued use of the assessment in all three studies. Upon conducting the three studies, Przybylski et al. (2013) suggested that further research examining FOMO and that they believe their research constituted the first of many important investigations on the construct of FOMO. Further use of this instrument would further support the validity of its use.

State Trait Anxiety Inventory (STAI) Trait Anxiety (T-Anxiety) Scale

The State Trait Anxiety Inventory (STAI) Trait (T-Anxiety) scale (see Appendix

D) measures clinical anxiety and anxiety problems. The STAI has been widely used in research and clinical settings, and continues to be a popular choice in psychological, psychiatric, counseling and treatment research. The inventory requires a fourth or fifth grade reading level for most persons. The STAI T-Anxiety instrument consists of 20 items each measured using a four-point Likert-type scale ranging from (1) almost always to (4) not at all. These items are used to assess how a participant generally feels. It is estimated that the T-Anxiety scale takes six minutes to complete. The T-Anxiety scale includes statements like “I feel secure” and “I wish I could be as happy as others seem to be” and “Some unimportant thought runs through my mind and bothers me” (Spielberger,

2015). One total score on the STAI-T Anxiety scale (cumulatively summing all 20 participant scores) was used for results in this study.

Trait anxiety is often thought of more of as a disposition (Spielberger, 2015). The rationale behind using trait anxiety is due to the stability of the construct. Spielberger

(2015) stated that trait anxiety reflects individual differences in how often or how intense a person may have manifested the state of anxiety in the past as well as the probability they will experience the state of anxiety in the future. He reported the stronger an individual exhibits the trait of anxiety, the more likely the person will experience the intensity of anxiety states in a threatening situation. Spielberger compared trait anxiety to potential energy. In this comparison, trait anxiety refers to the differences between people in the disposition to respond to stressful situations with varying amounts of state anxiety. For the purpose the study, the potential to experience anxiety and the disposition that one may experience it more often or generally feel more anxious was deemed a more appropriate measure of the construct than the subjective feelings of tension, apprehension, or worry referred to in describing the state of anxiety. State anxiety is reported to exist at a specific time in a given moment, with a specific intensity

(Spielberger, 2015). For this study, the disposition and potential were considered a more appropriate predictor.

Spielberger (2015) stated that in order to construct the most recent form, Form Y, over 5,000 subjects were tested. Using high school and college students, the T-Anxiety scale yielded test-retest correlations ranging from .73 to .86 for college students and from

.65 to .75 for high school students. Respectively, the median reliability coefficient for the

T-Anxiety scale for college and high school students were .764 and .695. In measuring the internal consistency of the T-Anxiety scale, the alpha coefficients were high, yielding a median coefficient of .90. The measure of internal consistency was based on a sample of working adults, students, and military recruits.

Form Y is the most recent form used and was revised in order to improve the balance of anxiety-present and anxiety-absent items on the scale. Factor analysis was used in order to improve this balance (Spielberger et al., 1980). Form X and Form Y have uniformly high correlations. Form Y is said to be a purer measure of anxiety that is somewhat more independent of measuring depression than Form X. Evidence to support the construct validity of the T-Anxiety scale is shown through discriminating between normal and psychiatric patients for whom anxiety is a major symptom.

Additionally, the correlations between the T-Anxiety and S-Anxiety scales provide further implications for construct validity as persons higher in T-Anxiety tend to be higher in S-Anxiety. In order to examine the correlation between the two scales under stressful and non-stressful conditions, the T-anxiety scale was given at the beginning and end of an exam session in which college students were exposed to different amounts and kinds of stress. The S-Anxiety scale was given at four separate occasions during the exam session. The mean S-Anxiety scores increased under greater stress conditions and decreased in relaxed conditions, while the T-Anxiety scores remained constant at the time

Form X of the STAI was being developed, the most commonly used measures of trait anxiety were the IPAT Anxiety Scale (Cattell & Scheier, 1963) and the Taylor Manifest

Anxiety Scale (TMAS, 1953). The correlations with these measures provided evidence of concurrent validity yielding somewhat high correlations ranging from .85 to .73

(Spielberger, 2015).

Inventory of Interpersonal Problems 32 (IIP-32)

The Inventory of Interpersonal Problems 32 (IIP-32) (see Appendix E) is a self- report instrument used to assist in identifying a person’s most significant interpersonal

difficulties. The IIP-32 is a brief version of the IIP-64. This instrument was developed from the theoretical framework that suggests social relationships and interactions are the core of psychopathology. The resulting 32 items in the IIP-32 were chosen from four items from each subscale that had the highest item-total correlations. The subscales include measuring individuals on the following constructs: domineering/controlling, vindictive/self-centered, cold/distant, socially inhibited, nonassertive, overly accommodating, self-sacrificing, and intrusive/needy. The IIP-32 uses a five-point

Likert-type scale ranging from (0) not at all to (4) extremely. It includes statements that begin with the following sentence stems: “It is hard for me to” and “The following are that you do too much,” (Horowitz et al., 2003). Participants’ total score on this the IIP-32

(cumulatively summing all 32 participant scores) were used for the results in this study.

With the exception of the overly accommodating and intrusive/need subscales when comparing the IIP-32 to the IIP-64, the coefficient alphas used to measure internal consistency were considered moderate to high (.68 to .87). Pearson correlations comparing the two scales scores have correlations ranging between .88 and .98 and are all significant at the p < .0001 level. This suggests that the scores on the IIP-32 are good estimates of the scores on the IIP-64 (Horowitz et al., 2003). Convergent validity was explored by correlating scores on the IIP-64 with scores on the Beck Depression

Inventory (BDI-11; Beck et al., 1996) and the Beck Anxiety Inventory (BAI; Beck &

Steer, 1990). These correlations between the IIP-64 and Beck scales ranged from .31 to.48. The results suggest that interpersonal difficulties may be related to but not predictive of symptoms of depression and anxiety. Correlations were also measured between the IIP-64 and the Global Severity Index (GSI) of the Brief Symptom Inventory

(BSI; Derogatis, 1993) and the Symptom Checklist-90-R (SCL-90-R; Derogatis, 1994).

Correlations with the BSI were generally higher than the correlations with the SCL-90-R.

The correlations for the BSI ranged from .57 to .76. This also suggested that interpersonal problems may be related to but not highly predictive of subjective distress.

General functioning and interpersonal problem correlations were examined between the IIP-64 and the Behavior and Symptom Identification Scale (BASIS-32;

Eisen, Dill, & Grob, 1994) and the Social Adjustment Scale-Self Report (SAS-SR;

Weissman & Bothwell, 1976). The total score, the psychosis scale, and the relation to self and others on the BASIS-32 correlated the highest with the IIP-64, which was consistent the IIP-64 total score as an overall indicator of interpersonal difficulty.

Correlations between the IIP-64 scales and SAS-SR were found to be mild to moderate

(.16 - .49) (Horowitz et al., 2003).

Internet Addiction Test (IAT) modified for Social Media

The Internet Addiction Test (IAT) (see Appendix F) is a 20-item scale used to measure the severity of an internet addiction. The scale is measured by a 5-point Likert scale ranging from (0) not applicable to your life to (5) something that you always engage in. For this study, items include questions like “How often do you check social media before something else that you need to do” and “How often do you find that you stay on social media longer than you intended” The IAT is the most widely used internet addiction scale and has been found to be a reliable measure that covers important aspects of problematic internet use including salience, excessive use, neglecting work, anticipation, lack of control, and neglecting social life. The IAT requires no more than ten minutes to complete. Results yield a normal level of usage or a mild, moderate, or

severe level of internet addiction (Young, 2017). One total score (cumulatively summing all 20 participant scores) was used for analysis in the results of this study.

Early investigations into the IAT showed strong internal consistency ( = .90 to

.93) and good test-retest reliability (r = 0.85) (Widyanto & McMurran, 2004; Yang et al.,

2005; Chang & Law, 2008; Khazaal et al., 2008; Milani et al., 2009; Korkeila et al.,

2010). Several researchers have adopted the IAT and modified for their specific research purposes (K.S. Young, personal communication, March 20, 2018). For the purpose of this study, the IAT has been modified to focus specifically on social media addiction.

Within the measure “social media” replaced “internet” and “on social media” replaced

“online” to focus only on addictive behaviors with social media, rather than the internet as a whole. Scores on the measure produced results in the normal, mild, moderate, and severe categories.

Procedures

Once the IRB approval was granted, the researcher created an online survey which included information related to the informed consent, the demographic questionnaire, and the instruments used in this study. Once completed, the researcher then sent a research participant request through two social media platform advertisements

(Facebook and Instagram). Because both platforms are owned by Facebook, advertisements were purchased through the Facebook platform and marketed to both.

