University of Florida Thesis Or Dissertation Formatting

THE EFFECTS OF SELF CONTROL AND SOCIAL LEARNING VARIABLES ON DEVIANCE AND SOCIAL CONTROL IN A VIRTUAL WORLD

AMANDA M. ADAMS

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2014

To my Mom and Dad, who knew I could do it even when I doubted

ACKNOWLEDGMENTS

I would like to thank my Chair, Dr. Richard Hollinger, for his wonderful guidance throughout this project. His time, energy and support were a tremendous help. I thank

Dr. Lonn Lanza-Kaduce for his time spent proofing my paper and discussing analysis techniques and directions. I would like to thank Dr. Ken Wald for his assistance with creating my survey. I thank Dr. Kendal Broad for her help with proofing and clarifying concepts. I thank my parents for their support and understanding while I worked toward my goal and for never letting me forget why I was here.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF ABBREVIATIONS AND TERMS ...... 9

ABSTRACT ...... 10

CHAPTER

1 INTRODUCTION ...... 12

2 LITERATURE REVIEW ...... 15

Self-Control ...... 15 Social Learning ...... 19 Social Control ...... 27 Online Gaming ...... 32

3 METHODS ...... 39

Sample Demographics...... 39 Gaming Demographics ...... 41 Survey Design and Data Collection ...... 43 Survey Response ...... 44 Variables ...... 45 Types of Deviance ...... 45 Self-Control ...... 48 Differential Association ...... 48 Virtual Friendship ...... 49 Definitions ...... 50 Imitation ...... 51 Reinforcement ...... 52 Social Control ...... 53 Social Bonding ...... 54 Control Variables ...... 56 Hypotheses ...... 58 Main Question 1 ...... 58 Main Question 2 ...... 58 Main Question 3 ...... 59 Analytic Plan ...... 59 Main Question 1 ...... 60 Main Question 2 ...... 61 Main Question 3 ...... 61

Descriptive Statistics ...... 62

4 RESULTS ...... 63

Main Question 1 ...... 63 Hypothesis 1 ...... 63 Hypothesis 2 ...... 64 Hypothesis 3 ...... 65 Hypothesis 4 ...... 66 Main Question 2 ...... 68 Hypothesis 5 ...... 68 Hypothesis 6 ...... 69 Hypothesis 7 ...... 70 Hypothesis 8 ...... 71 Main Question 3 ...... 72 Additional Analyses of Question Responses ...... 72 Informal Social Control ...... 73 Additional Comments and Insights ...... 75 Summary ...... 77

5 DISCUSSION ...... 90

Summary of Results...... 90 Self-Control and Use of Deviance Model ...... 90 Self-Control and Victimization Model ...... 91 Social Learning and Use of Deviance Model ...... 92 Social Learning and Victimization Model ...... 92 Self-Control and Experienced Informal Social Control Model ...... 93 Self-Control and the Use of Informal Social Control Model ...... 94 Social Learning and Experienced Informal Social Control Model ...... 95 Social Learning and the Use of Informal Social Control ...... 96 Summation of Trends ...... 97 Limitations ...... 98 Future Research ...... 100 Ignoring and Avoidance Behavior ...... 102 Conclusion ...... 103

APPENDIX A ...... 108

APPENDIX B ...... 118

REFERENCES ...... 153

BIOGRAPHICAL SKETCH ...... 159

LIST OF TABLES

Table page

4-1 Correlations for Self Control and Use of Deviant Actions Model** ...... 78

4-2 Regression for Self Control and Use of Deviant Actions (N=49)** ...... 78

4-3 Mult. Imput. Regression for Self Control and Use of Deviant Actions (N=77)** .. 78

4-4 Correlations for Self Control and Victimization Model** ...... 79

4-5 Regression for Self Control and Victimization (N=49)** ...... 79

4-6 Mult. Imput. Regression for Self Control and Victimization Model (N=77)** ...... 79

4-7 Correlations for Social Learning and Use of Deviance Model** ...... 80

4-8 Regression for Social Learning and Use of Deviance (N=44)** ...... 81

4-9 Mult. Imput. Regression for Social Learning and Use of Deviance (N=77)** ...... 81

4-10 Pearson Correlations for Social Learning and Victimization Model** ...... 82

4-11 Regression for Social Learning and Victimization (N=55)** ...... 83

4-12 Mult. Imput. Regression for Social Learning and Victimization (N=67)** ...... 83

4-13 Correlations for Self-Control and Experienced Social Control** ...... 84

4-14 Regression for Self-Control and Experienced Social Control (N=72)** ...... 84

4-15 Mult. Imput. Regression for Self-Control and Experienced Control (N=77)** ...... 84

4-16 Correlations for Self-Control and Use of Social Control** ...... 85

4-17 Regression for Self-Control and Use of Social Control (N=69)** ...... 85

4-18 Mult. Imput. Regression for Self-Control and Use of Social Control (N=77)** .... 85

4-19 Correlations for Social Learning and Experienced Informal Social Control** ...... 86

4-20 Regression for Social Learning and Experienced Informal Control (N=57)** ...... 87

4-21 Mult. Imput. Regression for Social Learning and Control (N=67)** ...... 87

4-22 Correlations for Social Learning and Use of Informal Social Control Model** .... 88

4-23 Regression for Social Learning and Use of Informal Control (N=54)** ...... 89

4-24 Mult. Imput. Regression for Social Learning and Use of Control (N=67)** ...... 89

A-1 Factor Analysis for Use of Deviance Scale* ...... 108

A-2 Factor Analysis for Victimization Scale* ...... 108

A-3 Factor Analysis for Self-Control Scale* ...... 109

A-4 Factor Analysis for Differential Association Scale* ...... 109

A-5 Factor Analysis for Deviant Definitions Scale* ...... 110

A-6 Factor Analysis for Deviant Attitudinal Definitions Scale* ...... 110

A-7 Factor Analysis for Imitation Scale* ...... 110

A-8 Factor Analysis for Differential Reinforcement Scale* ...... 110

A-9 Listwise Regression for Self Control and Use of Deviant Actions (N=49)** ...... 111

A-10 Meansub. Regression for Self Control and Use of Deviant Actions (N=87)** ... 111

A-11 Listwise Regression for Self Control and Victimization (N=67)** ...... 111

A-12 Meansub. Regression for Self Control and Victimization (N=90)** ...... 112

A-13 Listwise Regression for Social Learning and Deviance (N=67)** ...... 112

A-14 Meansub. Regression for Social Learning and Deviance (N=89)** ...... 113

A-15 Listwise Regression for Social Learning and Victimization (N=54)** ...... 113

A-16 Meansub. Regression for Social Learning and Victimization (N=90)** ...... 114

A-17 Listwise Regression for Self Control and Experienced Control (N=67)** ...... 114

A-18 Meansub. Regression for Self Control and Experienced Control (N=108)** ..... 114

A-19 Listwise Regression for Self Control and Use of Social Control (N=68)** ...... 115

A-20 Meansub. Regression for Self Control and Use of Social Control (N=112)**.... 115

A-21 Listwise Regression for Social Learning and Experienced Control (N=56)** .... 115

A-22 Meansub. Regression Social Learning and Experience Control (N=108)** ...... 116

A-23 Listwise Regression for Social Learning and Use of Control (N=53)** ...... 116

A-24 Meansub. Regression for Social Learning and Use of Control (N=112)** ...... 117

LIST OF ABBREVIATIONS AND TERMS

Begging Repeated unsolicited requests for items, money, or help while giving little or nothing in return.

Botting When the actions of a character can be automated to perform actions repeatedly in order to gain experience and level without actual player intervention.

Bullying Making fun of, insulting, criticizing, or picking on inexperienced and/or incompetent players.

Farming Using a high level character to accumulate currency or items by constantly killing a mob or repeatedly performing a series of actions.

Flaming Directly insulting another player, such as sending deliberately offensive remarks.

Ganking The unfair killing of another player who is severely outnumbered, outmatched, or unaware (such as being disconnected from the server).

Griefing Intentionally interfering with another player with the primary purpose of annoying or impeding game play / game progress; giving a player “grief,” such as killing low level players for no reason.

Guild A structured group that plays together and has a hierarchy of players. This type of organization is more permanent, united by a common purpose, and more organized than a short lived in-game group created for the purpose of advancement.

MMORPG Massively multiplayer online role-playing games.

MMOGs Massively multiplayer online games.

MUD Multi-user dungeon.

Ninjaing Purposefully stealing loot, drops, kills, etc. Players in a group who do not follow the set looting rules, such as rolling for loot they can’t use and then quitting once they have the item.

Pestering Repeated minor harassing and/or irritating behavior; mild, annoying behavior.

Spamming The mass sending of a message repeatedly in a populated area/chat channel.

Trolling Purposefully starting arguments with another player for the sole purpose of getting a reaction, typically negative in nature; posting purposefully provocative chat/messages in a public venue.

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

THE EFFECTS OF SELF CONTROL AND SOCIAL LEARNING VARIABLES ON DEVIANCE AND SOCIAL CONTROL IN A VIRTUAL WORLD

Amanda M. Adams

May 2014

Chair: Richard Hollinger Major: Criminology, Law, and Society

The setting of interpersonal contact is ever changing as new forms of social media, used in school, at work, and at play, become more and more mainstream.

Millions of individuals come into contact with each other beyond the domain of traditional face-to-face encounters which has served as the basis for understanding crime and deviance. Virtual worlds, such as those found in online video games like

World of Warcraft, Rift, or RuneScape, form a creative and innovative laboratory for conducting social research and serve as a mirror for social networking and communication media. Research into online communities and how these communities are regulated is important for the understanding of this virtual media, which is becoming increasingly critical in day to day lives.

The current study investigated methods of informal social control in an online, virtual setting and tested the ability of contemporary sociological theories, which are used to explain deviance in the real world, to see if they can explain deviance and control in the virtual world, in which the setting is anonymous. This case study also explored and examined the thoughts and feelings of participants in regards to virtual worlds, games, and informal methods of controlling deviance online. Overall, social

learning and self-control can adequately predict specific types of deviance as well as informal social control and victimization in an online gaming environment. Although interestingly, the most common mechanism for handling disruptive and deviant players was to ignore them, followed by moving to another area. Implications and future research directions are discussed.

CHAPTER 1 INTRODUCTION

The setting of interpersonal contact is ever changing as new forms of social media, used in school, at work, and at play, become more and more mainstream.

Computer and video games, once on the fringes of society, are now a part of popular culture, consumed by a large and growing demographic, and studied by an ever widening contingent of academics in multiple fields (Mayra, 2006). Millions of individuals come into contact with each other beyond the domain of traditional face-to-face encounters which has served as the basis for understanding crime and deviance.

Virtual worlds, such as those found in online video games like World of Warcraft,

Rift, or RuneScape, form a creative and innovative laboratory for conducting social research and serve as a mirror for social networking and communication media.

Massively multiplayer online role-playing (MMORPG) games are a large subsection of the online gaming community. In 2010, massively multiplayer online game (MMOG) subscriptions amassed $1.58 billion (Eurogamer Report, 2010). World of Warcraft, one of the largest online MMORPGs, was created by Blizzard Entertainment in 2004. It now has over 11 million users (Blizzard Entertainment Statistics, 2012).

Research into online communities and how these communities are regulated is important for the understanding of this virtual media, which is becoming increasingly critical in day to day lives. These communities are regulated through many means, both formal and informal, and the understanding of such governance is important on several levels. Government officials, corporate executives, and others who are concerned with formal and informal methods of controlling behavior online or invested in the alternate ways in which various online communities are regulated would be interested in the

results from such studies. The information gathered about this new, developing area of research could be used to inform policy and procedures in the future, help protect minors online, and maintain a comfortable online environment. Also, universities and even high school administrators and/or faculty may benefit from learning how to maintain control in a virtual classroom without resorting to official write-ups for misconduct or using grades as a means of control. These results could be applied to police agencies seeking to exert control over individuals in an online community before it becomes necessary to have official police contact which may result in a negative life outcome for those individuals. Considering the multiple levels on which online communities operate, the multiple avenues for the use and regulation of these communities, and the diverse groups that could benefit from this information, the potential benefits from this line of emerging research is obvious and extensive.

The current study investigated methods of informal social control in an online, virtual setting and tested the ability of contemporary sociological theories, which are used to explain deviance in the real world, to see if they can explain deviance and control in the virtual world, in which the setting is anonymous. This study into a new and emerging area of social research also explored and examined the thoughts and feelings of participants in regards to virtual worlds, games, and informal methods of controlling deviance online.

Research questions: The current study sought to provide answers to several theoretical and exploratory questions about informal social control and deviant behaviors within an online, virtual world:

1. Can two current sociological/criminological/deviance theories used to explain deviance in the real world explain deviance in the virtual world where the setting is anonymous?

2. Can current theories explain the control of deviant behavior in a virtual community by the community members?

3. What are the methods used for informal social control in a virtual on-line game world?

4. What are the types of deviant behaviors in a virtual on-line game world?

5. How do gamers react to deviant behavior in a virtual on-line game world?

CHAPTER 2 LITERATURE REVIEW

Self-Control

Considering the anonymous nature of online games and the lack of legal repercussions for deviance within this setting, it is appropriate to ask why people do not engage in deviant acts while in a gaming environment. What prevents individuals from doing whatever they want, with no regard for others, especially since it’s just a game?

No one is hurt, and no physical damage to items (gear, possessions, gold, etc.) occurs.

Why do most players conform to the norms of the gaming environment? Control theories are very different from all other theories of crime and deviance because rather than trying to determine why some people deviate from social and legal norms, these theories ask: why does anyone conform? Arguably, people conform because social controls prevent them from committing crimes and when controls break down or weaken, that is when deviance occurs. Control theories argue that people are motivated to conform by social controls, but they need no special motivation to violate the law

(Akers & Sellers, 2009).

Hirschi formulated a control theory that brought together elements from all previous control theories and offered new ways to account for delinquent behavior

(1969). “Delinquent acts results when an individual’s bond to society is weak or broken”

(Hirschi, 1969, p. 16). Four principal elements make up this bond (attachment, commitment, involvement and beliefs,) and the stronger these elements of social bonding are with parents, adults, school teachers and peers, the more the individual’s behavior will be controlled in the direction of conformity. The weaker they are, the more likely it is that the individual will violate the law.

Hirschi incorporated measures of techniques of neutralization as indicators of belief, but rejected Sykes and Matza’s (1957) central concept that techniques of neutralization were the delinquent’s way of breaking the bond of strongly held conventional beliefs. Hirschi proposed that endorsement of the techniques of neutralization simply indicates that general beliefs are weakly held by delinquents in the first place. Interestingly, Hirschi later backed away from this theoretical perspective and took a new direction.

In 1990, Gottfredson and Hirschi discussed a general theory that explains all individual differences in the propensity to refrain from or to commit crime, including all acts of crimes and deviance, at all ages and under all circumstances. They argued that low self-control also explains analogous behaviors, like smoking, drinking, and drug use. This became known as self-control theory, one of the most tested and utilized theories in criminology today. Gottfredson and Hirschi define crime as “acts of force or fraud undertaken in pursuit of self-interest” (1990, p. 15), which deliberately avoids equating criminal behavior with illegal behavior in order to avoid the tautology problem with analogous behavior, deviance, and crime measurements (Grasmick et al., 1993).

The researchers also concluded that lack of self-control does not require crime and can be counteracted by situational conditions or other properties of the individual.

Therefore, criminal or deviant actions are not preordained by one’s level of self-control.

Situations have to be right before the lack of self-control will produce crime, but they did not specify whether these circumstantial factors are external controls that make up for the lack of self-control, stronger positive motivations to commit crime, or positive motivation to refrain from crime. They also suggested that high self-control effectively

reduces the possibility of crime. More simply, individuals with high self-control will be substantially less likely at all periods of life to engage in criminal acts (Gottfredson &

Hirschi, 1990).

The theory combined elements of opportunity and control theories. From opportunity theory, the proposition was taken that environmental conditions influence criminal opportunities. From control theory, the proposition was borrowed that people differed in their propensity to take advantage of criminal opportunities. People who lack self-control will tend to be impulsive, insensitive, and physical (as opposed to mental), risk-taking, short-sighted and nonverbal, and they will tend therefore to engage in criminal and analogous acts. People with low self-control are impulsive, seek gratification immediately, prefer simple tasks, are risk seeking, prefer physical activity over contemplation or conversation, are self-centered; and have a volatile temper

(Gottfredson & Hirschi, 1990). These characteristics are often used to measure levels of self-control.

Gottfredson and Hirschi stated that the source of low self-control is ineffective or incomplete socialization, especially ineffective child rearing. Parents must help socialize a child by monitoring his/her behavior, recognizing deviance when it occurs, and punishing this behavior (1990). School and other social institutions contribute to socialization, but it is within the family that the most important socialization takes place.

Consequentially, peer groups are relatively unimportant in the development of self- control and, according to the theory, taking up with delinquent peers is without causal significance in the commission of delinquency or crime. Self-control, once developed,

remains relatively stable throughout a person’s life and is a major explanatory variable in crime (Longshore et al., 1996).

Self-control theory has been criticized for being tautological because criminal propensity is measured by analogous behaviors which are a part of criminal propensity

(Akers, 1991, 1998). Initial studies used analogous risk taking behaviors such as smoking of drinking as a proxy for low self-control. For example, individuals’ past drinking behavior was used as a measure of self-control for predicting drunk-driving arrests (Keane et al., 1999). Self-reports of smoking and drinking behavior were used to predict other forms of self-reported delinquency (LaGrange & Silverman, 1999).

