University of Nevada, Reno

An exploratory examination of the bystander effect in cyberbullying

A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Social Psychology

by

Megan M. Armstrong

Dr. William P. Evans/Dissertation Advisor

August, 2015 THE GRADUATE SCHOOL

We recommend that the dissertation prepared under our supervision by

MEGAN M. ARMSTRONG

Entitled

An Exploratory Examination Of The Bystander Effect In Cyberbullying

be accepted in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

William P. Evans, Advisor

Paul J. Devereux, Committee Member

Clayton Peoples, Committee Member

Anthony Papa, Committee Member

Cynthia Brock, Graduate School Representative

David W. Zeh, Ph. D., Dean, Graduate School

August, 2015 i

Abstract

There are many factors which can affect the bullying process. One such factor is the presence of bystanders during a bullying event. Bystanders can encourage the bullies to continue to bully their victims or they can defend the victim. In an online environment, the role of the bystander is less clear. Garcia, Weaver, Moskowitz, and Darley (2002) found evidence for an implicit bystander effect, the idea that people can experience the bystander effect in the physical absence of other people. In a cyberbullying setting, the idea of the implicit bystander effect could mean that online bystanders could aid cyberbully victims much like bystanders in a face-to-face bullying confrontation.

The current research investigated the bystander process in order to ascertain if the bystander effect and the associated mechanisms of diffusion of responsibility, evaluation apprehension, and pluralistic ignorance were apparent in cases of cyberbullying. There was no main effect found for personal intervention between high and low bystander frames, but an interesting trend emerged regarding intervention. Participants were more likely to intervene on the frames with the higher number of bystanders than those with the lower number of bystanders.

This trend was seen in every high/low vignette pair and provides an interesting contrast to what is normally seen in real-world bystander effect literature in which bystanders are less likely to intervene when there are more bystanders present. In addition to the counter-literature trend, there were significant group differences found for diffusion of responsibility in five of the eight vignette pairs, indicating that diffusion of responsibility can occur online as well as in the real- world.

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There also was a significant inverse relationship between likelihood of offering help, gender, and empathy. Males were more likely to intervene than females and those with lower IRI empathy scores were more likely to intervene than those with higher IRI empathy scores. This finding indicates that both gender and empathy might affect if and how people intervene in cases of cyberbullying.

As far as attributions, there were no significant effects found for any of the situational attributions but a significant effect found for one personal attribution ‘bullies are always bullies’ was related to lower levels of intervention and suggests that personal attributions that bystanders make about the people involved in a bullying situation can impact their decisions about intervention. Taken together, the findings from this study suggest that the bystander effect can be seen in an online environment and might impact how and when bystanders to a cyberbullying incident decide to intervene.

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Acknowledgements

This project could not have been completed without the aid and support of several people:

William Evans,

Nathan Hanawalt

Jenny Reichert,

Pete Martini,

Mike Kwiatkowski,

and Teresa and Randy Armstrong

I cannot thank each of you enough for the encouragement and support you gave me during this process. You are the reasons I made it through with what little sanity I have left intact.

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Table of Contents

Chapter 1...... 1 Prevalence of cyberbullying...... 2 Addressing the needs for research on cyberbullying…...... 4

Chapter 2...... 5 Operationalizing cyberbullying...... 5 Differentiating cyberbullying and traditional bullying...... 9 Differentiating cyberbullying and relational bullying...... 11 Tenets of the Bystander Effect...... 15 Bystander effect in online contexts...... 18 The Implicit Bystander Effect ...... 20 Tenets of Attribution Theory...... 21 The Current Project...... 23

Chapter 3...... 26 Materials...... 26 Scales...... 26 Vignette Construction...... 27 Advantages of factorial design...... 29 Disadvantages of factorial design…………………………………………….…………31 Vignette dimensions and levels………………………………………………………….32

Chapter 4...... 38 Procedure...... 38 Pilot study results...... 39 Social networking vignettes...... 39 Texting vignettes…………………………………………………………………………40 Bystander effect………………………………………………………………………….41 Interview reactions…………………….………………………………………………....44 Summary and changes…………………………………………………………………...47 Additional bystander response options...... 52 Example of old and new vignettes...... 55

Chapter 5…………………………………………………………………………………….….59 Participants……………………………………...... ………………………………………….59 Participant screening process…………………………………………………………………..60 Materials and Procedure……………………………...... ……………………………………65

Chapter 6…………………………………………………………………………………..……66 Results……………………………………………………...... ………………………………66 Descriptive analyses...... 67 Survey scales skewness and kurtosis...... 69 Follow-up questions...... 69 Summary of follow-up results...... 75 Research Question 1...... 79 v

Descriptive analyses...... 79 Paired t-tests...... 80 GLM...... 83 Multi-level model...... 86 Delivery mechanism...... 87 Follow-up t-tests...... 88 Findings for research question 1...... 96 Research Question 2 ...... 98 T-tests and GLM...... 99 Individual GLM...... 99 Research Question 3...... 101 Descriptive analyses...... 102 Paired sample t-test...... 106 Results summary...... 108 Chapter 7 ...... 111 Vignette pair findings...... 111 Bystander mechanism findings...... 113 BJW, IRI, and gender findings...... 114 Strengths and limitations...... 117 Implications and future research...... 119 Conclusion...... 122

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List of Tables

Table 1. Cronbach’s Alphas for Belief in a Just World and Interpersonal Reactivity Index……27

Table 2. Dimensions and Levels of Current Research Project…………………………………..33

Table 3. Follow-Up Questions for Each Vignette……………………………………………….37

Table 4. Pilot Study: Social Networking Vignettes Immediate Reaction Choices………………40

Table 5. Pilot Study: Texting Vignettes Immediate Reaction Choices………………………….41

Table 6. Examination of Likelihood of Intervention in High Bystander Vignettes……………..42

Table 7. Examination of Likelihood of Intervention in Low Bystander Vignettes……………...43

Table 8. Additional Bystander Response Options……………………………………………….52

Table 9. Demographic Characteristics of Full Sample…………………………………………..60

Table 10. Demographic Characteristics of Participants Removed at First Screen………………61

Table 11. Demographic Characteristics of Participants Removed at Second Screen…………..62

Table 12. Demographic Characteristics of Final Sample………………………………………..63

Table 13. Independent Samples T-Test Between SONA and M-Turk Samples…………………64

Table 14. Correlations between Intervention Variables...... 66

Table 15. Scale and Likelihood of Intervention Skewness and Kurtosis………………………..67

Table 16. Descriptions of Vignette Pairs………………………………………………………...68

Table 17. List of Follow-Up Actions and Likelihood/Help Questions…………………………..69

Table 18. Comparison of Anticipated Next Steps for Vignettes 1 and 2……………………...…70

Table 19. Comparison of Likelihood/Help Questions for Vignettes 1 and 2……………………70

Table 20. Comparison of Anticipated Next Steps for Vignettes 3 and 4……………………...…70

Table 21. Comparison of Likelihood/Help Questions for Vignettes 3 and 4………………..…..71

Table 22. Comparison of Anticipated Next Steps for Vignettes 5 and 6……………………..…71 vii

Table 23. Comparison of Likelihood/Help Questions for Vignettes 5 and 6……………………71

Table 24. Comparison of Anticipated Next Steps for Vignettes 7 and 8………………………...72

Table 25. Comparison of Likelihood/Help Questions for Vignettes 7 and 8……………………72

Table 26. Comparison of Anticipated Next Steps for Vignettes 9 and 10…………………….....73

Table 27. Comparison of Likelihood/Help Questions for Vignettes 9 and 10……………..……73

Table 28. Comparison of Anticipated Next Steps for Vignettes 11 and 12……………………...73

Table 29. Comparison of Likelihood/Help Questions for Vignettes 11 and 12………...……….74

Table 30. Comparison of Anticipated Next Steps for Vignettes 13 and 14…………...…………74

Table 31. Comparison of Likelihood/Help Questions for Vignettes 13 and 14…………………74

Table 32. Comparison of Anticipated Next Steps for Vignettes 15 and 16……………………...75

Table 33. Comparison of Likelihood/Help Questions for Vignettes 15 and 16…………………75

Table 34. Comparison of Anticipated Next Step Means for All High/Low SN Vignettes………76

Table 35. Comparison of Likelihood/Help Questions for All High/Low SN Vignettes...... 77

Table 36. Comparison of Anticipated Next Step Means for All High/Low TXT Vignettes...... 78

Table 37. Comparison of Likelihood/Help Questions for All High/Low TXT Vignettes...... 78

Table 38. Percentage of Participants’ Likelihood of Intervention for SN Vignettes...... 80

Table 39. Percentage of Participants’ Likelihood of Intervention for TXT Vignettes...... 80

Table 40. Vignette Pairwise T-Test Comparisons...... 81

Table 41. Comparison of Means for Each Vignette Pair...... 82

Table 42. Parameter Estimates for Overall, SN, and TXT Likelihood of Intervention...... 85

Table 43. T-Test Comparisons for Somebody Intervene Follow-Up Question by High/Low

Vignette Pair...... 89 viii

Table 44. Mean Comparisons for Somebody Intervene Follow-Up Question by High/Low

Vignette Pair...... 90

Table 45. T-Test Comparisons for Mutual Friend Follow-Up Question by High/Low Vignette

Pair...... 91

Table 46. Mean Comparisons for Mutual Friend Follow-Up Question by High/Low Vignette

Pair...... 92

Table 47. T-Test Comparisons for Email or Text a Mutual Friend In Order to Stop the Situation

Follow-Up Question by High/Low Vignette Pair...... 93

Table 48. Mean Comparisons for Email or Text a Mutual Friend In Order to Stop the Situation

Follow-Up Question by High/Low Vignette Pair...... 94

Table 49. T-Test Comparisons for Mutual Friend Email or Text You In Order to Stop the

Situation Follow-Up Question by High/Low Vignette Pair...... 95

Table 50. Mean Comparisons for Mutual Friend Email or Text You In Order to Stop the

Situation Follow-Up Question by High/Low Vignette Pair...... 96

Table 51. Attribution Questions about Cyberbullying...... 99

Table 52. Parameter Estimates for Personal Attributions GLM...... 100

Table 53. Parameter Estimates for Situation Attributions GLM...... 101

Table 54. Bystander Mechanism Questions...... 102

Table 55. Comparison of Bystander Mechanisms Questions for Vignettes 1 and 2...... 103

Table 56. Comparison of Bystander Mechanisms Questions for Vignettes 3 and 4...... 103

Table 57. Comparison of Bystander Mechanisms Questions for Vignettes 5 and 6...... 103

Table 58. Comparison of Bystander Mechanisms Questions for Vignettes 7 and 8...... 104

Table 59. Comparison of Bystander Mechanisms Questions for Vignettes 9 and 10...... 104 ix

Table 60. Comparison of Bystander Mechanisms Questions for Vignettes 11 and 12...... 104

Table 61. Comparison of Bystander Mechanisms Questions for Vignettes 13 and 14...... 105

Table 62. Comparison of Bystander Mechanisms Questions for Vignettes 15 and 16...... 105

Table 63. Comparison of Bystander Mechanisms for All High/Low SN Vignettes...... 106

Table 64. Comparison of Bystander Mechanisms for All High/Low TXT Vignettes...... 106

Table 65. Comparison of Follow-Up Mechanism Questions...... 108

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List of Figures

Figure 1. Latane and Darley (1970) Proposed Bystander Mechanisms...... 16

Figure 2. Example of Anticipated Next Steps for Social Networking Vignettes...... 35

Figure 3. Example of Anticipated Next Steps for Texting Vignettes...... 36

Figure 4. Path to Providing Help (Darley & Latane, 1970)...... 49

Figure 5. Pilot Study Social Networking Vignette Example...... 55

Figure 6. Dissertation Study Social Networking Vignette Example...... 56

Figure 7. Pilot Study Texting Vignette Example...... 57

Figure 8. Dissertation Study Texting Vignette Example...... 57

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Ch. 1--Introduction

According to the Centers for Disease Control (CDC) Massachusetts Youth Health Survey

(April, 2011), substantial prevalence indicate that approximately 44% of middle school and 31% of high school participants were involved or affected by bullying in some way. Among middle school students, approximately 27% reported being victims of bullying, 8% acknowledged being bullies, and 10% reported being bully-victims (both a bully and a victim of bullying). Among high school students, approximately 16% reported being victims of bullying, 8% acknowledged being bullies, and 7% reported being bully-victims. A 2009 nationwide CDC survey on bullying found that approximately 20% of high school students were bullied on school property in the preceding 12 months; in the 2007-2008 school year, 25% of public schools reported that bullying occurred among students on a daily or weekly basis (CDC, 2011).

The CDC also reports that approximately 9% to 35% of young people say they have been the victim of electronic aggression (Hertz & David-Ferdon, 2008). Electronic aggression was defined as “aggression perpetrated through technology—any type of harassment or bullying

(teasing, telling lies, making fun of someone, making rude or mean comments, spreading rumors, or making threatening or aggressive comments) that occurs through email, a chat room, instant messaging, a website (including blogs), or text messaging” (Hertz & David-Ferdon, 2008, p. 3).

Online aggression or ‘cyberbullying’ is a form of bullying that has become problematic as technology has become an increasingly pervasive part of everyday life, especially for younger generations.

According to the Pew Internet & American Life Project survey ‘Teens and mobile phones’, mobile phones are the favored form of communication for the majority of American teens (Lenhart, Ling, Campbell, & Purcell, 2010). Text messaging is the centerpiece of communications strategies between teens and friends (Lenhart et al., 2010). Seventy-five percent 2 of teens between the ages of 12 -17 own a cell phone and 88% of teen cell phone users also use text messaging as a primary method of communication (Lenhart et al.). The CDC reports that more than one-third of American homes had only cell phones in the home and no landline

(Blumberg & Luke, 2012). These reports indicate the dependence that people, adults and teenager alike, have on their cellular phone.

Lenhart and colleagues (2010) also found that about 33% of teens send more than 100 text messages a day, adding up to about 3000 texts per month. Females typically send and receive 80 messages per day and males send and receive 30. In addition to text and voice messaging, 21% of teens access the Internet on their phone and 23% access social network sites via their mobile phones. Mobile phones and new social networking technology are clearly a vital part of social interactions. Unfortunately, it is also through these technology mediums that cyberbullying is perpetrated.

Prevalence and Consequences of Cyberbullying

Cyberbullying is a problem on both a national and an international scale. Ybarra and

Mitchell (2004) reported that 19% of their sample of US Internet users between the ages of 10-17 experienced cyberbullying as either a victim or a perpetrator. A British survey found that approximately 25% of people between ages 11-19 had been cyberbullied (National Children’s

Home, 2002) and Mnet (2001) found that 25% of a sample of young Canadian Internet users reported receiving messages that contained hateful comments about others. Additionally, Li

(2008) examined both Canadian and Chinese samples and found that almost 25% of the

Canadian respondents and 33% of the Chinese respondents reported being cyberbullied at least once and 50% of respondents in each sample reported hearing about incidents of cyberbullying.