Advertisements were purchased for two days at 20 dollars per day and anticipated to draw 66 to 410 link clicks daily based on past campaign and market data (Facebook,

2018). The researcher also invited colleagues to share the research participant request on their own accounts to encourage their friends and families to participate in the study.

This invitation was sent out through listservs linked to counselor education doctoral students, counseling honor society students and alumni, and a state-wide counseling association.

The electronic participant request shared through these outlets included an explanation of the study, the estimated amount of time required to complete the survey, information regarding the participation incentive, and the link to the actual online survey.

Reminder requests, which included the same information as the original participant request, were sent after two and four weeks (or until the required minimum number of participants was obtained). Only individuals over the age of 18 were included in this study. If they did not identify as an adult, there were unable to answer any additional questions and were thanked for their participation in the study.

Every participant who used the link provided in the participation request were provided with an informed consent including the name, institutional affiliation, and contact information of the principal investigator (see Appendix A). Additionally, this form included the potential benefits of the study for the counseling profession’s understanding of social media addiction. The informed consent also included information pertaining to the confidentiality parameters of the participant’s resources. In order to maintain and preserve the anonymity of the survey responses, written informed consent was not requested. Rather, the outlined informed consent script contained all details about the research design and participation was requested through voluntary agreement with the statement “I acknowledge that I have read the information provided above and have no further questions regarding the research study at this time. I voluntarily agree to participate in this study.” As a way to increase the response rate, a

nominal incentive was offered (i.e., a 20.00 Amazon gift card) to ten random participants for their completion of the survey. In order to maintain the confidentiality of the participants, a separate page was added to the end of the survey asking participants to place their email address in the optional field, if they would like to be entered into a random drawing for the prize(s).

Upon completion of the informed consent page, research participants were asked to complete a demographic questionnaire and instruments measuring anxiety (Appendix

B), FOMO (Appendix C), interpersonal problems (Appendix D), and social media addiction (Appendix E). The approximate amount of time needed to complete the above instruments, including the informed consent, was estimated to be 20 minutes. As previously mentioned, participants were asked if they would like to include their email address at the end of the survey to be entered into the random drawing for the research incentive.

As previously noted, the research participant requests were sent out via social media and email listservs in attempts to secure the number of participants needed. The survey was created using Qualtrics, an online survey software program hosted by the researcher’s university (Qualtrics, 2018). The data collected through Qualtrics was then exported into the Statistical Package for the Social Sciences (SPSS), version 22 for analysis. Identifying information was not requested from research participants in efforts to protect their confidentiality, unless they entered their email to be entered into the random drawing.

Description of Dependent and Independent Variables

One dependent variable, degree of social media addition, was used in this study.

Social media addiction was measured using a modified version of the Internet Addiction

Test (IAT). In place of the word “internet” and “online” on the measure, the words

“social media” and “on social media” were used to shift the focus to addictive behaviors associated with social media only, rather than the internet as a whole. The IAT items address the salience of the problem, excessive use, neglecting work, anticipation of use, lack of control, and the neglect of social life (Young, 1998).

Three independent variables were used in this study. Each variable was measured by using total scores on three separate instruments. FOMO was measured using participants’ total score on the Fear of Missing Out Scale (FoMOs) (Przybylski et al.,

2013). This scale was used to identify how apprehension about missing out may affect a participant’s desire to stay continually connected with what others are doing. Anxiety was measured using participant’s total score on the T-Anxiety scale from the State Trait

Anxiety Inventory (STAI). Trait anxiety (T-Anxiety) refers more to how an individual generally feels, rather than how they may feel at a particular time. Therefore, the T-

Anxiety evaluated participants’ anxiety dispositions rather than possible reactions to a specific event (Spielberger, 2015). Trait anxiety was chosen in this study as it was a more constant experience and disposition rather than a measure of anxiety in a specific moment and intensity. Spielberger likened trait anxiety to potential energy, stating that trait anxiety implied how prone an individual is to experience anxiety. Interpersonal problems were measured using participant’s total score on the Inventory of Interpersonal Problems

32 (IIP-32). This instrument includes eight subscales to address the participant in regard to being: domineering/controlling, vindictive/self-centered, cold/distant, socially

inhibited, nonassertive, overly accommodating, self-sacrificing, and intrusive/needy

(Horowitz et al., 2003).

Data Analyses

In order to test the three null hypotheses proposed in this study, inferential statistical analyses were used. The researcher used the Statistical Package for Social

Sciences (SPSS) software to conduct data entry and statistical analyses. Initially, descriptive statistics were determined for all demographic, independent, and dependent variables. The descriptive statistics results included frequency distributions, means, standard deviations, and ranges for all variables.

To better understand the relationships between the independent and dependent variables, one hierarchical multiple regression analysis was used. Multiple regression analyses are said to be one the most used statistical analyses to examine if predictive relationships exist among variables (Gliner, Morgan, & Leech, 2009). In a hierarchical multiple regression, a researcher is able to examine multiple predictor independent variables in a specific order they have chosen ahead of time. The purpose in doing so is to explain the level of variance each variable can account for above and beyond what the previously entered variables accounted for (Mertler & Vannatta, 2013). Mertler and

Vannatta (2010) suggested this style of analysis is the most appropriate for studying the existence of relationships among a set of variables.

Before the completion of the main hierarchical multiple regression analysis in the study, testing was conducted for any covariates (i.e., the demographic variables measured and selected due to previous literature) to be entered in the model. The covariates with statistically significant correlations with the dependent variable were entered into the first

step of the hierarchical multiple regression in order to control for their effect. That is, the first block for the hierarchical multiple regression analysis contained the covariates determined to be statistically significant in their correlation with the dependent variable.

Those covariates were determined by a review of literature related to demographic correlates of social media use (see Demographic Correlates of Social Media Use section below): age, income, time spent using social media, race, gender, and social media platform most used.

The second block of the hierarchical multiple regression analysis included the three independent variables of primary interest independently; FOMO, trait anxiety, and interpersonal problems. An alpha level of p < .05 was used to determine statistical significance of the hierarchical multiple regression analysis. In addition to testing the covariates for correlations, assumptions for the multiple regressions were examined prior to running the regression analyses.

Prior to the analysis, specific criteria were set in order to establish the inclusion of participants’ surveys for final analyses in the study. The inclusion criteria were that the participants indicated they were at least 18 years old and completed all portions of the online questionnaire including the informed consent, the demographic questionnaire, the

IAT modified for social media, the STAI T-scale, the FoMOS, and the IIP-32. When fewer than 5% of questions on specific instruments were not completed, and those questions were randomly distributed throughout the instruments, the mean for each question was computed and blanks were filled in by the said mean for that question.

However, any response where “not applicable” was a response option was left blank in order to avoid swaying the mean. Mertler and Vannatta (2010) suggested that in this type

of situation, replacing missing data with overall item means using the available data is an effective way to address the issue of missing data. This method was used in order to complete the data set. In addition to this method of screening, assumptions for hierarchical multiple regression analysis were tested including linearity, normality, and homoscedasticity.

Demographic Correlates of Social Media Use

Demographic variables have shown their relevance in social media use since its advent. Smith and Anderson (2018) conducted a national sample via phone interviews.

The sample included 2002 adults, of at least 18 years of age, living in all of the 50 United

States and the District of Columbia. Smith and Anderson found that the youngest adults stand out as leaders in the amount of social media consumed. As was true in previous research related to social media use, age revealed definitive differences in it use

(Greenwood et al., 2016; Perrin, 2015). Roughly 88% of adults in the United States from

18-to-29-year-olds reported using any kind of social media. For other age groups reporting their social media use, that number dropped to 78% for the adults among ages

30 to 49, to 64% for those aged 50 to 64, and 37% for Americans aged 65 and older

(Smith & Anderson, 2018). Of designated age groups, growth of adults in the United

States who use at least one social media site has raised most significantly for younger adults since 2005. For adults from age 18 to 29, only 7% used at least one site and is now at 88% in 2018. With adults aged 30 to 49, the numbers went from 6% in 2005 to 78% in

2018. Next, findings showed that only 4% of adults ages 50 to 64 used at least one social media site in 2005. Now, the same group is at 64%. Finally, with adults 65 and older,

only 3% used at least one site in 2005. In 2018, that number is now at 37% of the adults over 65 (Pew Research Center, 2018).

Relatedly, Smith and Anderson (2018) discovered that age also contributed to the differences in us of specific social media platforms. Younger Americans aged 18 to 24 were more likely to be users of Snapchat, Instagram, and Twitter than even adults in their mid to late 20s. Finding pointed out this difference was most significant when looking at

Snapchat use. About 78% of 18 to 24-year-olds used Snapchat, while only 54% of those ages 25 to 29 also reported its use (Smith & Anderson, 2018). Overall, Facebook has proven to be the most used platform across a wide span of demographic variables.