Grasmick and colleagues (1993) directly tested self-control theory. Their measures were based on the various dimensions of self-control. This study found mixed results, but suggested that individual items corresponding to specific dimensions of self-control may be better predictors of criminal behavior. These items have since been used as a way to measure self-control.

Overall, self-control theory has been well supported by research. A thorough meta-analysis demonstrated that low self-control had a large effect and was a strong predictor of crime (Pratt & Cullen, 2000). Grasmick and colleagues designed a scale to measure self-control and, using factor analysis, concluded that self-control was unidimensional and was accurately measured by the scale. However, they also noted that opportunity for criminal activity had a significant main effect on use of force and use of financial fraud (Grasmick et al., 1993).

Self-control theory has been used to explain a lot of illegal online behavior, especially piracy, which is an anonymous online activity. Generally, the Grasmick scale

is used to measure self-control. If this theory is truly a general theory, then it should be able to predict deviant behavior in an online environment such as a video game. Higgins

(2005) found that students with low self-control were more likely to engage in software piracy, even when controlling for peers, attitudes, and moral beliefs. This suggests that low self-control could be expanded to include other forms of online and computer crime to test if low self-control remains a strong predictor of deviant behavior.

Therefore, when looking at deviance in an online game, individuals with high levels of self-control should be less likely to engage in deviant acts versus those with low levels of self-control. Many researchers suggest integrating social learning theory as well. For example, studies with social learning variables in conjunction with self-control variables explain 15.3% more variation in crime than did studies that did not control for social learning variables (Pratt & Cullen, 2000).

Social Learning

Edwin Sutherland proposed in 1947 that criminal behavior is learned through interactions with other people, just like any other behavior is learned. He called this idea differential association theory. During the process of learning behaviors, the individual also learns techniques, attitudes, and motives favorable to that behavior. These attitudes and motives create definitions favorable to that behavior. An individual will commit delinquent acts when there are more definitions favorable to criminal activity than those favorable to lawful activity (Sutherland, 1947).

Sutherland also explained that there could be variations in differential association because of differences in the frequency, priority, duration, and importance of the association. This, in turn, could affect the likelihood of developing pro-crime definitions.

For example, an individual would be more likely to develop favorable definitions towards 19

criminal behavior if he/she were first exposed to the pro-crime definitions, frequently, intensely, and for a longer period of time. If the exposure was to the pro-law definitions in the same manner, then the individual would be more likely to develop pro-law definitions. This theory eventually was modified by other researchers, most notably

Burgess and Akers (1966b).

Expanding from Sutherland’s differential association, Burgess and Akers outlined a differential reinforcement theory which utilized the major principles of behaviorism

(1966b). This full reformulation retained the principles of differential association, combining them with, and restating them in terms of, the learning principles of operant and respondent conditioning that had been developed by behavioral psychologists

(1966a). They retained the theory, but conceptualized the concepts in more behavioral terms and added concepts from behavioral learning theory. These concepts include differential reinforcement whereby the voluntary actions of the individual (operant behavior) is conditioned or shaped by rewards and punishments, classical conditioning

(the conditioning of involuntary reflex behavior), discriminative stimuli (the environmental and internal stimuli that provide cues or signals for behavior), and schedules of reinforcement (the rate and ratio in which rewards and punishments follow behavioral responses) (Burgess & Akers, 1966a, 1966b).

Akers followed up his early work with Burgess and further developed this differential association – reinforcement theory. He proposed his social learning theory, applying it to criminal, delinquent, and deviant behavior in general, and his theory is arguably the most frequently used and carefully tested of the differential association derived theories (Akers, 2000). According to this theory, criminal behavior is still

considered a learned behavior, as Sutherland proposed. The difference is, in social learning theory, the behavior is learned through operant conditioning and imitation

(Akers, 1985, 1998). By combining the modified differential association ideas with behavioral reinforcement components, Akers argued his was a broader theory that could explain more, yet did not reject differential association (Akers, 1985, 1998; Akers et al., 1979). Akers has argued that his theory is capable of subsuming most of the major sociological theories of crime (Akers, 1985, 1998).

According to Cullen and Agnew (2003), Akers’ theory is compatible with

Sutherland’s theory. Like Sutherland, Akers argued that people learn to engage in crime through exposure to and the adoption of definitions favorable to crime. However, Akers more fully described the nature of such definition and in doing so, he drew heavily on

Sykes and Matza’s (1957) description of the techniques of neutralization, though Akers argued that the definitions favorable to crime include more than neutralization techniques (Akers, 1998; Cullen & Agnew, 2003). On the other hand, Warr and Stafford

(1991) argued that Sutherland’s theory emphasized the attitudes of peers in the transmission of delinquency while social learning theory stressed the behavior of peers.

In an effort to investigate how peer groups are changing, Warr (2002) also looked at the presence and attitudes of deviant virtual peer groups, specifically due to the advancement and proliferation of technology.

Debates aside, the four main concepts in social learning theory are differential association, definitions, differential reinforcement, and imitation (Akers, 1985, 1998).

Differential association refers to the way in which an individual is exposed to definitions that are favorable or unfavorable towards criminal and legal behavior. Differential

association has both behavioral-interactional and normative dimensions. The interactional dimension is the direct association and interaction with others who engage in certain kinds of behavior; as well as the indirect association and identification with more distant reference groups. The normative dimension is the different patterns of norms and values to which an individual is exposed through this association. The groups with which one is in differential association provide the major social contexts in which all the mechanisms of social learning operate. They not only expose one to definitions, but they also present one with models to imitate and with differential reinforcement for criminal or conforming behavior. Those associations that occur earlier

(priority), last longer and occupy more of one’s time (duration), take place most often

(frequency), and involve others with whom one has the more important or closer relationship (intensity) will have the greater effect on behavior (Akers, 1985, 1998).

The next concept, definitions, refers to both an individual’s social and nonsocial attitudes and beliefs about a particular behavior. They are orientations, rationalizations, meanings of the situation, or other evaluative and moral attitudes that describe an act as right or wrong, good or bad, desirable or undesirable, justified or unjustified. These can be general, such as ethical, moral, and/or religious values, or specific, values attached to particular behaviors. These definitions favorable and unfavorable to criminal and delinquent behavior are developed through imitation and differential reinforcement.

Cognitively, they provide a mind-set that makes one more willing to commit the act when the opportunity occurs. Behaviorally, they affect the commission of deviant or criminal behavior by acting as internal discriminative stimuli. If an individual possesses attitudes that permit or approve of a certain behavior, the more likely he or she is to

engage in that behavior. Therefore, an individual whose attitudes/beliefs disapprove of a behavior will be less likely to engage in the behavior (Akers, 1985).

The third concept, differential reinforcement, involves anticipated or actual rewards and punishments associated with a behavior. Perceived positive or rewarding outcomes of a behavior (positive reinforcement) will increase the likelihood an individual will engage in the behavior as will the removal of a negative or undesirable outcome

(negative reinforcement). Again, there are variations in reinforcement. The greater the amount of reinforcement, the more often it is reinforced, and the higher the chance there is for reinforcement, the more likely the behavior will continue. The greater the value or amount of reinforcement for the person’s behavior, the more frequently it is reinforced, and the higher the probability that it will be reinforced, the greater the likelihood that it will occur and be repeated. Whether individuals will refrain from or commit a crime at any given time (and whether they will continue or desist from doing so in the future) depends on the past, present, and anticipated future rewards and punishments for their actions. The concept of social reinforcement (and punishment), goes beyond the direct reactions of others present while an act is committed and includes the whole range of actual and anticipated, tangible and intangible rewards valued in society or subgroups. Therefore, nonsocial reinforcement specifically refers to unconditioned physiological and physical stimuli (Akers, 1985).

Finally, the last concept in social learning theory, imitation or modeling, is when an individual engages in a particular behavior by observing that behavior in others and repeating it. Not all behaviors will be modeled by others. It depends upon the type of

individual models, the actual behavior being observed, and what are the consequences of the observed behavior (Akers, 1985).

The great preponderance of research conducted on social learning theory has found strong relationships in the theoretically expected direction between social learning variables and criminal, delinquent, and deviant behavior. Many studies using direct measures of one or more of the social learning variables of differential associations, imitation, definitions, and differential reinforcement find that the theory’s hypotheses are upheld. The relationships between the social learning variables and delinquent, criminal and deviant behavior found in the research are typically strong to moderate and there has been very little negative evidence reported in the literature. Pratt and Cullen’s

(2000) meta-analysis covering the findings of many studies found support for the impact of two social learning variables (differential associations and definitions) on offending, but the effects of those variables were not stronger than measures of self-control.

Akers and colleagues (1979) found support for social learning theory in their study. Differential association was the strongest predictor of deviant behavior, but the other social learning factors – definitions, differential reinforcement, and imitation – were also predictors. This study did find that imitation was the weakest of the four predictors of deviant behavior (Akers et al., 1979). Krohn, Skinner, Massey, and Akers examined how well social learning theory could explain deviance in teenagers. Specifically, this study investigated whether reinforcement was a necessary aspect of social learning theory, and they concluded that it was. Reinforcement was concluded to be separate from definitions even though reinforcement variables could be considered a form of

positive definitions (Krohn et al., 1985). However, social learning theory does not explain the onset of delinquency and delinquent behavior well.

There is abundant evidence to show the significant impact on criminal and deviant behavior of differential association in primary groups, especially family and peers. The family is a key primary group with which one is differentially associated and involves the social learning process in which interaction in the family exposes the children to normative values, behavioral models and vicarious reinforcement and differential reinforcement (Simons et al., 2004). Lee, Akers, and Borg found that social learning variables were useful in looking at the effects of social factors, such as age, gender, SES, etc., on delinquency. This study suggested that social learning variables mediated the effect of socioeconomic factors (Lee et al., 2004, p. 29).

Jones and Jones (2000) saw the influence of peer groups as embedded in two types of social networks involved in the contagious nature of antisocial behavior.

Specifically, they argued that there is a cohesion network in which there is direct communication in the network and a structural equivalence network that consists of people who occupy the niche in society. A teenage group exemplifies both kinds of networks and can function in both ways as a conduit of antisocial socialization because teenagers identify with one another in specific contrast to adults and a teenage group also communicate directly with one another. Haynie (2002) found strong support for the social learning principle of the balance or ratio of association with delinquent and non- delinquent friends. In effect, the higher the proportion of one’s friends who were delinquent, the greater the likelihood that he or she would become delinquent. Youth with only friends who had engaged in delinquency were twice as likely, and those with

some delinquent friends were also much more likely to commit delinquency acts than those youth who had only non-delinquent friends.

Social learning theory has been criticized because behavioral learning principles appear to be tautological, specifically, because reinforcement is defined as the strengthening of a behavior. This is a problem because, when testing the hypothesis “if a behavior is reinforced, then it is strengthened,” there is no way to prove it false. While this is an issue, it may be possible to address it in research studies. Burgess and Akers acknowledged this problem and suggested that the tautological aspects should be separated from the testable aspects of social learning (1966a, 1966b). Plus, reinforcement variables are separate from behavior measures.

Another critique revolves around the temporal ordering of the variables. The theory assumes that delinquent associations/peers lead to delinquent behavior. It is possible that an individual is first a delinquent individual and then searches for and associates with other delinquents. If this is the case, delinquency causes delinquent associations, not the other way around (Gottfredson & Hirschi, 1990; Hirschi, 1969;

Sampson & Laub, 1993). Social learning theory, however, allows for a reciprocal relationship. Delinquents can differentially associate with other delinquents and learn other definitions favorable to the delinquent behavior, and then the individual increases involvement in the behavior (Akers & Lee, 1996).

Overall, other than one’s own prior deviant behavior, the best single predicator of the onset, continuance or desistance of crime and delinquency is differential association with conforming or law-violating peers. More frequent, longer-term, and closer association with peers who do not support deviant behavior is strongly correlated with

conformity, whereas greater association with peers who commit and approve of delinquency is predictive of one’s own delinquent behavior.

Moreover, social learning variables have been linked to software piracy (Higgins,

2005, 2006; Higgins & Makin, 2004; Skinner & Fream, 1997). Individuals’ attitudes towards software piracy have been linked to offending in other studies (Christensen &

Eining, 1991; Rahim et al., 2001). While online gaming communities are relatively anonymous, individuals do learn from each other, associate with each other, and come together toward common goals. Deviant behavior may be learned in this manner. If the norms are learned from other players, it would make sense that deviance is too. There may be groups who support deviant behavior in a gaming setting. They would associate with each other, have common definitions of desirable behavior, and reward each other for this behavior. One way to learn how to play well is to watch and imitate a veteran player, so social learning theory and its four components should be readily applicable in this environment. Social learning variables are often used in conjunction with self- control variables in order to explain behavior, like piracy, so this theory would helpful for looking at deviance in a gaming setting.

Social Control

Social control is often divided into two categories: formal (such as a criminal justice response) and informal (such as peer disapproval). A discussion of these concepts as used in this study necessitates a brief look at control theory. Reiss’s and

Nye’s theories of internal and external controls are useful for understanding informal social control. They suggested that personal controls are internalized, whereas social controls operate through the external application of legal and informal social sanctions.

Nye (1958) later expanded on this and identified three main categories of social control 27

that prevent delinquency: direct control, by which punishment is imposed or threatened for misconduct and compliance is rewarded by parents; indirect control, by which a youth refrains from delinquency because his or her delinquent act might cause pain and disappointment for parents or others with whom one has close relationships; and internal control, by which a youth’s conscience or sense of guilt prevents his or her from engaging in delinquent acts.

Walter Reckless’ (1961) containment theory was built on the same concept of internal and external control, but they were termed inner and outer containment. Also he included factors that motivate youth to commit delinquent acts (i.e., pushes and pulls toward delinquency). These inner and outer pushes and pulls will produce delinquent behavior unless they are counteracted by inner and outer containment. When the motivations to deviance are strong and containment is weak, crime and delinquency is to be expected. Outer containment includes parental and school supervision and discipline, strong group cohesion and a consistent moral front. Inner containment consists primarily of a strong conscience or a good self-concept, which renders on less vulnerable to the pushes and pulls of a deviant environment, is the product of socialization in the family and is essentially formed by age 12.

Some researchers, especially in the psychological fields, prefer to discuss one’s locus of control as an important aspect of personality and the way in which one’s behavior may be reinforced. In this case, researchers utilize an internal versus external division for the locus of control as suggested by Rotter (1966). He and the researchers who follow him view locus of control as a predisposition towards reinforcement whereas internal control describes reinforcement that comes from one’s own traits or behavior

and external control describes reinforcement outside of one’s control, such as luck, fate, or other factors beyond one’s control.

Hirschi’s control theory was published in Causes of Delinquency (1969), in which he formulated a control theory that brought together elements from all previous control theories and offered new ways to account for delinquent behavior. The book is an internally consistent, coherent, and parsimonious theory that is applicable to any type of criminal behavior or deviant behavior, not only delinquency. There are four principal elements that make up this bond (i.e., attachment, commitment, involvement and beliefs) and the stronger these elements of social bonding with parents, adults, schoolteachers and peers the more the individual’s behavior will be controlled in the direction of conformity. The weaker they are the more likely it is that the individual will violate the law. Hirschi incorporated measures of techniques of neutralization as indicators of belief, but rejects Sykes and Matza’s central concept that applying the techniques of neutralization is the delinquent’s way of breaking the bond of strongly held conventional beliefs. He proposed that endorsement of the techniques of neutralization simply indicates that general beliefs are weakly held by delinquents in the first place.

Basically, “delinquent acts results when an individual’s bond to society is weak or broken” (Hirschi, 1969, p.16). Hirschi outlined the four components to social bonds

(1969).

Attachment to others is the extent to which people have close affectionate ties to others, admire them, and identify with them so that they care about their expectations.

The more insensitive we are to other’s opinions, the less we are constrained by the norms that we share with them; therefore, the more likely we are to violate these norms.

Commitment refers to the extent to which individual have built up an investment in conventionality or a stake in conformity that would be jeopardized or lost by engaging in law violation or other forms of deviance. Commitment, therefore, refers to a more or less rational element in the decision to commit crime. Involvement refers to one’s engrossment to conventional activities, such as studying, spending time with the family and participation in extracurricular activities. One is restrained from delinquency behavior because on is too busy, too preoccupied or too consumed in confirming pursuits to become involved in non-conforming pursuits. Beliefs is defined as the endorsement of general conventional values and norms, especially the belief that laws and society’s rules in general are morally correct and should be obeyed (Akers &

Sellers, 2009; Hirschi 1969).

Hirschi’s own research generally showed support for the theory but he found delinquency to be most strongly related to association with delinquent friends which was not anticipated by the theory. Hirschi tested his theory through a survey in 1965 using a stratified random sample of 3,605 adolescent males as part of the Richmond Youth

Project in California. He concluded that he found for support for his theoretical model but the theory underestimated the importance of peers and the data failed to tap all domains of this concept and that he had placed too much importance on involvement

(Akers & Sellers, 2009; Kempf, 1993).

Krohn and Massey (1980), using self-reported delinquency (minor and serious) measures, surveyed a sample of male and female adolescents. They found that commitment and belief were more strongly related to deviant behavior for females and attachment for males, and that the social bonding perspective can better explain less

serious forms of deviance than the more serious forms. Agnew (1985) wanted to build upon Paternoster et al. (1983) by using a national sample of adolescent boys and suggested again that social bonding did not explain serious deviance very well and the effects seemed to diminish as the adolescents aged.