Aricak et al. (2008) utilized a sample of Turkish adolescents and found that 36% of respondents 3 encountered unwanted and disturbing behaviors on the Internet. Like traditional bullying, the types of cyberbully behaviors can range from teasing and name calling (usually referred to as

‘flaming’, to texting or posting personal information online for all to see or even sexting, in which one individual sends sexually explicit texts or images to another person (Mishna, Cook,

Gadalla, Daciuk, & Solomon, 2010). And, like traditional bullying, cyberbullying can have considerable negative consequences, up to and including suicide (Hindjua & Patchin, 2010).

Suicide is the tenth leading cause of death among all age groups and the third leading cause of death among those age 15-24 (CDC, 2012). Among the latter age group, suicide accounts for 20% of all deaths annually (CDC, 2012). Prior to a suicide attempt, successful or not, many adolescents also engage in 'suicidal ideation' in which they contemplate the thoughts of suicide and ways to end their own life (Hindjua & Patchin, 2010). Suicide ideation and suicide are both correlated with involvement in bullying; both perpetrators and victims are at an elevated risk for suicidal thoughts, attempts, and completed suicides (Baldry & Winkel, 2003; Hindjua &

Patchin, 2010; Rigby & Slee, 1999). Bullying and victimization also contribute to loneliness, peer rejection, low self-esteem, poor mental health, and various other psychological and physiological ailments (Crick & Bigbee, 1998; Hershberger & D’Augelli, 1995; Prinstein,

Boergers, & Vemberg, 2001; Rigby & Slee, 1993).

Clearly suicide is related to traditional bullying in which the perpetrator and victim are physically present during the bullying confrontation. There is evidence to also support the idea that this kind of negative outcome can result from cyberbullying as well. Hindjua and Patchin

(2010) found both perpetrators and victims of bullying as well as cyberbullying experienced more suicidal thoughts and were more likely to attempt suicide than those who were not victimized, although victimization was more strongly related to suicidal thoughts and behaviors 4 than offending. There are also numerous anecdotal cases of teenagers committing suicide after the experience of being cyberbullied, cases such as Tyler Clementi, Megan Meier, and Phoebe

Prince. The suicide of Megan Meier, in fact, prompted some of the first anti-cyberbullying legislation in the US (Ruedy, 2008). The widespread and international nature of cyberbullying combined with the potential devastating consequences highlights the need for research on combating and stopping cyberbullying.

Addressing the needs for research on cyberbullying

Cyberbullying is a relatively new phenomenon, thus research on the topic is still in early stages. Much of the research thus far has examined demographics of cyberbullies and cybervictims (Twyman, Saylor, Taylor & Comeaux, 2010) and attempted to pinpoint reasons for engaging in cyberbullying and responses to the act (Huang & Chou, 2010; Li, 2010; Bauman,

2009; Li, 2008). As Tokunaga (2010) has observed, however, there is a dearth of theory-driven research on many aspects of cyberbullying. The present study is aimed at addressing this gap by examining how the bystander effect and its related mechanisms might impact cyberbystanders likelihood of intervention in cases of cyberbullying. In addition, there is little-to-no empirical research examining the role of the bystander as a potential source of encouragement or discouragement for cyberbullies, and another goal of the proposed study is to explore this topic.

A third goal is to extend the research of the bystander effect into the digital realm in order to support the idea of the implicit bystander effect (Garcia, Weaver, Moskowitz, & Darley 2002).

Finally, the present study will examine how attributions made about cyberbullying might potentially affect online bystanders’ decisions about intervention on behalf of a cyberbully victim. Understanding these concerns can aid in development of programs to combat this new form of bullying. 5

Ch. 2--Literature Review and Theoretical Framework

Operationalizing Cyberbullying

Face-to-face or ‘traditional’ bullying is commonly defined according to the Olweus

(1993) criteria as aggressive behavior in which an aggressor intentionally and repeatedly over time causes a weaker victim either physical and/or psychological harm. Lodge (2008) defines bullying as a power imbalance and deliberate acts that cause physical, psychological and emotional harm. Bullying can be direct, such as hitting, tripping, or teasing, or indirect, such as gossip, deliberately excluding others or social isolation.

Unlike traditional bullying, the definition of cyberbullying varies greatly. The movement of bullying from a face-to-face context into a digital context is one which has prompted a flurry of research on the topic. Unfortunately, the research has sometimes added to the confusion rather than cleared it up. One ongoing source of confusion in the cyberbullying research is the lack of conceptual clarity; each piece of new research, while providing helpful information, tends to conceptualize the nature of cyberbullying and the component players (e.g. the bully, the victim, and the bystander) differently.

Tokunaga (2010) conducted a synthesis on the available research on cyberbullying and stated that one of the major weaknesses is the lack of a common definition of the term.

According to Tokunaga (2010) the lack of consensus over a definition is problematic because it can affect researchers’ operational definitions and thus their conceptual measurements, leading to inconsistencies in the research. For example, Patchin and Hinduja (2006) found that 30% of the adolescents in their sample reported that they had been victims of online bullying. Vandebosch and Van Cleemput (2009) found that approximately 11% of their sample reported being a victim of bullying via the internet or mobile phone and approximately 62% of respondents were victims of potentially offensive internet and mobile phone practices. Li (2006) found that 17% of 6 respondents had been bullied using electronic communication tools. Obviously, these numbers vary greatly and support Tokunaga’s (2010) assertion that a more cohesive definition of the term

‘cyberbullying’ is needed.

Cyberbullying has been previously defined as: "willful and repeated harm inflicted through the medium of electronic text" (Patchin & Hinduja, 2006, p. 152); "bullying via electronic communication tools such as email, cell phones, Personal Digital Assistants (PDA), instant messaging, or the World Wide Web" (Li, 2008, p. 224); “bullying via the Internet or mobile phones” (Vandebosch & van Cleemput, 2009, p. 1360); "the use of technology to intentionally harm or harass others" (Bauman, 2010, p. 803); "the use of e-mail, cell phones, instant messaging, and/or Web sites by individuals or groups with intent to harm others"

(Twyman, Saylor, Taylor, & Comeaux, 2010, p. 195); "any behavior performed through electronic or digital media by individuals or groups that repeatedly communicate hostile or aggressive messages intended to inflict harm or discomfort on others" (Tokunaga, 2010, p. 278);

"the use of information and communication technologies to support deliberate, repeated, and hostile behavior by an individual or group, that is intended to harm others" (Belsey, n.d.).

These definitions all appear to share some common core characteristics: the use of technology, for example, and the idea of a harm being perpetrated against an individual. Most of the definitions are derived from Olweus' (1991) long standing definition of bullying as "an aggressive, intentional act or behavior that is carried out by a group or an individual repeatedly and over time against a victim who cannot easily defend him or herself". As Olweus is considered by many researchers to be one of the founding pioneers in the area of bullying research, the use of his definitions of bullying as a foundation for defining cyberbullying is a logical step. 7

According to Dehue, Bolman, and Vollink (2008) there are three elements necessary for cyberbullying: (1). the behaviors are repeated; (2). the behaviors involve psychological torment; and (3). the behaviors are carried out with intent. In keeping with the concept of parsimony within research, the definition of cyberbullying for this research is "willful and repeated harm inflicted through the use of computers, cell phones, and other electronic devices" (Hindjua &

Patchin, 2010, Hindjua & Patchin, 2009; Patchin & Hindjua, 2006). This definition was chosen for several reasons. Firstly, it incorporates all of the elements stated by Dehue, Bolman and

Vollink (2008), several of the definitions of cyberbullying, and even the elements of bullying stated by Olweus (1991). Secondly, the definition has been previously used and established in the cyberbullying literature. Thirdly, the definition is the most parsimonious but also the most comprehensive. In short, this definition covers all of the necessary elements of cyberbullying without any unnecessary elements or restrictions.

Utilizing this definition of cyberbullying, a cyberbully is defined here as anyone who has willfully inflicted harm upon another person via the use of computers, cell phones and other electronic devices. A cybervictim is any person who has had harm perpetrated against them through computers, cell phones, or other electronic devices. A cyberbystander is any person who has witnessed or been told about a cyberbullying incident.

Challenges for operationalized terms

These definitions are not without challenges. One issue with these definitions can be seen with the idea of a repeated harm. Given that a large portion of cyberbullying can occur over the

Internet, the idea of repeated harm takes on a different connotation. A post on a website or social networking site can be viewed by people hundreds of times, as well as captured in screenshots and constantly shared and redistributed (Viegas, 2005). This means that a single instance of 8 cyberbullying can actually become a repeated instance, through constant sharing and redistribution of the original post. Similarly, a text message can be disseminated to any number of people, far beyond the original message recipient.

A second issue is time. In traditional face-to-face bullying, there is a specific timeframe in which the incident occurs. With such a timeframe, there is usually a given starting point and a given end. For example, a person might experience bullying at school. The given timeframe would be that time at school. Bullying incidents can reoccur each day but are considered separate incidents. Counter to that, the timeframe for cyberbullying is much less clear. Again, due to the nature of the cyberbully mediums such as the Internet and text messaging, the cyberbully incident can be drawn out for much longer. These issues are especially pertinent when considering the role of the cyberbystander.

It can be difficult to determine the exact nature of a cyberbullying incident as a cyberbystander. In an online environment, the time lag between an incident and when a cyberbystander views the event can be days, weeks, or even months. Similarly, with text messaging, the cyberbystander might have received the text via many different sources (e.g. through the 'friend of a friend' who continued to pass the message along) for several days or even weeks. Should these kinds of incidents be considered cyberbullying and would these people be considered cyberbystanders to the act? Yes, because such people still have an opportunity to report the cyberbullying incident. Reporting such an incident, even months after it has occurred can be a solution to timeframe issues surrounding cyberbullying over the Internet.

Reporting issues are usually done to a website moderator or owner. Most social networking websites, often a popular forum for cyberbullying, have standard Terms of Service or

Terms of Use that prohibit cyberbullying activities and investigate any claims they receive 9

(Hindjua & Patchin, 2009). If the material is offensive or harmful, it is removed. The time from the initial post of the harmful material to the eventual removal of the material from the source can be one way to measure an online cyberbullying timeframe. This is not by any means the ideal way, but it is one potential solution.

A second solution is to count all versions of the cyberbullying incident (e.g. the initial harmful post or text message) as separate incidents. Each new iteration could then be considered a new cyberbullying incident. Of course, the problem here is that not all incidents would be known (e.g. how far the post spread or how many people received the text) but if a cybervictim is continually on the receiving end of harmful technological practices (e.g. texts and online posts), then keeping an estimated track of the perpetrators (e.g. through phone numbers and names or pseudonyms of online perpetrators) can be a proactive step in blocking such users and possibly having them removed from the online website. Many states have legal statutes prohibiting cyberbullying (NCSL, n.d.) and having a record of incidents might be helpful when building a case.

Differences between Traditional Bullying and Cyberbullying

Similar to traditional bullying, cyberbullying is differentiated from normal aggression and arguments because the behavior is targeted and persistent and intended to demean, intimidate, embarrass, or harass. There are other common elements between traditional bullying and cyberbullying; in fact, most definitions of cyberbullying rely on information gleaned from research on traditional bullying as a basis for exploration (see Bauman, 2009; Dooley, Pyzalski

& Cross, 2009; Patchin & Hinduja, 2006; Tokunaga, 2010 for a more thorough comparison).

Although there appear to be many similarities between the two concepts, Ybarra, Diener-West, 10 and Leaf (2007) and Dooley, Pyzalski and Cross (2009) both note that cyberbullying appears to be a distinct category with unique characteristics.

One difference between cyberbullying and traditional bullying is the medium; cyberbullies generally make use of a technology-driven medium such as text messaging on cell phones as well as social networking and other Internet sites (Tokunaga, 2010). In lieu of a face- to-face confrontation, cyberbullies utilize a medium which requires no actual contact, thereby placing themselves in a superior position over the victim. The distance also means that cyberbullies are not present to see the results of such bullying and, without visual cues to inform them of the distress of the bullied victim, cyberbullies might continue to bully or harass an individual much longer than a traditional bully might. Victims of cyberbullying are also subject to the whims of the cyberbully because the victims often do not know when another bullying incident might occur. This is another difference between traditional bullying and cyberbullying: the timeframe.

Patchin and Hinduja (2006) explain that in cases of cyberbullying, technology allows a cyberbully to reach a victim at any time, beyond the face-to-face interaction associated with traditional bullying. Traditional bullying tends to begin and end at school. Cyberbullying extends beyond the school, and the victim of a cyberbully can be forced to choose between maintaining social connections via technology or changing and even deleting everything in order to avoid the bully. This forced decision and impingement on another individual’s freedom demonstrates the power that cyberbullies can hold over cybervictims.

A third difference between traditional bullying and cyberbullying is the known identity of the bully. Victims of traditional bullying tend to know who is bullying them. Victims of a cyberbully might not know the identity of the bully. Vandebosch and van Cleemput (2008) found 11 that individuals who engaged in cyberbullying behaviors knew their victims in the offline world but deliberately concealed their identity online. The anonymity of the cyberbully can add to the stress and anxiety felt by the victim because the victim can feel like he is being attacked by an unknown assailant for highly personal reasons. In fact, cybervictims have reported that not knowing the identity of the cyberbully increased their feelings of frustration and powerlessness

(Vandebosch & van Cleemput, 2006). Anonymity for a cyberbully can extend to most technology spheres; faux social networking and email names can be used online and disposable or pay-as-you-go cell phones for calling and texting. Smith et al. (2008) conducted focus groups on cyberbullying and found that the student participants believed text messaging was the most common form of cyberbullying as it enabled the cyberbullies to remain anonymous.

Cyberbullying is clearly a separate issue from traditional bullying, but there are many areas of overlap. One such area is indirect or relational aggression. Some research suggests that cyberbullying also could be considered a form of relational aggression.

Cyberbullying, relational aggression and gender

Relational aggression is a subtle form of bullying; it generally consists of spreading rumors, innuendos and gossip, shunning and exclusion and other less overt attempts to damage relationships or social status (Coyne, Archer & Eslea, 2006; Underwood, 2003). The similar tactics of perpetuating bullying in a non face-to-face manner has had some researchers suggest that cyberbullying is a form of relational aggression (Raskauskas & Stoltz, 2007; Werner,

Bumpus & Rock, 2010). Raskauskas and Stoltz (2007) found that cyberbullying was significantly positively correlated with physical and verbal aggression as well as spreading rumors and excluding others offline. Likewise, Werner, Bumpus and Rock (2010) found that youths who were relationally aggressive offline were approximately ten times more likely than 12 non-relationally aggressive youths to demonstrate aggression over the Internet. Werner, Bumpus and Rock (2010) suggest that the Internet offers unique tools for adolescents to harm others through manipulation and exclusion; signs such as numbers of friends on social networking sites and personal webpages serve as visible indicators of social impact and can be utilized in a relationally aggressive manner. Wolak, Mitchell and Finklehor (2007) support this supposition.