Roughly 68% of all adult users shared they use Facebook, 35% reported Instagram use,

25% stated their use of LinkedIn, around 29% reported Pinterest use, 27% stated they used Snapchat, and about 24% shared they use Twitter (Pew Research Center, 2018).

Other platforms have often revealed demographic related differences. For example, Pinterest was found to be much more popular with women. Overall, social media use broken down by gender has shown growth for both men (6% in 2005 to 65% in 2018) and women (4% in 2005 to 73% in 2018; Pew Research Center, 2018). There were some similarities based off of platforms. Those with the biggest differences were

Facebook (62% men, 74% women), Instagram (30% men, 39% women), Snapchat (23% men, 31% women), and Pinterest (16% men, 41% women) as previously mentioned

(Smith & Anderson, 2018).

Other demographics differences in social media use and platforms were shown in regard to race/ethnicity and income. Smith and Anderson (2018) classified race / ethnicity into three categories; white, black, and Hispanic. Facebook use ranged from

67% for white adults in the United States, 70% for black adults, and 73% for Hispanic adults. Pinterest use ranged from 23% for both blacks and Hispanic adults to 32% for white adults. Instagram use ranged from 32% for white adults to 38% for Hispanic adults to 42% for black adults. Snapchat use ranged from 24% for white adults to 31% for

Hispanic adults, and finally 36% for black adults. LinkedIn use ranged from 13% for

Hispanic adults to 26% for white adults, and 28% for black adults. Lastly, Twitter use showed the range from 20% for Hispanic adults to 24% for white adults, and 26% for black adults (Pew Research Center, 2018).

Similarly, SES revealed similar ranges amongst use of different platforms. Smith and Anderson (2018) discovered that Facebook, Pinterest, LinkedIn, and Twitter use were highest among individuals earning over $75,000 per year. Instagram use was tied highest for highest use among adults earning between $30,000 and $49,999 and over

$75,000. Snapchat use ranked highest among adults earning between $30,000 and

$49,999.

Another characteristic of interest in research related to social media use has been the frequency of its use. Around 73% of the American adults reported using more than one of eight social media platforms studied with most using three of the sites (Smith &

Anderson, 2018). Younger adults tended to use a greater variety of platforms when compared to the older age groups. Given this information, knowing how often sites are used would be telling to show how much of a role they play in an individual’s daily functioning. Of those that reported they use Facebook, 74% reported using the site daily,

17% reported weekly use, and 10% stated they used it less often than the other options.

For Snapchat, 63% shared they use the platform daily, 21% stated they engage in weekly

use, and 15% reported their use was less often. Findings showed that of those who reported Instagram use, 60% used the platform daily, 21% used weekly, and 18% used less often. Lastly, of the Twitter users, 46% reported daily use, 25% used weekly, and

29% shared they used less often than that (Pew Research Center, 2018).

Summary

The purpose of this research study was to examine the associations between social media addiction and anxiety, fear of missing out, and interpersonal problems.

Participants completed the informed consent, a demographic questionnaire, the Internet

Addiction Test (IAT) modified for social media, State-Trait Anxiety Inventory (STAI), the Fear of Missing Out Scale (FoMOS), and the Inventory of Interpersonal Problems 32

(IIP-32). Descriptive statistics were analyzed using SPSS to determine associations among the variables. In addition to descriptive statistics, Pearson correlations and a hierarchical multiple regression analysis were used to determine if relationships existed between the dependent and independent variables.

CHAPTER IV

RESULTS

The purpose of this research study was to examine the associations between social media addiction and anxiety, fear of missing out (FOMO), and interpersonal problems.

Participants completed an informed consent, a demographic questionnaire, the Internet

Addiction Test (IAT) modified for social media to measure for social media addiction, the State-Trait Anxiety Inventory (STAI) T-scale to measure anxiety, the Fear of Missing

Out Scale (FoMOS) in order to measure FOMO, and the Inventory of Interpersonal

Problems 32 (IIP-32) to assess for interpersonal problems in the participant’s life.

Descriptive statistics were analyzed using SPSS to determine associations among the variables. In addition to descriptive statistics, Pearson correlations and a hierarchical multiple regression analysis were used to determine if any relationships existed between the dependent and independent variables. The first part of this chapter shares the pre- analysis and data screening which tested for any missing data as well as tested the assumptions of linearity, normality, and homoscedasticity. The remainder of the chapter presents the descriptive statistics for the study’s participants and the results of the hierarchical multiple regression analysis used to test the null hypotheses. A summary of the results is provided.

Pre-Analysis and Data Screening

Prior to the data analysis, criteria were determined in order to establish what cases were included or eliminated for final analyses in the study. The inclusion criteria were that the participants indicated they were at least 18 years old and completed all portions of the online questionnaire including the informed consent, the demographic questionnaire, the IAT modified for social media, the STAI T-scale, the FoMOS, and the

IIP-32. Upon screening all of the data from 225 participants, one case was eliminated because the participant left several questions blank on several instruments. Therefore, a total of 224 participants completed all of the survey participation requirements. When fewer than 5% of questions on particular instruments were not completed, and those questions were randomly distributed throughout the instruments, the mean for each question was computed and blanks were filled in by the mean for each question.

However, any response where “not applicable” was a response option was left blank so as not to sway the mean. Mertler and Vannatta (2010) reported that in this type of circumstance replacing missing data with overall item means using the available data is an effective way to address the issue of missing data. This method was used in order to complete the data set.

Testing of Assumptions

Prior to data analysis, assumptions for hierarchical multiple regression analyses were tested including linearity, normality, and homoscedasticity. According to the criteria that VIF should be less than 2.5 (Allison, 1999; Everitt, 1996; Miles & Shevlin,

2001), tests to determine if the data met the assumption of collinearity indicated that multicollinearity was not a concern in the present study (Age, Tolerance = .92, VIF =

1.09, Time Spent Using Social Media, Tolerance = .91, VIF = 1.10, FOMO, Tolerance =

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0.55, VIF = 1.83, Interpersonal Problems, Tolerance = .62, VIF = 1.61, Anxiety,

Tolerance = .76, VIF = 1.32). Next, Pearson correlations were analyzed to determine whether demographic factors were associated with the dependent variable, and therefore whether they should be considered as control variables in the primary (hierarchical multiple regression) analysis. Bivariate correlations indicated that age and time spent using social media were the only predictors that shared a statistically significant relationship to social media addiction (see Table 3).

Table 3

Bivariate Correlations of Demographic Factors with Dependent Variable

t Sig.

Age -3.61* .00 Income .99 .32 Time Spent 7.39 * .00 Race 1.85 .07 Gender -.33 .74 Platform Used Most .79 .43

Note. *p < .05. Categorical variables were broken into binary categories. Race = White vs. Non-White. Gender = Woman vs. Other. Platform Used Most = Facebook vs. Other

The assumption of normality for the dependent variable, social media addiction, was satisfied by a review of a normality plot, which indicated a linear pattern (see Figure

1). The residuals plot (see Figure 2) revealed a scattered or no pattern, thus the multivariate normality and homoscedasticity was also assumed. Residual statistics indicated a maximum of 3.47 and a minimum of -2.75, which is also deemed acceptable as most residuals fall between -3.0 and 3.0 (Mertler & Vannatta, 2010). The scatterplot for each independent variable survey (see Figures 3, 4, and 5) indicated the three survey

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totals (STAI T scale, IIP-32 and FoMOS) with the dependent survey total (IAT modified for social media) all followed roughly positive linear trends.

Figure 1. Normality plot for Social Media Addiction.

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Figure 2. Residuals plot for multivariate normality and homoscedasticity assumption of errors.

Figure 3. Scatterplot for anxiety and social media addiction.

Figure 4. Scatterplot for FOMO and social media addiction.

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Figure 5. Scatterplot for interpersonal problems and social media addiction. Descriptive Statistics

Nearly all of the participants indicated that Facebook was one of the platforms they use. Almost 60% of the participants indicated they used Instagram. Over 40% of the participants reported using Snapchat, Twitter, and Pinterest as well. Although many participants indicated using multiple platforms, 79% of the study’s respondents indicated that Facebook is the platform they use the most.

The instruments used in the present study included the following: (a) the FoMOs as a measure of fear of missing out; (b) the STAI T-scale as a measure of anxiety; (c) the

IIP-32 as a measure of interpersonal problems; and (d) the IAT modified for social media as a measure of social media addiction. Total scores were used for each were used in this study. For the total sample (N = 224), frequency distributions, means, standard deviations, and ranges are described in Table 4

The FoMOs items are measured by a five-item Likert-type scale ranging from (1) not at all true of me to (5) extremely true of me with higher scores signifying a greater fear of missing out on others’ rewarding experiences. The STAI T-scale items are measured using a four-point Likert-type scale ranging from (1) almost always to (4) not

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at all with higher scores meaning there is a greater presence of generally feeling more anxious. The IIP-32 items are measured using a five-point Likert-type scale ranging from

(0) not at all to (4) extremely with higher scores signifying a greater presence of interpersonal problems in the respondent’s life. The IAT items modified for social media are measured by using a 5-point Likert scale ranging from (0) not applicable to your life to (5) something that you always engage in with higher scores indicating the presence of an addiction to social media and the severity of said addiction.

