THE RELATIONSHIP BETWEEN VIDEO GAME USE, INTERNET USE, ADDICTION, AND SUBJECTIVE WELL-BEING
A THESIS Presented to the School of Social Work California State University, Long Beach
In Partial Fulfillment of the Requirements for the Degree Master of Social Work
Committee Members:
Janaki Santhiveeran, PhD. Venetta Campbell, Psy D. Lisa Jennings, PhD.
College Designee:
Nancy Meyer-Adams, PhD.
By Martin Molinos B.A, 2012, University of California, Riverside August 2012
ABSTRACT THE RELATIONSHIP BETWEEN VIDEO GAME USE, INTERNET USE, ADDICTION, AND SUBJECTIVE WELL-BEING By Martin Molinos August 2016 This quantitative study investigates the relationship between video game usage, video game addiction, compulsive Internet use, and subjective well-being. The key variables were measured using three different scales: The Game Addiction Scale; the Compulsive Internet Use Scale; and the Flourishing scale. One hundred and twenty-one participants over the age of 18 partook in the study. The empirical results demonstrate a statistically significant, negative correlation between addictive video game usage and well-being. Video game addiction and compulsive Internet use were both found to be negatively correlated with subjective well-being.
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ACKNOWLEDGEMENTS A big thanks to Dr. Santhiveeran and Dr. Brocato. An equal amount of thanks to my parents and sister. An almost identical amount of thanks to Mark, Jena, Matt, Megan, and Nicolas. I would like to thank the man who came to me when my heart needed mending. He opened me up and made my heart whole again, although leaving a slight scar when done. Dr. Razzouk, my cardiothoracic surgeon, you’re the best. To the clients, of which we all are at some point.
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TABLE OF CONTENTS ABSTRACT........................................................................................................................................ ii ACKNOWLEDGEMENTS……………………………………………………………………. iii LIST OF TABLES.............................................................................................................................. v 1. INTRODUCTION.................................................................................................................. 1 2. LITERATURE REVIEW ...................................................................................................... 4 3. METHODOLOGY .................................................................................................................19 4. RESULTS ...............................................................................................................................24 5. DISCUSSION……………………………………………………………………………35 APPENDICES ....................................................................................................................................39 A. CONSENT FORM .................................................................................................................40 B. STUDY INSTRUMENTS .....................................................................................................44 C. DEMOGRAPHICS SURVEY ..............................................................................................49 REFERENCES ...................................................................................................................................54
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LIST OF TABLES 1. Cronbach’s Alpha of CIUS, GAS, and FS ...........................................................................22 2. Demographic Characteristics (N = 121) ...............................................................................27 3. Gender Versus FS, GAS, and CIUS (N = 121) ....................................................................28 4. Ethnicity Versus FS, GAS, CIUS..........................................................................................28 5. Age Versus FS, GAS, and CIUS ...........................................................................................30 6. Tukey HSD of Age Versus GAS...........................................................................................31 7. Education Versus FS, GAS, and CIUS………………………………………….…………………………… 31 8. ANOVA of Employment Versus FS, GAS, and CIUS………………………………….……………. 32 9. Pearson’s r correlation between FS, GAS, and CIUS……………………………………………..……34
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CHAPTER 1 INTRODUCTION Two major innovations of the 20 th century are the Internet and video games. The Internet has made it possible for people miles apart to connect instantly. Improvements in technology have made these forms of entertainment more engaging and more accessible. Using surveys from 148 countries, Morales (2013) noted that worldwide, 32% of adults in the year 2011 had access to the Internet. This worldwide percentage of adults with access to the Internet increased from just 21% in 2007. In the United States alone, 80% of respondents declared that they had access to the Internet (Morales, 2013). With the ubiquity of smartphones, access to the Internet for the general population is ever increasing. Today, the various uses of the Internet range from e-mailing, chatting, instant messaging, researching, shopping, gambling to accessing social media (Sheldon, 2008). While the Internet can be described as multifaceted, video games are mostly used in a recreational sense. In a survey of 1,102 American teens, Princeton Survey Research Associates found that 97% of the teenagers disclosed that they play video games. The results were fairly equal across age, with 99% of the males, and 94% of the females admitting to playing video games (Lenhart et al., 2008). While this points to a large percentage of adolescents playing video games, this technical innovation is not limited to the young. In a study by the Entertainment Software Association (ESA), it was found that 27% of Americans over the age of 50 years play digital games (2015). Because both the Internet and video games have grown in popularity and technical sophistication, concerns have been raised that they can be addictive. 1
Overuse of these forms of entertainment could have consequences for personal levels of wellbeing. To gain a clearer understanding whether these concerns are justified, this study sought to investigate the risk of addiction to the Internet and video games using empirical methods. Research Questions This quantitative study examines the relationship between compulsive Internet usage and video game addiction as they relate to their subjective well-being. The study explored the following research questions: 1. Is there an association between video game use and Internet addiction? 2. Is there an association between video game use and subjective well-being? 3. Is there an association between Internet use and addiction and subjective well-being? 4. What is the association between demographic characteristics (age, gender, ethnicity, job status, generation type) and subjective well-being? Definitions of Terms Internet addiction: The continued use of the Internet despite interferences in biological, psychological, or social functioning (Demirer, Bozoglan, & Sahin, 2013). Internet use: Includes watching media online, searching the Internet, using e-mail, using instant messaging, or using social networking (Demirer et al., 2013). Subjective well-being: The absence of mental illness or disorders and a positive flourishing in biological, emotional, mental, spiritual, social, and relationship domains (Keyes, 2007).