The authors found that online harassment by a known peer was more likely to involve a person sending or posting messages for others to see.

In traditional bullying, relational aggression is more commonly found among girls than boys

(Underwood, 2003). This has led many researchers to speculate that there might be noticeable gender differences in cases of cyberbullying. Such differences have not been consistently found.

Some studies have found that girls are more likely to engage in cyberbullying (Keith & Martin,

2005). Erdur-Baker (2009) found that males are more likely to engage in cyberbullying as either a cybervictim or a cyberbully. Other studies have not found any connection between cyberbullying and gender (Beran & Li, 2007; Hinduja & Patchin, 2008; Li, 2007; Patchin &

Hinduja, 2006; Williams & Guerra, 2007; Wolak, Mitchell & Finklehor, 2007). Although relational aggression is related to cyberbullying there is not enough evidence to suggest that it encompasses all of the behaviors associated with cyberbullying. Much like the complicated relationship between cyberbullying and traditional bullying, there are areas of overlap.

Cyberbullying, however, is definitively different from traditional bullying, either direct or indirect, due to the nature of the medium, the distance, time and anonymity. This uniqueness can mean that interventions and techniques developed to combat traditional bullying might not be as 13 effective against cyberbullying. New interventions must be developed and one way of developing them is through empirical research.

Prior research on bullying has noted that the role of the bystander in a bullying incident is an understudied role because much of the research focuses on the bully/victim dyad (Gini,

Pozzoli, Borghi & Franzoni 2008). Focusing on victim/bully dyads has been criticized in the research due to the conceptualization of bullying as an individual or dyadic process (Gini, 2007;

Nesdale & Scarlett, 2004). In most instances, bullies and victims are in the presence of other bystanders who witness the incident (Craig & Pepler, 1997; Craig, Pepler & Atlas, 2000).

Although these bystanders are present, they may choose not to intervene or even join in the bullying (O’Connell, Pepler, & Craig, 1999). These bystanders are usually categorized according to their participant role (Salmivalli et al, 1996). These categories include: bullies, victims, bully reinforcers who help the bully and reinforce the behavior, defenders who stand up for the victims, and outsiders who are not directly involved in the situation (Salmivalli et al., 1996).

Outsiders, or any bystander who does not intervene in any manner, such as telling an authority figure about the incident, are termed ‘passive’ bystanders (Cowie, 2000).

The limited research on bystanders in traditional bullying scenarios shows that passive bystanders and active bystanders differ on several different variables. Gini, Albiero, Benelli and

Altoe (2008) found that active defenders and passive bystanders had high levels of empathetic responsiveness towards bullying behavior and that helping behavior was associated with high self-efficacy. Pozzoli and Gini (2010) found that problem solving coping strategies and 14 perceived peer normative pressure for intervention were associated with help for a bullying victim whereas passive bystanders were associated more with distancing strategies.

Understanding the characteristics and situations that influence bullying occurrences is important; however the role of the bystander is also important if only because the presence of bystanders can affect the context of the bullying situation. The presence or absence of bystanders can have a critical impact on stopping or encouraging the bullying act (Twemlow, Fonagy, &

Sacco, 2006). As such, cyberbystanders might also play an important role in encouraging or inhibiting cyberbullying. An examination of the cyberbystander, however, can be difficult to define due to the nature of the cyber medium (Coloroso, 2003). The audience of an act of cyberbullying is not just those who witness the event at the time it occurs; bystanders can view images or videos several days, weeks, months, or even years later (Shariff, 2008). Although the exact nature of the cyberbystander can be difficult to define, Willard (2007) believes that cyberbystanders play an important role in preventing acts of cyberbullying. By teaching cyberbystanders how to influence the online climate and report incidents to others, cyberbystanders will be in a better position to stop the bullying (Willard, 2007).

Of the few studies that include data on cyberbystanders, Li (2010) found that most cyberbystanders join in the bullying, watch but not participate, or simply leave the online environment without comment. About 9% of Li’s sample stated that they would object to others but not the cyberbully, about 23% would object directly to the cyberbully and less than 10% reported the incident to someone who could help the cybervictim. A reason often given for not reporting cyberbullying is the fear of an adult or parent limiting the use of technology, such as limiting Internet time or taking away a cell phone (Li, 2010). According to the Pew Internet &

American Life Project survey on teens and mobile phones, 62% of parents of teens with mobile 15 phones have taken away their child’s phone as a form of punishment (Lenhart et al., 2010). In a different study, Li (2006) asked cyberbystanders and cybervictims why they would not report cyberbullying and found that many participants did not think anybody would try to stop it. One overlooked reason people might not intercede might be due to the bystander effect.

The Bystander Effect

The bystander effect refers to situations in which an individual's likelihood of helping another person decreases when other passive bystanders are present (Darley & Latane, 1968;

Latane & Nida, 1981). Originally prompted by the 1964 rape and murder of Kitty Genovese in

Queens, New York, Darley and Latane (1968) wanted to examine why none of the thirty-eight neighbors or witnesses stepped forward to aid or stop the attack or even call the police. In an initial piece of research, in which they examined response time to a confederate faking a seizure via an intercom, Darley and Latane (1968) found that the number of bystanders a participant perceived to be present had a significant impact on the likelihood of the participant reporting the emergency. Specifically, 85% of the participants who believed they were alone reported the seizure before the intercom was cut off; as a comparison, only 31% of participants who believed themselves to be in a group of four reported the seizure. Subsequent research on the subject has consistently shown and corroborated the idea that helping behavior is reduced when the number of bystanders increased or when the situation is ambiguous (Latane & Nida, 1981).

There are three psychological mechanisms which Latane and Darley (1970) proposed to explain the action or non-action of bystanders (Figure 1). The first mechanism is diffusion of responsibility. This refers to the tendency of people to divide the perceived personal responsibility among the number of bystanders present; the more bystanders in a given situation, the less personal responsibility any one bystander will feel. The second mechanism is evaluation 16 apprehension. This refers to the fear people have of being judged by others, especially in situations where they could be making a mistake or doing something wrong. The third mechanism is pluralistic ignorance. This refers to the tendency of people to rely on the overt reactions of others when defining an ambiguous situation.

Figure 1 Latane and Darley (1970) proposed bystander mechanisms 1. Notice event is occurring

Ambiguity: what is actually happening?

Pluralistic ignorance: nobody else appears worried

2. Determine help is required

Diffusion of responsibility: someone else will do something

3. Assume personal responsibility

Audience inhibition: do not know how to help

4. Determine way to help

Audience inhibition: might have made mistake

5. Provide help

The bystander effect can be seen in both emergency and non-emergency situations. In a meta-analysis summarizing the bystander research, Latane and Nida (1981) examined aspects of the situations besides the number of bystanders. These aspects included attributes of the incident 17

(e.g. incident occurred in rural or urban areas), artificialness (e.g. whether the experiment was conducted in a lab or natural setting), bystander or participant attributes (e.g. competency, sex of participant), victim attributes (e.g. sex of victim), attributes of other bystanders (e.g. friends or strangers), and the extent to which bystanders could communicate with each other. According to

Latane and Nida (1981), situations which involve other passive bystanders, or are perceived as ambiguous, are situations in which bystander helping is decreased.

In an updated meta-analysis, Fischer et al. (2011) examined bystander research spanning a time frame from 1981 to 2010. The authors found several new contributions to the literature on the bystander effect. Specifically, Fischer et al. (2011) found empirical evidence to support the idea for a reduced attenuation of the bystander effect in situations perceived to be dangerous.

That is, in situations where people perceive greater amounts of danger, the effect of non- intervention due to the presence of other bystanders is reduced. Harari, Harari, and White (1985) found that men in a bystander situation (in the presence of another man) were more likely to intervene in a staged rape attempt than men who were not in the presence of another bystander, a difference of 85% intervention with bystander to 65% intervention by self. Fischer et al. (2006) found similar results. The researchers exposed participants to a videotaped emergency situation in which a man was sexually harassing a woman. They found that when the man was physically imposing (the ‘dangerous’ situation) bystanders were as likely to help in the presence of other bystanders as they were by themselves (40% by self and 44% with other bystanders), however when the man was of smaller stature (the ‘low-danger' situation), participants were much less likely to help when in the presence of other bystanders (50% by self and 5.9% with other bystanders). These studies show that, in the face of situations which are unambiguous or dangerous, the bystander effect can be decreased or even reversed. 18

The bystander effect in an online environment

One of the biggest issues in examining cyberbullying is that much of the related bullying and theoretical research takes place in 'real life' contexts. That is, the research base from which conclusions are drawn is one which utilizes physical and face-to-face contexts. This can present a problem when examining cyberbullying because the cyberbully does not operate in a face-to- face or physical environment; instead the bullying takes place via electronic and technological means, such as cell phones and the Internet.

Research utilizing online contexts. The Internet has become a ubiquitous part of everyday life. In an era of rapidly developing technologies, expanding into the online realm has become standard practice for many different disciplines. Online tutoring (e.g. services such as smarthinking), online education (Garrison, Anderson & Archer, 2003) and even online psychotherapy (Rochlen, Zack & Speyer, 2004) are now offered as a convenience for people who are unable to access such services in real life. Online availability has allowed for a greater connectivity between people and other people, between people and necessary services (e.g. therapy or education), between people and information (e.g. online academic journals), and between people and recreational activities (e.g. online gaming). Online environments are increasingly pervasive and therefore it is logical to extend the research of social psychology into these online environments as well.

Social psychology research into online environments has been conducted on a variety of subjects. Such subjects include the effect of anonymity and deindividuation on communication and aggression (McKenna & Bargh, 2000), personal identity and role construction (McKenna &

Bargh, 2000), attraction in the absence of physical cues (McKenna, Green & Gleason, 2002), and social norms (Yee, Bailenson, Urbanek, Chang & Merget, 2007). The breadth of research on 19 human behavior and interaction online is quite extensive and with this research comes new understanding of human behaviors and processes in real life as well. People do not partition themselves into tiny pieces, so belief and motivations that spur behavior in a real life environment are often the same that spur behavior in an online environment. One such area of behavior is cyberbullying. The bullying might occur in an online environment but the consequences can overlap into real life as well (Hinduja & Patchin, 2010).

Megan Meier and Tyler Clementi both committed suicide after becoming victims of cyberbullying; Ryan Halligan and Phoebe Prince were both bullied and cyberbullied until they also committed suicide. The link between online actions and real life reactions is quite clear: what an individual faces online can have carryover consequences into real life (Hinduja &

Patchin, 2010). In these cases, the consequences were tragic. Examining the online nature of cyberbullying can help prevent further tragedies such as those that befell these victims.

The bystander effect, as applied in the real world, relies on the idea that a person visually assesses the number of bystanders in a given situation and utilizes that knowledge in order to make a decision about helping a victim (Darley & Latane, 1968). The number of other bystanders might prompt psychological processes such as diffusion of responsibility, evaluation apprehension, and pluralistic ignorance. In each of these cases, the psychological processes underlying the decision are based on a strict frequency (e.g. the number of other people).

Further research has expanded the bystander effect to include social categorization of both the victim as well as the other bystanders (Levine, Brazier & Reicher, 2002; Levine & Crowther,

2008).

20

Implicit bystander effect processes

An important factor to consider is that most of the research on the bystander effect has occurred in the real world, (e.g. with an individual in the physical presence of others). The extension of the bystander effect to the online world can be seen with Garcia, Weaver,

Moskowitz and Darley's (2002) series of studies on the implicit bystander effect. In a set of studies, Garcia et al. (2002) were able to prime participants with prompts about imagining themselves in their social group or in a group of strangers. Participants were given a filler task and then asked to engage in a helping behavior, either a contribution towards charity or a pledge of money towards a university. Participants who initially imagined themselves in a group of 30 friends or with a group of strangers contributed significantly less than participants who envisioned themselves with a small group of friends, one stranger, or by themselves. Garcia et al.

(2002) further extended this research by examining helping behavior. The researchers asked participants to imagine that they were eating dinner with either one friend or with 10 friends.

After a filler task, participants were then asked to help with an experiment taking place next to the one in which they were currently participating. Participants in the 10 friend condition offered significantly less helping time than participants in the one person or neutral condition.

Levine and Crowther (2008) also utilized the implicit bystander effect by asking their participants to imagine the presence of other bystanders. In a series of four studies, they found that group size and membership impacted the bystander effect. When the participants imagined bystanders to be strangers, greater group size was related to lower bystander helping behavior; however, when the participants imagined bystanders to be friends, greater group size was related to more participants willing to help. This finding was further extended to gender and social category; females were more likely to intervene in a situation if they were in the imagined 21 presence of friends and less likely to intervene in the imagined presence of strangers. More importantly, Levine and Crowther (2008) demonstrated that the implicit bystander effect can impact people’s decision about intervention in different situations.

Attribution Theory

As demonstrated by the expanding research on the bystander effect, more than just the number of people can affect whether helping behavior is enacted on behalf of a victim. To further investigate this line of research, the current study will also examine how attributions made about the victims and the perpetrators of cyberbullying might affect the decision of a bystander to offer help or intervene. According to Heider (1958), there are two ways that people attempt to explain behavior: through internal and external attributions. Internal attributions are those considered inherent to an individual, like personality traits; external attributions are attributions not inherent but instead assumed to be the result of an external situation or the environment (Heider, 1958).

Jones and Davis (1965) further explicated the tenets of attribution theory by examining the process by which people form and make internal attributions about behavior. According to

Jones and Davis, internal, or dispositional, attributions are made when people see correspondence between a motive and a behavior; these correspondences then form the basis for predictions about expected future behavior. Dispositional attributions are formed via five sources of information: choice, cause, social desirability, non-common effects, and hedonistic relevance

(Jones & Davis, 1965). Choice refers to the enactment of a behavior; if a person chooses a behavior due to their own volition, it is assumed that this choice is reflective of an internal or dispositional attribute. Cause refers to the cause of the behavior; behavior deemed to be intentional is also believed to be the result of dispositional attributes whereas behavior deemed 22 accidental is assumed to be attributed to the situation or external causes. Social desirability refers to how desirable the behavior is; enacting less desirable behavior is linked to dispositional attributions. Non-common effects refers to the consequences of the behavior: if the effect is non- common then dispositional attributions are inferred. The last, hedonistic relevance, refers to the benefit of the behavior; behavior that appears to be directly beneficial or harmful is assumed to be due to dispositional attributions.