Table 4

Descriptive Statistics of Study Variables (N = 224)

Instrument M (SE) SD Actual Possible Range Range

IAT modified for social media 35.11 (1.27) 19.07 22 - 44 0 - 100 STAI T scale 47.51 (0.39) 5.90 43 - 65 20 - 80 FoMOS 25.26 (0.59) 8.86 10 – 50 10 -50 IIP-32 78.43 (1.58) 23.61 32 – 147 32 - 160

Note. IAT modified for social media = social media addiction, STAI T scale = anxiety, FoMOs = fear of missing out, IIP-32 = interpersonal problems.

Inferential Results

This section reviews the inferential statistical results. A multiple hierarchical regression analysis was conducted to determine whether instruments testing, anxiety,

FOMO, and interpersonal problems were statistically significantly related to the criterion variable of social media addiction. An alpha level of .05 was used as the criterion to determine the significance of statistical results. Prior to completing the main hierarchical multiple regression analysis, covariate testing through bivariate correlations were

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conducted to determine if any of the demographic variables had statistically significant correlations with the dependent variable, social media addiction. Age and the amount of time spent using social media were the two demographic variables which revealed statistically significant relationships with social media addiction. Age had an inverse relationship with the dependent variable, meaning as age increased, scores for social media addiction decreased. As time spent using social media increased, so did scores for social media addiction. As a result, age and the amount of time spent using social media were the variables entered into the first step of the hierarchical multiple regression in order to control for their effects.

The directional hypothesis stated that higher scores on all surveys, the STAI T- scale, the FoMOS, and the IIP-32 would be associated with higher scores on the IAT modified for social media. Overall regression results with all five variables (age, time spent using social media, anxiety, FOMO, and interpersonal problems) yielded R2 = .49,

2 R adj = .48, F (5, 218) = 42.10, p < .001. This model cumulatively explained 49.1% of the variability in social media addiction. Overall then, these five variables taken together revealed a positive relationship with social media addiction. When controlling for age and time spent using social media, the model explained 26.1% of the variability in social media addiction; F (2, 221) = 32.98, p < .001. Even when controlling for demographic variables, anxiety, FOMO, and interpersonal problems collectively had a statistically significant positive relationship with social media addiction. Regression coefficients (see

Table 5) indicated that among the independent variables, FOMO and interpersonal problem scores positively contributed to the model meaning that as those scores increased, social media addiction scores increased as well. Anxiety did not significantly

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correlate with social media addiction scores in this model with the other variables factored in.

Positive relationships between FOMO and interpersonal problems scores and social media addiction were consistent with previous research as will later be discussed.

Simply put, these positive relationships revealed further evidence to support the correlation between the overuse or addiction to social media and mental health concerns, as was anticipated. Regression coefficients revealed that anxiety did not have a strong relationship with social media addiction, especially compared to the other independent variables.

Table 5

Regression Coefficients for All Variables Correlated with Social Media Addiction

Variables B  t Bivariate r Partial r

Anxiety .07 .02 .41 .57 .34 FOMO .56 .27 4.09* .57 .27 Interpersonal Problems .27 .33 5.41* .57 .34

Note. *p < .05

Anxiety Regression Results

The first directional sub-hypothesis stated that higher scores related to anxiety would be positively associated with higher scores of social media addiction. The

2 2 regression results yielded R = .29, R adj = .28, F (3, 220) = 29.46, p < .001. When controlling for age and time spent using social media, the model for anxiety explained only 5.7% of the variability in social media addiction. Regression coefficients (see Table

6) indicated a statistically significant positive but weak relationship with anxiety and a

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positive, moderate relationship with the time spent using social media. The relationship with age and social media addiction in this model revealed a negative, yet weak relationship.

Table 6

Regression Coefficients for Anxiety Model – Associations with Social Media Addiction

Variables B  t Bivariate r Partial r Age -0.18 -0.13 -2.24* -0.22 -0.15 Time Spent 2.61 0.42 7.37** 0.44 0.45 Anxiety 0.79 0.25 4.18** 0.29 0.27

Note. *p < 0.05, **p < 0.01

FOMO Regression Results

The second sub-hypothesis expected that higher scores of FOMO would be positively correlated with higher scores of social media addiction. The regression model

2 2 produced the results of R = .420, R adj = .412, F (3, 220) = 53.038, p < .001. This model explained 19.0% of the variability in social media addiction when controlling for age and time spent using social media. Regression coefficients (see Table 7) indicated statistically significant and moderate positive relationships with FOMO and the time spent using social media. There was not a significant relationship with age in this model.

Table 7

Regression Coefficients for FOMO Model – Associations with Social Media Addiction

Variables B  t Bivariate r Partial r Age -0.112 -0.083 -1.575 -0.223 -0.106 Time Spent 1.886 0.304 5.686* 0.442 0.358 FOMO 1.003 0.466 8.483* 0.572 0.496

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Note. *p < 0.01

Interpersonal Problems Regression Results

The third sub-hypothesis suggested that higher scores for interpersonal problems would be associated with higher scores of social media addiction. The regression results

2 2 yielded R = .445, R adj = .437, F (3, 220) = 58.806, p = .000. This model explained

21.5% of the variability in social media addiction scores, when controlling for age and time spent using social media. Regression coefficients (see Table 8) revealed a statistically significant and moderate positive relationships with interpersonal problems and time spent using social media. As with the FOMO model, there was not a significant relationship with age in this model.

Table 8

Regression Coefficients for Interpersonal Problems Model – Associations with Social Media Addiction

Variables B  t Bivariate r Partial r Age -0.12 -0.09 -1.66 -0.22 -0.11 Time Spent 2.08 0.34 6.52* 0.44 0.40 Interpersonal Problems 0.39 0.49 9.24* 0.57 0.53

Note. *p = 0.000 Summary of Results

When reviewing research hypothesis one, the overall model showed that when all independent variables were considered together, three of the independent variables (time spent using social media, FOMO, and interpersonal problems) had positive relationships with social media addiction. For each additional hour spent on social media, social media addiction scores increased. Also, as scores on the FoMOS and IIP-32 increased, social

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media addiction scores increased as well. Age and anxiety had no significant associations with social media addiction in this model.

Regarding the first sub-hypothesis, the overall model revealed three somewhat weak relationships between the independent variables (age, time spent using social media, and anxiety) and social media addiction which seemed to be the most inconsistent with previous literature and will be further explored in the next chapter. The second sub- hypothesis showed that two of the independent variables (time spent using social media and FOMO) were positively associated with social media addiction, while age had no significant relationship with the dependent variable (social media addiction) in this model. Lastly, results of the third sub-hypothesis were much like the second sub- hypothesis. Results revealed moderate positive relationships between two of the independent variables (time spent using social media and interpersonal problems) with social media addiction, while age had no significant relationship.

CHAPTER V

DISCUSSION

This chapter is organized into five sections including a summary and interpretation of the statistical results, a comparison of the results to previous research, a comparison of the results to the related theory, implications of the results, and the limitations and recommendations for future research.

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The purpose of this study was to examine the relations between participants’ social media addiction and trait anxiety, fear of missing out (FOMO), and interpersonal problems. This was accomplished by controlling for relevant demographic variables (i.e., age and time spent using social media). Participants in the study (N = 224) completed a demographic questionnaire and the following scales: the STAI T-scale (Spielberger,

2015) to measure anxiety, the FoMOS (Przybylski et al., 2013) to measure FOMO, the

IIP-32 (Horowitz et al., 2003) to measure interpersonal problems, and the IAT modified for social media (Young, 2017) to measure social media addiction.

In the present study Facebook was the most used platform of the sample (79%).

In this sample, the average score for the IAT was 35.11 indicating a mild level of social media addiction (Young, 2017). For anxiety, the average score was 47.51, which indicated a moderate level of anxiety. The range for the STAI T-scale was 20 to 80

(Spielberger, 2015). The FOMO scores yielded an average score of 25.26 in this study.

These results indicate a moderate level of a fear of missing out (Przybylski et al., 2013).

Lastly, the average score for interpersonal problems was 78.43, which indicated slight to a moderate presence of interpersonal problems (Horowitz et al., 2003).

Preliminary results were reviewed in order to determine demographic variables related to the dependent variable. Age and the time spent using social media were the two variables from the demographic questionnaire that revealed significant relationships with social media addiction. Age had an inverse relationship with the dependent variable, meaning as age increased, scores for social media addiction decreased. As time spent using social media increased, so did scores for social media addiction.