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Video game: For the intent of the study, the term video game is operationalized as described previously by Jones, Scholes, Johnson, Katsikitis, and Carras (2014) as, “electronic/digital games played on personal computers, home consoles (e.g., Microsoft Xbox, Sony Playstation, Nintendo Wii), tablets (e.g., iPads), and mobile devices (e.g., smart phones, handhelds like Nintendo 3DS)” (p. 260). Video game addiction: Addiction, is measured by the Game Addiction Scale, which includes the seven criteria of addiction: salience; tolerance; mood modification; relapse; withdrawal; conflict; and problems (Lemmens, Valkenburg, & Peters, 2009).
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CHAPTER 2 LITERATURE REVIEW The Internet and video games have become ubiquitous over the past 20 years. They enhance our ability to do research, connect with people around the world, entertain ourselves, and much more. Over the foreseeable future, these technologies will continue to grow at a rapid pace and the number of people affected by them will grow. Therefore, it is imperative to understand how these technologies impact our lives. While these two technologies have positive aspects to them, with overuse they can have a darker side. In a study of 531 college students, 13% of respondents reported that their usage levels interfered with their ability to function (Sherer, 1997). Neither Internet addiction nor video game addiction is classified as a disorder in the Diagnostic and Statistical Manual of Mental Disorders 5th edition (DSM-V); however, they are both classified as conditions that require further study (American Psychiatric Association, 2013). While there is disagreement over the treatment of and classification of non-chemical addictions such as video game or Internet addictions, the consensus is to follow the operational definition posited by Griffiths (2005) for any addictive behavior. This definition requires the fulfillment of six conditions to classify behaviors as an addiction. These six criteria are salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse (Griffiths, 2005). To assess the effects of Internet and video game use, this study uses a strengths-based approach. The reason for this approach is the view proposed by Keyes (2007), which is that the absence of mental illness does not, in itself, constitute positive mental health. Huppert and So 4
(2013) found that higher well-being is associated with positive mental health. Based on this view of positive mental health, this study examines the effect of the Internet and video games on subjective well-being. There are several different measures of well-being. Huppert and So (2013) state that, for well-being to be accurately measured, a multi-faceted approach which accounts for emotional stability, meaning, positive emotion, optimism, and engagement should be taken. Diener et al. (2009) created the flourishing scale to assess “meaning and purpose, supportive and rewarding relationships, engaged and interested, contribution to the well-being of others, competence, selfacceptance, optimism, and being respected” (p. 252). Much of the current literature focuses on the negative aspects of video games and the Internet, mainly regarding addiction. Nevertheless, other studies highlight the positive qualities of these technologies. Boot, Blakely, and Simons (2011) studied individuals who played action video games, and found that, compared to non-players, players performed better on tests of perception and cognition. In a 3-year longitudinal study of video game usage among adolescents and children in Singapore, researchers noted both negative and positive effects. Negative effects included gaming addiction and aggression, while the positive effects included prosocial behavior, empathy, and improved social relationships (Khoo, Chen, & Hyekyung 2015). Although the positive effects of playing video games have been established for adolescents, there is a gap in the literature on the consequences of video games on adults. Psychological Well-Being, Media Use, and Demographic Variables
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Current research suggests that both age and gender might be related to the propensity for an individual to show and experience problematic behavior surrounding video game and internet usage. In a study investigating problematic Internet usage, Beutal et al. (2011) found that individuals under 30 years of age tended to show more signs of compulsive Internet usage. In a separate study, Vitak, Crouse, and LaRose (2011) found that younger males, as opposed to older females, tended to have higher rates of Internet usage and these higher rates could be classified as problematic. A study of 247 teachers in Turkey (87 males and 160 females) analyzed addiction levels with respect to gender and self-rated life satisfaction (Demirer et al., 2013). This study found a statistically significant difference between males and females, where males showed more instances of Internet addiction, as measured by the Internet Addiction Scale (Demirer et al., 2013). Frangos, Fragkos, and Kiohos (2010) surveyed 1,876 students from ages 18 to 27 studying at universities in Greece and found 11.6% of the students sampled met the criteria for Internet addiction. Additionally, the researchers also found that males were more likely than females to exhibit Problem Internet Use (PIU) behaviors. The literature addressing gender and video game addiction does not have a clear consensus regarding the relationship between gender and video game playing amongst adults. Some studies found that males are more likely to play video games than females. Ogletree and Drake (2007) found that, in a sample of 206 college students in the United States, it was significantly more likely for males to play over 2 hours per day more than females. These results, however, were not found by Haagsma, Pieterse, and Peters (2012) who studied a wider age range between 14 to 81 year olds by surveying 902 respondents. They find a higher 6