Kelley's (1967) covariation model, similar to Jones and Davis (1965), is an explanatory mechanism for why people make internal or external attributions. According to Kelley (1967), a person takes information from multiple sources at different times in order to determine the cause of an observed effect. There are three types of causal information utilized: consensus, distinctiveness, and consistency. Consensus refers to the extent to which all people involved in a certain situation behave and act in the same manner. Distinctiveness is the extent to which an individual person behaves in the same manner in similar situations. Consistency is the extent to which an individual person behaves across situations. The appearance and combination of these factors help people to determine external or dispositional attributions. For example, a person who is displaying low consensus, high distinctiveness and high consistency (e.g. smoking across different situations) might be considered to have a dispositional attribute (e.g. be a smoker). If, however, the person demonstrates low consensus, high distinctiveness, and low consistency (e.g. only smokes when at a bar) then the inference would be external or situational (e.g. being in a bar is conducive to smoking).

In cyberbullying, attributions about the perpetrator and the victim might shape the subjective perceptions bystanders have of both the bully and of the victim. These attributions therefore may affect the cognitive decision for a bystander to intervene. Research on attributions 23 towards victims has shown that many people tend to blame victims for the circumstances regarding the victimization, therefore these attributions might prevent a bystander from interceding.

The Current Project

Collectively, the research by Garica et al. (2002), Markey (2000) and Levine and

Crowther (2008) demonstrates that the bystander effect can be an implicit process. If and when a person is primed to think about other people, this cognitive priming can later affect their behavior. Indicators of friend groups are very prevalent online: there are friend lists on social networking sites such as Facebook and Myspace, often with a picture and count of the total number of friends an individual has and total number on the site at that point in time. These pictures and friend numbers can serve as visual indicators about an individual's social group, essentially acting as a priming mechanism for the bystander effect. In cell phone usage, most smartphones (e.g. iPhones, Droids, and Windows phones) have a contact list which lists names of contacts along with pictures and accompanying information. The Windows smartphone will even link a person's contact information with their Facebook social networking page. Again, the listing and picture can serve as a prompt for an individual to envision a social group.

A logical extension of research on the implicit bystander effect is to examine how this concept affects helping behavior of bystanders who witness cyberbullying. Because cyberbullying occurs in an online environment, it would allow for a natural extension of the research on the implicit bystander effect. Applying the implicit bystander effect solely in an online environment (e.g. an online survey or utilizing online resources) is also uncharted territory because there is a certain amount of uncertainty regarding the bystander primes in an online context. It is logical to infer that pictures associated with online social networking profiles might 24 act as a priming mechanism because such pictures can serve as a visible indicator of the number of other ‘bystanders’ present online at the same time.

It is less certain, however, if noting the number of friends an individual has through a social networking site (e.g. Facebook, Myspace, or even followers on Twitter) or if the contacts in a cell phone would serve as a strong enough prime. Perhaps there is not enough inherent psychological recognition of cell phone contacts as other 'bystanders' to warrant the bystander effect; likewise simply noting that a person has a certain amount of friends and the number currently online might not be enough to provoke any cognitive reaction. These gaps in the literature notwithstanding, the extension of the implicit bystander effect to cyberbullying provides a chance to elucidate some of the psychological mechanisms which prevent bystanders from helping others.

A separate line of inquiry into mechanisms which might prevent online bystanders from helping others utilizes attribution theory. At its core, attribution theory is concerned with how people use incoming information in order to make decisions regarding the behavior of other people. Online, the amount of available information is limited, therefore any attributions made about a victim or perpetrator of cyberbullying would, logically, utilize whatever information was available for public consumption, such as other people’s posts and comments. To that end, the current project will also examine how attributions made about cyberbullying perpetrators and victims might impact bystanders’ decisions towards intervention. The more that is known about online mechanisms which impact cyberbullying, then the better chance there is of developing interventions to overcome them and, hopefully, prevent further tragic consequences of cyberbullying. For the current project, several research questions and hypotheses are proposed. 25

Research question 1: Is there evidence to support the idea of an online or implicit bystander effect?

Hypothesis 1a: Decisions to intervene will be moderated by the number of perceived

bystanders involved in the situation; bystanders who perceive more people will be less

likely to intervene than those who perceive fewer bystanders.

Hypothesis 1b: Decisions to intervene will be moderated by participants BJW and IRI

scores; participants with high BJW will be less likely to intervene whereas participants

with a higher IRI score will be more likely to intervene.

Research question 2: Do the kinds of attributions, personal v. situational, made about perpetrators and victims of cyberbullying impact bystanders’ decisions to intervene?

Hypothesis 2a: Participants who are more likely to make personal attributions about the

cyberbully and victim will be less likely to intervene.

Hypothesis 2b: Participant who are more likely to make situational attributions about the

cyberbully and victim will be more likely to intervene.

After completion of the pilot project, an additional Research Question was added to the dissertation study:

Research Question 3: Do the decision mechanisms associated with the bystander effect appear in an online environment and, if so, how do they impact participants?

26

Ch. 3—Materials

The materials for both the pilot study and the dissertation study consisted of vignettes constructed specifically for this research and two scales which have previously utilized and vetted in academic research: the Interpersonal Reactivity Index (IRI), and the Belief in a Just

World (BJW).

Belief in a Just World

Belief in a just world is the idea that most people believe that the world around them is fair and just and people generally ‘deserve’ what they get (i.e. rewards or punishments).

According to Lerner (1980), the belief in a just world is one way individuals orient themselves to their environment; the assumptions of a just world allow individuals to view their world as manageable and predictable. The Belief in a Just World-Other scale (Lipkus, 1991; Lipkus,

Dalbert, & Siegler, 1996) is a measure which examines the extent to which people believe the world is just for other people. It is often used as a measure of social attitudes (Bègue &

Bastounis, 2003). The scale contains eight items measured on a 5-point Likert scale with a

Cronbach’s reliability alpha of .866 (Table 1). A higher BJW score correlates to a stronger belief that the world at large is just and fair and people deserve what they get. Sample items on the

BJW scale include: “I feel that the world treats people fairly”; “I feel that people get what they deserve”; “I feel that the people earn the rewards and punishments they get in life”; and “I feel that a person’s efforts are noticed and rewarded”.

Interpersonal Reactivity Index

The Interpersonal reactivity index (IRI: Davis, 1980, 1983) is a scale that is used to measure empathy. The total scale contain 28 items that are measured on a 5-point Likert scale

(Table 1). For the current study, only the empathetic concern subscale was utilized as it is a 27 measure of ‘other-oriented’ feelings of sympathy and concern for others (Davis, 1980). The IRI subscale is comprised of seven items measured on a 5-point Likert scale with a Cronbach’s reliability alpha of .853. A higher score on the IRI is associated with higher empathetic concern for others. Sample items on the IRI include: “I often have tender, concerned feelings for people less fortunate than me”; “When I see someone being taken advantage of, I feel kind of protective towards them”; and “I would describe myself as a pretty soft-hearted person”.

Table 1

Cronbach’s Alphas for Belief in a Just World and Interpersonal Reactivity Index Scale # of items Cronbach’s Alpha Grand Mean

Belief in a Just world 8 .866 2.5221

Interpersonal Reactivity Index 7 .853 3.9126

Vignette construction

The content of the vignettes was based on Willard's (2007) taxonomy of cyberbullying acts. Flaming, or sending angry, rude, vulgar messages about a person to an online group or to that person, was chosen as it was commonly believed to be one of the more pervasive form of cyberbullying (Hazelden Foundation, 2011). The mechanisms of texting and a popular social networking site are based on Lenhart, Ling, Campbell, and Purcell's (2010) examination of mobile phone use amongst teenagers as well as the evidence which suggests that cyberbullying occurs frequently on social networking sites (Cassidy, Brown, Jackson, 2012; Kowalski, Limber

& Agatston, 2012). Although there are many different forms that cyberbullying can take, the ability to study each separate form is outside the scope and ability of the current exploratory project; in order to lay foundational research and to address the gaps in the literature a more parsimonious approach will be taken. 28

The vignettes were constructed based on the idea of factorial design. Factorial survey design was developed by Rossi and Nock (1982) as a way to incorporate experimental design into survey research and address methodology issues with vignette research. It is quasi- experimental as there is no true experimental designation of group randomization or manipulation, but there is randomization within the elements of a factorial survey and the vignettes given to participants (Wallander, 2009). This randomization allows for a more experimental form of vignettes than was previously utilized as well as a more 'true-to-life' description of circumstances for participants to judge (Wallander, 2009). In fact, Taylor (2006) notes that factorial design approximates real-life decision-making better than typical vignettes.

Factorial designs and vignette research has been utilized in a variety of fields in order to study the contexts and conditions which can affect how people make judgments (Wallander,

2009). Some such judgments include the influence of victim and perpetrator characteristics on sentencing decisions (Applegate, Turner, Sanborn, Latessa & Moon, 2000), norms of political action (Jasso & Opp, 1997), teachers' recognition and reporting of child abuse (O'Toole,

Webster, O'Toole & Lucal, 1999) and social care professionals referrals of elder abuse (Killick &

Taylor, 2011). The vast and disparate fields in which factorial survey design can be utilized demonstrates the strength of the design in capturing the complexities in judgment and decision- making. In the current project, factorial vignette design is utilized as a way to examine the complex decision-making process behind bystanders intent to intervene in cases of cyberbullying.

Factorial survey design is based on the concept of orthogonality; all vignettes are different and no duplicate vignettes exist (Rossi & Nock, 1982). In factorial vignettes each variable, or dimension, is given a certain number of levels. These dimensions and levels are then 29 randomly varied independently of each other in order to produce vignettes that capture every possible combination of dimensions and levels (Wallander, 2009). For example, a vignette on end-of-life decision making might have variables such a gender and illness of the decision maker. Gender would have two levels: male and female. Illness could be construed many different ways; serious vs. not serious (two levels), serious, moderate, and not serious (three levels), or even different kinds of illnesses like cancer, tumor, HV/AIDS, or pneumonia (four levels). The decisions about the variables should be guided by prior theory and research, extra- theoretical reasoning, and conventional wisdom (Jasso, 2006). Likewise, dimension levels of each variable should be considered in relation to the objective of the study and the aims of the researcher (Wallander, 2009). The inclusion of additional dimensions and levels increases the number of vignettes.

Methodologically speaking, increasing the number of vignettes can have a large impact on the research being conducted. Asking participants to read several vignettes can cause fatigue

(Wallander, 2009). Another possible problem is seen with demand characteristics; the participant might evaluate differences between vignettes instead of their own decisions or judgments about the content of the vignette (Wallander, 2009). Care must be taken, then, to write vignettes which encompass all the necessary dimensions and levels for thorough research but are still parsimonious enough to not cause fatigue in the participants.

Advantages of factorial survey design. One advantage of utilizing a factorial design in research, and specifically for research in cyberbullying, is that the design would allow for approximation of the circumstances surrounding a cyberbullying incident. People utilize a number of different factors when making decisions and judgments (Rossi & Nock, 1982) and using a factorial survey is one way in which multiple elements can be incorporated into a 30 situation approximating cyberbullying. Factors such as age, gender, and type of cyberbullying incident can be manipulated in order to see what kinds of decisions bystanders make in cyberbullying incidents.

The ability to manipulate the level or type of cyberbullying is especially relevant to the current research project. According to Willard (2007), there are seven categorizes of common cyberbullying actions: flaming, online harassment, cyberstalking, denigration or put-downs, masquerade, outing, and exclusion. Flaming refers to the action of sending angry, rude, vulgar messages about a person to an online group or to that person. Online harassment is repeatedly sending offensive messages to another person. Cyberstalking is online harassment that includes threats of harm or is excessively intimidating. Denigration or put-downs refers to sending harmful, untrue, or cruel statements about a person to other people of posting such material online. Masquerade is when a person pretends to be someone else and sends or posts material that makes that person look bad. Outing occurs when someone sends or posts material about a person that contains sensitive, private, or embarrassing information, including forwarding private messages or images. Finally, exclusion is cruelly excluding someone from an online groups.

Each of these categories represents a possible level of the variable of cyberbullying behavior or even a variable by themselves with their own levels. For example, flaming as a variable could have levels such as offensiveness of message content. Outing could have levels comparing text versus images for perceived harmfulness of the impact. Factorial survey design is one way in which judgments of each of these levels or variables could be adequately examined and measured without great cost on the part of the researcher.

A second advantage of factorial survey design is that the randomization of the variables, the levels, and the participants allows for a better external validity (Wallander, 2009). The ability 31 to generalize from given research is an important aspect in many experiments but in cyberbullying the ability to generalize from the research might also allow for workable interventions to be developed. This is especially important due to some of the consequences associated with cyberbullying. Victims of cyberbullies have been linked to poor concentration, low school achievement and increased absenteeism (Beran & Li, 2005), depressive symptoms

(Ybarra, & Mitchell, 2004), and even increased suicidal thoughts and suicide attempts (Hindjua

& Patchin, 2010). A third advantage is that factorial survey vignettes can be administered online or in a written pen-and-paper methods and therefore are potentially available to a wider sample than might be available otherwise. The ability to utilize an online or Internet environment is especially apt due to the nature of cyberbullying. The behavior is not one especially desirable and participants who are given anonymity are more likely to give more honest answers

(Birnbaum, 2004).

Disadvantages to factorial survey design. There also are disadvantages to utilizing factorial survey vignettes. The biggest disadvantage is that there is little ability to measure a behavioral component, especially if the survey is given at a distance, such as over the Internet.

The link between attitudes, beliefs, and behavior is not highly correlated but there is some research to suggest that broad aggregate behaviors or dispositions are more apt to be reflective of a person's attitudes and beliefs (Ajzen & Fishbein, 2005). Incorporating an ‘easy-out’ escape question which allows participants a chance to take a socially desirable option in order to avoid the cyberbullying situation is one way to try and measure a behavioral component as well as a control for social desirability.

A second disadvantage is that sometimes the vignettes, in considering all possible variables and levels, end up in combining elements that render them useless or unbelievable 32

(Wallander, 2009). One way to overcome this disadvantage is to pilot test vignettes before utilizing them for research, as was the approach in the current research. Along the same lines, however, is that sometimes the sheer number of variables and levels can require hundreds of vignettes in order for all orthogonal combinations to be met (Wallander, 2009). This disadvantage is accounted for in the current research project by a reduction in the number of dimensions and therefore the number of possible vignette frames. In order to form an adequate, exploratory project, only those dimensions and levels considered to be absolutely vital will be included.