Social control theories are varied, each with differing levels of empirical support.

To further complicate matters, social control has been used as a generic term for all the ways in which individuals express their approval or disapproval of others. Currently, many of the negative reactions received by individuals who violate norms has been subsumed under the general term “social control” (Brauer & Chekroun, 2006). Several researchers have acknowledged the complex relationships between the social control theories and the term social control and have subsequently synthesized many of the theoretical constructs into two categories: formal and informal social control (Downing,

2010).

The vast majority of informal social control literature comes out of social disorganization research. These studies typically point to the importance of informal social control in the achievement of lower neighborhood crime rates and deviance (Carr,

2003; Sampson et al., 2002; Triplett et al., 2003). Informal social control may take the form of neighbors looking for and questioning strangers in the community, looking out for each other’s property, supervising neighborhood youth, and intervening during local disturbances (Bursik, 1988; Sampson, 1987).

Research suggests a reciprocal link between formal and informal social control

(Downing 2009; Triplett, et al., 2003). More specifically, the decrease or weakening (real or perceived) of formal social control can lead to an increase in informal social control.

The form and level of informal social control has also be suggested to impact the likelihood of individuals contact legal authorities (formal control) when a crime is committed (Goudriaan et al., 2006). In environments where there is perceivably weak formal social control, informal control can be a deterrent to deviance (Pasternoster et al., 1983). Research also suggests that disadvantaged neighborhoods, even in neighborhoods with more signs of social disorganization, residents may be more likely to intervene informally (Atkinson & Flint, 2004), even in cases where there are high levels of formal social control. These same themes emerge when examined in other situations. Hollinger and Clark, in their study on workplace deviance, found that informal social control mediates and influences behavior more than the threat of more formal social control, such as official sanctions (1982). Downing (2010) conducted a qualitative study on a subculture of online piracy and examined formal and informal social controls.

The virtual world presents new challenges to formal and informal social control as well.

Formal social control within a game can be limited by access to official game administrators, long wait times after incidents are formally reported, and corruption

(actual or perceived) of game officials.

Online Gaming

Virtual worlds are more commonly known as Massive Multiplayer Online Role-

Playing Games (MMORPGs). Although the first ‘Multi-User Dungeon’ (MUD), written in

1979, featured a text-only interface, the rapid development of computing power and bandwidth has led to modern popular MMORPGs. Now, the player is immersed in a rich, three-dimensional virtual reality image as if seen from the ‘avatar’s’ perspective

(the latter being the virtual projection of the player within the game). The Internet offers a virtual meeting place whereby persons can meet at any time and develop influential 32

peer groups (Flache, 2004) that may remain online or extend into physical space (Warr,

2002).

As virtual worlds and Massive Multiple Online Games (MMOGs) expand, more and more uses are being found outside of strict gaming applications. In 2005, the United

States Military investigated the use of MMOGs as tools for learning and training (Bonk &

Dennen, 2005). These games are unique in that they step away from single player, solitary games and allow users to engage others in social interactions as a part of game play. The worlds in which users play are typically complex and dynamic and allow for the development and homing on critical thinking, problem-solving, and teamwork skills.

Additionally, research on group decision support systems (GDSS) has revealed that these systems result in more time to make a decision, higher decision quality, better overall performance, and a higher degree of satisfaction with the decision

(Dasgupta, 2003). Dasgupta points out that most GDSS studies have taken place in highly controlled environments. Considering the increased use of the Internet to support groups and group functioning, there is a need to replicate some of these studies in more dynamic and decentralized online environments. Instead of being located in a computer lab, individuals and groups can be located across different departments, organizations, countries, and continents. This increases the realism and true functionality of group interactions better than controlled lab environments.

MMOGs offer one such environment to explore the quality, confidence, and speed of online decision-making. Aspects of decision-making that may be explored include the quality of the decision, the level of satisfaction with the decision among group members, the confidence in the soundness or correctness of a decision, and the

extent of conflict during the decision-making process (Dasgupta et al., 2000). In terms of the latter, styles of handling interpersonal conflict (e.g., compromising, obliging, dominating, avoiding, integrating, etc.) might be compared to the quality of decisions or solutions. These styles also are important to help understand the principles and practices of leadership within MMOGs.

Online gaming is an area which has seen little research (Jones, 2003), even less in MMOGs, though this is changing as more researchers see the value of this type of environment. Instead, the prevalent research tends to be centered on adolescent youth in single user settings. There are many reports about the potential addictive nature of computer games (Griffiths & Davies, 2002). In fact, there are a host of social, psychological, and emotional problems associated with overuse of the Internet (e.g., depression, deviant behaviors, academic troubles, job burnout, over-involvement in online behaviors, unemployment, etc. (Bonk & Dennen, 2005).

Males still dominate the gaming culture, but a notable increase in female gamers appears to be occurring. In 2001, Yee reported that about 84% of EverQuest players were male. Griffiths and colleagues (2004) found 81% of players were male. More recently, male players accounted for 71% of participants and females accounted for

29%. Furthermore, 76.2% of male and 74.7% of female players had made good friends within the game. This suggests that MMORPGs are highly socially interactive (Cole &

Griffiths, 2007).

Survey research from the Human Computer Interaction Institute at Carnegie

Mellon University investigated the social aspects of MMOGs, both within and outside participant gaming environments (Seay et al., 2004). Over 1,800 players, who played

EverQuest (EQ), Dark Ages of Camelot, Asherton’s Call, or Ultima Online, responded to the 69 item survey. The results indicated 90 percent of the players were males who played MMOGs for 15-21 hour per week. Those who claimed to be members of guilds were likely to spend more time playing online than those who did not. Nearly 40 percent played, at least in part, for the social experience. However, when asked to indicate the

“main” reason for playing MMOGs, the primary responses were fun (20 percent) and character growth (21 percent), while social contacts was listed as the main reason for just 15 percent of respondents.

Similarly, Yee (2004) conducted a series of survey research studies on

EverQuest (EQ) and other MMOGs. During the year of his study, he collected more than 20,000 surveys from roughly 4,000 individual participants. Most of his surveys were multiple choice and consisted of 30-50 questions, requiring 5-10 minutes to complete. Yee became an active player in EQ to gain respect in the MMOG community and learn the relevant online gaming lingo. He found that more than half of respondents indicated they learned mediation and overall leadership skills and other conflict resolution skills, such as reducing group conflicts and tensions. Nearly half indicated that they also learned persuasion skills and how to instill loyalty or encourage and motivate group members (Yee, 2004).

In another study investigating MMOGs, Steinkuehler used cognitive ethnography to investigate the cultural practices and consequences of a MMOG called Lineage

(2003). Her description of this particular MMOG environment illustrated the different virtual gaming communities. She focused on how new learners are “apprenticed” into the community through different activities, the role that different technology or cognitive

tools play in such an environment, and the contexts in which different learning community events occur. Steinkuehler’s ethnography was conducted through participant observation in order to be immersed in the gaming experience.

The author chose this because a participant observer can record and transcribe naturally occurring game-related activities and events, game related communications

(e.g., discussion board posts, chat and instant messages, bulletin board posts, etc.), and all relevant documents of the online community. Steinkuehler (2004) found that those who have mastered the social and material practices of Lineage tend to “scaffold” or assist new gamers who lack sufficient knowledge and skill to perform well (see also

Tharp, 1993; Tharp & Gallimore, 1988). Such “scaffolded” assistance might be evident when modeling successful performance, sending key information on how to navigate a difficult terrain, offering opportunities for practicing new skills, and providing timely and situated feedback on one’s performance.

Bartle (1996) details four key roles in a MUD environment in his taxonomy: achievers, explorers, socializers, and killers. Some may seek high achievement in the system and the status brought about by winning. The focus on accumulating points, wealth, or whatever the currency or goal of the game is a key marker of “achievers.”

Others may be motivated to understand the system or game well enough to be able to train others in it. They delight in finding unusual or unknown places and interesting game features (e.g., bugs) and sharing that knowledge. These players, known as explorers, might help with training manuals and FAQs and usually will be highly valued as team members for their knowledge. Other game players are often online simply to search for a social forum rich in personal or professional relationships. These

“socializers” might excel at mentoring those new to the game, brokering connections, recruiting new team members, and holding groups together. (Bartle notes that someone in this role is interested in people and human relations and tends to have a high level of empathy, good listening skills, and a ready ability to joke or share humor, and generally entertain others.) Finally, there are players who are dubbed “killers.” They tend to cause other players grief or discomfort. They focus on acquiring weapons of some type and using them on other players to cause death, havoc, or distress within the game (Bartle,

1996).

Early researchers suggested that online social interaction would not be as bound to social restraints, such as those used in traditional, face to face communication

(Sproull & Kiesler, 1986). There are general rules and norms which are shared by the overall online multiplayer gaming community and norms and rules that are specific to that community (game). If someone breaks the general rules and norms, which can typically be punished through the game administrators (account bans, official warning, etc.)

However, not every action can be controlled through official actors. Sometimes the individual players act. They generally can respond with public humiliation, barring the transgressor from community groups (guilds, squads, raids, etc.), or circulating the incident and giving the transgressor a bad reputation (Siitonen, 2007). Social regulation in any particular virtual space can be thought of as a mixture of several types of regulation.

A useful way of classifying regulation types is by the amount of work that is delegated to a non-human actor, in this particular case, actual software code. This

results in a continuum of regulatory types with the two end points being human-driven regulation and regulation driven by delegated computer software routines (Muramatsu,

2003).

CHAPTER 3 METHODS

Sample Demographics

The respondents for the current study are part of a specialized online gaming population, and therefore, it was logical to recruit over the Internet. Participants were required to be over the age of 18 and answered a gatekeeping question to confirm this.

Out of the 176 surveys, 88% confirmed they were over 18, and 12% were not. Those participants who were underage were then automatically dropped from the study.

The Entertainment Software Association (ESA) reported that in 2012, the average age of the game players (computer, console, and online) was 37, with 53% of gamers falling between the ages of 18-48 years of age. The ESA also determined that

58% of all game players were male and 42% female.

Of those who answered the corresponding items, the sample makeup for this study reflects the population makeup from the ESA report. The average age of respondents was 25, slightly younger than the ESA average, but the age ranged between 18 and 47, which was similar to the report. In the current study, 58% were male, 41% were female, and 1% preferred to not answer, which also follows the ESA’s findings. However, when respondents were asked about the sex of the characters they choose to play when given the option, 53% reported choosing males, 46% reported choosing female, and 1% preferred to not answer. This suggests that traditional demographic designations may not play a large role in online behavior as assumed.

Instead, it could be argued that the persona one adopts while online may be a more important identifier.

The racial breakdown was interesting, especially considering the lack of response to this specific item. Of the initial 176 respondents, 52% did not respond to the question about race and ethnicity. Of those who responded, 36% considered themselves to be Caucasian, 6% African-American, 3.4 Asian, and 3% considered themselves to be another race/ethnicity. Roughly 11% of respondents identified as

Hispanic, Latino/a, or of Spanish origin. Considering the large number of respondents who skipped this question, racial identifiers may not be a necessary nor even valid demographic measure.

Participants were also asked about their relationship status. Of those who answered, 62% identified as single, 15% as living together but not married, 13% as married, 1% as divorced, and 9% prefer to not answer. When asked about their living status, 30% of respondents live with a roommate(s), 25% with parents, 25% with their spouse/significant other, 15% live alone, and 4% reported other living arrangements. An overwhelming 85% of respondents do not have any children under 18 living with them, but of those who do, only 19% are considered the primary caregiver for the child.

In terms of education, 2% had less than a high school degree, 18% had a high school degree/GED, 35% completed some college, 9% had a two year college degree,

24% had a four year degree, 9% had a master’s degree, and 3% had a doctoral degree or professional degree. Of those who responded to the employment item, 6% were part time students, 7% were employed part time, 19% were full time students, 16% were employed full time, 7% were unemployed, 1% were homemakers, 1% were retired, and

1% preferred not to answer.

While 57% of respondents chose to skip the income question, of those who answered, 33% made less than $10,000 per year, 18% made between $10, 000 to

$29,000, 21% made $30, 000 to $49,000, 7% made between $50,000 to $69,000, and

1% made over $70,000 per year. Respondents connected to the internet differently, but

56% connected using cable modem, 22% connect via DSL, 10% connected using a T1 connection, 1% used satellite, 3% did not know, and 8% connected using a different method.

Apart from weddings and funerals, 25% of respondents only attend religious services of special holidays or occasions, 1% attend once a week, 1% attend once a month, and 1% attend more than once a month but less than once a week. 68% do not attend services at all or attendance does not apply in their circumstances, and 4% prefer not to answer this item.

Gaming Demographics

When asked about playing online multiplayer games in general, approximately

82% responded that they did play, 6% did not play at all and were excluded from the study, and 12% did not respond to the question. Respondents were asked how often they played specific genres of games on a scale of frequently, often, sometimes, occasionally, and never.

Of those who responded, 80% played “Action” games at least sometimes, 16% played “Family” games, 23% played “Racing” games, 74% played “Shooter” games,

15% played “Sports” games and 90% played “Role-playing” games at least sometimes.

Participants were also asked to self-identify what kind of player they considered themselves to be. 29% saw themselves as role players, 32% social players, 6% player

killers, 22% achievement players, and 11% responded with other type of player. Only

23% of respondents skipped this question.

Additionally, players were also asked which gender they preferred to play as if given a choice. Of those who responded, 53% would choose male, 46% would choose female, and 1% preferred not to answer. Only 29% skipped this question versus the

55% of respondents who chose to skip the question about their sex. This suggests typical demographic variables may not be useful controls in online environments where one’s persona becomes their identity.

Respondents were asked to rank the reasons why they played online multiplayer games on a scale of very important, somewhat important, neither important nor unimportant, somewhat unimportant, and unimportant. The items were not mutually exclusive. Of those who responded, 74% found interacting with friends at least somewhat important, 42% thought role-playing was at least somewhat important, 97% believe having fun was at least somewhat important, 87% believe enjoying a challenge was at least somewhat important, 47% found beating others to be at least somewhat important, 66% believed that participating in a story was at least somewhat important,

71% believed it was at least somewhat important to escape from day to day life, and

31% said there were other reasons at least somewhat important in their decision to play online multiplayer games. When asked to select the top reason to play, 57% said it was to have fun.

Respondents were also asked if they played in groups. Of those who responded,

79% group with others, and 21% do not. Of those who group, 45% do so in order to level up their character, 39% to gear up their character, 43% to assist other players, 5%

had other reasons, and 2% had no opinion. When asked about who respondents grouped with, 35% grouped with friends known outside the game, 42% group with friends from within the game, and 33% group with random people within the game.

While there were many variables with more than a 50% skip rate, there did not appear to be any trend to the missing data. The above demographics create a picture of who responded and willing answered the demographic questions. Therefore, any results or trends should be interpreted with caution as this sample may not be generalizable outside of this very specific environment.

Survey Design and Data Collection

The researcher used Survey Monkey (a commonly used survey research site) as the design and dissemination tool for the survey and posted a link to the Survey Monkey website, where respondents would take the survey (see appendix for survey questions), on message boards for online multiplayer gaming sites as well as posted to guilds/clans/group sites. The sites selected came from the top gaming websites as well as the list of top guilds for the most popular games in 2012. Web-based recruitment strategies and surveys have been utilized in the past with success (see, e.g., Seay et al., 2004; Yee, 2004, 2006, 2007a, 2007b). Since the targeted population was online gamers, this design and collection method was considered an appropriate way to elicit responses.

Participants were not paid; however, they were given the opportunity to receive a final report, a tactic previously utilized by Yee (2004, 2006, 2007a, 2007b). Participants were asked to complete a survey concerning their thoughts of online gaming, virtual worlds, and methods for controlling online behavior. First, the participants were asked to confirm they were over the age of 18. If not, they were thanked for their time and the 43

survey was concluded. If the respondents were of age, they were asked to read a consent page and advised to print it out for their records. This allowed participants to keep the researcher’s contact information should they have any questions or concerns after completing the survey and also maintained their anonymity. Next, a survey was administered. Participants were reminded that they could stop the survey at any time with no penalty. The survey took approximately 40 minutes to complete.

Survey Response

The targeted population of massively multiplayer gamers is relatively small, but even taking the population into account, the response rate was surprisingly low. Even though this is an emerging area of research, the low completion rate was unexpected. A total of 176 individuals began the survey. Only 57.4% completed the full survey, though enough respondents completed critical sections necessary for specific analyses in the current study. Because of the issue of losing cases, non-critical items were dropped from analysis, such as race and sex.

Considering how the current study focused on online behaviors in online environments and consisted of collecting response through an online survey, it was counterintuitive to see such a disparity in surveys initiated and completed. There did not appear to be any particular pattern to the theoretical items’ missing data, but there was an increase in missing data for the control variables – particularly race/ethnicity, income, and employment/school status. This may be a result of survey fatigue or an indication of which variables are not as relevant to an online population.

The majority of studies in this emerging area of research have consisted of mostly qualitative analysis focusing on identity formation and development as well as relationship style. Considering how this type of research is continuing to develop, the 44

current study utilized a longer survey in an attempt to more finely investigate relationships among several theoretical components and online behavior using quantitative measures. However, this may have been a detriment to sample size by making the survey too long for this particular population.

Variables

The following section defines and describes the various scales and variables used in the study. Additional tables containing information on factor analysis and descriptive statistics can be found in Appendix A.