incidence of problematic use by females than in other similar studies (Haagsma et al., 2012). Differences in the preferred genre of video game play have also been observed between genders. Jansz, Avis, and Vosmeer (2010) studied the preferences in video game play, and found that males were far more likely to choose games that included elements of social interaction with other players, including hints of fantasy and role-playing, whereas women preferred casual video game playing. Furthermore, Haagsma et al. (2012) showed that the type of game and level of engagement can also differ according to gender. It was found that females were more likely to play casual games such as those in web browsers, whereas males were more likely to play video games that required offline use. Regarding whether male players preferred certain genres of video games, Koo (2009) found similar results in that males were more apt to enjoy video games that had fantastical elements, because these elements tended to help them escape into the game. Koo also found that males tended to gravitate more towards the idea that games gave a sense of social affiliation, one of the main components of well-being. Many video games have multiplayer capabilities, and as such, can have positive effects on domains of well-being. A study of British young adults (10 to 15 years of age, n=5,000) examined the effects of videogame playing on psychosocial factors. In this study, low levels of video game use were defined as playing less than 1 hour of video games per day (Przybylski, 2014). The study found that those individuals who averaged 1 hour of video game play or less were significantly more likely than non-game players to have increased life satisfaction, higher prosocial behavior, and a lower instance of both externalizing and internalizing problems (Przybylski, 2014). Additionally, the possible therapeutic aspects of video games in multiple dimensions has 7
been studied. The effects of video games on positive and clinically relevant health outcomes were studied in a meta-analysis of 38 studies by Primack et al. (2012). The study measured 195 health outcomes from different interventions that utilized video games. The different interventions included psychological therapy, physical therapy, physical activity, clinician skill, pain distraction, and health education outcomes. Primack et al. found that when video games were included in treatment plans, the inclusion of video games improved 69% of psychological therapy outcomes and 59% of physical therapy outcomes. Apart from their effect on demographics on Internet use and gameplay, demographic factors can influence personal well-being directly. In a study of 177 adults, Chen, Lee, Pethtel, Gutowitz, and Kirk (2012) measured well-being against age. They found that young adults (ages 18 to 27) did not differ from older adults (ages 40 to 89) in their well-being (Chen et al., 2012). Positive effects of video games have been shown to be present in domains of attention as well. In an fMRI study comparing video game players to non-video game players, the researchers found that the mechanisms governing the control of attentional resources were less active during challenging tasks than in non-gamers. The researchers conclude that this difference is due to the video game players having more developed cognitive abilities, and being able to utilize their attentional resources more efficiently than the non-gamers (Granic, Lobel, & Engels, 2014). Video Game Use and Video Game Addiction The amount of time spent playing video games has been linked to pathological video game use in various studies. In a comparison study of pathological versus non-pathological gamers, Grusser, Thalemann, and Griffiths (2007) found that pathological gamers spent a 8
significantly longer amount of time playing than non-pathological gamers. Participants who met three out of six criteria for addiction, according to the World Health Organization’s International Classification of Diseases (ICD-10), were defined as pathological gamers. The pathological gamers played an average of 4.7 hours daily, whereas the non-pathological gamers spent an average of 2.49 hours playing daily (Grusser et al., 2007). In a study of Internet users in the Netherlands, however, the average weekly time participants who met the criteria for problematic video game use was 55.3 hours, with a mean of 7.9 hours per day (Rooij, Schoenmakers, Vermulst, Eijnden, & Mheen, 2010). The correlation between the time that is allocated towards video games and problematic use has been supported by other studies as well (Donati, Chiesi, Ammannato, & Primi, 2015; Porter Porter, Starcevic, Berle, & Fenech, 2010). There is still disagreement over the cutoff point for the amount of time spent playing video games that warrants a classification of addiction. As stated by Griffiths (2005), “the difference between an excessive healthy enthusiasm and an addiction is that healthy enthusiasms add to life whereas addictions take away from it” (p. 195). In an attempt to find correlates of gaming and Internet addictions, Kuss and Griffiths (2012) conducted a systematic literature review of neuroimaging studies. They found that those who showed gaming and Internet addictions had brain structures characteristics similar to individuals with substance abuse characteristics. Although they are not chemical in nature, video game and Internet use can affect the dopamine and rewards pathways in the brain similar to substances that have abuse potential. Ream, Elliott, and Dunlap (2011) conducted a study assessing problematic video game use along with substance use in a study of adults (n = 1,196) 9
who reported playing video games on a regular or occasional basis. They found that problematic video game use was associated with problem caffeine, tobacco, alcohol, marijuana, and painkiller use (Ream et al., 2011). While this study focuses on any subjects using video games, research has been done studying gaming addiction and avoidant coping. Kwon, Chung-Suk, and Lee (2011) find that among middle school students, those who report higher levels of gaming addiction often experienced difficulties coping with negative moods, and used the games to escape their negative emotions (Kwon et al., 2011). The correlates and predictive factors pertaining to pathological use have also been studied. The age of the person playing video games has been tied to the risk of developing behaviors linked to pathological video game use. In a study of 1,319 people who responded to a survey in the United Kingdom, aged 16 to 51 years, Morrison and Gore (2010) found that younger respondents are more likely to meet the classification for problematic video game use. As a relatively new category of research, studies vary in their findings on the prevalence of video game addiction. Ferguson, Coulson, and Barnett (2011) found the rate of video game addiction in their population of video game players to be approximately 3%. Similarly, Haagsma et al. (2012) found a rate of 3.3% of problematic video game use among adolescents and young adults in the Netherlands. The respondents whose preferred game was Massively Multiplayer Online Role Playing Games (MMORPG) spent significantly larger amounts of time playing video games than those who play other video game genres. There is a direct association between video game addiction 10