Vignette dimensions. The vignette frames were created in a manner similar to Ludwick

(2004), using random numbers generators to determine the order of levels of dimensions, and a word processing program to create the vignettes (Table 2). The design of the vignettes was modeled after popular social networking websites and text messaging services available on cell phones. The content of the vignettes was based on anecdotal evidence provided by the researcher, contacts which allowed the researcher access to their cell phone conversations and social networking pages, and mainstream media coverage of cyberbullying cases. Content from these sources was collated together and compared to the definition of ‘flaming’ given by Willard

(2007) to ensure that the vignettes were representative of these kinds of cyberbullying events.

Once this was completed, the vignettes were created using Microsoft Word.

Dimension one: Number of bystanders. Dimension one is perceived number of bystanders with two levels: low and high. In most bystander literature, the number of bystander in groups can vary from one to eight (see Darley & Latane, 1968; Latane & Nida, 1981; Garcia,

Weaver, Moskowitz & Darley, 2002; Levine & Crowther, 2008). In these studies, the bystander effect was utilized in a real-life context. Garcia et al. (2002) and Markey (2000) used implicit 33 and online contexts; their group numbers ranged from 2 to 30. The need for such a larger group size reflects the larger context of the Internet; online the number of people available and online can be in the millions. The group size for the current research reflects this larger potential population. The low group frames had 5 members and the high group frames had 50 members.

Dimension two: Delivery mechanism. The two delivery mechanisms were chosen for three reasons. First, they reflect the limited research (Lenhart, Ling, Campbell, & Purcell, 2010) on the use of cellular phones, texting, and Internet use. Second, there is supporting anecdotal evidence to support these two mechanisms as prevalent among adolescents and young adults, especially the tragic cyberbullying cases of Megan Meier, Phoebe Prince, and Tyler Clemente.

Finally, these mechanisms provide a good basic starting point and, as this is largely exploratory research, attempting to capture every possible element associated with cyberbullying would prove to be an almost insurmountable task.

Table 2 Dimensions and Levels of Current Research Project Dimension Levels

Perceived number of bystanders Small number of perceived bystanders Large number of perceived bystanders

Delivery mechanism Facebook Text

In addition to the vignettes, there were a series of immediate response questions for each vignette (seven questions for social networking, five questions for texting) and 10 follow-up questions (Table 3) for all vignettes measured on a Likert scale of likelihood, ranging from 1,

‘Very unlikely’ to 7 ‘Very likely’. The purpose of the immediate response was as a way to gauge both the participants’ anticipated next steps but also to give the participants an ‘easy-out’ step in 34 order to avoid the potential for social desirability in the research. The ‘easy-out’ questions were constructed with a Likert style rating so that a participant could determine how applicable each question or action would be to their own course of action (Figure 2 and 3). It was thought that by having participants rate the strength of their anticipated actions, including those actions that might be considered as socially undesirable, such as joining in the bullying or leaving without offering help, then the findings would be more accurate and not reflective of the bystanders desire to give responses that would paint them in a good light but might not necessarily reflect their true motivations or actions.

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Figure 2 Example of Anticipated Next Steps for Social Networking Vignettes Please read statement below and rate them on how likely it is that you would respond in a similar way

Would you... Very unlikely Somewhat unlikely Neither Somewhat likely Very likely

(1) (2) (3) (4) (5)

1. Tease or make fun of Matt

2. Feel bad about the act

3. Just close the browser

4. Check later to see if the incident had stopped

5. Post a comment for Matt

6. Post a comment telling people to knock it off

7. Alert the webmaster to the events

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Figure 3 Example of Anticipated Next Steps for Texting Vignettes Please read statement below and rate them on how likely it is that you would respond in a similar way

Would you.. Very unlikely Somewhat Neither Somewhat likely Very likely unlikely (1) (3) (4) (5) (2)

1. Just put my phone away

2. Tease or make fun of Jennifer

3. Ignore or delete the text

4. Send a text telling people to knock it off

5. Send a text chastising the original texter

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Table 3 Follow-Up Questions for Each Vignette 1. Do you think somebody will intervene in the situation? 2. Do you think a mutual friend will intervene and help? 3. Do you think a mutual friend will alert somebody? 4. How likely are you to intervene? 5. How likely are you to email or text a mutual friend in order to stop the situation? 6. How likely is it that a mutual friend will text or email you in order to stop the situation? 7. How much is the subject of the post or text to blame for the situation? 8. How much are the people who sent the disparaging text or made disparaging posts to blame for the situation? 9. How much is anybody who encourages the texts or posts to blame for the situation? 10. How much are people who see the texts or posts but do not do anything to blame for the situation?

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Ch. 4: Pilot Study Findings

The purpose of this chapter is to present the results of the pilot study. The novel application of the bystander effect theory in an online environment along with the use of factorial design vignettes prompted the consideration and use of a pilot study. The goals of the pilot study were to refine the survey instrument questions and scales, ensure that the vignettes were comprehensible and readable, to gauge how much time was needed to complete the survey, to see if the initial results supported the bystander effect hypothesis and, if not, to modify or add to the project as necessary based on the pilot findings.

The pilot study was conducted between May 2013 and August 2013. Participants were required to be at least 18 years of age or older and comprehend and speak English at a middle- school level. In addition, for the pilot study, participants were required to appear in-person to a designated location on-campus for an estimated 60 minutes. This was enough time for the participant to read all the vignettes and respond to the survey in its entirety, and then participate in the follow-up semi-structured interview. In total, 10 people volunteered to participate in exchange for course credit at the discretion of their instructors. Certification for the Pilot and

Dissertation Studies was sought and obtained from the University of Nevada (UNR) Institutional

Review Board (IRB).

Procedure

Upon arrival, participants were directed to a conference room secured by the researcher for the length of the pilot study. Participants were directed to read and follow the directions given in the survey instrument and then the researcher left the room. Upon completion of the survey, participants signaled the researcher by opening the door. The researcher then re-entered the room and, after ensuring the participant was willing to continue, explained the purposes of 39 the overall cyberbullying study as well as the smaller pilot study. The researcher told the participant that the interview would be recorded and gained consent to continue. If the participant did not wish to be electronically recorded, the researcher then asked for permission to take written notes. Once consent was given, the researcher conducted a semi-structured interview

[Appendix A for complete list of guiding questions].

Survey Results

Social networking vignettes and follow-up questions. Analysis of the survey items and vignettes was done primarily utilizing frequencies and measures of central tendency (mean, mode, and variance) due to the low number of participants. Although the quantitative pilot data must be very cautiously interpreted, given the small sample, they are presented here as part of the assessment of how items performed and were primarily used for understanding potential item trends, variance, and ceiling/floor effects. Examining the social networking vignettes (vignettes

1, 4, 6, 7), revealed that participants were very unlikely to tease the person who made the post or to report the incident to the webmaster. A greater variability was seen in the “Check back later”,

“Post a comment for the victim” and “Post a comment telling other people to knock it off” options (Table 4).

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Table 4 Pilot Study: Social Networking Vignettes Immediate Reaction Choices Feel bad Post Tell Tease Close Check Alert the for comment others victim browser later webmaster victim for victim to stop Vignette 1 Mean 1.1 3.9 2.7 3.7 3.0 2.9 2.2 Mode 1.0 5.0 1.0 4.0 1.0 1.0 1.0 Variance .10 1.2 2.5 1.1 2.2 2.5 1.9 Vignette 4 Mean 1.3 3.0 2.8 3.1 2.9 2.8 1.8 Mode 1.0 4.0 3.0 4.0 4.0 4.0 1.0 Variance .45 1.3 1.3 2.1 2.3 1.5 1.3 Vignette 6 Mean 1.2 4.0 3.0 3.3 3.0 2.9 2.0 Mode 1.0 4.0 2.0 4.0 2.0 2.0 1.0 Variance .40 .67 1.6 1.6 1.8 1.7 1.6 Vignette 7 Mean 1.1 3.6 2.9 3.1 3.3 3.0 2.0 Mode 1.0 4.0 2.0 2.0 3.0 3.0 1.0 Variance .10 1.8 2.3 1.9 2.0 1.8 1.3

Examination of the follow-up questions for the vignettes revealed that most respondents thought somebody else would intervene but were not likely to intervene or alert someone themselves. Most participants placed blame on the bullies or people who made comments and little to none on the victims, even in the vignettes with the victim being aggressive. The participants also placed little blame on people who witness the acts but do nothing.

Texting vignettes and follow-up questions. Analysis of the texting vignettes revealed that the majority of participants were unlikely to engage in any of the given immediate behaviors. Of the given options, the highest rated mean and mode was found in the “Send a text chastising the original texter” option followed by “Send a text telling people to knock it off”

(Table 5).

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Table 5 Pilot Study: Texting Vignettes Immediate Reaction Choices Ignore or Tell people Send text Put the Tease delete the to knock it chastising phone away victim text off original texter Vignette 2 Mean 2.1 1.3 2.2 3.6 3.7 Mode 1.0 1.0 1.0 4.0 4.0 Variance 1.4 .23 2.2 1.8 .90 Vignette 3 Mean 2.0 1.4 2.3 3.4 3.4 Mode 1.0 1.0 1.0 2.0 2.0 Variance 2.0 .93 2.2 1.4 1.4 Vignette 5 Mean 2.4 1.3 2.2 3.8 3.8 Mode 1.0 1.0 1.0 5.0 4.0 Variance 2.9 .46 1.7 2.2 1.3 Vignette 8 Mean 2.3 1.3 2.5 3.6 2.9 Mode 1.0 1.0 1.0 4.0 4.0 Variance 2.0 .46 2.3 1.2 2.1

The same 10 follow-up questions regarding likelihood of intervention and assignment of blame that followed the social networking vignettes also were given after the texting vignettes.

These follow-up questions provided answers that followed in a similar pattern: participants were more likely to assume that somebody would intervene but were themselves unlikely to do so.

Participants were unlikely to ignore the text and more likely to send a text telling the original texter to ‘knock it off’.

Bystander effect questions. In order to see if the displayed number of bystanders had any effect on participants’ survey answers, the vignettes were broken out by number of bystanders as high (vignettes 1, 2, 4, 8) and low (vignettes 3, 5, 6, 7). Comparison of means, modes, and variance showed that, unexpectedly, participants reported a higher likelihood of 42 intervention for the vignettes with the higher number of bystanders than the lower number of bystanders (see Table 6).

Further examination of this effect was undertaken by incorporating the victim status (in red, vignettes 3, 4, 7, 8) in which the victim responded or fought back. These vignettes also varied considerably within the high and low bystander conditions, which suggested that there might be other factors which participants consider in making their intervention decision. Both vignettes 4 and 5 diverge from the pattern set by the other vignettes, 4 is much lower than the other high bystander vignette responses and 5 is much higher than the other low bystander vignette responses. Vignette 4 is one in which the victim actively fights back against the bullies via social networking, it might be that participants were less likely to intervene in this instance because the victim appeared to already be doing so. Vignette 5, however, is not one of the vignettes in which the victim responds or fights back against the bullies. In this case, the high mean for intervention might be due to the content of the actual vignette, in this vignette the bullying is done via a threat on the victim’s life (i.e. “Matthew is a nark and should just kill himself). The perceived seriousness of the situation might be the cause for why participants felt they would intervene which follows the theory of the bystander effect. According to Fischer et al. (2009) in situations perceived as emergencies, the bystander effect can be overcome and bystanders are more likely to act or, in this case, actively intervene.

Table 6 Examination of Likelihood of Intervention in High Bystander Vignettes How likely are you to Vignette 1 Vignette 2 Vignette 4 Vignette 8 intervene? High High High High bystander bystander bystander bystander Mean 4.2 4.9 3.4 4.9 Mode 3 7 4 4 Variance 4.4 3.4 1.8 3.7

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Table 7 Examination of Likelihood of Intervention in Low Bystander Vignettes How likely are you to Vignette 3 Vignette 5 Vignette 6 Vignette 7 intervene? Low Low Low Low bystander bystander bystander bystander Mean 3.9 4.8 3.6 3.4 Mode 4 4 3 2 Variance 2.1 3.9 2.7 4.7

In addition to the follow up questions, the immediate answer questions also were examined by number of bystanders (Table 6 and 7). Comparatively, the number of bystanders did not appear to affect the immediate response answers for either social networking or for texting. In the social networking vignettes, participants appeared most likely to engage in a passive or non-intervention response such as feel bad for the victim and check back later to see if the incident had stopped. The active intervention responses, such as actively posting a comment for the victim or telling others to knock it off, or alerting the webmaster, showed slightly more variance among participants but overall, were less likely to occur.

The texting vignettes followed an interesting pattern in that the more active

‘intervention’-type of actions (i.e. actually texting others to either tell them to knock it off or to chastise the original perpetrator) were rated by the participants more highly than the other, passive or non-intervention type of responses or actions.

Scale results. Four scales were utilized in the survey: Belief in a Just World (BJW, 8 items), a shortened version of the Marlowe Crowne Social Desirability Scale (MCDS, 10 items), a scale of Social Dominance Orientation (SDO, 22 items) and the Interpersonal Reactivity Index

(IRI, 6 items). All scales with the exception of the SDO scale demonstrated good variability in participant response. The SDO scale appeared to suffer from a floor effect in which all the participants had very low scores with little variability between them. 44

Ranking cyberbullying acts. Participants were asked to rank seven different cyberbullying acts from least harmful (1) to most harmful (7). Almost all participants ranked all acts as very or most harmful (6 or 7) with the exception of exclusion (excluding someone from an online group), which was usually ranked as one of the least harmful acts. Again, this could be a reflection of social desirability that participants feel they need to say all acts are harmful.

Interview results

The interviews were semi-structured with a guide and question prompts. This approach allowed the research to have control over the interview with the necessary questions but also allowed for freedom for the participant to talk and discuss new topics as they arose. The interviews were recorded and then transcribed by the research. Once transcribed, each question in each interview was coded separately by the researcher in order to determine the core themes per question (Glaser, 1968). Open coding was utilized first in order to understand the general themes and discussion patterns and develop a coding rubric for each question. In deference to the small sample size, keywords and phrases were considered to be a common theme if they were spoken or used by at least two participants. After each interview was read and coded, selective coding was employed in order to determine how the specific themes and patterns emerged across all the interviews collectively.

Participant reactions to the vignettes. When participants were asked about their reactions to the vignettes, including reactions about the content, all participants responded that the vignettes seemed believable and realistic (see Appendix A for Pilot Interview Question

Guide). Half the participants said they had seen similar postings or situations occur on Facebook, three of those participants said that the language was usually worse. One participant felt it was an

“age thing”, he believed that younger people tended to bully with crude language and older 45 people would bully by exclusion or sarcasm. Almost all of the participants remarked that they would like more information about the situation or the relationship between them and the victim before they could definitively state what their reaction would be. Participants often commented that they were unsure if the situation was a joke or the result of a previous encounter between the bullies and victims, in addition, many participants stated that their reactions would be different if they knew their relationship to the victim, (e.g. close friend or family member). All participants said that the vignettes were easy to visually read and understand.