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Results from the hierarchical multiple regression analysis indicated that interpersonal problems, FOMO, and time spent using social media were the most strongly correlated to social media addiction. In the first analyses, all five variables (age, time spent using social media, anxiety, FOMO, and interpersonal problems) explained nearly

50% of the variability in social media addiction scores. When controlling for age and time spent using social media, 26.1% of the variability in social media addiction was explained by anxiety, FOMO, and interpersonal problems collectively.

When controlling for age and time spent using social media, and considering each independent variable on its own, the variability in social media addiction scores revealed that two independent variables statistically significantly correlated with social media addiction. For interpersonal problems, 21.5% of the variability in social media addiction scores was explained by this variable alone. For FOMO, 19% of the variability in social media addiction scores was explained by this variable alone. Anxiety alone only explained 5.7% of the variability in social media addiction scores and did not statistically significantly associate with the dependent variable.

Comparison of Results to Previous Research

Much of the previous research has suggested that relationships exist between social media addiction and the independent variables (anxiety, FOMO, and interpersonal problems). However, little research has examined the three variables together in relation to social media addiction. Results of the present study examining the relationship among anxiety, FOMO, interpersonal problems, and social media addiction are discussed and compared to previous studies.

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The null hypothesis in this study asserted that the total scores of anxiety, FOMO, and interpersonal problems will not be statistically significantly associated with total scores of social media addiction when controlling for demographic variables (i.e. age and time spent using social media). This null hypothesis was rejected as the outcome of the analysis indicated an overall model of significance containing all independent variables

(anxiety, FOMO, and interpersonal problems) with social media addiction.

As was previously stated, there is little research that has examined these three independent variables together. With that being said, there is previous research where some if not all of the variables were studied jointly. Pryzbylski et al. (2013) found increased amounts of FOMO related to lower levels of relatedness to others and increased engagement with Facebook. Additionally, they found that younger participants reported higher levels of FOMO, which is similar as age was one of the significant demographic variables in this study. Findings in this study were also consistent with other research.

Lee (2015) also found that age was a significant predictor of Facebook addiction, which is closely related to age being linked to social media addiction in this study. Fox and

Moreland (2015) linked FOMO and interpersonal problems, specifically relationship dissatisfaction, jealousy, and tension with being tethered to Facebook. Although not the internet or social media, Elhai et al. (2016) found problematic smartphone use was significantly related to anxiety and FOMO, especially FOMO, which is somewhat consistent with the current findings in this research.

The null sub hypotheses took each independent variable separately in relationship to social media addiction when controlling for age and time spent using social media.

The first null sub-hypothesis stated that there was not a statistically significant

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relationship between anxiety and social media addiction when controlling for demographic variables. This null sub-hypothesis was accepted as the relationship between anxiety and social media addiction was quite weak. This was inconsistent with much of the previous literature in which problematic internet use was linked to higher anxiety. However, there is some research that may support the findings of a weak relationship in this study.

Kim and Davis (2009) found higher levels of problematic internet use was linked to increased time spent online and on SNS, and higher anxiety. Similarly, Ho et al.

(2014) found that the symptoms of anxiety were much higher for those who met criteria for internet addiction disorder (IAD). Ni et al. (2013) discovered a positive relationship between self-reported anxiety and internet addiction. Much of the other research found that anxiety was a strong, significant predictor of either problematic internet use (PIU), for connecting with people on Facebook, and preference for online social interactions

(POSI) (Kaess et al., 2014; Jenaro et al., 2007; Clayton et al., 2013; Caplan, 2007).

Although this vast amount of literature would support a relationship between social media and anxiety, there is a fair amount that may support an explanation or rationale behind why the relationship was not strong in this study. Yen et al. (2012) found that their participants had lower levels of social anxiety in their online interactions.

Additionally, Yen et al. yielded results that revealed internet addiction or activity had no significant effect on social anxiety online or otherwise. Lee and Stapinski (2012) found that social anxiety was related to and also a significant predictor of PIU. Those who reported higher levels of social anxiety also engaged in more online communication.

What may help explain why this is consistent with the current study, is that those who

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had higher levels of social anxiety viewed their online relationships as better than those they held offline (Lee & Stapinksi). It is possible that anxiety had a weak relationship to social media addiction in this study due to the idea that participants may use social media in order to cope with their social anxiety. Campbell, Cunning, and Hughes (2006) found similar results that people who chatted online more often were less socially fearful and that chat users believed the internet was beneficial to their psychological well-being.

Romano et al. (2013) found a significant, yet weaker relationship between internet addiction and anxiety, when compared to other variables including depression, schizotypal impulsive nonconformity, and autism traits. Clayton et al. (2013) found anxiety as a reason that their participants connected to Facebook, which may explain that the use of the platform was used to cope in a positive way rather than developing problematic use. In understanding these studies, this could help to explain the weaker relationship anxiety had with social media addiction in this study. Further research to better understand this relationship may help to explore mediating factors or additional variables that may impact the relationship between anxiety and social media addiction.

The second null sub-hypothesis affirmed that there was no statistically significant relationship between the total scores of FOMO and social media addiction when controlling for age and time spent using social media. In this case, the null sub- hypothesis was rejected as positive relationship was discovered. Although there is little research on FOMO to date, this study’s results are consistent with the existing data as it asserts that FOMO is linked to problematic social media use.

This finding becomes even more important as it will contribute to the research of the newer construct of FOMO. This is consistent with the previous research as similar

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positive relationships have been found. Pryzybylski et al. (2013) found that increased levels of FOMO were linked to lower levels of general mood, life satisfaction, relatedness to others, and increased levels of Facebook engagement. Alt (2015) found that FOMO mediated students’ motivation for engaging in social media use. In other words, their participants chose to use social media by being driven by external rewards.

Fox and Moreland (2015) discovered that even though their participants recognized the negative feelings they had toward Facebook, they feared being without it. They also revealed this feeling of FOMO also led to increased interpersonal problems. Lastly,

Elhai et al. (2016) linked problematic smartphone use to anxiety, and especially FOMO.

While related and similar, this research links social media addiction directly to FOMO and as previously stated, adds to the growing body of research related to it.

Finally, the third null sub-hypothesis stated that there was no statistically significant relationship between the total scores of interpersonal problems and social media addiction when controlling for the demographic covariates (age and time spent using social media). In this instance, the null sub-hypothesis was rejected as a positive relationship between interpersonal problems and social media addiction was revealed. .

Kim and Ahn (2013) found that conflict was present with Facebook users and was more likely to be provoked in closer relationships among users as opposed to non-closer relationships. Tokunaga (2011) found ten common negative events that occurred over social networking sites that caused interpersonal strain in their lives. Suhail and Bargees

(2006) also found that negative effects of internet use had a positive relationship with interpersonal problems, including preferences for their online friends and family complaints about their time spent using the internet. Finally, Tonioni et al. (2012) found

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positive links between higher levels of internet addiction with the increased amount of time using the internet. The research suggested that this was due to an avoidance of interpersonal relationships by the participants. This result is consistent with previous literature that reported increased conflict and strain in interpersonal relationships associated with social media use

Comparison of Results to Related Theory

As previously discussed in the first chapter, the field of counseling has a strong emphasis on professional identity. As such, it is important to note that counselors focus on an empowerment and wellness orientation toward helping their clients which tends to be considered a more growth-oriented approach to helping others (Sangganjanavanich &

Reynolds, 2015). In doing so, it is important to highlight that a core component of counselor identity is having the specific education and training to be prepared for the counseling profession (Granello & Young, 2012).

The education and training of many in the profession incorporates the Council for

Accreditation of Counseling and Related Educational Programs (CACREP), the accrediting body that creates standards for graduate school counseling programs promoting excellence and unity in the counseling profession. One key component promoted by CACREP is an understanding of the biopsychosocial approach as it applies to client assessment and client case conceptualization (CACREP, 2016). This approach is used in efforts to adequately and appropriately understand and respond to the suffering clients may experience. In order to best do so, clinicians are expected to attend to the biological, psychological, and social dimensions of their clients (Borrell-Carrio, Sucman,

& Epstein, 2004).

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In attempts to conceptualize and better understand social media addiction, the biopsychosocial approach may lend itself to understanding the relationships with other aspects of the lives of those experiencing this type of behavioral addiction. Trait anxiety was the type of anxiety assessed in this study, which is a longstanding and stable form of anxiety over time and across many situations (Spielberger, 2015). Arguably, this type of anxiety because of its length and stability, could explain a biological tie to social media addiction. In further conceptualizing the ties to the independent variables in this study,

FOMO lends itself to a psychological association to this pervasive fear that one may be absent from others’ rewarding experiences (Pryzbylski et al., 2013). In linking social media addiction to the social component of the biopsychosocial approach, reviewing interpersonal problems individuals experience was fitting.