and the player’s preferred genre. Not only the preferred genre, but also having a variety of genres has been linked to addiction. In a study of the behavioral risk factors associated with video game addiction, researchers have found that those who meet the classification for gaming addiction tend to play a wider variety of video game genres (Donati et al., 2015). Male adolescents (n = 701) were assessed via the Gaming Addiction Scale (GAS) to assess pathological, or addictive, use against a list of 14 video game genres. The study found that gaming versatility was correlated positively to pathological gaming use (Donati et al., 2015). While these researchers found results linking variety of genre to addiction levels, specific video game genres have also been linked to gaming addiction. In a study of college age students (M = 21.36) attending Eastern Michigan University (n = 1,013), preferred genre of video game play was assessed versus game addiction scores (Pouliot, 2014). The results showed that there were higher rates of addiction in the video game players who preferred the MMORPG and Shooter genres (Pouliot, 2014). The contributing factor of the type of video game genre on video game addiction was also studied by Elliott, Ream, McGinsky, and Dunlap (2012). The researchers used a national online survey to assess video game addiction in adults (n = 3,380) and found that the Role Playing Game and Shooter genres were both positively correlated with addiction (Elliott et al., 2012). Internet Use and Internet Addiction As a relatively new subject, the occurrence of Internet addiction (IA) has had little study. Kuss, Griffiths, and Binder (2013) studied 2,257 university students in the United Kingdom. The 11
participants took an online survey assessing IA and their online activities suspected of being correlates. The results of the survey showed that 3.2% of the students met the classification for IA. The rate of Internet addiction in sampled populations is fairly similar across studies. A study of German students (n = 4,436) examined well-being and levels of compulsive Internet use. It was found that utilizing the compulsive Internet use scale (CIUS), 4.7% (n = 200) of the sample met the criteria for IA (Rehbein & Mößle, 2013). Additionally, it was found that those meeting pathological Internet use levels also differed in their preferred activity while using the internet. For females, 96% stated social networking as the highest contributor to their addiction, while for males only 62% cited social networking, and 38% of the males stating different problems as the root of the cause. For the males, 17.2% stated that viewing online pornography contributed to their problem. Aboujaoude, Koran, Gamel, Large, and Serpe (2006) conducted a random telephone survey of 2,513 adults in the United States. This survey was designed to assess IA using four diagnostic criteria. These four markers were interference with relationships, preoccupation while offline, unsuccessfully cutting down, and staying online longer than intended. More rigid criteria also included the respondent finding it hard to stay away from the Internet, the respondent using the Internet for escape, and the respondent concealing Internet use. The researchers found that 0.7% of the sample met the criteria for the first marker; however, when the most rigid criteria were added, the number of respondents who met the criteria dropped to 0.3% (Aboujaoude et al., 2006). Durkee et al. (2012) found that the mean hours spent online by European Internet users was significantly correlated with PIU. This study found a higher amount of mean time was 12
correlated with those meeting the classification of PIU. Lastly, the mean time spent by those who met the classification of PIU spent 3.75 hours of Internet use daily (Durkee et al., 2012). Hawi (2012) found that those who classified into PIU groups averaged 7.5 hours daily in their Internet usage. In this study of 833 students in public and private schools, students were given a questionnaire with Young’s Internet Addiction Test (YIAT). The data from the study indicated that 4.2% of the sample population had problems with their Internet usage at a level that could be labeled as compulsive, which is a criterion for addiction (Hawi, 2012). In their study of Internet addiction rates of Turkish teachers, Demirer et al. (2013) found that one of the significant factors predicting IA was the access to the Internet. Those who had personal computers and Internet access were far more likely to exhibit behaviors associated with PIU (Demirer et al., 2013). Lastly, it was found that there were no differences between adults and youth in their time spent online (Mitchell, Becker-Blease, & Finkelhor, 2005). Because IA is a relatively new area of study, various research studies have been performed attempting to investigate possible correlates and comorbidities as well as predictive factors. In an Italian study of 50 outpatient clients who reported to a clinic for conditions other than IA, Bernardi and Pallanti (2009) screened the individuals for IA using Young’s Internet Addiction Scale (IAS). The researchers found that those who met the criteria for IA were 15% co-morbid with attention deficit and hyperactive disorder, 7% hypomania, 15% generalized anxiety disorder, 15% social anxiety disorder, 7% dysthymia, 7% obsessive compulsive personality disorder, and 7% avoidant personality disorder. Internet Use, Video Game Use, and Well-Being 13
Previous studies can inform our expectations regarding the effects of Internet use and video games on positive well-being. In a meta-analysis surrounding psychosocial well-being and video game use literature, Jones et al. (2014) found that moderate use of video games correlated with increases in well-being, whereas very high levels of video game play were associated with lower levels of well-being. Based on the results reported by Jones et al. the researcher expects to find correlations between IA, video game addiction, and well-being. The effects of Internet use on well-being have been researched in other studies as well, and like Jones et al. (2014), many of these studies find a positive effect between particular online activities and well-being. In a study of 1,365 adolescents, Boniel-Nissim and Barak (2013) asked participants to maintain a blog (online journal) over the course of 10 weeks. After preand post-test measures had been calculated, they found that the participants showed less emotional distress and had higher emotional well-being (Boniel-Nissim & Barak, 2013). These findings have also been seen in other studies as well. In a study surveying 692 respondents who regularly made use of online journals and blogs, H. C. Ko and Kuo (2009) found that these online activities significantly increased subjective well-being in the respondents. They posit that these positive outcomes are a result of the bloggers receiving social support through these online communities. Other studies have also found positive relationship between video games and well-being. Allaire et al. (2013) assessed older adults for well-being, negative affect, depression, and social functioning, comparing gamers and non-gamers. They found that the older adults who participated in video games showed higher levels of well-being, lower levels of depression, and higher levels of social 14