Participant reactions to the survey. When asked about the survey questions, the language, readability, comprehension and survey length, almost all participants said they understood the language. Two participants were unsure about the word “chastise” and several participants were unsure about what “feel bad about the act” meant, (i.e. should they feel bad about it occurring or that they were not doing anything about it). A few participants pointed out the deliberate typos made in the vignette and four participants suggested added an open-ended question to the immediate response action questions. None of the participants felt the survey was overly long or tiring but a few mentioned that having the same 10 follow-up questions was repetitive.

Participant attributions of blame. When asked about assignment of blame in the vignettes, almost all participants blamed both the people making the comments or who sent the text. Almost half of the participants felt that the subject of the comments or text (i.e. the ‘victim’

Matthew or Jennifer) shared some of the blame in the social networking scenarios. One participant noted “…they have to know that when they put this stuff out there, people are going to say stuff”. Several participants noted that scenarios in which the poster or victim appeared to be in crisis (i.e. when Matthew or Jennifer post about being sad or unable to stop crying), they 46 felt this was vignette was more ‘serious’ than the others because of the post and that they felt such a person is seeking help and therefore blamed all people who made comments or didn’t help. These also were the vignettes in which most participants said they would intervene or say something, either online or directly in person if they knew the victim. When asked about the kinds of comments or interventions, participants said they would be most likely to post or text an encouraging comment to the victim and ignore the others, or somehow find a way to redirect the conversational thread by changing the topic or asking about something else. Participants also reported that they were most likely to intervene in situations in which the victim was a friend or family member.

Participants described the people bullying with a range of adjectives; some said they were just ‘young and stupid’ others commented on their ‘immaturity and lack of real-world experience’ and other just referred to them as ‘jerks or not pleasant’. Matthew and Jennifer (the victims in the vignettes) were usually described as ‘socially awkward’, ‘reaching for help’,

‘passive-aggressive’ and ‘sometimes instigating’.

Participant reaction to number of bystanders. Participants were asked if they noticed the number of bystanders and if this number changed or altered their responses in any way. Even though participants said they noticed the different numbers, almost all of the participants said the number did not change their responses. This is interesting to note because the data revealed that there was variance between participants on their decision to intervene for low bystander numbers versus high bystander numbers although not in the expected direction. That is, participants appeared to be more likely to intervene when the number of bystanders was high, compared to the traditional concept of the bystander effect in which more bystanders is associated with a lower likelihood of intervention by any one bystander. 47

One participant said the different number of people seemed indicative of different situations, specifically that when 50 were involved versus just 5, the situation seemed to be worse or to have larger possible consequences. Interestingly, another participant remarked something in contrast and said that 5 bystanders seemed more sinister and 50 seemed like someone just trying to get attention from friends.

When asked if they thought about their social group when online, most participants said they would if they saw something specific, such as an article or picture or product that reminded them of a specific person (i.e. if their sister had a cat and they saw a funny cat picture). Other participants said that they use their computer for only schoolwork and that they rarely spent any time online or even had a social networking website.

Interview results-General comments. Participants were asked to make any general comments about the survey that they thought would be helpful. More than half of the participants believed that having an indication of the relationship between the survey-taker and the cyberbully victim would sway their answers (i.e. they would definitely intervene for family but not for somebody they did not really know). One participant commented that the number of social networking ‘friends’ is not indicative of actual friends and that adding ‘friends’ can be done due to a shared period of time (e.g. job, or camp or specific event) but these friends are not considered close friends. Several participants said they had never received a mass-text message to 50 people and therefore would assume such a message was a hoax or meant to be for marketing and would ignore it.

Summary and potential changes to dissertation project. Analysis of the pilot study data revealed several positive factors and several areas for change before the full dissertation study. All participants found that the content of the vignettes was believable and, with a few 48 minor word changes, comprehensible. None of the participants felt the survey questions were confusing or repetitive, nor did they find the length of the vignettes or survey to be too long, even when reading all eight vignettes. The participants all noticed the number of bystanders present in each vignette and were able to articulate such. Additionally, participants felt that the vignettes were realistic depictions of what one might see in a potential cyberbullying scenario.

Utilizing the feedback provided by the pilot study, and after consultation with the dissertation committee, several changes were made to the survey instrument for the dissertation study.

The first change was a tightening of focus on the bystander effect. Due to the lack of significant results in the pilot study, new questions were developed and added for the dissertation study in order to collect more specific data about the bystander effect. These questions were based on the common bystander effect mechanisms: situation ambiguity/pluralistic ignorance, diffusion of responsibility, and evaluation apprehension or audience inhibition as proposed by

Latane and Darley (1970) on their ‘path to providing help’. The full path model has five steps

(Figure 4). First a bystander must notice an event. Second, the bystander must determine that help is required. Third, the bystander assumes personal responsibility in the situation. Fourth, the bystander determines a way to help. Fifth, the bystander provides help. At each step along the way, there is the potential for a bystander mechanism to occur. Pluralistic ignorance and situational ambiguity occur around steps one and two: if the bystander notices an event but also notices that nobody else seems worried or is unsure of what is exactly occurring in the event, then one of these mechanisms might occur. Diffusion of responsibility occurs around steps two and three: if the bystander does not interpret the event as an emergency, or assumes another person will take personal responsibility, then this mechanism might occur.

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Figure 4 Path to Providing Help (Darley & Latane, 1970)

1. Notice event is occurring

Ambiguity: what is actually happening?

Pluralistic ignorance: nobody else appears worried

2. Determine help is required

Diffusion of responsibility: someone else will do something

3. Assume personal responsibility

Audience inhibition: do not know how to help

4. Determine way to help

Audience inhibition: might have made mistake

5. Provide help

In the original bystander effect research, researchers relied on auditory and visual cues to alert the participants to the presence of other bystanders. In an online environment, these cues are entirely visual. Similar to classic research on the bystander effect, it is expected that once participants are alerted to the online presence of other bystanders, one or all of the bystander effect mechanisms might occur.

The three bystander effect mechanisms discussed here, situation ambiguity/pluralistic ignorance, diffusion of responsibility, and evaluation apprehension, are sometimes referred to by different names in the literature but all have the same basic operational definition. The 50 operational definitions for the current project will follow those given by Fischer et al. (2011).

Diffusion of responsibility is the tendency for people to subjectively divide the personal responsibility to help by the number of bystanders present; the more bystanders the less personal responsibility any individual bystander will feel. Evaluation apprehension, called audience inhibition by Latane & Darley (1970) is the fear people hold of behind judged by others when acting publically; people fear making a mistake or acting inadequately when observed so they are reluctant to intervene. Pluralistic ignorance is the tendency to rely on the overt reactions of others when defining an ambiguous situation; a bystander effect occurs when no one intervenes because they do not perceive other people intervening.

For the purposes of the dissertation study, an additional research question was added.

Research Question 3 is “Do the mechanisms associated with the bystander effect appear in an online environment and, if so, how do they impact participants?” The mechanisms were assessed via the follow-up self-report questions specifically developed for this study (see Table 8 for full list of additional bystander mechanism questions). A new statement, “If you answered 'Very' or

'Somewhat' unlikely, please move to the next questions.” was added. This statement then branched into the follow-up mechanism questions (Table 8). These follow-up questions were developed based on the definitions for each mechanism given by Fischer et al. (2011) and Latane

& Darley (1970) and were written to encompass the individual mechanism as much as possible.

Although the original aim was to develop three questions per mechanism to capture each construct as fully as possible, upon review of the developed items, each mechanism had items that were deemed too identical to be useful. This resulted in two discrete items per mechanism that, as closely as possible captured the construct of each mechanism as defined by Fischer et al.

(2011) and Latane & Darley (1970). In addition, each follow-up question response set were 51 written with Likert style ratings so that participants’ could denote how strong or applicable each mechanism was to them for each vignette.

Adding Research Question 3 and the mechanism questions to the study helped to tighten the focus on the bystander effect but, at the same time, increased the amount of information gained on the bystander effect process. Just as in real life, there are individuals who choose not to intervene. The number of bystanders can play a pivotal role, but the addition of these follow- up items could help explain why bystanders intervene or not, and what affects such decisions.

These mechanisms, often listed as factors in the real-life scenarios, also might be occurring in an online environment. In addition, the findings of the pilot study, although preliminary, were contradictory to the theory of the bystander effect. Adding the mechanism questions was done to help determine if the lack of differences found in the pilot between the high/low bystander groups could be due to the influence of one of these mechanisms.

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Table 8 Additional Bystander Response Options Survey Question Bystander Effect Mechanism

I am not likely to intervene because somebody else probably Diffusion of responsibility will. I am not likely to intervene because there are enough people Diffusion of responsibility

I am not likely to intervene because I might make a mistake. Evaluation Apprehension

I am not likely to intervene because nobody else is, so it’s Evaluation Apprehension probably no big deal.

I am not likely to intervene because I am not confident I Pluralistic Ignorance know what is happening in the situation.

I am not likely to intervene because nobody else is and they Pluralistic Ignorance probably know something I don’t

The second change was to the number and content of the vignettes. The original content of the vignettes was based on Willard's (2007) taxonomy of cyberbullying acts. Flaming was chosen as the cyberbullying act for all of the vignettes as it is believed to be one of the more pervasive forms of cyberbullying (Hazelden Foundation, 2011). The mechanisms of texting and a popular social networking site are based on Lenhart, Ling, Campbell, and Purcell's (2010) examination of mobile phone use amongst teenagers as well as the evidence which suggests that cyberbullying occurs frequently on social networking sites (Cassidy, Brown, Jackson, 2012;

Kowalski, Limber & Agatston, 2012). Once the type of cyberbullying act was determined, the content of the vignettes (e.g. the actual responses said in the scenarios) was based on anecdotal cyberbullying experiences, the typical examples given across previous cyberbullying literature

(see Willard, 2007; Hinduja & Patchin, 2012; Patchin & Hinduja, 2010 among others for examples) and Internet searches of popular social networking websites. 53

After the pilot study, the vignettes were altered so that the encounter would mimic, as closely as possible, a potential bystander encounter in the real world. To that end, the initial social networking posts and text messages by the victims were removed so that just the replies by the cyberbullies remained. This was done because, as research shows, often bystanders are in circumstances in which they must make decisions based on a limited amount of information. The origin of the conflict or event is not always known and so, by presenting these cyberbullying scenarios in a similar manner, the vignettes were a closer approximation of a bystander event that could occur in real life. After the vignettes were modified to not include the initial post or text, a cyberbullying victim response was added at the end of the bullying responses. This was done to give the perspective of the victim and show that this encounter was an actual bullying encounter and not simply a case of friends sharing a joke or having fun at the expense of another friend.

Finally, an additional ‘more comments’ header was made and placed at the top of the comment table in the social networking vignettes. The purpose of the header was to reinforce for the participants’ the high/low bystander pair. The higher bystander vignettes had a corresponding higher number of previous comments than the lower bystander vignettes.

Along with the changes made to the text, the number of bystanders present in each vignette was bolded and underlined so that it would be clear to the participant, and the sidebar on the social networking vignettes was altered to show the presences of bystanders along with the creation of an ‘you are online’ notification button on the bottom of the sidebar. This notification button was added to further imprint in the participants’ mind the presence of other bystanders currently online with them (see Figure 5 and Figure 6 for examples of original and revised vignette design and content for social networking vignettes and Figure 7 and Figure 8 for examples of original and revised vignette design and content for texting vignettes). The names of 54 the online bystanders in the sidebar also were bolded to further make the point of an online bystander group currently engaged in the online activity along with the participant.

In addition to these content and format changes, another change was to increase the amount of vignettes so that each vignette had both a high number of bystanders and a low number of bystanders--but were otherwise identical. This change allowed for a cleaner design where the paired vignette content was constant and only the number of bystanders varied.

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Figure 5 Pilot Study Social Networking Vignette Example You are on a popular online social networking site and you see the following:

Matthew: Sometimes I feel like I’m crying and nobody can hear me Sharon is online

Philip is online James ha ur such a bitch August 27 at 10:37am · Like Amy is online

Jose is online Kevin stop with the drama Matt. your being stupid August 27 at 10:38am · Like Malcolm is online

Hank matt maybe you should go jump off a cliff Julia is online August 28 at 9:08pm · Like ·

Vera is online

Jamie ur such a jerk! August 30 at 11:59am · Like Calum is online

Wesley is online Aaron thats so stupid. matt ur such a dumbass! August 31 at 5:33pm · Like Henry is online

...plus 40 others! Eric @Hank—hahaha. matt should just jump and do us all a favor September 2 at 3:42pm · Like

According to the sidebar ticker, you and Matt currently have 50 friends-in-common online right now.

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Figure 6 Dissertation Study Social Networking Vignette Example You are on a popular online social networking site and you see this ongoing comment thread on your friend Matthew’s page after one of his updates: Sharon is online View 26 more comments Philip is online

James ha ur such a bitch August 27 at 10:37am · Like Amy is online

Jose is online

Kevin stop with the drama Matt. your being stupid August 27 at 10:38am · Like Malcolm is online

Julia is online Hank matt maybe you should go jump off a cliff August 28 at 9:08pm · Like · Vera is online

Matthew quit being such jerks you guys! September 2 at 3:42pm · Like Calum is online

** You are online

* ...plus 42 others!

According to the sidebar ticker, you and Matt currently have 50 friends-in-common online right now.

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Figure 7 Pilot Study Texting Vignette Example You receive the following group MMS text message:

jennifer is a giant slut!

Sep 25, 2012 1:53 PM

Several more texts follow with people insulting Jennifer. The original message was sent to 50 people total and also is being recirculated by the same phone number.

Figure 8 Dissertation Study Texting Vignette Example

You receive the following group MMS text message:

jennifer is a giant slut!

Sep 25, 2012 1:53 PM Seconds later you receive the following message from Jennifer:

U guys r such jerks! knock it off!

Sep 25, 2012 1:53 PM

Several more texts follow with people insulting Jennifer. The original message was sent to 50 people total and also is being recirculated by the same phone number.

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A third change from the pilot study was to remove the ranking of cyberbullying acts and the scale of social dominance orientation as both suffered from strong ceiling and floor effects respectively. Each were originally included in order to expand the potential pool of available information should the main effect not be seen. Due to the conflicting nature of the pilot findings of the bystander effect, however, and the decision to tighten the focus of the study and increase the number of vignettes, eliminating these scales helped to streamline the focus of the bystander effect mechanisms and aid in preventing cognitive fatigue on the part of the participants.

The fourth change was to alter the immediate response answers to remove ‘chastise’, as some of the participants did not understand this word and the response ‘report to the webmaster’ was changed to ‘report this’ as this is how such an action is given on most social-networking pages. Participants also were unsure about the ‘feel bad about the act’ response, so this was clarified to ‘Feel bad that (victim) is being teased or made fun of by other people’. A space for open-ended answers also will be given as many participants suggested it during interviews.