In comparison to the results of this study, the biopsychosocial approach can be considered in multiple ways. First, another biological component, age, was found in the preliminary covariate analysis to show that there was a biological connection to social media addiction, as age is a biological construct / consideration. Even when controlling for age and time spent using social media, the independent variables (anxiety, FOMO, and interpersonal problems) explained 26.1% of the variability in the social media addiction scores indicating that each variable representing part of the biopsychosocial model was represented in this study’s overall hypothesis results.

In reviewing the sub hypotheses, the first null sub-hypothesis was accepted as the relationship with anxiety and social media addiction was weak when controlling for age and time spent using social media. To explain this in context of a biopsychosocial model, it is possible that other factors like using social media to relieve anxiety may impact a

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person enough that the moderate amount of anxiety they experience is reduced through social media use. In turn, this may relieve the biological and psychological symptoms, and increase the opportunity for individuals to connect socially through the social media platform(s).

The second null sub-hypothesis in this study was rejected, as a positive relationship was revealed between FOMO and social media addiction when controlling for age and time spent using social media. This confirms that the psychological context of the independent variables was present in this study. In understanding FOMO and social media addiction from a biopsychosocial lens, it is important to note that biological and social factors may also be at play. For example, FOMO is a specific fear so symptoms of anxiety, possibly considered biological, may be present at play.

Additionally, when understanding that FOMO is a fear of missing out on others’ experiences, the social context is then also included.

With the final sub-hypothesis, the null hypothesis was rejected because a positive relationship was found between interpersonal problems and social media addiction scores, when controlling for age and time spent using social media. This finding confirms that the expected social construct for the biopsychosocial model was present in the study. It is important to keep in mind that while interpersonal problems are obviously social in nature, the biological or psychological components, like an addiction may contribute to the social context as well.

Implications of Results

The following section describes the implications of the study’s findings as it applies to different areas in the field. Implications are presented for counseling education

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and training, counseling practice, and future research. The results are discussed as they related to specific ethical code and accreditation standards in the field of counseling.

Implications for Counselor Education and Training

This study results further confirm what other research has stated in regard to problematic social media use being linked to mental health concerns, specifically interpersonal problems and FOMO. While anxiety showed much a much weaker relationship with social media addiction, it may also remain important, especially when clients are exhibit other ties to addiction through FOMO or interpersonal problems, or even through demographic data, like age and the amount of time spent using social media platforms. Analyzing and assessing clients’ use of social media then becomes important in better understanding their concerns. Informed practice is important in counselor education and training as it helps to provide a quality education to counseling students.

CACREP (2016) provides accreditation to programs showing their commitment to excellence in doing just that. CACREP also encourages their accredited programs to reflect current knowledge and address needs in a multicultural society. In other words, knowing society’s trends is not only important, but necessary.

CACREP (2016) requires that current counseling-related research be in infused into curriculum in counseling programs, which then makes these findings connecting social media addiction with FOMO, interpersonal problems, and anxiety relevant to being shared in the educating of new professionals in our field. CACREP also requires that students from an CACREP accredited program be well-versed in foundational counseling knowledge including: how technology impacts the counseling profession and processes; theories and understanding of addictions and addictive behaviors; systemic and

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environmental influences that affect human development, functioning, and behavior; strategies for promoting growth and wellness across the lifespan; how to effectively conduct assessments, case conceptualize, diagnose, treatment plan, and use interventions with clients (CACREP, 2016). Because of this, it is important that counselor educators share the need for counselors and counselors-to-be to understand how social media use and/or addiction is or has the potential to be related to their clients’ functioning and/or presenting concern(s). Counselor educators must also stress competency in assessing clients for issues related to social media use like FOMO and interpersonal problems and recognize how this study’s findings and other existing research exhibit a need to assess for and develop interventions related to it.

CACREP (2016) focuses on the importance of research in advancing the counseling profession and also using research to inform practice. Counselor educators and supervisors are then expected to demonstrate a level of expertise and competency in training clinical mental health counselors. As such, counselor educators and supervisors must use research to inform their methods in education counselors-in-training. Knowing that a social media addiction is significantly associated with age, time spent on social media, FOMO, and interpersonal problems then informs counselor educators and supervisors to ensure they are teaching their students the following: techniques to assess their clients in relation to their social media use and associated concerns or demographics; case conceptualizations and effective interventions for clients in different settings and diverse populations in relation to their social media use and associated concerns; and how these current trends or issues impact counselors’ daily work as well as the profession altogether.

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The results of the present study may suggest that counselor educators and supervisors focus on a strong foundation in understanding the theory of behavioral addictions defined by six core components of salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse (Rosenberg & Feder, 2014). . In the same vein, it is important that counselor educators and supervisors work with their students and trainees for them to recognize the changes made to the DSM-5 that added Gambling

Disorder rather than the previous diagnosis of in “Impulse-Control Disorder Not

Elsewhere Classified” in the earlier version, DSM-IV-TR. Other behavioral addictions were proposed including internet-related behavioral addictions and Internet Gaming

Disorder. Internet Gaming Disorder was published as a condition for further study. In its explanation, other similar internet-related behavioral addiction were not included due to a lack of current peer-reviewed research to further establish them as new mental disorders.

(American Psychiatric Association, 2013). Understanding this helps provide implications for further research while also informing counselor educators and supervisors to help their students and trainees to understand how to currently diagnose problems associated with social media use and what could influence more specific diagnoses in the future. In reviewing the above, the present study’s results paired with this information suggest that teaching these concepts would fit well in curriculum tied to assessment, diagnosis, ethics, and treatment quite well.

Because of CACREP’s highlighting the importance of advancing the counseling profession through research, it is then also important that continued research on social media addiction in relation to mental health concerns like anxiety, FOMO, and interpersonal problems occurs as this would continue to better understand the role of

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counselors in assessing, diagnosing, and treating these concerns. Continued research on the topic could also contribute to new methods in assessment, diagnosis, and interventions as well.

Implications for Counseling Practice

As technology continues to advance in society, it has been suggested that even clinicians with experience under their belt will struggle to understand their clients, especially younger clients who tend to gravitate toward new ways of connection through technology like social media platforms (Nitzburg & Farber, 2013). Results revealed that as the age of participants increased, social media addiction decreased. Nitzburg and

Farber (2013) suggested that clinicians use this kind of knowledge to inform practice by a continued effort to assess and understand how their clients attach to other people through technology as well as their rationale or purpose in doing so. Given that age and time spent on social media were the covariates with significant relationships to social media addiction, these constructs may be important considerations for clinicians to consider when evaluating their clients’ concerns.

The ACA Code of Ethics (2014) stresses the importance of professional responsibility in Section C. The section shares that counselors are required to monitor their effectiveness, continue their education, and contribute to the public good. As a result, it can be argued that expanding knowledge about the impact of social media on mental health issues contributes to the maintenance of professional responsibility.

Understanding the links between age, time spent on social media, FOMO, and interpersonal problems with social media addiction fulfills the continued education

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requirement of professional responsibility. This further knowledge may also increase the effectiveness and contribution to public good.

In addition to professional responsibility, assessment is another area addressed by the ACA Code of Ethics (2014). Ethical code E.1 states:

In understanding this code, it can be gathered that assessment allows counselors to gather information for a variety of purposes – all of which help counselors to better understand the life of their clients. Much of the previous research shared the need for assessing problematic Internet or social media use, and the current findings of this study further confirm the need to assess how a client’s social media use or overuse may be related to other concerns in their life, like problems with others or fear of missing out.

Similarly to counselor educators and supervisors, results of this study and ethical codes suggest that practitioners become familiar with the key aspects of behavioral addictions including salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse and understand their implications for diagnosing issues associated with social media use currently and how that could possibly change and further research aids for more specific internet-related diagnoses (Rosenberg & Feder, 2014).

It is imperative counselors understand the role of technology as it relates to the profession. The ACA Code of Ethics (2014) covers distance counseling, technology, and social media in an entire section of their code. As technology evolves, so must counselors in understanding its impact to clients and associated concerns the use of it

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may raise. Knowing that social media addiction has positive, significant relationships with age, time spent on social media, FOMO, and interpersonal problems helps to show the evolution of technology, specifically as it pertains to social media use. Using this knowledge, the code of ethics would then suggest counselors be knowledgeable about the results and their influence in the lives of our clients.

Implications for Future Research

As was a limitation in much of the previous literature, this study did not take a longitudinal approach which would prove to be helpful in better understanding the development of the presenting concerns. Additionally, a longitudinal approach would allow for the discovery of changes over time. In this study, age had a negative relationship with social media addiction, so as age increased, the scores for social media addiction decreased. Taking an approach to study a population over time may also allow for future researchers to understand if this would be true over time or if those who exhibit more addictive behaviors remain the same as time goes on. A longitudinal approach offers even more in that reviewing a population over time may also allow for development of interventions in treating social media addiction and associated concerns, as well as monitoring the effectiveness of said interventions. This could be extremely beneficial in future research as we know that nearly 80% of Americans adults use social media (Perrin, 2015). Should that continue to increase, counselors must be aware of how to work with clients struggling with concerns or problems related to or exacerbated by their social media use.