functioning. In another study based in Singapore, seniors 56 to 92 years old who resided in a long term care facility showed higher scores on the Rosenberg Self Esteem Scale, as well as the Bradburn Affect Balance Scale, after playing video games (Jung, Li, Janissa, Gladys, & Lee, 2009). The authors of this study postulate that the higher affect and self-esteem scores were the result of the physical activity subjects engaged in while playing the Wii game (Jung et al., 2009). A study conducted in Germany followed 4,500 video game players over a 2-year period (Kowert, Vogelgesang, Festl, & Quandt, 2015). To measure well-being, the authors used separate scales to assess life satisfaction, social competence, self-esteem, and loneliness. The participants were stratified into three age groups, 14-18 ( n = 110), younger adults aged 19 to39 (n = 358), and older adults aged 40 and over (n = 423). After 2 years, participants were assessed on variables of video game play against the measures of psychosocial well-being used in the study. It was found that lower scores in psychosocial well-being predicted higher outcomes on video game addiction, indicating that the lower scores on psychosocial well-being are predictors of addictive game play behavior (Kowert et al., 2015). According to Przybylski, Weinstein, Murayama, Lynch, and Ryan (2011), SelfDetermination Theory (SDT) is a macro theory of well-being, and can be used to contextualize the underlying mechanisms through which well-being works. Autonomy is defined as the need for individuals to feel that they have control in their life, and that they can make choices in the actions and events that pertain to their life. The psychological need of competence is defined as the need for individuals to feel as though they have mastery over their environment, and can effectively navigate it through their own power and volition. Relatedness is the psychological 15
need for individuals to feel as though they are connected to a group and experience love and care (Przybylski et al., 2011). Broeck, Vansteenkiste, Witte, Soenens, and Lens (2010, p.982) found that “all three needs are considered important for individuals’ flourishing,” and through their satisfaction, a human’s intrinsic motivation, growth, integrity, and well-being increases. Self-Determination Theory has also been used by many researchers as a means to add context to the motivations behind Internet use, as well as provide an explanation for how Internet use can start to gain inertia and become compulsive. For example, in a survey of 1,045 Internet users, Bessiere, Kiesler, Kraut, and Boneva (2008) found the respondents who reported less offline relationships tended to seek compensation for these needs online. Ebeling-Witte, Frank, and Lester (2007) found similar results in individuals attempting to connect with others online, namely that the Internet was fulfilling the SDT motivator of relatedness. These researchers posit that expressions of SDT motivators are possible predictors that an individual will eventually start using the Internet excessively. They also speculate that poor social skills and a proclivity toward shyness are predictors of excessive use (Ebeling-Witte et al., 2007). The lack of fulfillment in this social need, and its effect on utilizing the Internet has been seen in other studies as well. Sheldon, Abad, and Hinsch (2011) studied relatedness and how the internet satisfied this need. Specifically, they studied how socializing though Facebook can be seen as a means of coping with loneliness. Their results confirmed their expectations, namely that participants were driven to use the Internet as a way to cope with loneliness (Sheldon et al., 2011). In a set of three studies, Ryan, Rigby, and Przybylski (2006) measured competence, one 16
of the components of SDT, which was seen to mediate effects on well-being. In measuring competence, the researchers measured the participants’ assessment of how much mastery they felt over being able to direct the controls required to play the game. It was found that not only did the participants gain a boost to well-being after their sessions of play, but their judgments of how much competence they had also mediated the effect on well-being. A higher feeling of competency or mastery over the game predicted higher scores of well-being. Internet Addiction, Video Game Addiction, and Well-Being To investigate the motivation exhibited by some video game players, Neys, Jansz, and Tan (2014) administered a survey to) video game players (n = 7,252) who frequented video game websites. They found that the player’s motivations for playing video games closely fell in line with SDT. To study whether the pathological use of video games was a cause or effect on psychosocial well-being, Lemmens, Valkenburg, and Peter (2011) conducted a longitudinal study of 543 video game players between 11 and 17 years old (M = 13.9, SD = 1.4). They used the constructs of loneliness, life satisfaction, self-esteem and social competence to assess psychosocial well-being, and the GAS was used to assess pathological video game use. They found that lower scores on well-being predicted higher scores on the GAS. These results support the conclusion that lower psychosocial well-being is a cause, rather than a consequence, of video game addiction. In a longitudinal study of a sample of adults (n = 398), Muusses, Finkenauer, Kerkhof, and Billedo (2014) found that compulsive Internet use predicted higher scores in stress, 17
loneliness, and depression over time. To measure the effects of positive well-being, they used the subjective happiness scale, and also found that compulsive internet use predicted a decrease in positive well-being (Muusses et al., 2014). These findings have been supported by different measures of well-being, as well as in culturally diverse samples. In a sample of 479 Turkish university students, Çardak (2013) assessed possible relationships of IA and well-being. Çardak found that students with increased levels of IA were more likely to have low psychological well-being. Çardak also found that lower well-being was predicted by several other factors, including loneliness, depression, social comfort, distraction, and diminished impulse control (Çardak, 2013). These findings are supported by C. H. Ko et al. (2008) who found that PIU is correlated with low life-satisfaction.