Types of Deviance

Several common types of deviant behavior found in online gaming were measured. Respondents were asked to rank behaviors with the following survey item:

“Some gamers find certain behaviors disruptive to gameplay. In your experience, please rank these behaviors in order from most to least disruptive, with “1” as the LEAST disruptive behavior and 5 = MOST disruptive behavior. Hover your mouse over a blue term to see the definition.” Respondents were able to self-report any additional items they felt should be included by answering the following question: “Are there any terms you feel should be listed that aren’t? Please list these terms and indicate their meaning.”

The specific types of deviance measured in the current study were ninjaing

(purposefully stealing loot, drops, kills, etc.; players in a group who do not follow the set looting rules, such as rolling for loot they can’t use and then quitting once they have the item), bullying (making fun of, insulting, criticizing, or picking on inexperienced and/or incompetent players), griefing (intentionally interfering with another player with the primary purpose of annoying or impeding game play / game progress; giving a player

“grief,” such as killing low level players for no reason), trolling (purposefully starting

arguments with another player for the sole purpose of getting a reaction, typically negative in nature; posting purposefully provocative chat/messages in a public venue), ganking (the unfair killing of another player who is severely outnumbered, outmatched, or unaware (such as being disconnected from the server), pestering (repeated minor harassing and/or irritating behavior; mild, annoying behavior), begging (repeated unsolicited requests for items, money, or help while giving little or nothing in return), flaming (directly insulting another player, such as sending deliberately offensive remarks), spamming (the mass sending of a message repeatedly in a populated area/chat channel), farming (using a high level character to accumulate currency or items by constantly killing a mob or repeatedly performing a series of actions), and botting (when the actions of a character can be automated to perform actions repeatedly in order to gain experience and level without actual player intervention).

These terms were also defined for the respondents in the survey (see attached survey).

The responses were then summed and averaged to create a disruptive behavior scale. After reliability analyses, this scale included the following nine behaviors: ninjaing, bullying, griefing, trolling, ganking, pestering, begging, flaming, and spamming.

This resulted in a moderate Cronbach’s Alpha score of 0.752. As is often the case with deviance, this deviance scale violated normality assumptions and required additional transformation. In this particular case, it was necessary to reflect the variable and apply the logarithm to handle the significant negative skew of the raw data. This transformed disruptive behavior scale now passes a test of normality (Kolmogorov-Smirnov statistics

= 0.084, p=0.116).

Respondents were asked to indicate their frequency of use of deviant behavior in the following question: “In the past year, how often have you done this to another player? Please select the number corresponding to the response that best describes your behavior.” This scale was assessed in a Likert-type scale, with 1 = very often, 2 = often, 3 = sometimes, 4 = occasionally, and 5 = never. These items were recoded so higher scores indicated higher frequency of use. The scores were summed and then averaged for each respondent, with a higher score indicating more use of deviant behavior. The types of deviance measured were ninjaing, bullying, griefing, trolling, ganking, pestering, begging, flaming, and spamming. This scale is moderately reliable, with a Cronbach’s Alpha of 0.705. This scale need an inverse logarithmic transformation and then passed the tests of normality (Kolmogorov-Smirnov’s statistic = 0.111, p=0.059).

Motivation for deviance was measured through an open ended question “Why would you do any of these actions against another player?”

Frequency of victimization was measured by asking players “Has another player ever done this to you?” This scale was assessed in a Likert-type scale, with 1 = very often, 2 = often, 3 = sometimes, 4 = occasionally, and 5 = never. The scores were then recoded, summed and averaged for each respondent which allowed for higher scores to indicate more victimization from deviant behavior. To be consistent, the types of deviance measured were ninjaing, bullying, griefing, trolling, ganking, pestering, begging, flaming, and spamming. This scale was also moderately reliable with a

Cronbach’s Alpha of 0.725. This scale need an inverse logarithmic transformation and then passed the tests of normality (Kolmogorov-Smirnov’s statistic = 0.089, p=0.091).

Determining how players stop others from continuing deviance was measured with the open ended question, “How do you get the other player to stop doing these to you?”

Self-Control

The measure for self-control was the commonly utilized 24-item Grasmick et al.

(1993) scale. Grasmick and colleagues designed a single attitudinal scale to measure self-control. Low levels of self-control are associated with higher numerical scores on the scale. Originally, six dimensions were created, and, using factor analysis, the researchers concluded that self-control was unidimensional. The dimensions loaded heavily on the first factor – self-control, indicating that self-control was accurately measured by the scale. The scale has good internal validity (Grasmick et al., 1993).

The six dimensions (impulsivity, physical tasks, risk taking, simple tasks, temper, and self-centeredness) contained 4 items each. Respondents indicated their level of agreement with statements on a Likert-type scale. The answer choices were as follows:

1 = strongly disagree; 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. The scores were recoded, so higher scores indicate higher levels of self-control. The mean self-control score was calculated by summing and then averaging the scores to provide each respondent with a self-control value. This scale passed the tests of normality

(Kolmogorov-Smirnov’s statistic = 0.078, p=0.200).The reliability of the scale was tested by measuring and assessing the scale’s Cronbach’s Alpha. This scale had an alpha of

0.888, indicating a reliable scale.

Differential Association

Differential association for deviant behavior was measured in the same way respondents’ own deviant behavior was assessed. Respondents were asked how often

in the past year their friends had done the following behaviors: ninjaing, bullying, griefing, trolling, ganking, pestering, begging, flaming, and spamming.

Respondents indicated their estimation of the frequency their friends had done each behavior using a Likert-type scale. The answer choices were as follows: 1 = very often, 2 = often, 3 = sometimes, 4 = occasionally, and 5 = never. These items were recoded so higher scores indicated higher levels of deviant peer association. The mean differential association score was calculated to provide each respondent with a differential association value, and reliability of the scale was tested by measuring and assessing the scale’s Cronbach’s Alpha of 0.843. This scale needed an inverse logarithmic transformation and then passed the tests of normality (Kolmogorov-

Smirnov’s statistic = 0.098, p=0.075).

Virtual Friendship

Virtual friendship was measured as a separate concept because theoretically, the differences between relationships online and in-person may be important. Few researchers have looked at the difference in influence held by real and virtual friends.

This is an important section because many theoretical components, such as differential reinforcement and imitation as well as attachment, require peers as part of the defining measurements. Respondents were asked to indicate how many friends, on average, regularly play online games. The answer choices were 1 = almost all, 2 = most, 3 = about half, 4 = a few, and 5 = almost none.

Real world friendships were assessed by two questions: “How many of your close male friends you have known in the real world that regularly play online games?” and “How many of your close female friends who you have known in the real world regularly play online games?” Virtual friends were assessed by the following two 49

questions: “How many of your virtual or online male friends regularly play online games?” and “How many of your virtual or online female friends regularly play online games?” The two sets of questions were each summed to provide a virtual versus real world friend score.

Definitions

Definitions were measured with two scales. First, a general scale was derived from Yee (2006, 2007b) to assess attitudes. The following items were used: (1) If you want to do well in the game, you need to take advantage of other players when you can.”; (2) “’Noobies’ are asking for players to take advantage of them.”; (3) “Taking advantage of other players is just the way gaming works.”; (4) “Taking loot that did not belong to me is the easiest way to advance my character.”; (5) “Tricking other people out of their money or equipment is a useful skill.”; (6) “Spamming a chat channel is a legitimate use of the channel.”; and (7) “It is important to play without taking unfair advantage of others (recode).”

Respondents indicated their level of agreement with statements on a Likert-type scale. The answer choices were as follows: 1 = strongly disagree; 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. The scores were then summed and then averaged for each respondent. The mean deviant behavior definitions score was calculated to provide each respondent with a differential association value, and reliability of the scale was tested by measuring and assessing the scale’s Cronbach’s

Alpha of 0.896. This scale needed an inverse transformation and then passed the tests for normality (Kolmogorov-Smirnov’s statistic = 0.097, p=0.064).

Additionally, a second scale was created to measure specific deviant behavior definitions. Respondents were asked how wrong they felt it was to do the following 50

behaviors: ninjaing in an online game, troll in a chat channel in an online game, bully other players in an online game, gank other players in an online game, pester other players in an online game, beg other players in an online game, flame other players in an online game, send spam messages in a chat channel, and to give other players

“grief” in an online game.

Respondents indicated their assessment of the how wrong it was to do each item on a Likert-type scale, with 1 = very wrong; 2 = wrong, 3 = neither right nor wrong, 4 = a little bit wrong, and 5 = not wrong at all. Therefore, higher scores indicate a higher level of deviant definitions. The scores were then summed and then averaged for each respondent. The mean deviant behavior definitions score was calculated to provide each respondent with a differential association value, and reliability of the scale was tested by measuring and assessing the scale’s Cronbach’s Alpha of 0.919. This scale needed an inverse square root transformation and then passed the tests of normality

(Kolmogorov-Smirnov’s statistic = 0.098, p=0.075).

Imitation

Imitation was assessed using an approximate attitudinal measure derived from

Yee (2006; 2007b). While the items do not directly measure deviant imitation, the items do capture modeling of behavior. Because of the imperfect nature of this scale, it may not completely capture imitation of deviant behavior which is a difficult concept to measure. The following items were used: (1) “The best way to learn how to play my character is watching my friends play.”; (2) “I can become a better player by watching how high level characters/players act.”; (3) “It is useful to watch YouTube videos to learn how to play my character better.”; and (4) “I watch other players to learn how to play the game.” 51

Respondents indicated their level of agreement with statements on a Likert-type scale. The answer choices were as follows: 1 = strongly disagree; 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. The scores were then summed and then averaged for each respondent. The mean imitation score was calculated to provide each respondent with a value, and reliability of the scale was tested by measuring and assessing the scale’s moderate Cronbach’s Alpha of 0.782. This scale needed an inverse square root transformation and then passed the tests of normality (Kolmogorov-

Smirnov’s statistic = 0.102, p=0.061).

Reinforcement

Reinforcement was measured as a scale derived from items used by Yee (2006,

2007b) to assess attitudes. Items used were the following: (1) “I would not try to steal from another member of my group because I would worry about the penalty/ punishment (recoded).”; (2) “If my friends knew that I ganked other players, they would support me.”; (3) “Stealing from another player is exciting.”; (4) “My friends and I enjoy taunting new or incompetent players.”; (5) “Ganking another player is exciting.”; (6) “It is a lot of fun to troll in chat channels.”; (7) “I get a thrill from being able to gank low level players.”; (8) “My friends think it is funny when I troll chat channels.”; (9) “I feel better about myself as a gamer when I am able to take advantage of players online.”; (10) “My friends encourage me to violate the rules.”; and (11) “I like to steal from others players because it makes me feel powerful.”

Respondents marked their level of agreement with statements on a Likert-type scale. The answer choices were as follows: 1 = strongly disagree; 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. The scores were then summed and then averaged for each respondent. The mean reinforcement score was calculated to 52

provide each respondent with a differential reinforcement value, and reliability of the scale was tested by measuring and assessing the scale’s Cronbach’s Alpha of 0.883.

This scale need a square root transformation and then passed the tests of normality

(Kolmogorov-Smirnov’s statistic = 0.098, p=0.071).

Social Control

Several types of informal social control and the way in which respondents respond to deviant behavior were also measured. Respondents were asked to explain how they control other players through the qualitative question, “Multiplayer online gaming environments can be very social and interactive. How do you handle players who are bothering or annoying you during gameplay?”

A social control response scale was also created. Respondents were asked to choose how they react to another player who bothers them. The respondents indicated their level of agreement with statements on a Likert-type scale. The answer choices were as follows: 1= very unlikely, 2 = somewhat unlikely, 3 = neutral, 4 = somewhat likely, and 5 = very likely. The scores were then summed and then averaged for each respondent, with higher score indicating more use of informal social control. The social control options included the following: “Publicly call them out,” “Criticize or taunt them,”

“Retaliate with the same behavior,” “Threaten them,” and “Call on your friends to retaliate against the player.” This scale passes the reliability test with a Cronbach’s

Alpha of 0.768. This scale need a logarithmic transformation and then passed the tests of normality (Kolmogorov-Smirnov’s statistic = 0.111, p=0.059).

Respondents were also asked to choose the level of official involvement by answering the following question: “Who do you think should handle these instances when players are causing problems? Please check all that apply.” The answer options 53

were as follows: “The individuals involved,” “The guild or group,” “GMs or Administrators

(make these actions able to be punished officially),” and “Informally handled in some other way (please specify).”

Frequency of experienced social control used on the respondent was measured in a Likert-type scale, with 1 = frequently, 2 = often, 3 = sometimes, 4 = occasionally, and 5 = never as the answer choices. The scale items were assessed with the question

“When thinking about playing a game online, how often have YOU…” The answer items are as follows: “Been reported to an administrator,” “Been publicly called out for your behavior,” “Been taunted or criticized for your behavior,” and “Had a player retaliate based on your behavior.” The scores were then be summed and then averaged for each respondent, with higher score indicating more experienced informal social control. This scale need a logarithmic transformation and then passed the tests of normality

(Kolmogorov-Smirnov’s statistic = 0.111, p=0.059).The scale was then assessed and found to be moderately reliable with a Cronbach’s Alpha score of 0.742.

Social Bonding

Social bonding variables were also measured. Though this was not a focus of the research, the concepts were interesting in this context and proved somewhat useful for understanding how social control works in this specific environment. The four components of social bonding (attachment, commitment, involvement and belief) were each measured separately.

Attachment, commitment, and belief were measured through attitudinal scales developed from Yee (2006, 2007a, 2007b). Respondents indicated their level of agreement with statements on a Likert-type scale. The answer choices were as follows:

1 = strongly disagree; 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. The scores were then summed and then averaged for each respondent.

Attachment was measured with the following items: (1) “I like to manipulate other players so they do what I want them to (recode).”; (2) “It is important to play without taking unfair advantage over others.”; (3) “It is important for my teachers/boss to respect me.”; (4) “My friends would approve of how I interact with players in the games I play.”; and (5) “It is important for my parent/guardian to respect me.” This scale was assessed a reliability score using Cronbach’s Alpha and was moderately strong at 0.739.

Commitment was measured by the following items: (1) “Higher level players should help out those at a lower level.”; (2)“My friends encourage me to play by the rules (recode).”; (3) “Taking loot that did not belong to me is the easiest way to advance my character (recode).”; (4) “I try hard in school or at work.”; and (5) “Taking advantage of other players is just the way gaming works (recode).” This scale was assessed a reliability score using Cronbach’s Alpha and was moderately strong at 0.743.

Belief was measured by the following items: (1) “I would not try to steal from another member of my group because I would worry about the penalty/punishment.”; (2)

“It is unfair to pick on less experienced players.”; (3) “I feel better about myself as a gamer when I am able to take advantage of players online (recode)”; (4) “Spamming a chat channel is a legitimate use of the channel (recode).”; (5) “Everyone has a duty to obey the law.”; and (6) “You should respect the police.” This scale was assessed a reliability score using Cronbach’s Alpha and was relatively strong at 0.699.

Involvement was measured through several questions. Relationship status, presence of children, education, employment, and religiosity all can be used to create a

proxy involvement scale. However, after factor analysis and reliability tests, the best scale actually consisted of the following two items: (1) “I often participate in clubs or other activities (volunteer work, community clubs, or other school/work activities).” and

(2) “I spend a lot of time studying or at work.” For this scale, higher values indicate more involvement, and it is somewhat reliable with an alpha score of 0.569. A free response question cataloging the respondent’s typical weekly playtime plus open ended questions about other regular time commitments were included to measure involvement, but the response rate was low. However, the data received through this journal was interesting and varied widely among the participants.

Control Variables

Several variables were used as controls. Demographic variables needed to be dummy coded as follows: sex (1 = male; 0 = female), race (1 = Caucasian; 0 =all others), and relationship status (1 = single; 0 = all others). Age, education, employment, income, and internet connection were also recorded. Religiosity, presence of children, and an open-ended question on hobbies were also collected to measure time commitments from the respondent.

Type of gamer was measured to determine if this has an effect on social control or deviance. Respondents were asked to self-select the best description of themselves as gamers. They were given the following as options: role player, social player, player killer, and achievement player. They were also able to specify with an “other” option if they did not feel the provided options best fit them as gamers. Of those who answered this question, 29% saw themselves as role players, 32% identified as social players, 6% as player killers, 22% as achievement players, and 11% responded with other types of players. 56

Motivation was measured in rank order with the following options from Yee’s

(2006, 2007b) motivation survey: to interact with friends, to role play, to have fun, to enjoy a challenge, to beat others, and to participate in the story. The items were not mutually exclusive. Of those who responded, 74% found interacting with friends at least somewhat important, 42% thought role-playing was at least somewhat important, 97% believe having fun was at least somewhat important, 87% believe enjoying a challenge was at least somewhat important, 47% found beating others to be at least somewhat important, 66% believed that participating in a story was at least somewhat important,

71% believed it was at least somewhat important to escape from day to day life, and

31% said there were other reasons at least somewhat important in their decision to play online multiplayer games. When asked to select the top reason to play, 57% said “to have fun.”

Solo versus group player was assessed first as a dichotomous variable, “Do you group with other players?” Respondents were able to choose 0 = No and 1 = Yes. Of those who responded, 79% group with others, and 21% do not.

Respondents were then asked to indicate when they group with other players through the following options: “Leveling your character,” gearing up your character,”

“Storyline advancement,” and “assisting another player.” Of those who group, 45% do so in order to level up their character, 39% to gear up their character, 43% to assist other players, 5% had other reasons, and 2% had no opinion.