The final change was an expansion of the research pool in order to include a sample of participants drawn from Amazon Mechanical Turk (M-Turk). M-Turk is a crowdsourcing

Internet marketplace in which individuals (referred to as Providers or Workers) are able to perform tasks (such as taking surveys) for monetary compensation. Incorporating M-Turk allowed for a more diverse population from which a sample can be drawn, in addition to expansion of the project beyond the convenience sample of college students utilized in the pilot study (See Appendix B for complete Dissertation Instrument).

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Ch. 5: Dissertation Study

After the changes proposed from the pilot study findings were implemented, the dissertation study was conducted from March 2014 until August 2014.

Participants

There were a total of 855 participants randomly recruited from undergraduate students at the University of Nevada, Reno (UNR) and through M-Turk. UNR participants received course credit for participation and M-Turk participants received monetary compensation for participation. Participants were chosen if they were over the age of 18, could read and speak

English at a middle-school level, and could access the online survey (Table 9). However, after screening, the total overall sample was composed of 150 participants from SONA and 404 participants from M-Turk.

There were several reasons for utilizing both SONA and M-Turk. The SONA subject pool was utilized in the pilot study and was therefore utilized again in the dissertation study.

SONA, however, is a convenience sample of college undergraduate students; therefore M-Turk was also utilized as a sample pool in order to increase the diversity of the overall sample.

Cyberbullying research has been somewhat homogenous, most studies utilize samples of middle school students and children of younger ages. By utilizing the SONA and M-Turk subject pools, it is the hope that the study will capture a more diverse set of responses to cyberbullying and see how the patterns of bystanders might change due to age. As Privitera and Campbell (2009) found, cyberbullying behaviors can continue into adulthood and can be seen in the workplace.

SONA participants ranged in age from 18 to 47, with a mean age of 22 and a median age of 21. Of the SONA sample, 61% were female and 30% were male. Thirty-two percent of SONA 60 participants had experienced cyberbullying and 42% of SONA participants reported that their friends or family had experienced cyberbullying.

M-Turk participants ranged in age from 18 to 62 with a mean age of 33 and a median age of 30. Of the M-Turk sample, 62% were female and 38% were male. Approximately 31% of M-

Turk participants reported that they had experienced cyberbullying and 35% of M-Turk participants reported that their friends or family had previously experienced cyberbullying.

Table 9 Demographic Characteristics of Full Sample (n=847) SONA SONA SONA M-Turk M-Turk M-Turk n % of % overall n % of M- % overall SONA Turk Age Ranged between 18-47 Ranged between 18-62 # who answered 150 - 18% 405 - 48% Gender Male 59 39% 7% 153 37% 18% Female 91 61% 11% 258 63% 29% Experience cyberbullying Yes 48 32% 6% 127 31% 15% No 102 68% 12% 277 69% 32% Friends/family experienced cyberbullying Yes 63 42% 7% 143 36% 17% No 87 58% 10% 260 64% 30%

Participant screening.

A two-step process was employed in determining which participants would be retained for analysis. The first criterion was a correct answer to the manipulation check question. This ensured that two important criteria were met. One, that participants had competed at least 80% of the survey due to the position of the question toward the end of the survey. Two, that participants were aware of the differing number of bystanders and able to select the correct answer. One of the main tenets of the bystander theory is that people will respond differently in the presence of a different number of bystanders, therefore it was imperative to make certain that participants were 61 aware of the number of bystanders. After filtering the data by the manipulation check question, a total of 497 participants remained.

A total of 350 participants were removed; 60 participants who were removed answered the manipulation check question incorrectly, and 290 participants did answer the manipulation check question at all, meaning they did not complete at least 80% of the survey. Of the participants who were removed for an incorrect answer (and for which data were available), 25 participants were removed from the SONA sample and 30 were removed from the M-Turk sample. For the SONA sample, 9 participants were male and 16 were female, for the M-Turk sample, 22 participants were male and 12 were female. Table 10 shows the breakdown of participants remaining after the manipulation check question.

Table 10 Demographic Characteristics of Participants Removed After First Screen (n=350) SONA SONA M-Turk M-Turk New n Number removed New n Number removed # who answered 125 25 370 30 Gender Male 50 9 131 22 Female 75 16 239 12 Number of bystanders chosen (manipulation check question) 0 and 20 9 13 6 and 40 8 9 4 and 30 5 10 5 and 50* 125 370 1 and 60 3 3 *Denotes the correct answer to the manipulation check question

Once participants were filtered via the manipulation check question, the data were examined for indicators of social desirability bias via a shortened version of the Marlowe

Crowne Social Desirability Assessment (MCSD). Participants’ responses were tabulated and aggregated into a total score, which ranged from 10 ‘no social desirability bias’ to 17 ‘high social 62 desirability bias’. Of the 497 participants, approximately 24% (n=123) had a social desirability score greater than 1 standard deviation above the mean of 13.53, in this case, a score of 15 or higher. These participants were eliminated because they were deemed to have a high social desirability bias, therefore their responses could skew the results and lower validity.

Table 11 Demographic Characteristics of Participants Remaining After Second Screen (n=123) SONA SONA M-Turk M-Turk New n Number removed New n Number removed # who answered 88 37 285 85 Gender Male 32 18 96 35 Female 56 19 189 50 Social desirability score 10 2 1 11 10 28 12 17 66 13 23 91 14 34 88 15 23 23 55 55 16 10 10 23 23 17 4 4 7 7

These cumulative screens resulted in a final sample size for the dissertation study of 374

(Table 12). Eighty-eight (24%) participants from the SONA pool were retained into the final sample, along with 285 (76%) participants from the M-Turk sample pool. A larger percentage of

SONA participants were screened out due to high social desirability scores, one reason for this could be due to the demographics of the SONA pool, most participants were undergraduate college students seeking credit and therefore they might have felt more inclined to give socially desirable responses.

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Table 12 Demographic Characteristics of Final Sample (n=373) SONA SONA M-Turk M-Turk n % total n % total Age Range 18-45 Range 18-45 Range 18-62 Range 18-62 # who started survey 88 24% 285 76% Gender Male 32 9% 96 26% Female 56 15% 189 51% Experience cyberbullying Yes 32 9% 90 24% No 56 15% 195 52% Friends/family experienced cyberbullying Yes 42 11% 98 26% No 46 12% 186 50%

In total, 62 participants were removed from the SONA sample and 120 were removed from the M-Turk sample. Of the SONA participants, 27 were male and 35 were female; of the

M-Turk participants, 57 were male and 63 were female. The majority (n=56) of the removed

SONA sample were between the ages of 18-30 and most (n=81) were between the ages of 21-40.

Sixteen of the removed SONA participants had experienced cyberbullying as well as 38 of the removed M-Turk participants. In addition, 21 SONA participants and 45 M-Turk participants reported close friends or family members had experienced cyberbullying.

Merging of participant pools.

Participants were drawn from two subject pools: M-Turk (n=285) and SONA (n=88). An independent samples t-test was conducted to determine if there were any significant difference between these subject pools on the scales and likelihood of intervention questions in order to determine if the subject pools could be merged.

Analysis revealed only one significant difference for the IRI measure of empathy, F (1,

363) = .039, p = .043; M-Turk users reported a higher mean than SONA users (see Table 13 for 64 full between group comparisons). This could be due to the difference in the size and diversity of the population samples from each pool; the M-Turk sample was larger and more diverse than the

SONA sample, which could have contributed to a higher mean score in total. Given the lack of significant differences between the two groups on any other scale or the likelihood of intervention, the data were merged as a single dataset for analysis.

Table 13 Independent Samples T-Test Between SONA and M-Turk Samples t df p Mean difference Likelihood of Intervention Vignette 1 .731 167 .466 .10905 Vignette 2 -.716 371 .474 -.11571 Vignette 3 .774 172 .440 .11555 Vignette 4 .716 162 .475 .10869 Vignette 5 1.193 164 .235 .17644 Vignette 6 .502 371 .616 .08018 Vignette 7 -.384 371 701 -.06527 Vignette 8 -.577 371 .564 -.09434 Vignette 9 2.176 185 0.31 .29976 Vignette 10 1.061 173 .290 .14920 Vignette 11 -.030 371 .976 -.00474 Vignette 12 -1.079 371 .281 -.17285 Vignette 13 .183 371 .855 .03082 Vignette 14 .600 162 .550 .09382 Vignette 15 1.072 173 .285 .15937 Vignette 16 .379 371 .705 .05961 IRI -2.027 363 .043 -.89019 BJW 1.631 154 .105 1.08799

Total participants for study.

Of the 374 participants, 66% (n=245) were female and 34% (n=129) were male. Ages ranged from 18 (n=18) to 62 (n=1) with an average age of 30.23 and a median age of 28. Most

(67%; n=252) had never experienced cyberbullying before and those who had experienced it

(33%) reported ranges from once to over 100 times. Participants also reported that approximately

62% (n=233) of their close friends or family had never experienced cyberbullying. Of the 37% of 65 close friends and family who had experienced cyberbullying, the number of times again was estimated to be one to over 100 times. Participants were asked if there was a time when they had seen a case of cyberbullying but not intervened. Over half (57%) reported no and 42% reported yes. Participants who had not intervened were asked if, given a chance, they would go back and intervene; 57% said they would whereas 31% of participants would not.

Materials and Procedure

Participants were given an information screen and link to the survey. Once participants agreed to take the survey they were taken to the online survey, housed on the Survey Monkey website. Participants were shown 16 cyberbullying vignettes in random order. Each vignette was followed by the same set of follow-up questions regarding the participants’ anticipated course of action regarding the hypothetical scenario they just read, their likelihood of intervention, and their belief about other bystanders’ intervention. After the vignettes, participants were given a series of survey questions regarding attributions of blame, and several scales measuring belief in a just world, empathy, and social desirability. Following the measurement scales, participants were asked demographic questions, a manipulation check question, and questions regarding their own close friends and family member’s experiences with cyberbullying. Participants then entered an identification number so they could receive credit or compensation, and allowed to exit the survey.

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Chapter 6--Results

Variable construction

The first two research questions and hypotheses were addressed by using a mix of variables specifically created for this research project and scales previously developed and validated via prior research. For research question 1 and the associated hypotheses, the variable of interest was the participants’ decision to intervene based on the number of bystanders present in the vignette. This likelihood was assessed with five point Likert style rating with the question

“How likely are you to intervene?” Each of these intervention questions were added together to form an overall likelihood of intervention score for all sixteen vignettes. In addition, all social networking likelihood scores were added together to form an overall social networking likelihood of intervention score and all texting likelihood scores were added together to form an overall texting likelihood of intervention score (Table X for correlations). These scores were used as the dependent variables for the regression and multi-level modeling analyses. The added bystander mechanism and other follow-up and help questions also were given a five point Likert style rating scale.

Table 14 Correlations between Intervention Variables Overall SN likelihood TXT likelihood likelihood of of intervention of intervention Intervention Overall likelihood of Intervention 1 .973** .920** SN likelihood of Intervention .973** 1 .806**

TXT likelihood of Intervention .920** .806** 1

**correlation is significant at the p<.001 level

Research question 2 and the associated hypotheses were tested with variables of interest comprised of scores on the personal and situational attribution questions. Similar to the 67 likelihood to intervene questions, these questions were created specifically for this study and developed with a five point Likert style rating. There were three personal attribution questions and three situation attribution questions. In addition to the attribution questions, the Belief in a

Just World (BJW) and the empathic concern subscale of the Interpersonal Reactivity Index (IRI) were employed. Each of these scales has been previously validated with prior studies; questions from each scale used Likert style ratings and the overall additive total in this study represents the strength of the participants’ belief in a just world or empathic concern.

Descriptive analyses

Survey scales. Descriptive analyses initially were used to examine the survey scales, as well as all of the vignette follow-up questions. The scales included the Belief in a Just World

Scale (BJW) and the Interpersonal Reactivity Index (IRI). There was a slight negative skew in the IRI scale, along with a slight positive kurtosis but, according to Curran, West and Finch

(1996), raw skewness scores of less than 2.0 and kurtosis scores of less than 7 are not likely to distort the results, so the data were left untransformed (see Table 15). A Pearson bivariate correlation analysis revealed a significant negative correlation between the scales r = -173, n =

356, p<.001.

Table 15 Interpersonal Reactivity Index and Belief in a Just World Skewness and Kurtosis IRI BJW

Mean 22.5410 20.1768 Skewness -0.840 .053

Kurtosis .963 .294

Follow-up questions. The follow-up questions included ratings of the participants’ anticipated next steps, the likelihood that the participant would intervene in the situation, and the 68 participants’ belief that other bystanders would intervene in the situation (see Table 17 for list of follow-up questions). These questions were identical per vignette pair and type, meaning that all the social networking (SN) vignettes had identical follow-up questions and all texting (TXT) vignettes had identical follow-up questions (Table 16).

Table 16 Description of Vignette Pairs Vignette number Type of Vignette Number of bystanders

Vignette 1 Pair 1, Social Networking High

Vignette 2 Pair 1, Social Networking Low

Vignette 3 Pair 2, Social Networking High

Vignette 4 Pair 2, Social Networking Low

Vignette 5 Pair 3, Texting Low

Vignette 6 Pair 3, Texting High

Vignette 7 Pair 4, Social Networking High

Vignette 8 Pair 4, Social Networking Low

Vignette 9 Pair 5, Texting Low

Vignette 10 Pair 5, Texting High

Vignette 11 Pair 6, Social Networking Low

Vignette 12 Pair 6, Social Networking High

Vignette 13 Pair 7, Social Networking Low

Vignette 14 Pair 7, Social Networking High

Vignette 15 Pair 8, Texting High

Vignette 16 Pair 8, Texting Low

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The likelihood/help questions were constructed to capture the full range of possible bystander effect mechanisms beyond singular individual’s decisions to intervene. Therefore these questions also asked participants if they thought someone else might intervene and the likelihood that they or someone else would text or email about the situation.

Table 17 List of Follow-Up Actions and Likelihood/Help Questions Follow-up immediate steps for Tease or make fun of victim social networking vignettes Feel bad about the act Just close the browser Check back later to see if the incident had stopped Post a comment for the victim Post a comment telling people to knock it off

Follow-up immediate steps for Just put my phone away texting vignettes Tease or make fun of the victim Ignore or delete the text Send a text telling people to knock it off Send a text scolding the original texter

Likelihood/help questions Do you think somebody will intervene in the situation? Do you think a mutual friend will intervene and help? How likely are you to email or text a mutual friend in order to stop the situation? How likely is it that a mutual friend will text or email you in order to stop the situation?