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CHAPTER 3 METHODOLOGY Study Design This study uses a quantitative design, and is descriptive in nature. The variables examined in the study include compulsive Internet use, subjective well-being, video game addiction and how these relate to levels of video game and Internet use. The primary data were collected using a self-administered survey website, www.google.com/forms. Sampling The study sample included adults, aged 18 years or older, who have access to the Internet and can read English. To recruit participants, non-probability snowball sampling was utilized. Participants were recruited through Facebook and the website Reddit. The procedure for recruiting by Facebook was to post a link to the survey, followed by a pre-written prompt. The same prompt and link were also posted on relevant forums on Reddit. Recipients who received the script were encouraged to forward or re-post the message on their Facebook wall to increase visibility. Hello, I am finishing my Master’s Degree in Social Work. In fulfillment of the degree, I am writing a thesis in which I am studying the relationship between levels of video game use, addiction, Internet addiction, and subjective well-being. If you are 18 years of age or older, have access to the Internet, and are fluent in English I’m inviting you to participate in my research. Please note your participation in this is completely voluntary, your 19
responses will be kept confidential as no identifying information is kept, and you can opt out of the survey at any time should you so choose. The survey should take about 20 minutes. If you have any questions, please email me at …
[email protected] Thank you! If you would like more information, please follow click following link: http://goo.gl/forms/mpKfYUbwM2 Study Instrument If the individual chose to participate in this study, they were asked to complete a survey about their Internet usage, video game usage, and some demographic information. The entire survey consisted of 39 questions. Ten questions related to demographics, seven consisted of the Game Addiction Scale, 14 consisted of the Compulsive Internet Use Scale, and eight consisted of the Flourishing Scale. The survey allowed subjects to skip questions. The Compulsive Internet Use Scale (CIUS) has shown internal consistency, validity, and reliability. The scale is based on criteria used to assess addiction in gambling, but has been changed to assess Internet use. The second scale used was the Game Addiction Scale (GAS). The GAS is composed of seven questions created by Lemmens et al. (2009), and is designed to assess the level of video game addiction in adolescents. The scale is designed to assess the characteristics commonly associated with addiction: salience, tolerance, mood modification, relapse, withdrawal, conflict, and problems. Each question is scored on a Likert scale, where responses range from 1 indicating a response of “never” to 5 indicating a response of “very often.” Select sample questions include, “Have others unsuccessfully tried to reduce your game use?” and “Have you felt bad when you were unable to play?” To meet the criteria for addiction on the GAS, a 20
respondent must answer 3 (“sometimes”) or higher on every single item, or they would not meet the criteria. To clarify, if a respondent had a score of 5 on six items, and a score of 1 on the last they would not meet the criteria. Although the GAS was created to assess video game addiction in adolescents, it can also be used to assess adults. MacGregor (2014) established concurrent validity of the GAS when used to assess a population of 2,820 adult video game players. MacGregor examined the relationship between life satisfaction, loneliness, social competence, and the GAS, and found good concurrent validity and inter-item reliability (Cronbach’s alpha = 0.90). The Flourishing Scale (Diener et al., 2010) is used to measure Psychosocial well-being. Before this scale, the Satisfaction with Life Scale was used to assess positive functioning, although it did not measure competence, self-acceptance, meaning, or engagement (Diener et al., 2010). The Flourishing Scale is a short 8-item measure, which summarizes the respondent’s reported success in the following areas: relationships; self- esteem; purpose; and optimism. This scale has good validity, and correlates with other measures of psychosocial well-being. It consists of a 7-level Likert scale, where an answer of 1 is coded as, “strongly disagree” and an answer of 7 is coded as “strongly agree”. For example, to the statement “I am engaged and interested in my daily activities” a respondent would answer on the scale of 1 to 7. Table 1 contains the reliability statistics for the CIUS, GAS, and FS for the sample in this study. Data Collection Methodology The collection of data started in October of 2015, after the approval letter from the Institutional Review Board was received. The recruiting process was as follows. First, 21
the Initial Contact form (see Appendix A) was both posted on Reddit and the researcher’s Facebook page. Once the prospective participants had read the initial message, they were able to click a link allowing them to continue with the survey, where they could decide if they were eligible and would like to continue. After the participants clicked “agree” on the Consent Form (see Appendix B), they were allowed to access the final portion of the survey. Data collection began October 2015, and ended at the end of November 2015, at which point the researcher began to analyze the data. TABLE 1. Cronbach's Alpha of the CIUS, GAS, and FS Instrument
Possible Range
Observed Range
M
SD
Alpha(α)
CIUS
0-56
0-50
18.95
11.09
.876
GAS
10-40
7-30
14.67
5.8
.800
FS
8-56
11-48
38.79
7.00
.786
Data Analysis For the current research study, the researcher utilized the Statistical Package for the Social Sciences (SPSS) in analyzing the information received from participants. Depending on