Group makeup was determined through the following question: “Who do you typically group with?” The answer choices were as follows: “Friends known outside of the game,” “Friends met within the game,” “Random people found within the game,” and

“Other (please explain).” Of those who responded to this item, 35% grouped with friends known outside the game, 42% group with friends from within the game, and 33% group with random people within the game.

Hypotheses

The current study sought to provide insight into three theoretical questions about informal social control and deviant behaviors within an online, virtual world:

Main Question 1

Can the current sociological/criminological/deviance theories used to explain deviance in the “real” world explain deviance in the virtual world where the setting is anonymous?

Hypothesis One (H1) stated that higher levels of self-control will predict lower levels of the use of deviant actions. Hypothesis Two (H2) stated that higher levels of self-control will predict lower levels of victimization. Hypothesis Three (H3) stated that social learning variables will predict deviance as follows: More pro-deviance definitions will predict higher levels of deviance, higher levels of deviant peer association will predict higher levels of deviance, higher scores of imitation will predict higher levels of deviance, and higher levels of peer reinforcement will predict higher levels of deviance.

Hypothesis Four (H4) stated that social learning variables will predict victimization as follows: More pro-deviance definitions will predict higher levels of victimization, higher levels of deviant peer association will predict higher levels of victimization, higher scores of imitation will predict higher levels of victimization, and higher levels of peer reinforcement will predict higher levels of victimization.

Main Question 2

Can current theories explain the control of deviant behavior in a virtual community by the community members?

Hypothesis Five (H5) stated that higher levels of self-control will predict lower levels of experienced social control. Hypothesis Six (H6) stated that higher levels of self-control will predict less use of informal social control. Hypothesis Seven (H7) stated that social learning variables will predict experienced informal social control as follows:

More pro-deviance definitions will predict more experienced informal social control, higher levels of deviant peer association will predict more experienced informal control, higher scores of imitation will predict higher levels of experienced social control, and higher levels of peer reinforcement will predict higher levels of experienced informal social control. Hypothesis Eight (H8) stated that social learning variables will predict use of informal social control as follows: More pro-deviance definitions will predict more use of informal social control, higher levels of deviant peer association will predict more use of informal control, higher scores of imitation will predict higher levels of use of social control, and higher levels of peer reinforcement will predict more use of informal social control.

Main Question 3

How do gamers react to deviant behavior in a virtual/online game world?

This question was assessed through the use of the open-ended questions and free-response questions in the survey. Therefore, there is no traditional hypothesis.

Analytic Plan

The independent variables were self-control and social learning variables.

Deviance use, deviance victimization, social control use, and social control experienced were the dependent variables. Therefore, several separate models were run to explore the associations between the variables. Ordinary Least Squares (OLS) regression models was used to estimate the effects of self-control and social learning on informal

social control and deviance. While these models were run using listwise exclusion, all models were also run using mean replacement to increase the number of included cases. As this did not change any of the findings, these results have been omitted.

Additionally, while it is customary to use age, race, and sex as control variables, these variables may not be an accurate representation of the sample online. Additional analyses were run without using these control variables.

While Ordinary Least Squares (OLS) regression models are useful explanatory models, there can be bias introduced into the models that may affect the outcomes.

Also, due to the amount of missing data, multiple imputation was used in order to counter the low sample size. After the multiple imputations were run for the target variables, linear regression models were estimated. Results from these models are also reported

Main Question 1

To test H1, the effect of self-control on deviance use, OLS regressions were estimated with self-control and age as independent variables and use of deviant actions as the dependent variable. Then multiple imputations were run, and a linear regression model was estimated with this data. To test H2, OLS regressions were estimated with self-control and age as independent variables and victimization as the dependent variable. Then multiple imputations were run, and a linear regression model was estimated with this data.

OLS regressions were also used to test H3, the effect of social learning variables on deviance, with social learning variables (definitions, peers, reinforcement, and imitation) and age as independent variables and use of deviant actions as the dependent variable. Then multiple imputations were run, and a linear regression model 60

was estimated with this data. To test H4, OLS regressions were estimated, using social learning variables (definitions, peers, reinforcement, and imitation) and age as independent variables and victimization as the dependent variable. Then multiple imputations were run, and a linear regression model was estimated with this data.

Main Question 2

To test H5, the effect of self-control on informal social control, OLS regressions were estimated with self-control and age as independent variables and experienced social control as the dependent variable. Then multiple imputations were run, and a linear regression model was estimated with this data. To test H6, OLS regressions were estimated with self-control and age as independent variables and use of social control as the dependent variable. Then multiple imputations were run, and a linear regression model was estimated with this data.

OLS regressions were be used to test H7, the effect of social learning variables on experienced social control, with social learning variables (definitions, peers, reinforcement, and imitation) and age as independent variables and experienced social control as the dependent variable. Then multiple imputations were run, and a linear regression model was estimated with this data. To test H8, OLS regressions were be estimated, using social learning variables (definitions, peers, reinforcement, and imitation) and age as independent variables and use of social control as the dependent variable. Then multiple imputations were run, and a linear regression model was estimated with this data.

Main Question 3

Quotations from respondents were used to illustrate the overarching themes discovered through the open-ended and free-response questions. This information

helps to provide a basis for the interpretation of results generated by the other hypotheses.

Descriptive Statistics

Descriptive statistics were also run on all variables to determine means, frequencies, ranges, and other categorical summaries useful for comparisons. These variables included demographics, types of gamers, types of informal control, as well as types of deviance.

CHAPTER 4 RESULTS

The current study investigated several hypotheses directed at answering three main questions. Due to the relative new nature of this research topic, a significance level of 0.10 will be used, although the actual significance value will be reported as well.

Listwise regression was used first, but mean substitution models were run to help provide additional information. This technique is not very strong, and the results did not change, so the mean substitution models are not reported here. Instead, due to the amount of missing data, multiple imputation was used, and then linear regression models were estimated using this additional data. Both the OLS and multiple imputation models’ results were reported, and the following section summarizes the results from the statistical analyses of the nine hypotheses.

Main Question 1

Can the current sociological/criminological/deviance theories used to explain deviance in the real world explain deviance in the virtual world where the setting is anonymous?

Hypothesis 1

Hypothesis One (H1) stated that higher levels of self-control will predict lower levels of the use of deviant actions and low self-control will predict higher use of deviant actions. First, correlations were estimated on use of deviance, self-control, age, race, and sex. Deviance use was positively correlated with sex, so males were associated with higher levels of deviance use. Deviance use was negatively correlated with self- control, which indicates that as deviance use increases, self-control levels decrease.

Self-control was negatively correlated with sex, so higher levels of self-control were

correlated with females. Age was not significantly correlated to any other variable (see

Table 4-1).

To test the effect of self-control on use of deviant actions, an OLS listwise regression was estimated with self-control and age as independent variables and use of deviant actions as the dependent variable. This model explained 26.3% of the variance.

The model was highly significant (F = 8.367, p < .001). Self-control was a significant predictor (t = -3.843, p = .000) of use of deviance. Age was not significant in this model

(see Table 4-2).

A model was estimated after using multiple imputation (set of five imputations) to address the missing data. This model was also highly significant (F = 13.494, p = .000) and explained 42.5% of the variance. Using the pooled data, self-control (t = -3.601, p =

.001) was highly predictive of the use of deviant actions, but age and race were not (see

Table 4-3).

When the model was run with sex and race, it was also highly predictive. The same was true when a model was run using mean substitution (without sex and race dummy variables). Self-control was still highly predictive. See the Appendix for additional information on these models.

Hypothesis 2

Hypothesis Two (H2) stated that higher levels of self-control will predict lower levels of victimization. First, correlations were estimated on victimization, self-control, age, race, and sex. Self-control was not significantly correlated with victimization, but is was negatively associated with sex. While not a significant relationship, as self-control increased, victimization decreased (see Table 4-4).

To test the effect of self-control on victimization, an OLS listwise regression was estimated with self-control and age as independent variables and victimization as the dependent variable. This model explained 1.5% of variance and was not significant (p =

.603). None of the variables were significant (see Table 4-5).

A model was estimated after using multiple imputation (set of five imputations) to address the missing data. This model was not significant either and only explained 5.0% of the variance. Using the pooled data, self-control (t = -1.324, p = .186) was not predictive of victimization, and neither were age and race (see Table 4-6).

When the model was run with sex and race, it was also not predictive. The same was true when a model was run using mean substitution (without sex and race dummy variables). See the Appendix for additional information on these models.

Hypothesis 3

Hypothesis Three (H3) stated that social learning variables will predict deviance as follows: More pro-deviance definitions will predict higher levels of deviance, higher levels of deviant peers will predict higher levels of deviance, higher scores of imitation will predict higher levels of deviance, and higher levels of friend reinforcement will predict higher levels of deviance. First, correlations were estimated on use of deviant behavior. Use of deviance was positively correlated with pro-deviant definitions (p =

.000). Deviance use was also positively correlated with sex (p = .000), differential reinforcement (p=.000), and differential association (p=.000). Use of deviant actions were also positively correlated with sex (p = .000) but negatively correlated with race (p

= .007) (see Table 4-7).

To test the effect of social learning variables on use of deviant actions, an OLS listwise regression was estimated with social learning variables and age as independent 65

variables and use of deviance actions as the dependent variable. This model explained

50.8% of the variance and was highly significant (F=7.447, p = .000). Differential reinforcement was predictive (t=2.557, p = .015). Differential association was also significant (t=2.883, p=.007). The other variables were not significant predictors of use of deviant actions (see Table 4-8).

A model was estimated after using multiple imputation (set of five imputations) to address the missing data. This model was also significant (F = 17.334, p = .000) and explained 66.9% of the variance. Reinforcement (p = .018), imitation (p = .066), and differential association (p = .047) were significant predictors of deviance (see Table 4-

9).

When the OLS listwise regression model was run with sex and race, it was also highly predictive (p = .000) and explained 58.3% of variance. Differential association and sex were predictive variables, while differential reinforcement was approaching significance. When the model (without sex and race dummy variables) was run with mean substitution, the model was still significant, and differential reinforcement was the only significant predictor. See the Appendix for additional information on these models.

Hypothesis 4

Hypothesis Four (H4) stated that social learning variables will predict victimization as follows: Higher levels of pro-deviance definitions will predict higher levels of victimization, higher levels of deviant peers will predict higher levels of victimization, higher scores of imitation will predict higher levels of victimization, and higher levels of friend reinforcement will predict higher levels of victimization. First, correlations were run. Victimization significantly correlated with differential association but was not significantly correlated with any of the other predictors. However, 66

correlations approached significance for victimization and deviant definitions (p=.142).

Definitions was positively correlated with sex (p = .015), imitation (p = .011), and reinforcement (p = .000). Imitation was also positively correlated with differential reinforcement (p = .064) (see Table 4-10).

To test the effect of social learning variables on use of victimization, an OLS listwise regression was estimated with social learning variables (definitions, differential association, reinforcement, and imitation) and age as independent variables and victimization as the dependent variable. This model explained 42.2% of variance. The model was significant (F = 7.294, p = .000). Differential association (t = 5.635, p = .000) and reinforcement (t = 2.179, p = .034) were significant predictors of victimization. None of the other variables were significant (see Table 4-11).

A second regression model was estimated after using multiple imputation (set of five imputations) to address the missing data. Victimization was the dependent variable, and the independent variables were differential association, differential reinforcement, definitions, imitation, age, sex, and race. This model was significant (F = 7.861, p =

.000) and explained 47.8% of variance. Differential association was significant (p =

.000). No other variables were significant (see Table 4-12).

Additionally, an OLS regression model with race and sex. This model was also significant, and differential association was still highly predictive. The same was true when a model was run using mean substitution (without sex and race dummy variables). See the Appendix for additional information on these models.

Main Question 2

Can current theories explain the control of deviant behavior in a virtual community by the community members?

Hypothesis 5

Hypothesis Five (H5) stated that higher levels of self-control will predict lower levels of experienced social control. First, correlations were estimated on experienced social control, self-control, age, race, and gender. Experienced social control was negatively correlated with age (p = .057). Self-control was correlated with positively race

(p = .001) and negatively correlated with sex (p = .032) (see Table 4-13).

To test the effect of self-control on informal social control, an OLS listwise regression was estimated with self-control, and age as independent variables and experienced social control as the dependent variable. This model explained 3.8% of variance and was not significant (F = 1.366, p = .262). None of the predictors were significant, although age was approaching significance (p = .103) (see Table 4-14).

A second regression model was estimated after using multiple imputation (set of five imputations) to address the missing data. Experienced social control was the dependent variable, and self-control, age, sex, and race were the independent variables. This model was significant (F = 2.712, p = .036) and explained 12.9% of variance. Age was a significant predictor of experienced social control (t = -2.343, p =

.022). No other variables were significant (see Table 4-15).

Additionally, an OLS regression model with race and sex. This model was not significant, and none of the variables were predictive for experienced informal social control. The same was true when a model was run using mean substitution (without sex

and race dummy variables). See the Appendix for additional information on these models.

Hypothesis 6

Hypothesis Six (H6) stated that higher levels of self-control will predict less use of informal social control. First correlations were assessed. Use of social control was highly correlated with self-control (p = .003) and negatively correlated with race (p

=.091). Self-control was correlated with sex (p = .032) and race (p = .001) (see Table 4-

16).

To test H6, an OLS listwise regression was estimated with self-control and age as independent variables and use of social control as the dependent variable. This model was significant (F = 4.573, p = .014) and explained 12.0% of variance. Self- control was a significant predictor (t = 3.009, p = .004), but no other variables were significant (see Table 4-17).

A second regression model was estimated after using multiple imputation (set of five imputations) to address the missing data. Use of informal social control was the dependent variable, and self-control, age, sex, and race were the independent variables. This model was also significant (F = 4.245, p = .004) and explained 14.4% of variance. Using the pooled data, race was a significant predictor of use of informal social control (t = -1.885, p = .063) and so was self-control (t = 1.834, p = .071). No other variables were significant (see Table 4-18).

Additionally, an OLS regression model with race and sex. This model was significant. Self-control and race were still predictive for use of informal social control.

When a model was run using mean substitution (without sex and race dummy

variables), it was also significant, though only self-control was predictive of use of informal social control. See the Appendix for additional information on these models.

Hypothesis 7

Hypothesis Seven (H7) stated that social learning variables will predict experienced informal social control as follows: More pro-deviance definitions will predict more experienced informal social control, higher levels of deviant peers will predict more experienced informal control, higher scores of imitation will predict higher levels of experienced social control, and higher levels of friend reinforcement will predict higher levels of experienced informal social control. First correlations were assessed for experienced informal social control, age, sex, race, definitions, differential association, imitation, and reinforcement. Experienced informal social control was negatively correlated with age (p = .044). Reinforcement and definitions were negatively correlated with race and positively correlated with sex (see Table 4-19).

An OLS listwise regression model was used to test H7, the effect of social learning variables on experienced informal social control, with social learning variables

(definitions, association, reinforcement, and imitation) and age as independent variables and experienced informal social control as the dependent variable. This model explained 10.8% of the variance but was not significant (F = 1.257, p = .296).

Differential reinforcement (t = 2.206, p = .032) and deviant definitions (t = 1.811, p =

.076) were significant predictors of experienced informal social control. None of the other variables were significant predictors (see Table 4-20).

A second regression model was estimated after using multiple imputation (set of five imputations) to address the missing data. Experienced informal social control was the dependent variable, and differential association, differential reinforcement, deviant 70

definitions, imitation, age, sex, and race were the independent variables. This model was also not significant (F = 1.683, p = .131) and explained 16.4% of variance. No variables were significant, although definitions was approaching significance (p = .182)

(see Table 4-21).

Additionally, an OLS regression model with race and sex. This model was not significant. Definitions was the only variable approaching significance (p = .118). None of the other variables were predictive in this model. When a model was run using mean substitution (without sex and race dummy variables), it was also not significant, though only reinforcement was approaching significance (p = .124). See the Appendix for additional information on these models.

Hypothesis 8

Hypothesis Eight (H8) stated that social learning variables will predict use of informal social control as follows: More pro-deviance definitions will predict more use of informal social control, higher levels of deviant peer association will predict more use of informal control, higher scores of imitation will predict higher levels of use of social control, and higher levels of friend reinforcement will predict more use of informal social control. First correlations were assessed for use of informal social control, age, sex, race, definitions, differential association, imitation, and reinforcement. Use of informal social control was positively correlated with definitions (p = .004) and reinforcement (p =

.000). Use of informal control was also negatively associated with race (p = .091) (see

Table 4-22).

OLS regressions were be used to test H8, the effect of social learning variables on use of informal social control, with social learning variables (definitions, differential association, differential reinforcement, and imitation) and age as independent variables 71

and use of informal social control as the dependent variable. This model explains 31.6% of variance and was highly significant (F = 4.527, p = .002). Reinforcement (t = 4.022, p

= .000) was the only significant variable, although definitions (p = .110) was approaching significance as a predictor of use of informal social control (see Table 4-

23).

A second regression model was estimated after using multiple imputation (set of five imputations) to address the missing data. Use of informal social control was the dependent variable, and differential association, differential reinforcement, deviant definitions, imitation, age, sex, and race were the independent variables. This model was also significant (F = 5.989, p = .000) and explained 41.1% of variance. Differential reinforcement (p = .000) and definitions (p = .022) were the only significant predictors of the use of informal social control (see Table 4-21).