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Although efforts were taken to advertise this study to attract a diverse sample, the respondents in the study resulted in a largely White / European American and woman- identifying sample. For future research, it would be helpful to ensure that a more diverse sample participated. This would be beneficial so that results could be more generalizable to the population of social media users in the United States. This would be helpful in better understanding if there are groups of people who suffer or struggle more with concerns related to their social media use.

Another implication that resulted from this study was to better understand the relationship between social media addiction and anxiety. Unlike much of the previous research, this study found weak relationships between the two constructs. Future research could explore this further to determine potential mediating factors or explanations. Other instrumentation or more diverse samples could lead to answers to improve our understanding surrounding the relationship. Additionally, it may be helpful to continue exploratory research as social media addiction is still an experience that is relatively in its infancy compared to other behavioral addictions.

In addition, the aforementioned implications for future research, post hoc analyses of the present study could also reveal further detail when looking at the subscales of the measures used. By conducting this type of analyses, the relationships discovered in the present study may be even further explained by the strength of the relationships with said subscales. Given that age was a demographic variable associated with social media addiction, future research could also be conducted with younger adults and even adolescents as results showed that the relationship between social media addiction and age were stronger with the younger participants. Other areas for future consideration

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would be to include sites like YouTube and other messaging apps. Another aspect that could be helpful in exploring with further research would participants’ motivatio n for their social media use.

Limitations and Recommendations

Multiple limitations of this study should be considered when interpreting and applying the results. First, the primary researcher recruited participants through advertisements on Facebook and Instagram, considered a convenience sampling technique, which did not provide an equal opportunity to all participants, especially those that may use other platforms more often. Additionally, as previously mentioned, the majority of the participants identified as women (83%) and/or White / European

American (76%). These issues may impact the generalizability of the findings of this study. Because the participants were recruited through social media, it is important to note that the findings would be more likely generalizable to the population of social media users in the United States, rather than the entire population of the United States.

Future researchers could use alternative sampling methods in efforts to obtain a more diverse and accurate representation of social media users or the general population on the whole.

Second, although invitations to participants were sent out nationally via the

Facebook and Instagram ads, geographic location was not included in the demographic survey. As a result, stating that the sample was nationally representative could be inaccurate as well. Future research could include geographic variables to ensure the location of the participants as well as verifying national representation in the research.

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Third, similar to other research, the cross-sectional design was a limitation in this study as it is difficult to determine the direction of the interaction between the variables.

Specifically, the study does not determine whether social media addiction stems from anxiety, interpersonal problems, and/or FOMO; or if instead, anxiety, interpersonal problems, and/or FOMO causes a social media addiction. The cross-sectional nature of this study does not allow for a determination of causality.

Fourth, compared to other addictive behaviors, social media is considered to be a somewhat newer phenomenon. As such, this study only gathered information at one point in time. In the future, to better understand the relationships amongst the variables, longitudinal designs and study would be helpful to explore. This approach may also contribute to better understanding direction of relationships. Additionally, a longitudinal study may also assist in better detecting the developments or changes in the population at both the individual and group levels (Institute for Work & Health, 2015).

Fifth, because the survey was online, and participants completed it via self-report, their responses are considered subjective and potentially, biased. Specifically, the participants may have reported responses based on socially desirable response bias. This type of bias in self-report is frequently motivated by the desire to avoid embarrassment or any consequences from disclosing sensitive personal information. Addiction has negative connotations and people may be less likely to report due to wanting to appear better and manage the way others perceive them (Tourangeau & Yan, 2007). It is possible participants would want to be perceived as not having a problem with their social media use and associated mental health concerns. Additionally, participants may also not deem their use as problematic, which could also influence their responses.

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Sixth, social media and an addiction to it, as well as any related mental health concerns associated with use of social media are still a relatively new topics or concepts.

Because of this, continued and further research is necessary. Hitlin (2018) reported that social media use on the whole has remained stable over the past two years, yet the dynamic of how and what platforms are used continues to evolve. For example, users now are often “smartphone-only” users, meaning they only access social media from their phones rather than a traditional at home internet service provider. As such, the need to continue learning and better understanding the phenomenon remains.

Seventh, another limitation of this study is understanding that social media use requires an internet connection or data from cell or smartphone. Because of this, access to devices or internet service may be a challenge to individuals engaging in social media use. Challenges or limitations to access can be financial, geographical, preferential, and/or based on age. Because of said challenges, the sample may have not been a full national representation. As mentioned previously, the demographic survey could include more to assess for variables that may impact or prevent access to social media platforms.

Additionally, future research could also be sent out via other methods in order to gain participants to gain a sample more reflective of the general population in the United

States.

Summary of Discussion and Implications

The purpose of this study was to explore the relationships between social media addiction with anxiety, FOMO, and interpersonal problems when controlling for demographic and social media related variables (i.e. age and time spent using social media). The results of this study indicated that when taken together, anxiety, FOMO,

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interpersonal problems, demographic, and social media related variables are significantly related to social media addiction. When examining the study even further, results revealed that interpersonal problems had the strongest relationship with social media addiction when controlling for the demographic and social media related variables, followed closely by the relationship with FOMO. The relationship with anxiety was the weakest of the three independent variables.

The current study advanced the knowledge of social media addiction and its relationship with other mental health concerns, like anxiety, FOMO, and interpersonal problems. Additionally, it contributed to a stronger understanding of FOMO, as it is a newer construct and less research has been conducted studying it. This study indicated that age and the amount of time spent using social media were the demographic and social media related variables with the most significant relationships to social media addiction. Understanding that each of these variables (anxiety, FOMO, interpersonal problems, age, and time spent using social media) had a significant tie to social media addiction whether collectively or independently offers further support to ensure that social media use and/or addiction is assessed and given consideration in counseling, especially when clients report concerns related to anxiety, FOMO, or interpersonal problems. In addition to that, this study indicated that there is a need to develop interventions related to treating social media addiction and its associated concerns.

Counselors, counselor supervisors, and counselor educators alike can benefit from the results of this study. Given this information, counselors can ensure they are assessing their clients’ social media use and how it is impacting their lives or presenting concerns.

Additionally, counselors may then also work toward developing or using effective

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interventions for treating behavioral addictions, specifically for a social media addiction.

Counselor supervisors can support and encourage their supervisees in the process of assessing and/or monitoring clients’ social media use and its effect on their functioning and concerns or in developing the best ways to do so. Similarly, counselor educators can support this as best practice for their students and incorporate the results of this research regarding social media into their instruction and coursework for their students. As a result, students would be knowledgeable about trends in society, how said trends impact clients, and how they can work with clients who experience presenting problems related to social media.

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APPENDICES

141 APPENDIX A

THE UNIVERSITY OF AKRON INFORMED CONSENT

Title of Study: The relationship between social media addiction and anxiety, the fear of missing out, and interpersonal problems.

My name is Marisa Cargill and I am a doctoral student in the Department of Counselor

Education and Supervision at the University of Akron. I am conducting a study for my dissertation and invite you to consider participating.

What is this study about? The purpose of this study is to examine the relationship between social media addiction and anxiety, the fear of missing out, and interpersonal problems.

Who can participate in this study? Only individuals age 18 or older will be included in this study. All research participants must identify as a social media user.

What will your participation involve? You will be asked to respond to an online survey as an adult who uses social media. Questions included in this survey are related to your social media use, the fear of missing out, anxiety, and interpersonal problems. This survey will take approximately 20 minutes to complete and you will be asked to provide some demographic information as well, so we can generally describe who participated in the study.

What are the risks involved in participating? There are no known physical, social, legal, or economic consequences or risks related to the completion of this study.

142 What are the benefits involved in participating? The benefits of this study are indirect as results may increase the counseling profession’s understanding of mental health concerns associated with social media use and social media addiction. One direct benefit of this study includes receipt of a $20.00 gift card from Amazon by ten randomly chosen participants for their completion of the online survey.

Right to refuse of withdraw: Your participation in this research study is strictly voluntary and you may refuse to participate, or may discontinue participation, at any time without penalty.

Anonymous data collection: No identifying information will be collected in this study, unless participants provide their information for the drawing. Participants will have the option to include their email address at the end of the survey, if they would like to enter in the drawing for the research incentive. However, anonymity will be preserved throughout the research design.