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the measurement levels of obtained data, relationships between variables were assessed with independent group t-tests, bivariate correlations, or one-way ANOVAs. Ethics The participants of the study were recruited voluntarily and were required to acknowledge the informed consent form before initiation of the survey. Confidentiality was maintained through google.com, which maintains the anonymity as well as the security of the participant’s data. No identifiable information was asked, and Google Forms does not obtain the IP address of participants, which ensures that the data obtained from a participant cannot be identifiable to a certain computer, work station, or geographic area. Participants were informed of the ability to skip questions, or to stop taking the survey at any time. Contact information was provided to the participants, as well as any other party in the interest of transparency. Relevance to Social Work with Older Adults and Families The National Association of Social Workers (NASW) Code of Ethics states that in the area of competence, social workers should work to improve the knowledge base of the profession (2008). By attempting to test an adult scale of the flourishing and increase understanding of video game addiction, this study aims to improve the knowledge base regarding both measurement instruments and a growing social problem. Additionally, prior to this study, the GAS was rarely used to study an adult population. This study increases the research base of video game addiction in adults, and also investigates the efficacy of the GAS scale when used to assess addiction. This study expands research into the adult population, and also investigates the possible positive effects of video game use. 23
CHAPTER 4 RESULTS Descriptive Statistics Table 2 contains the descriptive demographic statistics for the sample. In the sample there were more male respondents (66.1%) than female respondents (32.2%). A large portion of the sample identified their ethnicity as Caucasian (73.6%), with a smaller number reporting as Latino (20.7%) and as others (5.8%). The age of the respondents was diverse, with a majority of the respondents identifying as 18-25 year olds (54.5%). The second largest age group was the 26-35 year olds (30.6%). Following this, the smallest age group turned out to be 36-75 year olds (14.9%). A majority of the respondents reported no current employment (43.0%), followed by full time employment (30.6%), and then part time employment (26.4%). Less than half of the population had received a GED or High school diploma (47.9%). In terms of college degrees, 39.7% of the population had Associate’s or Bachelor’s degrees, and 12.4% had either a Master's or Doctoral degree. Differences between Gender, Ethnicity, and Age The current study utilized an independent samples t-test (Table 3) to compare the difference between males and females in all three scales. A significant difference between male and female was found on the Game Addiction Scale (GAS); t(117) = 6.92, p = .005. Males (M = 16.74, SD = 5.26) scored higher than females (M = 9.89, SD = 4.69). A significant difference
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was found between males and females in their FS; t(117) = -2.59, p = .011. Males (M = 38.24, SD = 6.89) scored significantly lower than females (M = 41.33, SD = 4.12). Ethnicity was significantly associated with GAS [F(2, 118) = 5.5, p = .005] with the Caucasian group (M = 15.44, SD = 11.12) scored significantly higher (Table 4) on the GAS than the Latino group (M = 38.28, SD = 7.62). While no significant associations were found between age and the FS or CIUS, a significant association was found when analyzing age and the GAS [F(2, 118) = 10.21, p = .005]. Analysis utilizing the Tukey HSD (Table 6), revealed that the 1825 year olds (M = 16.38, SD = 4.95) scored significantly higher than the 26-35 year olds (M = 13.24, SD = 6.74) and the 36-75 year olds (M = 10.17, SD = 4.89). No significant associations were found when analyzing differences between scores on the FS and CIUS when comparing to age. Differences in Education and Employment There is an association between education level and GAS [F(2, 118) = 14.326, p = .005] (Table 7). The GED/High school group (M = 17.06, SD = 4.93) scored significantly higher on the GAS than those with the Associate and Bachelors group (M = 11.73, SD = 5.24) and the Masters and Doctoral group (M = 14.50, SD = 17.35). In addition to these findings, a significant association was found between education level and the score of compulsive Internet use [F(2, 118) = 6.51, p = .002]. The score of the GED/Highschool group (M = 23.19, SD = 10.81) differed significantly from those in the Associate and Bachelors group (M = 15.93, SD = 8.88) on the CIUS. Significance was also found between employment conditions and the FS [F(2, 118) = 4.58, p = 25
.012], (Table 8). The subjects who worked full-time (M = 41.51, SD = 4.25) scored significantly higher on the FS than the not employed group (M = 37.15, SD = 7.14). A difference was also found when comparing both the full-time group and the non-employed group to GAS [F( 2, 118) = 6.90, p = .001] and CIUS scores [F( 2, 118) = 3.34, p = .039]. The participants who belonged to the group who were employed full-time (M = 11.84, SD = 5.40), scored significantly lower than the non-employed group (M = 16.38, SD = 5.93) in GAS group. The group which was employed full time (M = 16.41, SD = 9.20) scored significantly lower than the non-employed group (M = 22.23, SD = 11.19) when the scores on the CIUS were compared against differences in employment. Hours of Video Game Played and Scales A significant association was found between the hours of weekly reported video game use and the GAS [F ( 4, 116) = 22.97, p = .005]. It was found that the group reporting no video game use (M = 3.39, SD = 4.28) scored significantly lower on the GAS than the group that played video games 1-5 hours (M = 12.16, SD = 4.78). This indicates a lower gaming addiction score with less video game use. Both the 1-5 hour group and the group reporting no video game hours scored significantly less than the group that played video games 6-10 hours (M = 17.19, SD = 3.69). Additionally, the 1-5 hour group scored significantly lower than the 11-15 hours group (M = 15.47, SD = 4.27), and more than 16 hours (M = 18.65, SD = 5.30). There is a significant association between hours of video game use and the FS scores [F ( 4, 116) = 3.44, p = .011].