Additionally, an OLS regression model with race and sex. This model was also significant and explained 31.0% of the variance. Reinforcement was the only variable approaching significance (p = .001), and definitions was approaching significance (p =

.111). None of the other variables were predictive in this model. When a model was run using mean substitution (without sex and race dummy variables), it was also significant, though only reinforcement was significant (p = .000). See the Appendix for additional information on these models.

Main Question 3

How do gamers react to deviant behavior in a virtual on-line game world?

Additional Analyses of Question Responses

While the majority of the survey focused on theoretical concepts, respondents were given free response opportunities to further explain their experiences or positions

on a variety of topics. Their responses are detailed in the following section, with special attention paid to the various responses in regards to deviant behavior and informal social control in an online game.

Informal Social Control

Participants were asked about their response to disruptive or deviant behavior in the following question: “Multiplayer online gaming environments can be very social and interactive. How do you handle players who are bothering or annoying you during gameplay?”

While most of the participants skipped this open ended question, several provided interesting responses. Most players seem to choose an option that allows them to continue playing without encountering the troublesome player. When ranking the informal social control options, over 90% reported they would ignore the other player. Specifically, participants reported using the following methods to handle other players: countering, ignoring, actively confronting, and reporting.

Respondent 2 Briefly tell them off if the behavior is negatively affecting myself or a guild/party member, then place them on ignore list if the behavior persists.

Respondent 5 I have a fairly high tolerance and will just avoid paying attention to them in general... In actual gameplay, I assess whether I can totally overpower the annoying person (counter-annoy), but if I'm not 100% sure I can do it then I will just attempt to continue as if they weren't even there (possibly mocking their inability to affect my gameplay and telling them they can't hurt or annoy me just once or twice).

Respondent 5 If you intend to use a zone that's frequently occupied and you find someone at inappropriate level/skill in such an area, tell them what they should be doing and even give them a small gift to help them along. Sometimes being nice works.

Respondent 13 Usually when this is happening in match-up settings (our team vs. the other team usually consisting of strangers) my friend goes over and verbally confronts them. Otherwise, they get muted and no one pays much attention to them. When I am by myself, however, I put up with it in silence until the end of the match where I can leave the lobby and join another.

Respondent 17 Hey what about just dealing with it like an adult? Just keep playing and not give them attention. Generally they just go away without the ignore feature in game.

Respondent 18 Block/Ignore - never feed the trolls

Respondent 19 Address them directly, and if that's not successful, ignore them.

Respondent 35 With guilds, you can call higher-level players in order to get the point across that KSing (kill-stealing) is wrong because it makes the game less fun. One call, and a good friend from the guild shows up and gets the other player to go away.

Respondent 94 Depends on the game, and how extreme their behavior (sic) is. If they're just griefing me for kicks, I'll usually ignore them or get out of their way until they've found some other shiny thing. Loot ninjas I call out publicly, because they're parasites and other players need to be warned not to trust them. But if they're engaging in actual harassment, like PM spamming me with threats or following me around for more than a few hours or taking the harassment outside of the game, I'll report them to an admin.

Respondent 101 Avoid them and ignore them in the future

Respondent 128 I try my best to avoid and/or ignore them. Sometimes logging off for a while, hoping they will be gone when I get back on.

As the above quotes further illustrate, the vast majority of responding gamers choose an option that allows them to continue playing while ignoring or without encountering the troublesome player. Specifically, participants reported countering the behavior, ignoring the offender, actively confronting the offender, and albeit rarely, reporting the offender. Interestingly, countering and confronting disruptive behavior was not widely reported. Instead, players were almost passive as they ignored, muted, avoided, or left the area of the offender.

As with any behavior, there is a range of responses. Some players taunt back and others seek assistance. And, as some participants noted, the type of game and the situation in which the offensive behavior occurs also influence the reactions. Even in these circumstances, most players do not act out, retaliate, or harm the disruptive player. The irritating player is ignored. This action and lack of action reflect an interesting response to and tolerance of disruptive online behavior. Considering the assumed anonymous nature of online gaming, it is striking how little aggressive and retaliatory actions were reported by players.

Additional Comments and Insights

Participants were also asked about any additional comments they had concerning the topic of controlling online behavior, especially in games. As is typical of generic, open-ended questions, 85% of the respondents chose to not answer or quit the survey by this point. Of those who answered, several had interesting points about context specific behavior and issues of anonymity.

Respondent 5 Every game has its own set of gamer expectations.

Respondent 6 Whether a game is subscription or free-to-play, the amount of coordination/skill required to be competent, and the advertised demographic range affects not only who plays but what is expected of a player.

Respondent 35 I think it's sad that people think it's fun to ruin other people's fun, real life and online.

Respondent 65 When you ask about taking advantage of a player, sometimes it is acceptable. If someone makes a mistake, then you should capitalize on it to win (ie RTS games). Not because winning is everything, but because that's is how you win RTS games. But if a person doesn't have a clue what to do, it is unfair to take advantage of them, and it wouldn't be satisfying to beat a person who can't play anyway.

Respondent 94 Anonymity makes “shitty” behavior easy for people. It's very revealing of who people really are when there are no consequences to their actions, and providing social in-game consequences goes a long way towards keeping those people in check. It's not a complete solution, but it helps.

Respondent 148 I find when players are given the ability to police themselves the general atmosphere is slightly more morally sound. The punishments players doll out between themselves is often considerably harsher than that given out by a GM/Admin. In my experience this only holds true for games with shared resources (all open world spawns, finite materials etc). The punishments are often griefing to a point where the offender can no longer play that character.

When respondents were questioned further about issues with controlling online behavior, especially in games, they had very intriguing responses. Many respondents indicated a preference for self-regulation in online games. Some respondents took a more simplistic view of the world as they wished for players to be fair and play well with others. In this same vein, some players were concerned with the inherent fairness of play and the satisfaction derived from honest competition and combat.

On the other hand, several respondents acknowledged the dangers of anonymity and the necessity of consequences for behavior. In general, however, participants did not appear to require nor request official sanctions except in extreme situations. Many felt that social and public retribution would be harsher and longer lasting than those from administration. As with behaviors, these sanctions are obviously situational. In some games, killing enemy players isn’t just acceptable, it’s the whole point of the game.

However, some behaviors, specifically harassment, seem to be regarded as unacceptable under the majority of circumstances and therefore susceptible to being sanctioned the harshest by players. This trend bodes well for the future of online

communication and interaction as it demonstrates the ability of online communities to police themselves, although the tolerance for disruptive or deviant behavior seems to be context specific.

Summary

Overall, ignoring or avoiding a player who is problematic is a common choice for the responding gamers. Rarely, participants indicated a need for formal action to be taken. This was typically around issues of harassment or cheating, which seem to be considered the more serious of offenses. Players instead chose to remove themselves from the situation when necessary, though typically, they just go on with their lives and ignore the offending player or situation. These comments offer a unique look into the complex interplay between context, behavior, control, and anonymity which could serve as fertile ground for additional research.

Table 4-1. Correlations for Self Control and Use of Deviant Actions Model** Frequency of Race Age Sex Self- use control Freq. Use 1.0 Race -.339(.007)* 1.0

Age -.184(.184) -.191(.092)* 1.0

Sex .512(.000)* -.230(.043)* -.139(.259) 1.0

Self- -.494(.000)* .371(.001)* -.076(.519) -.252(.032)* 1.00 control ** Significance levels are recorded in parentheses. * = Significant at p ≤ .10

Table 4-2. Regression for Self Control and Use of Deviant Actions (N=49)** Unstandardized Standard Error Standardized t-test B B Self-control -.275 .072 -.483 -3.843(.000)* Age -.008 .007 -.135 -1.078(.287)

Construct .708 .291 ------2.435(.019)* R Squared .263 F 8.367 Significance .001* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-3. Mult. Imput. Regression for Self Control and Use of Deviant Actions (N=77)** Pooled Unstandardized Standard Error Standardized t-test B B Self-control -.262 .073 ------3.601(.001)* Age -.008 .007 ------1.109(.279) Sex .147 .082 ------1.801(.073)* Race -.151 .103 ------1.468(.143)

Construct .661 .308 ------2.147(.034)* R Squared .425 F 13.494 Significance .000* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-4. Correlations for Self Control and Victimization Model** Victimization Race Age Sex Self - Control Victimization 1.0 Race .026(.814) 1.0 Age .011(.926) -.191(.092)* 1.0 Sex -.091(.445) -.230(.043)* -.129(.259) 1.0 Self-control -.116(.340) .371(.001)* -.076(.519) -.252(.032)* 1.00 ** Significance levels are recorded in parentheses. * = Significant at p ≤ .10

Table 4-5. Regression for Self Control and Victimization (N=49)** Unstandardized Standard Error Standardized t-test B B Self-control -.022 .023 -.115 -.938(.352) Age .001 .002 .041 .335(.738)

Construct .447 .099 ------4.519(.000)* R Squared .015 F .510 Significance .603 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-6. Mult. Imput. Regression for Self Control and Victimization Model (N=77)** Pooled Unstandardized Standard Error Standardized t-test B B Self-control -.032 .024 ------1.324(.186) Age .000 .002 ------.210(.834) Sex -.028 .029 ------.975(.330) Race .009 .036 ------.255(.798)

Construct .525 .108 ------4.854(.000)* R Squared .050 F .900 Significance .469 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-7. Correlations for Social Learning and Use of Deviance Model** Use of Race Age Sex Definition Deviance Use of 1.00 Deviance Race -.339(.007)* 1.00 Age -.184(.184) -.191(.092)* 1.00 Sex .512(.000)* -.230(.043)* -.129(.259) 1.00 Definition .513(.000)* -.292(.014)* .003(.978) .294(.015)* 1.00 Assoc. .467(.000) .047(.686) .048(.695) .020(.869) .473(.000)* Imitation .104(.469) -.136(.246) .126(.284) .230(.050)* .306(.011)* Reinforc. .716(.000)* -.448(.000)* -.047(.694) .510(.000)* .794(.000)*

Diff. Assoc. Imitation Reinforc. Use of

Deviance Race Age Sex Definition Diff. 1.00 Assoc. Imitation .014(.911) 1.00 Reinforc. .360(.003) .219(.064)* 1.00

** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-8. Regression for Social Learning and Use of Deviance (N=44)** Unstandardized Standard Error Standardized t-test B B Age -.011 .008 -.161 -1.354(.184) Diff. Assoc. .212 .074 .360 2.883(.007)* Imitation .260 .190 .177 1.371(.179) Reinforcement 1.234 .483 .177 2.557(.015)* Definitions .081 .568 .030 .143(.887)

Construct -.236 .648 ------.364(.718) R Squared .508 F 7.447 Significance .000 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-9. Mult. Imput. Regression for Social Learning and Use of Deviance (N=77)** Pooled Unstandardized Standard Error Standardized t-test B B Age -.006 .006 ------1.087(.278) Sex .138 .075 ------1.832(.067)* Race -.075 .108 ------.698(.489) Reinforc. 1.015 .414 ------2.455(.018)* Definitions .041 .459 ------.090(.929) Imitation .283 .151 ------1.872(.066)* Assoc. .150 .069 ------2.152(.047)*

Construct -.573 .527 ------1.087(.278) R Squared .669 F 17.334 Significance .000* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-10. Pearson Correlations for Social Learning and Victimization Model** Victim Race Age Sex Definition Victim. 1.00 Race .026(.814) 1.00 Age .011(.926) -.191(.092)* 1.00 Sex -.091(.445) -.230(.043)* -.129(.259) 1.00 Definition .183(.142) -.292(.014)* .003(.978) .294(.015)* 1.00 Assoc. .530(.000)* .047(.686) -.048(.695) .020(.869) .473(.000)* Imitation .115(.343) -.136(.246) .126(.284) .230(.050)* .306(.011)* Reinforc. .044(.716) -.448(.000) -.047(.694) .510(.000)* .7994(.000)*

Diff. Assoc. Imitation Reinforc. Victim. Race Age Sex Definition Diff. 1.00 Assoc. Imitation .014(.911) 1.00 Reinforc. .360(.003) .219(.064)* 1.00 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-11. Regression for Social Learning and Victimization (N=55)** Unstandardized Standard Error Standardized t-test B B Age -.001 .002 -.049 -.437(.664) Diff. Assoc. .107 .019 .698 5.635(.000)* Imitation .057 .053 .120 1.089(.282) Reinforcement .282 .129 .366 2.179(.034)* Definitions .107 .162 .111 .658(.513)

Construct .979 .189 ------5.184(.000)* R Squared .422 F 7.294 Significance .000* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-12. Mult. Imput. Regression for Social Learning and Victimization (N=67)** Pooled Unstandardized Standard Error Standardized t-test B B Age -.002 .186 ------.866(.388) Sex -.026 .026 ------1.009(.313) Race -.007 .034 ------.213(.832) Reinforc. .176 .129 ------1.360(.175) Definitions .046 .149 ------.308(.758) Imitation .069 .048 ------1.434(.153) Assoc. .102 .018 ------5.680(.000)*

Construct 1.019 .186 ------5.492(.000)* R Squared .478 F 7.861 Significance .000* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-13. Correlations for Self-Control and Experienced Social Control** Exp. Social Race Age Sex Self - Control Control Exp. Social 1.0 Control Race -.112(.247) 1.0 Age -.216(.057)* -.191(.092)* 1.0 Sex .140(.223) -.230(.043)* -.129(.259) 1.0 Self-control -.001(.994) .371(.001)* -.076(.519) -.252(.032)* 1.00 ** Significance levels are recorded in parentheses. * = Significant at p ≤ .10

Table 4-14. Regression for Self-Control and Experienced Social Control (N=72)** Unstandardized B Standard Error Standardized t-test B Self-control -.011 .080 -.016 -.133(.894) Age -.013 .008 -.195 -1.653(.103)

Construct 1.650 .357 ------4.627(.000)* R Squared .038 F 1.366 Significance .262 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-15. Mult. Imput. Regression for Self-Control and Experienced Control (N=77)** Pooled Unstandardized Standard Error Standardized t-test B B Age -.019 .008 ------2.343(.022)* Sex .143 .112 ------1.281(.204) Race -.190 .141 ------1.343(.183) Self-control -.139 .090 ------1.549(.126)

Construct ------1.087(.278) R Squared .129 F 2.712 Significance .036* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-16. Correlations for Self-Control and Use of Social Control** Use Social Race Age Sex Self - Control Control Use Social 1.0 Control Race -.163(.091)* 1.0 Age .004(.972) -.191(.092)* 1.0 Sex .189(.106) -.230(.043)* -.129(.259) 1.0 Self-control .345(.003)* .371(.001)* -.076(.519) -.252(.032)* 1.00 ** Significance levels are recorded in parentheses. * = Significant at p ≤ .10

Table 4-17. Regression for Self-Control and Use of Social Control (N=69)** Unstandardized B Standard Error Standardized t-test B Self-control .491 .163 .345 3.009(.004)* Age .003 .015 .024 .209(.835)

Construct 3.932 .699 ------5.624(.000)* R Squared .120 F 4.573 Significance .014* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-18. Mult. Imput. Regression for Self-Control and Use of Social Control (N=77)** Pooled Unstandardized Standard Error Standardized t-test B B Age -.006 .014 ------.451(.653) Sex .077 .199 ------.388(.699) Race -.472 .251 ------1.885(.063)* Self-control .290 .158 ------1.834(.071)*

Construct 3.614 .807 ------5.271(.000)* R Squared .144 F 4.245 Significance .004* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-19. Correlations for Social Learning and Experienced Informal Social Control** Exp. Race Age Sex Control Exp. Control 1.00 Race -.122(.247) 1.00 Age -.216(.057)* -.191(.092)* 1.00 Sex .140(.223) -.230(.043)* -.129(.259) 1.00 Definition .043(.723) -.292(.014)* .003(.978) .294(.015)* Assoc. .026(.825) .047(.686) .048(.695) .020(.869) Imitation -.076(.522) -.136(.246) .126(.284) .230(.050)* Reinforc. .136(.245) -.448(.000)* -.047(.694) .510(.000)*

Definition Assoc. Imitation Reinforc. Exp. Control Race Age Sex Definition 1.00 Assoc. .473(.000)* 1.00 Imitation .306(.011)* .014(.911) 1.00 Reinforc. .794(.000)* .360(.003)* .219(.064)* 1.00 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-20. Regression for Social Learning and Experienced Informal Control (N=57)** Unstandardized Standard Error Standardized t-test B B Age -.005 .011 -.065 -.481(.633) Assoc. .052 .091 .086 .567(.573) Imitation .015 .251 .008 .058(.954) Reinforc. 1.351 .612 .469 2.206(.032)* Definitions 1.368 .756 .393 1.811(.076)*

Construct 2.212 .901 ------2.455(.017)* R Squared .108 F 1.257 Significance .296 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-21. Mult. Imput. Regression for Social Learning and Experienced Control (N=67)** Pooled Unstandardized Standard Error Standardized t-test B B Age -.012 .010 ------1.193(.234) Sex .135 .134 ------1.009(.313) Race -.216 .169 ------1.280(.201) Reinforc. .434 .646 ------.671(.502) Definitions 1.018 .762 ------1.336(.182) Imitation .125 .243 ------.513(.608) Assoc. .056 .096 ------.581(.562)

Construct 2.301 .946 ------2.433(.015)* R Squared .164 F 1.683 Significance .131 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-22. Correlations for Social Learning and Use of Informal Social Control Model** Use of Control Race Age Sex Use of 1.00 Control Race -.163(.091)* 1.00 Age .004(.972) -.191(.092)* 1.00 Sex .189(.106) -.230(.043)* -.139(.259) 1.00 Definition .349(.004)* -.292(.014)* .003(.978) .294(.015)* Assoc. .164(.166) .047(.686) -.048(.695) -.020(.869) Imitation .162(.178) -.136(.246) .126(.284) .230(.050)* Reinforc. .557(.000)* -.448(.000)* -.047(.694) .510(.000)

Definition Assoc. Imitation Reinforc. Use of

Control Race Age Sex Definition 1.00 Assoc. .473(.000)* 1.00 Imitation .306(.011)* .014(.911) 1.00 Reinforc. .794(.000)* .360(.003)* .219(.064)* 1.00 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-23. Regression for Social Learning and Use of Informal Control (N=54)** Unstandardized Standard Error Standardized t-test B B Age .013 .018 .089 .739(.464) Assoc. .056 .175 .042 .319(.751) Imitation .217 .448 .060 .484(.631) Reinforc. 4.304 1.070 .796 4.022(.000)* Definitions 2.156 1.326 .328 1.626(.110)

Construct 2.255 1.513 ------1.491(.142) R Squared .316 F 4.527 Significance .002* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table 4-24. Mult. Imput. Regression for Social Learning and Use of Control (N=67)** Pooled Unstandardized Standard Error Standardized t-test B B Age .009 .014 ------.594(.554) Sex -.140 .206 ------.681(.498) Race -.254 .260 ------.977(.332) Reinforc. 4.776 1.014 ------4.709(.000)* Definitions 2.771 1.176 ------2.357(.022)* Imitation .172 .367 ------.468(.642) Assoc. .051 .141 ------.363(.718)

Construct 3.238 1.428 ------2.267(.027)* R Squared .411 F 5.989 Significance .000* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

CHAPTER 5 DISCUSSION

The following section summarizes and interprets the trends and results for each of the hypotheses covered in the prior section. Limitations of the current study and the potential for future research studies will also be discussed in the conclusion.