Confidentiality of Records: The data will be entered into a secure password-protected online survey software platform, secured in a password protected computer, and then exported only using de-identified survey responses into the Statistical Package for the

Social Sciences (SPSS) for analysis. Data will not be released to third parties and will be destroyed upon completion of the entire study protocol.

Whom to contact with questions: If you have any questions about this study, you may contact Marisa Cargill, Principle Investigator, at [email protected] or Robert C.

Schwartz, Faculty Advisor, at [email protected] or 330.972.8155.

143 Informed Consent: I acknowledge that I have read the information provided above and have no further questions regarding the research study at this time. I voluntarily agree to participate in this study.

144 APPENDIX B

DEMOGRAPHIC QUESTIONNAIRE

Instructions: Please circle or write your response.

1. Age: ______

2. Gender: Man Woman Other (specify) Prefer not to answer

3. Race (please circle one):

a. American Indian or Native Alaskan

b. Asian

c. Black / African

d. Black / African American

e. Hispanic / Latin American

f. Indian / Pakistani

g. Middle Eastern

h. Native Hawaiian or Other Pacific Islander

i. White / European American

j. Bi-Racial (Please explain): ______

k. Multi-Racial (Please explain): ______

l. Other (Please explain): ______

4. What is your estimated annual income?

a. Less than $20,000

b. $20,000 to $34,999

145 c. $35,000 to $49,999

d. $50,000 to $74,999

e. $75,000 to $99,999

f. Over $100,000

5. How much time do you spend using social media daily?

a. Fewer than 2 hours

b. 2-4 hours

c. 5-7 hours

d. 8-10 hours

e. More than 10 hours

6. What social media platform do you use most?

a. Facebook

b. Instagram

c. Twitter

d. SnapChat

e. Pinterest

f. LinkedIn

g. Other (please specify): ______

146 APPENDIX C

THE FEAR OF MISSING OUT SCALE (FoMOS)

Below is a collection of statements about your everyday experience. Using the scale provided please indicate how true each statement is of your general experiences. Please answer according to what really reflects your experiences rather than what you think your experiences should be. Please treat each item separately from every other item.

Not at all true of me = 1 Slightly true of me = 2 Moderately true of me =3 Very true of me = 4 Extremely true of me = 5

1. I fear others have more rewarding experiences than me. 1 2 3 4 5

2. I fear my friends have more rewarding experiences than me. 1 2 3 4 5

3. I get worried when I find out my friends are having fun without me. 1 2 3 4 5

4. I get anxious when I don’t know what my friends are up to. 1 2 3 4 5

5. It is important that I understand my friends ‘‘in jokes’’. 1 2 3 4 5

6. Sometimes, I wonder if I spend too much time keeping up with what is going on. 1 2 3 4 5

7. It bothers me when I miss an opportunity to meet up with friends. 1 2 3 4 5

8. When I have a good time it is important for me to share the details online (e.g. updating status). 1 2 3 4 5

9. When I miss out on a planned get-together it bothers me. 1 2 3 4 5

10. When I go on vacation, I continue to keep tabs on what my friends are doing. 1 2 3 4 5

147 APPENDIX D

STATE TRAIT ANXIETY INVENTORY – T SCALE

A number of statements which people have used to describe themselves are given below. Read each statement and then select the appropriate number below the statement to indicate how you generally feel.

1 = Almost Never 2 = Sometimes 3 = Often 4 = Almost Always

1. I feel pleasant. 1 2 3 4 2. I feel nervous and restless. 1 2 3 4 3. I feel satisfied with myself. 1 2 3 4 4. I wish I could be as happy as others seem to be. 1 2 3 4 5. I feel like a failure. 1 2 3 4 6. I feel rested. 1 2 3 4 7. I am “calm, cool, and collected.” 1 2 3 4 8. I feel that difficulties are piling up so that I cannot overcome them. 1 2 3 4 9. I worry too much over something that doesn’t really matter. 1 2 3 4 10. I am happy. 1 2 3 4 11. I have disturbing thoughts. 1 2 3 4 12. I lack self-confidence. 1 2 3 4 13. I feel secure. 1 2 3 4 14. I make decisions easily. 1 2 3 4 15. I feel inadequate. 1 2 3 4

148 16. I am content. 1 2 3 4 17. Some unimportant thought runs through my mind and bothers me. 1 2 3 4

18. I take disappointments so keenly that I can’t put them out of my mind. 1 2 3 4 19. I am a steady person. 1 2 3 4 20. I get in a state of tension or turmoil as I think over my recent concerns and interests. 1 2 3 4

149 APPENDIX E

INVENTORY OF INTERPERSONAL PROBLEMS (IIP-32)

People have reported having the following problems in relating to other people. Please read the list below, and for each item, consider whether it has been a problem for you with respect to any significant person in your life. Then, using the following choices, circle the response that describes how distressing that problem has been for you.

0 = Not at all 1 = A little bit 2 = Moderately 3 = Quite a bit 4 = Extremely

It is hard for me to: 1. Say “no” to other people 0 1 2 3 4 2. Join in on groups 0 1 2 3 4 3. Keep things private from other people 0 1 2 3 4 4. Tell a person to stop bothering me 0 1 2 3 4 5. Introduce myself to new people 0 1 2 3 4 6. Confront people with problems that come up 0 1 2 3 4 7. Be assertive with another person 0 1 2 3 4 8. Let other people know when I am angry 0 1 2 3 4 9. Socialize with other people 0 1 2 3 4 10. Show affection to people 0 1 2 3 4 11. Get along with people 0 1 2 3 4 12. Be firm when I need to be 0 1 2 3 4

150 13. Experience a feeling of love for another person 0 1 2 3 4 14. Be supportive of another person’s goals in life 0 1 2 3 4 15. Feel close to other people 0 1 2 3 4 16. Really care about other people’s problems 0 1 2 3 4 17. Put somebody else’s needs before my own 0 1 2 3 4 18. Feel good about another person’s happiness 0 1 2 3 4 19. Ask other people to get together socially with me 0 1 2 3 4 20. Be assertive without worrying about hurting the other person’s feelings 0 1 2 3 4

The following are things that you do too much. 21. I open up to people too much. 0 1 2 3 4 22. I am too aggressive toward other people. 0 1 2 3 4 23. I try to please other people too much. 0 1 2 3 4 24. I want to be noticed too much. 0 1 2 3 4 25. I try to control other people too much. 0 1 2 3 4 26. I put other people’s needs before my own too much. 0 1 2 3 4 27. I am overly generous to other people. 0 1 2 3 4 28. I manipulate other people too much to get what I want. 0 1 2 3 4 29. I tell personal things to other people too much. 0 1 2 3 4 30. I argue with other people too much, 0 1 2 3 4 31. I let other people take advantage of me too much. 0 1 2 3 4 32. I am affected by another person’s misery too much. 0 1 2 3 4

151 APPENDIX F

INTERNET ADDICTION TEST (MODIFIED FOR SOCIAL MEDIA)

Name______Men _____ Women ______Age ______Years Online ______Do you use the Internet for work? ______Yes ______No

This questionnaire consists of 20 statements. After reading each statement carefully, based upon the 5-point Likert scale, please select the response (0, 1, 2, 3, 4 or 5) which best describes you. If two choices seem to apply equally well, circle the choice that best represents how you are most of the time during the past month. Be sure to read all the statements carefully before making your choice. The statements refer to offline situations or actions unless otherwise specified.

0 = Not Applicable 1 = Rarely 2 = Occasionally 3 = Frequently 4 = Often 5 = Always

1. ___How often do you find that you stay on 10. ___How often do you block out disturbing social media longer than you intended? thoughts about your life with soothing 2. ___How often do you neglect household thoughts of social media? chores to spend more time on social media? 11. ___How often do you find yourself 3. ___How often do you prefer the excitement anticipating when you will go on social of social media to intimacy with your media again? partner? 12. ___How often do you fear that life without 4. ___How often do you form new social media would be boring, empty, and relationships with fellow social media users? joyless? 5. ___How often do others in your life 13. ___How often do you snap, yell, or act complain to you about the amount of time annoyed if someone bothers you while you you spend on social media? are on social media? 6. ___How often do your grades or school 14. ___How often do you lose sleep due to work suffer because of the amount of time being on social media? you spend on social media? 15. ___How often do you feel preoccupied with 7. ___How often do you check your social social media when off-line, or fantasize media accounts before something else that about being online? you need to do? 16. ___How often do you find yourself saying 8. ___How often does your job performance or "just a few more minutes" when on social productivity suffer because of social media? media? 9. ___How often do you become defensive or 17. ___How often do you try to cut down the secretive when anyone asks you what you amount of time you spend on social media do on social media? and fail? 18. ___How often do you try to hide how long you've been on social media?

152 19. ___How often do you choose to spend more time on social media over going out with others? 20. ___How often do you feel depressed, moody, or nervous when you are off-line, which goes away once you are back on social media?

153