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TABLE 2. Demographic Characteristics Variable Gender
Age
Ethnicity
Employment Status
Highest Level of
n
Percent
Male
80
66.1
Female
39
32.2
Other
2
1.7
18-25
66
54.5
26-35
37
30.6
36-75
18
14.9
Caucasian
89
73.6
Latino/a
25
20.7
Other
7
5.8
Full-time
37
30.6
Part-time
32
26.4
Not-employed/NA
52
43.0
High School diploma/GED
58
47.9
Associate’s or Bachelor’s
48
39.7
Master’s or Doctoral
15
12.4
Education
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TABLE 3. Gender Versus FS, GAS, and CIUS Scale FS
GAS
CIUS
n
df
Mean (SD)
t
Sig. (p)
Male
80
117
21.12(10.23)
1.39
.167
Female
39
Male
80
6.92
.005
Female
39
Male
80
-2.59
.245
Female
39
18.21(11.85) 117
16.74(5.26) 9.87(4.69)
117
38.24(6.88) 41.33(4.12)
TABLE 4. Ethnicity Versus FS, GAS, and CIUS
FS
GAS
CIU
n
df
Mean(SD)
F
Sig.
Caucasian
89
2
61.816
1.30
.278
Latino
25
Other
7 5.50
.005
2.90
.059
Caucasian
89
Latino
25
Other
7
Caucasian
89
Latino
25
Other
7
28
47.714
2
181.992 33.070
2
329.227 113.410
TABLE 5. Age Versus the FS, GAS, and CIUS Age
FS
GAS
CIUS
n
df
F
38.58(6.97)
1.255
.289
10.211
.000
1.942
.148
18-25
66
26-35
37
38.24(7.67)
36-75
18
41.22(4.56)
18-25
66
26-35
37
13.24(6.75)
36-75
18
10.17(4.89)
18-25
66
26-35
37
19.95(10.39)
36-75
18
15.67(11.96)
29
2
Mean(SD)
2
2
16.38(4.95)
21.29(10.58)
Sig.
TABLE 6. Tukey HSD of Age Versus Game Addiction Score (I)
(J)
Age
Age
Mean Difference
Std.
(I-J)
Error
Sig.
95% Confidence Interval
Lower Bound
Upper Bound
26-35
3.13554*
1.14016
.019
.4292
5.8419
36-75
6.21212*
1.47623
.000
2.7081
9.7162
18-25
-3.13554*
1.14016
.019
-5.8419
-.4292
36-75
3.07658
1.59538
.135
-.7103
6.8635
18-25
-6.21212*
1.47623
.000
-9.7162
-2.7081
26-35
-3.07658
1.59538
.135
-6.8635
.7103
18-25
26-35
36-75
*. The mean difference is significant at the 0.05 level.
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TABLE 7. Education Versus FS, GAS, and CIUS Scale
Education level
FS GED/Highschool
n 58
df 2
Mean(SD) 38.14(7.45)
Associate’s/Bachelor’s
48
Master’s and Doctoral
15
GED/Highschool
58
Associate’s/Bachelor’s
48
11.73(5.24)
Master’s and Doctoral
15
12.87(7.35)
GED/Highschool
58
Associate’s/Bachelor’s
48
15.94(8.88)
Master’s and Doctoral
15
12.76(3.29)
F
Sig.
1.065
.348
14.326
.000
6.519
.002
40.00(5.19) 38.07(9.35)
GAS 2
17.21(4.93)
CIUS
31
2
23.19(10.81)
TABLE 8. ANOVA of Employment Versus FS, GAS, and CIUS Employment
FS
n
df
Mean(SD)
F 4.576
.012
6.904
.001
.338
.039
Part-Time
32
2
38.59(8.20)
Full-Time
37
118
41.51(4.25)
52
120
Sig.
NA/Not 37.15(7.14)
Employed
32 Part-Time GAS
Full-Time
2
14.50(5.61)
37
118
11.84(5.40)
52
120
NA/Not 16.38(5.93)
Employed
32 Part-Time
CIUS
Full-Time
2
20.69(11.16)
37
118
16.40(9.20)
52
120
22.23(11.19)
NA/Not Employed
32
The group who reported greater than 16 hours of video game use scored significantly lower (M = 36.80, SD = 8.21) on the FS than those who used video games 11-15 hours (M = 41.35, SD = 41.35). Moreover, in addition to those who used video games 1-5 hours (M = 41.05, SD = 4.52), as well as the group reporting zero hours of video game use (M = 40.68, SD = 6.31). Further analysis also revealed that those who used video games 11-15 hours scored significantly higher (M = 41.35, SD = 6.17) than the group reporting 6-10 hours of video game use (M = 36.15, SD = 6.54) in their FS score. Association Among Scale Scores The Flourishing scale was significantly correlated with both addiction scales. There was a moderate, negative correlation between the FS and the GAS (r = -0.386, p