Overall, online behavior is an emerging area of study, and as such, may need to be considered as entirely separate to behavior in the physical world. Social learning and self-control can somewhat predict deviance, informal social control, and victimization in an online gaming environment, and interestingly, the most common mechanism for handling disruptive and deviant players was to ignore them, followed by moving to another area.

Summary of Results

Self-Control and Use of Deviance Model

The self-control listwise regression model explained 26.3% of the variance and was highly significant. Self-control, with a standardized beta of -.483, was the strongest predictor of deviant actions. Lower levels of self-control predicted higher use of deviant actions. Age, with a standardized beta of -.135, was not a significant predictor of deviant actions. However this suggests that younger individuals tended to report higher levels of the use of deviance and/or disruptive behavior. Considering the amount of missing data, additional analyses were utilized to confirm these conclusions using more robust techniques.

The multiple imputation regression model was also significant and explained

42.5% of the variance. Self-control and sex were significant predictors. This model suggests higher levels of self-control predict lower levels of deviance and that males are

more likely to report using deviant actions. Although not significant, non-whites and younger individuals were also more likely to use deviant and disruptive actions.

These trends are not surprising considering current research on self-control and deviance. The results make sense intuitively. Typically, young males with low self- control demonstrate more aggressive and deviant or disruptive behavior. Even though the behaviors in question are online in nature and the setting in which these individuals interact in virtual, low self-control still appears to predict deviance under the conditions of the current study.

Self-Control and Victimization Model

This self-control listwise regression model only explained 1.5% of the variance and was not significant at all. Self-control was not a significant predictor of victimization, and neither was age. The multiple imputation regression model explained 5% of the variance but was also not significant, and none of the variables were predictive. This may be due to the small nature of the sample and the relatively small incident rate of victimization. This also could be a reflection of unequal scale reliability. While the self- control scale was a reliability coefficient of .888, the victimization scale had a reliability coefficient of .725. However, while these models are not significant, there are some interesting trends.

As previously mentioned, lower self-control were related to higher incidents of victimization. Females were also more likely to be victimized. Interestingly, age was positively related to victimization, so as the age of the players increased, the victimization did as well. This may be due to more exposure as the players age, or possibly due to more available time and the freedom to play more often. High school students, for example, are unable to play during the school day, whereas college 91

students and some employed professionals may have more flexibility in their playtime.

Therefore, they are exposed to more opportunities for victimization to occur. On the other hand, older players may not necessarily have been playing longer and are not as immersed in gaming culture, which could lead to higher levels of victimization. These players may also not be as good at the game as some younger gamers are, and this could create opportunities for victimization to occur.

Social Learning and Use of Deviance Model

The social learning listwise regression model explained 50.8% of the variance and is highly significant. Of the social learning variables, only differential reinforcement and differential association were significant. Differential association, with a standardized beta coefficient of .360, was the strongest predictor, indicating that as association with deviant peers increased, use of deviance increased as well. Additionally, the higher the level of deviant reinforcement, the use of deviance increased. Though not a significant relationship, age had a negative beta, which indicated that younger participants were more likely to have higher levels of deviance. As imitation and pro-deviant definition scores increased, deviance use increased.

The multiple imputation regression model was also significant. It explained 66.9% of the variance. Reinforcement, imitation, and differential association were significant predictors or deviance, and reinforcement was the strongest predictor. As reinforcement, imitation, and pro-deviant association increased, so did deviance use.

Although not a significant relationship, as age increased, deviance use decreased.

Social Learning and Victimization Model

The listwise regression model was significant and explained 42.2%% of the variance. Differential association and reinforcement were the only significant predictors,

and they both had positive beta coefficients, indicating that as these variables increased, so did reported victimization. As for the social learning variables, higher scores of pro-deviant definitions was related to higher levels of victimization. Higher levels of imitation (though not specifically deviant) were related to higher levels of victimization. The multiple imputation regression model was also significant and explained 47.8% of the variance. Differential association was the only significant predictor, where higher levels of deviant peer association was related to higher levels of deviance.

There were some interesting trends within the models. Differential association was significant in both models. In these models, as seen with the previous victimization model, being female was associated with higher levels of victimization. Interestingly, so was being non-white, which is counterintuitive since the online environment is anonymous in nature. Contrary to the prior models, victimization decreased with age, though this was not a significant nor powerful relationship.

Self-Control and Experienced Informal Social Control Model

The listwise regression model was not significant and only explained 3.8% of variance. However, several trends are of interest from this model. Age was the only variable approaching significance. In this case, as age increased, the level of experiencing informal social control mechanisms decreased. Again, this may be a function of available time and younger players having more exposure to potential informal control mechanisms. More likely, the decrease in experiencing control mechanisms is related to the decrease in deviance use by older players. Players who do not engage in deviant actions are not going to experience informal social control. In the current sample, there were not many individuals over 30, so full time professionals 93

who would be unable to play during the day are not particularly influential for this model and suggest that time exposed to the game may not be the critical factor.

Interestingly, lower levels of self-control was associated with more experienced social control. While this is not a significant variable, it does suggest a relationship that is on par conventional wisdom. Those who are more impulsive and exhibit lower self- control tend to report higher levels of informal control mechanisms. Perhaps these individuals are also more likely to use deviance in their play and therefore face the repercussions more often.

The multiple imputation regression model was significant and explained 12.9% of the variance. This model confirms the trends seen in the listwise model. Age was the only significant predictor of experienced informal social control. Younger players typically reported experiencing more informal social control. Non-whites and males were also more likely to experience informal social control, though not a significant relationship. These trends are interesting because online gaming is an anonymous environment; as such, these relationships suggest that there could be additional factors that may come into play when determining who experiences informal social control.

Self-Control and the Use of Informal Social Control Model

Overall, this model was highly significant and explained 12.0% of the variance.

Interestingly, self-control was the only significant predictor, with those with higher levels of self-control more likely to use informal social control. This makes sense as individuals with higher levels of control would be expected to use informal methods of control rather than accept chaos and disruption. As age increased, so did the use of informal control.

This makes intuitive sense. Older players probably have more experience in social settings and in using informal control mechanisms simply because they have lived 94

through it. While this does not necessarily translate into online experience, obviously informal control mechanisms can be used in real world situations as well as online.

These trends follow conventional findings.

Similar trends were observed in the multiple imputation regression model. This model was also significant and explained 14.4% of the variance. Self-control and race were significant predictors of the use of informal social control. Specifically, higher levels of self-control were related to more reported use of informal social control.

Interestingly though, non-whites were more likely to report using informal social control.

As for gender, males were more likely to use informal means of control. This may be an interesting dynamic to further explore considering the anonymous setting of this study. Behavioral difference still appear to exist between males and females.

Social Learning and Experienced Informal Social Control Model

While this listwise regression model only explain 10.8% of variance and was not significant, there were interesting trends among the variables. Reinforcement was a significant predictor, where higher levels of deviant reinforcement related to higher levels of experienced informal social control. Definitions was also significant, where higher scores of pro-deviant definitions was related to higher levels of experienced informal social control. Though not significant, younger participants were more likely to experience informal social control, and higher levels of deviant differential association and imitation were related to higher levels of experienced informal social control.

As for the multiple imputation regression model, this was also not significant, and none of the variables were significant predictors. However, the trends were similar. Age was inversely related to experienced informal social control. Non-white were more likely to report experiencing informal social control. Higher levels of deviant reinforcement, 95

deviant definitions, imitation, and deviant association were related to higher levels of experienced informal social control. Males were more likely to report experiencing informal social control. These trends make sense. Those who are more likely to use deviance or are more likely to have higher levels of the social learning components that may lead to increased deviance would experience more mechanisms of control.

Social Learning and the Use of Informal Social Control

This model was highly significant and explained 31.6% of the variance.

Reinforcement was the most significant and strongest predictor with a standardized beta coefficient of .796, so the more reinforcement an individual experiences, the more likely they are to use informal social control techniques. Though not significant, as age increased, so did the use of informal social control. Higher levels of deviant association, imitation, and deviant definitions also were related to the use of more informal social control.

The multiple imputation regression model was also significant. This model explained 41.1% of the variance. Differential reinforcement and definitions were the only significant predictors, with higher levels associated with higher use of informal control.

Though not significant, as age increased, so did the use of informal control. This may be a reflection of experience in life in general, whereas older gamers use informal methods for curbing undesired behavior because they have learned to do so over time. Perhaps younger gamers are less experienced and more impulsive. These trends are interesting and suggest further investigation may be needed to parse out the relationships between these factors.

Summation of Trends

Overall, self-control and social learning variables seem to be able to somewhat predict use of deviance and use of informal social control. The relationships are less clear in regards to victimization and experienced social control. Generally, lower levels of self-control were related to higher levels of deviance, victimization, and experiences with informal social control. Higher levels of self-control were related to more use of informal social control.

On the other hand, higher levels of deviant reinforcement, deviant definitions, deviant association, and imitation were associated with higher levels of deviance, victimization, use of informal social control, and experiences with informal social control.

Additionally, as age increased, deviance, victimization, and experience with informal social control typically decreased. As age increased, use of informal social control typically increased as well.

Victimization rates are difficult to measure accurately, and online victimization within a gaming environment may present differently than other forms of online victimization as the definitions of the behaviors are different and online gaming environments are generally considered a play or fantasy environment. Self-control did not predict victimization well, but differential association and reinforcement were related to higher levels of victimization

Age was only a significant predictor in the self-control and experienced social control model, but the trends suggest that experience may influence the exposure to informal social control which individuals may receive. As age increased, deviance decreased. The relationship to victimization is complicated, but in the only significant model, younger players reported more victimization. As age increased, so did the 97

reported use of informal social control, but younger players reported experiencing more informal social control. Again, being in a gaming environment alters the severity of informal social control mechanisms though online identity can be incredible important, especially as more on more socialization occurs in online settings.

Overall, self-control models significantly predicted use of deviance, experienced informal social control, and use of informal social control, and the social learning models significantly predicted deviance, victimization, and use of informal social control. Again, this indicates that these sociological theories can predict online behavior in a specific online gaming environment using an online survey methodology. However, caution is advised when interpreting these results since the number of items in the social learning model may inflate the variance explained.

Although these current theories are able to at least partially predict specific behaviors in certain online interactions, the highly contextual environment suggests that other factors may be important to the understanding of this behavior. Further research is needed to pick apart these relationships, but these findings and trend provide a fertile starting point into this relatively new area of study.

Limitations

While research studies require a lot of effort and attention to detail, there is always room for improvement and to learn from past experiences. When initial conducting research online using an online environment and online population, it was expected to be straight forward. However, even though the targeted population of massively multiplayer gamers is relatively small and needed to be appropriately addressed, the response rate was surprisingly low.

A total of 176 individuals began the survey. Only 57.4% completed the full survey. Although several attempts were made by the researcher to encourage responses, message board posts and email communications were often ignored. Part of the issue may have been the length of the survey, though the survey needed to cover several aspects of behavior, social control, theory, and demographics. It may be possible to utilize multiple waves of the survey and then match results, critically examine the questions to reduce the number to a core few items to incorporate in a scale, or perhaps utilize a combination of both.

Although attempts were made to decrease the number of survey questions through factor analysis after a focus group discussion, length is an obvious issue for survey completion rates. Interestingly, certain questions seemed to be skipped more often. Race/ethnicity, employment status, education level, and religiosity were among the most troublesome items. This may be an aspect of survey fatigue or part of a larger issue concerning anonymity on the internet. No matter the reason, all attempts should be made to correct the sample size and survey completion issue in future studies. Small sample sizes are appropriate for initial studies, such as the current investigation into theory and online behavior. However, in order to have a robust study and be able to draw firm conclusions, a larger sample is needed.

The method by which the survey was delivered was appropriate for the online gaming environment. However, it is possible that certain groups of casual gamers were never given an opportunity to complete the survey. While posting the survey on gaming sites and message boards, only the gamers who frequent those can participate.

Therefore, future studies should try and secure support from the online gaming

administrators as well as large groups who can assist in reaching different types of gamers. An attempt was made to do this in the current study, but support was withdrawn after the study began. There may not be a difference between gamers who frequent message boards seeking information and those who casually play without seeking outside information, but there could be a significant difference in these types of gamers so this should be taken into consideration.

Future Research

Considering the developing nature of online environments and the user- constructed scale of deviance behavior, further research is needed to determine where the threshold for behavior lies, either by gamer, game type, or situation. A study of the interplay between context, behavior, control, and anonymity would also be interesting and may provide significant insight into decision making and social control online. The participants’ responses to these issues suggest a rich topic that has not yet been fully explored.

The entire deviance scale was created through feedback from the initial focus group and discussions with gamers. Additionally, the deviant behavior covered in this study was highly context and potentially game specific. There were situations where such behavior was expected, and there were discrepancies with what counted as deviant behavior depending on the participant. When asked to rank the top most disruptive behaviors, 29% chose griefing and 23% chose bullying. Therefore, while some view ganking and ninjaing as serious behavioral issues, perhaps those behaviors are only an issue for certain types of gamers (again, maybe casual versus hard-core) or certain types of games. On the other hand, harassment and bullying seemed to be considered more serious in general which suggests a sliding scale of ethical, non- 100

deviant behavior and a tolerance for game-specific behavior that in the real world would be considered serious.

Based on the responses of participants, it appears that gamers do not just create their own sliding moral behavior scale. They also create their own set of values and react accordingly. This makes sense considering the nature of online environments.

The lack of physical space and serious, real world repercussions for behavior allow for the development of a group driven subculture. Many gamers neutralize or justify their own behavior as “part of the game.” Some dismiss their behavior by blaming the other player. While anonymity may give them freedom to act in a less structured way, some gamers are obviously still influenced by their real world values and learned responses.

Players cannot just leave behind the real world even if they can act differently online, and this adds an interesting interplay to further discover.

Additionally, it would be interesting to see just how well self-control to an extent and social learning theory in particular handle predicting the type of social reaction to deviance online. Theory may not be able to predict the frequency of such behavior, but it should be able to inform researchers as to the type of reaction. Considering the social aspects of social learning theory, this would be an appropriate theory. Players may learn how to react to given situations based on their friends and associations in game, but they may be influenced by real world associations as well. Informal social control is measured across a broad range of actions. The gamers are able to use shaming, threatening, and retaliating, but they are also able to use techniques not available in the real world, like quitting the game or muting players. For the purposes of this study, formal reporting and the actual removal of the player from the situation (by moving to

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another area or quitting) had been removed from the scale as these are not specifically informal control of other’s behavior. It is possible that the investigation into these responses could yield information for more effective ways to elicit conformity of pro- social behavior online.

This also introduces a new dynamic unique to deviance in online environments.

In the physical world, people cannot always remove themselves from situations. Even when the behavior is a mere annoyance (versus a serious crime), most people cannot walk away from neighbors, family, school, work, and so forth. This is a context-specific and environmentally constrained reaction to deviant or unwanted, annoying behavior.

Further research should investigate how much of online behavior can actually translate into the physical world and how such behaviors may parallel other social reactions.

Ignoring and Avoidance Behavior

Interestingly, when given a wide range of social control mechanisms, “ignoring” an offender was the most common mechanism for handling disruptive and deviant players. This was followed by the player moving to another area away from the offending person (avoidance). In the open-ended, additional comments, respondents specifically mentioned ignoring or avoiding players as an often-used technique. In fact, most players chose an option that allowed them to continue playing without encountering the troublesome player, and given the vast space on the Internet and within an online game specifically, avoidance is a feasible option rather than direct confrontation or official intervention.

As mentioned previously, this is a unique reaction that has several implications in the real world. Most people cannot physically remove themselves from situations beyond walking away from confrontations when possible, and even this is highly context 102

specific. It would be ill-advised to walk away from a violent confrontation and impossible to just leave and find another apartment to sleep in if neighbors are having a loud party.

Perhaps this behavior trend is only truly feasible online. If this is the case, and common sense suggests it is, then ignoring and avoidance behavior still can be effective methods for handling deviance, but only under these conditions. Considering the amount of time people spend online, this may become an important option to include in all chat rooms, message boards, or other online gathering places.

The use of avoidance and ignoring may also be related to their perceived level of risk, even if the risk is only to a virtual avatar (“death” of the character) or virtual currency (a “loss” penalty). If the player is not certain they can win against the offender, they will often chose not to engage. Players appear to utilize a hierarchy at times where they will ignore average irritants but publically “call out” gaming “parasites” who negatively impact the gaming experience and may continue to be a problem in the future.

This bears further research as more and more people move their social interactions online. While these mechanisms may work in a gaming environment or on message boards and chat rooms, these controls may not work in other settings, such as an online classroom. Perhaps other effective techniques involving shaming or some other way of impacting an online persona or even a muting mechanism could be utilized. More research into effective control mechanisms is needed.

Conclusion

In conclusion, social learning and self-control, to an extent, can adequately predict deviance, informal social control, and victimization in an online gaming environment. While self-control models significantly predicted use of deviance, 103

experienced informal social control, and use of informal social control, social learning models significantly predicted deviance, victimization, and use of informal social control.

This indicates that these sociological theories can predict specific online behavior, at least in a gaming environment using Internet-based survey methodology.

The social learning models, on average, predicted more variance, and differential reinforcement was the strongest significant predictor. Differential association and definitions were also significant. Of these individual predictors, differential reinforcement was a predictor of deviance use, victimization, experienced informal social control, and use of informal social control. Differential association was a significant predictor of deviance use and victimization. Definitions were significant predictors of experienced informal social control and use of informal social control. Social learning theory predicted over 50% of the variance in the deviance model but only 40% of victimization.

The model predicted under 40% for the use of informal social control but only around

10% of experienced informal social control. Caution is advised when interpreting these results since the number of items in the social learning model may inflate the variance explained.

As for the self-control models, self-control was significant predictor for use of deviance and the use of informal social control. Self-control predicted around 30% of deviance and around 12% of the use of informal social control. Specifically, low self- control led to more deviance, but higher self-control led to an increase in the use of informal social control. Self-control was not a good predictor of victimization or experienced informal social control. In fact, these were the weakest models. While self-

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control itself was not a significant variable, the self-control model significantly predicted roughly 12% of experienced informal social control.

Because online behavior takes place in an anonymous environment where an individual’s persona is more important, sex and race are not critical variables. In fact, explaining deviance was the only time sex was a significant factor. Males were more likely to report committing deviant actions. This trend follows traditional findings, but social learning and self-control should not be overly affected by these variables.

Considering the sedentary nature of online computer games, it is interesting that low self-control still has an effect. Social learning theory intuitively makes sense considering the social nature of multiplayer online games. Still, these results suggest that sociological theories are appropriate when investigating online behavior using an online population, although adjustments to the depth and length of survey instruments are recommended if utilizing a similar population.

Surprisingly, and perhaps most interesting, when ranking the informal social control options, over 90% of respondents reported they would ignore the other player.

This is by far the strongest finding of the study, and considering the population, could explain a lot about online behavior. Retaliatory behavior was relatively rare. Obviously, there are some troublesome players and some who aggressively defend their gaming experience, but the majority of respondents appear to just want to mind their own business. The space in online games is relatively unlimited so confrontations are easily resolved by adding space between the player and the offending situation. Simply put, the easiest thing to do is move when there is generally little to lose except some time spent traveling to a new location. Rarely did participants indicate the need for formal

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intervention. These circumstances typically revolved around harassment of some kind, which suggests a threshold for action and reaction as well as a hierarchy of offenses.

Additionally, the participants’ open-ended responses about ignoring things that annoy them offer a possible reason for the surprisingly low response rate, lack of significant relationships, and the low level of reported use of control mechanisms. If participants were not interested in a question, did not like a question, did not want to answer a question, or got frustrated with the survey, they could skip the questions and effectively ignore the situation. The response to just ignore/avoid players could also apply to whatever may be considered an issue online, and this behavior, in turn, could be a reflection of the conditions in which the respondents find themselves when online.

Every day, people are inundated with material like special online offers, emails, surveys, spam, and phishing scams. People receive so much information via the

Internet, some of which may not be accurate or even safe to open. This group may be more difficult to reach online because of this. So, while it may make intuitive sense to study online behavior via online surveys, more care may be needed to reach and keep respondents. Still, this research offers a unique look into the complex interplay between context, behavior, control, and anonymity which could serve as fertile ground for additional research.

As technology rapidly changes and more and more people migrate to online social interactions, it is important to study the differences and similarities between online and offline behavior, socialization, and control. While the anonymity of the internet may seem to block sociological study, offline socialization appears to have carried over into online interaction. It is important to learn how online interactions influence control or the

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lack thereof. If ignoring or avoiding is the typical response to low level deviance and disruption, adjustments can be made to online platforms such as blogs, forums, or chat rooms to allow for this type of control. On the other hand, ignoring irritating students in an online classroom setting may not be advisable. This type of behavior and social response appears to be context driven and specific to online interactions.

While current sociological theories may be able to somewhat predict low level online deviance and informal control, these responses are not limited by the physical environment. Players can ignore an annoying player or drop a cheating group member, but most people cannot exit situations so easily in the real world. Instead, these trends have real world implications that are constrained by the physical limitations in which people live. In these circumstances, people have to use more traditional, formal control mechanisms such as calling management, reporting behavior, or even calling the police. So, while there are parallels, it is context-specific. Researchers should continue to study these phenomena to help develop tools and techniques for controlling behavior informally in this new, online environment that is quickly spreading to all aspect of life.

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APPENDIX A FACTOR ANALYSIS

Table A-1. Factor Analysis for Use of Deviance Scale* Communalities Component 1 Component 2 Griefing .696 .650 .289 Trolling .774 .784 .120 Begging .719 .413 -.321 Flaming .528 .591 -.412 Spamming .659 .496 -.538 Bullying .668 .779 -.239 Ganking .655 .336 .652 Ninjaing .714 .365 .674 Pestering .461 .638 .233 *Cronbach’s alpha = .705

Table A-2. Factor Analysis for Victimization Scale* Communalities Component 1 Component 2 Griefing .556 .689 Trolling .745 .510 .517 Begging .845 .465 Flaming .594 .629 .435 Spamming .718 .340 .606 Bullying .636 .789 Ganking .597 .375 -.604 Ninjaing .566 .597 -.328 Pestering .625 .669 -.352 *Cronbach’s alpha = .725

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Table A-3. Factor Analysis for Self-Control Scale* Communalities Component 1 Component 2 Get the things I want .776 .700 Look out for myself .699 .655 If things I do upset people .718 .651 Take a risk for fun .641 .641 -.418 Hard to talk calmly .732 .632 Rather something physical .730 .628 -.389 Pleasure here and now .639 .625 Exciting might get in trouble .592 .600 Avoid difficult projects .705 .585 .327 Angry feel like hurting .684 .541 Excitement over security .530 .529 -.417 Not sympathetic .771 .517 .403 Get out and do .524 .508 -.450 Quit complicated tasks .658 .467 .313 Spur of the moment .463 .413 Test myself with risk .536 .349 -.642 Dislike hard tasks .700 .410 .564 More energy .578 .522 -.538 Easiest things for pleasure .560 .492 .314 Stay away when angry .691 .473 Lose temper easily .674 .514 Not much thought of future .714 .335 .436 More concern for short run .695 .468 .434 Better on the move .694 .517 *Cronbach’s alpha = .888

Table A-4. Factor Analysis for Differential Association Scale* Communalities Component 1 Component 2 Pestering .936 .761 Griefing .888 .721 Begging .867 .717 -.409 Trolling .944 .704 Bullying .897 .681 .436 Ninjaing .880 .668 -.557 Ganking .892 .614 -.468 Flaming .931 .592 .551 Spamming .969 .587 -.653 *Cronbach’s alpha = .843.

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Table A-5. Factor Analysis for Deviant Definitions Scale* Communalities Component 1 Component 2 Trolling .834 .848 Griefing .808 .826 Pestering .932 .811 Spamming .917 .803 Begging .952 .795 Ganking .912 .776 -.324 Ninjaing .917 .771 -.391 Bullying .998 .722 Flaming .951 .671 .511 *Cronbach’s alpha = .919

Table A-6. Factor Analysis for Deviant Attitudinal Definitions Scale* Communalities Component 1 Component 2 How gaming works .765 .874 Tricking others .652 .807 Do well taking advantage .645 .803 Taking loot is easier .625 .790 “Noobies” ask for it .578 .760 .304 Play fair .544 .737 Spamming is legit .356 .597 .376 *Cronbach’s alpha = .896

Table A-7. Factor Analysis for Imitation Scale* Communalities Component 1 Watch high level player .677 .823 Watch other players .556 .746 Watch my friends .527 .726 Watch YouTube .524 .724 *Cronbach’s alpha = .782

Table A-8. Factor Analysis for Differential Reinforcement Scale* Communalities Component 1 Fun to taunt .652 .807 Stealing is exciting .596 .772 Ninjaing disappoint .583 .764 Steal for power .575 .758 Better about self .531 .729 Ganking excitement .452 .673 Thrill to gank .440 .663 Enjoy taunting .397 .630 Encourage violation .373 .611 Ganked support .363 .602 *Cronbach’s alpha = .904

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Table A-9. Listwise Regression for Self Control and Use of Deviant Actions (N=49)** Unstandardized Standard Standardized t-test B Error B Self-control -.165 .072 -.289 -2.296(.026)* Age -.007 .007 -.128 -1.112(.272) Sex .225 .087 .319 2.573(.013)* Race -.213 .113 -.235 -1.886(.066)

Construct .368 .072 1.203(.235) R Squared .426 F 8.339 Significance .000* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table A-10. Meansub. Regression for Self Control and Use of Deviant Actions (N=87)** Unstandardized Standard Standardized t-test B Error B Self-control -.190 .052 -.368 -3.668(.000)* Age -.007 .005 -.154 -1.539(.128)

Construct .454 .225 2.019(.047)* R Squared .150 F 7.528 Significance .000* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table A-11. Listwise Regression for Self Control and Victimization (N=67)** Unstandardized Standard Standardized t-test B Error B Self-control -.038 .026 -.202 -1.496(.140) Age .000 .002 .028 .221(.826) Sex -.038 .030 -.167 -1.279(.205) Race .010 .038 .036 .263(.794)

Construct .522 .111 4.712(.000) R Squared .050 F .834 Significance .506 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

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Table A-12. Meansub. Regression for Self Control and Victimization (N=90)** Unstandardized Standard Standardized t-test B Error B Self-control -.019 .021 -.095 -.896(.373) Age .005 .002 .003 .030(.976)

Construct .457 .093 4.917(.000) R Squared .090 F .406 Significance .667 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table A-13. Listwise Regression for Social Learning and Deviance (N=67)** Unstandardized Standard Standardized t-test B Error B Reinforc. .773 .512 .314 1.510(.140) Imitation .267 .181 .181 1.477(.149) Assoc. .222 .070 .377 3.171(.003)* Definitions .125 .539 .046 .232(.818) Age -.008 .008 -.117 -1.018(.316) Sex .206 .089 .285 2.317(.027)* Race -.134 .146 -.121 -.921(.364)

Construct -.148 .657 -.225(.823) R Squared .583 F 6.793 Significance .000 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

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Table A-14. Meansub. Regression for Social Learning and Deviance (N=89)** Unstandardized Standard Standardized t-test B Error B Reinforc. .865 .263 .469 3.287(.001)* Imitation .032 .121 .026 .263(.794) Assoc. .057 .044 .135 1.312(.193) Definitions .015 .352 .006 .042(.966) Age -.006 .004 -.118 -1.275(.206)

Construct -.330 .432 -.764(.447) R Squared .295 F 7.034 Significance .000* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table A-15. Listwise Regression for Social Learning and Victimization (N=54)** Unstandardized Standard Standardized t-test B Error B Reinforc. .222 .144 .282 1.541(.130) Imitation .068 .053 .143 1.280(.207) Assoc. .106 .019 .697 5.488(.000)* Definitions .074 .165 .077 .451(.654) Age -.001 .002 -.061 -.529(.599) Sex -.014 .029 -.060 -.482(.632) Race -.012 .038 -.037 -.327(.745)

Construct 1.021 .197 5.187(.000)* R Squared .441 F 5.286 Significance .000* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

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Table A-16. Meansub. Regression for Social Learning and Victimization (N=90)** Unstandardized Standard Standardized t-test B Error B Reinforc. .128 .104 .179 1.235(.220) Imitation .060 .048 .124 1.250(.215) Assoc. .092 .017 .551 5.303(.000)* Definitions .018 .139 .019 .131(.896) Age -.001 .002 -.031 -.330(.742)

Construct .919 .171 5.387(.000)* R Squared .267 F 6.201 Significance .000* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table A-17. Listwise Regression for Self Control and Experienced Control (N=67)** Unstandardized Standard Standardized t-test B Error B Self-control -.059 .088 -.086 -.668(.506) Age -.013 .008 -.198 -1.626(.109) Sex .137 .107 .159 1.283(.204) Race -.114 .137 -.109 -.827(.411)

Construct 1.426 .394 3.620(.001)* R Squared .076 F 1.383 Significance .249 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table A-18. Meansub. Regression for Self Control and Experienced Control (N=108)** Unstandardized Standard Standardized t-test B Error B Self-control -.012 .099 -.012 -.122(.903) Age -.014 .009 -.153 -1.591(.115)

Construct 1.769 .429 4.127(.000)* R Squared .023 F 1.266 Significance .286 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

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Table A-19. Listwise Regression for Self Control and Use of Social Control (N=68)** Unstandardized Standard Standardized t-test B Error B Self-control .317 .183 .221 1.736(.087)* Age -.002 .015 -.018 -.151(.880) Sex .046 .220 .027 .211(.834) Race -.583 .281 -.267 -2.071(.042)*

Construct 3.904 .811 4.814(.000)* R Squared .174 F 3.361 Significance .015* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table A-20. Meansub. Regression for Self Control and Use of Social Control (N=112)** Unstandardized Standard Standardized t-test B Error B Self-control .440 .161 .253 2.731(.007)* Age -.003 .015 -.016 -.173(.863)

Construct 3.895 .698 5.583(.000) R Squared .064 F 3.730 Significance .027* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table A-21. Listwise Regression for Social Learning and Experienced Control (N=56)** Unstandardized Standard Standardized t-test B Error B Reinforc. .694 .669 .237 1.038(.304) Imitation .051 .250 .028 .203(.840) Assoc. .081 .091 .135 .888(.379) Definitions 1.189 .748 .343 1.590(.118) Age -.005 .011 -.065 -.465(.644) Sex .212 .138 .231 1.543(.129) Race -.259 .174 -.205 -1.489(.143)

Construct 2.404 .920 2.611(.012)* R Squared .173 F 1.465 Significance .202 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

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Table A-22. Meansub. Regression Social Learning and Experience Control (N=108)** Unstandardized Standard Standardized t-test B Error B Reinforc. .852 .549 .232 1.551(.124) Imitation .125 .252 .051 .497(.620) Assoc. .013 .091 .016 .144(.886) Definitions .957 .733 .198 1.304(.195) Age -.013 .009 -.141 -1.446(.151)

Construct 1.893 .900 2.103(.038)* R Squared .050 F 1.077 Significance .378 ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

Table A-23. Listwise Regression for Social Learning and Use of Control (N=53)** Unstandardized Standard Standardized t-test B Error B Reinforc. 4.439 1.211 .802 3.667(.001)* Imitation .197 .469 .054 .419(.677) Assoc. .051 .186 .039 .277(.783) Definitions 2.244 1.379 .342 1.627(.111) Age .010 .019 .068 .522(.604) Sex -.133 .246 -.077 -.540(.595) Race -.176 .342 -.069 -.515(.609)

Construct 2.558 1.624 1.575(.122) R Squared .310 F 2.949 Significance .012* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

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Table A-24. Meansub. Regression for Social Learning and Use of Control (N=112)** Unstandardized Standard Standardized t-test B Error B Reinforc. 3.815 .826 .614 4.619(.000)* Imitation .061 .380 .015 .161(.872) Assoc. .095 .137 .066 .695(.488) Definitions 1.548 1.103 .189 1.403(.163) Age .003 .014 .018 .204(.839)

Construct 1.768 1.354 1.306(.194) R Squared .221 F 6.087 Significance .000* ** Significance levels are recorded in parentheses. * Significant at = p ≤ .10

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BIOGRAPHICAL SKETCH

Amanda Adams completed her undergraduate degree in the spring of 2005 at

Emory University in Atlanta, Georgia. She graduated with a Bachelor of Arts in psychology and minored in sociology with a concentration in criminology. From there, she moved to Florida. She received her Master of Arts in Criminology in the summer of

2008 from the University of Florida, where she continued her studies. She was awarded her criminology doctorate from the Department of Sociology and Criminology & Law in the spring of 2014.

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