Pair one, consisting of vignettes one (high bystander) and two (low bystander), was a social networking scenario. For both vignettes, the three anticipated next steps with the highest averages for participants were: feel sorry for the victim, check back later to see if the incident had stopped, and post a comment telling people to knock it off (Table 18). The averages for intervention or help questions were higher on vignette 1 than vignette 2, and the only content difference between the two vignettes was the number of bystanders present (Table 19). These differences in averages echo a trend seen in the pilot study in which the vignettes with the higher number of bystanders had higher reported likelihood of intervention scores. 70

Table 18 Comparison of Anticipated Next Steps for Vignettes 1 and 2 Tease Feel Close Check Comment Comment sorry browser later support knock it off Vignette 1 Mean 1.14 4.02 2.63 3.82 3.33 3.52 SD .52 1.26 1.41 1.23 1.45 1.43 Vignette 2 Mean 1.18 3.92 2.66 3.75 3.32 3.48 SD .58 1.31 1.41 1.25 1.42 1.42

Table 19 Comparison of Likelihood/Help Questions for Vignettes 1 and 2 Somebody Mutual You email Friend will friend will or text email text intervene intervene friend you Vignette 1 Mean 3.63 3.66 3.28 3.12 SD 1.15 1.10 1.37 1.31 Vignette 2 Mean 3.44 3.41 3.32 3.03 SD 1.17 1.17 1.38 1.32

Pair two consisted of vignettes three (high bystander) and four (low bystander) and was another social networking scenario. The results were similar to pair one; the anticipated next steps for both vignettes were to feel sorry, check back later, and post a comment (Table 20).

And, just as in pair one, the averages for intervention and help questions were higher for vignette

3, the high bystander vignette (Table 21).

Table 20 Comparison of Anticipated Next Steps for Vignettes 3 and 4 Tease Feel Close Check Comment Comment sorry browser later support knock it off Vignette 3 Mean 1.19 3.91 2.73 3.77 3.31 3.43 SD .60 1.31 1.40 1.29 1.42 1.39 Vignette 4 Mean 1.12 3.91 2.73 3.75 3.30 3.42 SD .46 1.27 1.40 1.25 1.36 1.41

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Table 21 Comparison of Likelihood/Help Questions for Vignettes 3 and 4 Somebody Mutual You email Friend will friend will or text email text intervene intervene friend you Vignette 3 Mean 3.61 3.63 3.32 3.11 SD 1.10 1.07 1.34 1.29 Vignette 4 Mean 3.39 3.36 3.16 3.00 SD 1.11 1.14 1.35 1.30 Vignettes five (low bystander) and six (high bystander) were texting vignettes and made up pair three. Both vignettes had the highest averages for the same three next steps. These steps were: send a text telling people to knock it off, sent a text scolding the original texter, and ignore or delete the text (Table 22). As was seen in the prior pairings, averages for intervention and help questions were higher for vignette six, the vignette with the higher number of bystanders (Table

23).

Table 22 Comparison of Anticipated Next Steps for Vignettes 5 and 6 Put Tease or Ignore or Text Text phone make fun delete knock it scolding away off texter Vignette 5 Mean 2.20 1.23 2.43 3.75 3.70 SD 1.43 .72 1.46 1.38 1.44 Vignette 6 Mean 2.25 1.16 2.54 3.76 3.74 SD 1.44 .58 1.49 1.37 1.40

Table 23 Comparison of Likelihood/Help Questions for Vignettes 5 and 6 Somebody will Mutual friend you email or friend intervene will intervene text friend email text you Vignette 5 Mean 3.62 3.58 3.63 3.29 SD 1.18 1.17 1.36 1.33 Vignette 6 Mean 3.83 3.82 3.70 3.42 SD 1.08 1.02 1.29 1.26

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Pair four, consisting of vignettes seven (high bystander) and eight (low bystander) were social networking vignettes. The next step questions with the highest averages for both vignettes were to feel sorry for the victim, followed by check back later, and then post a comment telling others to knock it off (Table 24). Continuing in the trend seen in the other vignette pair comparisons, the help and intervene question averages were higher for vignette seven, the vignette with the higher number of bystanders (Table 25).

Table 24 Comparison of Anticipated Next Steps for Vignettes 7 and 8 Tease Feel Close Check Comment Comment sorry browser later support knock it off Vignette 7 Mean 1.16 3.93 2.72 3.74 3.24 3.42 SD .56 1.31 1.44 1.30 1.43 1.44 Vignette 8 Mean 1.16 3.92 2.66 3.71 3.22 3.43 SD .52 1.27 1.43 1.28 1.40 1.41

Table 25 Comparison of Likelihood/Help Questions for Vignettes 7 and 8 Somebody Mutual You email Friend will friend will or text email text intervene intervene friend you Vignette 7 Mean 3.56 3.59 3.26 3.08 SD 1.14 1.15 1.35 1.31 Vignette 8 Mean 3.40 3.36 3.23 2.99 SD 1.18 1.14 1.36 1.30

Pair five, vignettes nine (low bystander) and ten (high bystander), were texting vignettes.

Similar to the prior texting vignette pair, the most common action steps were: send a text telling people to knock it off, send a text scolding the original texter, and ignore or delete the text (Table

26). In keeping with the prior pair trends, the vignette with the high number of bystanders, in this case vignette ten, had higher averages for the intervention and helping questions than vignette nine (Table 27). 73

Table 26 Comparison of Anticipated Next Steps for Vignettes 9 and 10 Put Tease or Ignore or Text Text phone make fun delete knock it scolding away off texter Vignette 9 Mean 2.26 1.17 2.48 3.69 3.67 SD 1.47 .56 1.49 1.39 1.41 Vignette 10 Mean 2.17 1.16 2.46 3.74 3.69 SD 1.46 .58 1.49 1.38 1.45

Table 27 Comparison of Likelihood/Help Questions for Vignettes 9 and 10 Somebody will Mutual friend You email or Friend intervene will intervene text friend email text you Vignette 9 Mean 3.58 3.55 3.55 3.23 SD 1.19 1.18 1.34 1.30 Vignette 10 Mean 3.85 3.78 3.70 3.42 SD 1.11 1.10 1.33 1.27

Vignettes eleven (low bystander) and twelve (high bystander) made up pair six. This pair, comprised of social networking vignettes, had the highest averages for the same three immediate next steps: feel bad about the act, check back later to see if the incident had stopped and post a comment telling people to knock it off (Table 28). Once again, the averages for the intervention and helping questions were highest for vignette twelve, the vignette with the higher number of bystanders (Table 29).

Table 28 Comparison of Anticipated Next Steps for Vignettes 11 and 12 Tease Feel Close Check Comment Comment sorry browser later support knock it off Vignette 11 Mean 1.18 3.81 2.64 3.81 3.46 3.59 SD 1.16 3.87 2.66 3.90 3.47 3.60 Vignette 12 Mean .56 1.38 1.45 1.24 1.42 1.41 SD 1.18 3.81 2.64 3.81 3.46 3.59

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Table 29 Comparison of Likelihood/Help Questions for Vignettes 11 and 12 Somebody Mutual You email Friend will friend will or text email text intervene intervene friend you Vignette 11 Mean 3.52 3.48 3.35 3.10 SD 3.74 3.77 3.42 3.16 Vignette 12 Mean 1.13 1.08 1.41 1.31 SD 3.52 3.48 3.35 3.10

Pair seven, comprised of vignettes thirteen (low bystander) and fourteen (high bystander), was a social networking pair. The immediate steps with the highest average for vignette thirteen were to feel bad about the act, check back later, and then post a comment for the victim (Table

30). Vignette fourteen had the same first two steps but differed on the third step; the step with the third highest average for vignette fourteen was to post a comment telling people to knock it off

(Table 30).

Table 30 Comparison of Anticipated Next Steps for Vignettes 13 and 14 Tease Feel Close Check Comment Comment sorry browser later support knock it off Vignette 13 Mean 1.18 3.70 2.70 3.73 3.39 3.43 SD .66 1.41 1.40 1.25 1.41 1.43 Vignette 14 Mean 1.20 3.82 2.72 3.78 3.40 3.42 SD .67 1.36 1.42 1.27 1.44 1.47

Table 31 Comparison of Likelihood/Help Questions for Vignettes 13 and 14 Somebody Mutual You email Friend will friend will or text email text intervene intervene friend you Vignette 13 Mean 3.29 3.27 3.15 2.94 SD 1.20 1.22 1.41 1.32 Vignette 14 Mean 3.52 3.53 3.30 3.07 SD 1.16 1.14 1.37 1.30

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Finally, pair eight was comprised of two texting vignettes, vignette fifteen (high bystander) and vignette sixteen (low bystander). The three immediate steps with the highest averages were the same for both vignettes: send a text telling people to knock it off, send a text scolding the original texter, and ignore or delete the text (Table 32). And, as in the other pairs, the high bystander vignette also had the higher averages on the help and intervention questions

(Table 33).

Table 32 Comparison of Anticipated Next Steps for Vignettes 15 and 16 Put Tease or Ignore or Text Text phone make fun delete knock it scolding away off texter Vignette 15 Mean 2.33 1.20 2.62 3.64 3.59 SD 1.52 .65 1.53 1.42 1.47 Vignette 16 Mean 2.39 1.19 2.68 3.68 3.57 SD 1.54 .62 1.51 1.33 1.43

Table 33 Comparison of Likelihood/Help Questions for Vignettes 15 and 16 Somebody will Mutual friend You email or Friend intervene will intervene text friend email text you Vignette 15 Mean 3.71 3.75 3.58 3.32 SD 1.13 1.08 1.34 1.31 Vignette 16 Mean 3.50 3.50 3.49 3.22 SD 1.20 1.15 1.33 1.32

Summary of Follow-up Results

Overall, there were some interesting trends that emerged from the descriptive analysis.

The follow-up action steps tended to be similar among each of the delivery types. That is, the next steps among social networking vignettes and next steps among texting vignettes all tended to be the same next steps (see Tables 33--36 for high/low comparisons). For social networking vignettes, the most commonly cited follow-up steps were: feel bad, check back later and then 76 either posting a comment for the victim or posting a comment telling people to knock it off. For texting vignettes, the most commonly cited follow-up steps were: send a text telling people to knock it off, send a text scolding the original texter, or ignore or delete the text. It is interesting to note that in the social networking vignettes, passive actions tended to have the higher averages, like feeling bad or checking back later, whereas for the texting vignettes, the steps with the highest averages tended to be the active steps, such as sending a text to someone. Another interesting trend, one that was seen in the pilot study as well, was that the vignettes with the higher number of bystanders also were the vignettes that had the higher averages for the follow- up intervention/help questions. This trend is counter to what most of the bystander effect theory research has found in real world settings.

Table 34 Comparison of Anticipated Next Step Means for all High/Low SN Vignettes Tease Feel Close Check Comment Comment sorry browser later support knock it off

High SN Vignette 1 1.14 4.02 2.63 3.82 3.33 3.52 bystander Vignette 3 1.19 3.91 2.73 3.77 3.31 3.43 Vignette 7 1.16 3.93 2.72 3.74 3.24 3.42 Vignette 12 1.16 3.87 2.66 3.90 3.47 3.60 Vignette 14 1.20 3.82 2.72 3.78 3.40 3.42 Low SN Vignette 2 1.18 3.92 2.66 3.75 3.32 3.48 bystander Vignette 4 1.12 3.91 2.73 3.75 3.30 3.42 Vignette 8 1.16 3.92 2.66 3.71 3.22 3.43 Vignette 11 1.18 3.81 2.64 3.81 3.46 3.59 Vignette 13 1.18 3.70 2.70 3.73 3.39 3.43

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Table 35 Comparison of Means for Likelihood/Help Questions for All High/Low SN Vignettes Somebody Mutual You email A friend will will intervene friend will or text email or text intervene friend you

High SN Vignette 1 3.63 3.66 3.28 3.12 bystander Vignette 3 3.61 3.63 3.32 3.11 Vignette 7 3.56 3.59 3.26 3.08 Vignette 12 3.74 3.77 3.42 3.16 Vignette 14 3.52 3.53 3.30 3.07 Low SN Vignette 2 3.44 3.41 3.32 3.03 bystander Vignette 4 3.39 3.36 3.16 3.00 Vignette 8 3.40 3.36 3.23 2.99 Vignette 11 3.52 3.48 3.35 3.10 Vignette 13 3.29 3.27 3.15 2.94

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Table 36 Comparison of Anticipated Next Step Means for all High/Low TXT Vignettes Put Tease or Ignore or Text Text phone make fun delete knock it scolding away off texter

High TXT Vignette 6 2.25 1.16 2.54 3.76 3.74 bystander Vignette 10 2.17 1.16 2.46 3.74 3.69 Vignette 15 2.33 1.20 2.62 3.64 3.59 Low TXT Vignette 5 2.20 1.23 2.43 3.75 3.70 bystander Vignette 9 2.26 1.17 2.48 3.69 3.67 vignette 16 2.39 1.19 2.68 3.68 3.57

Table 37 Comparison of Likelihood/Help Question Means for all High/Low TXT Vignettes Somebody Mutual You will A friend will will intervene friend will email or email or text intervene text friend you

High TXT Vignette 6 3.83 3.82 3.70 3.42 bystander Vignette 10 3.85 3.78 3.70 3.42 Vignette 15 3.71 3.75 3.58 3.32 Low TXT Vignette 5 3.62 3.58 3.63 3.29 bystander Vignette 9 3.58 3.55 3.55 3.23 vignette 16 3.50 3.50 3.49 3.22

Examination of the frequencies of the follow-up questions for social networking vignettes reveals that most participants were unlikely to make fun of the victim (n=327 to 340, 87% to

90%) and, in fact, no participants endorsed this option in vignettes one or 4. Participants were somewhat likely (n=139 to 115, 37% to 31%) or very likely (n=172 to 132, 46% to 35%) to feel sorry for the victim. Participants were very unlikely (n=111 to 93, 30% to 25%) to simply close the browser but the reported variance (n =18) was less extreme for this option. Participants were somewhat likely (n=165 to 146, 44% to 39%) to check back later and to post a comment in 79 support of the victim (n= 119 to 97, 32% to 26%) and were very likely (n=129 to 104, 34% to

28%) to comment and tell people to knock it off (Appendix C for full frequency chart).

Examination of the frequencies of the follow-up questions for texting vignettes reveals that participants were very unlikely (n=189 to 164, 50% to 44%) to just put their phone away after viewing the text and also were very unlikely (n=335 to 326, 90% to 87%) to tease or make fun of the victim. Participants also were very unlikely to ignore or delete the text (n=118 to 146,

32% to 39%). Participants were very likely (n=145 to 129, 39% to 34%) to send a text telling people to knock it off and also were very likely (n=153 to 134, 41% to 36%) to send a text scolding the original texter (Appendix D for full frequency chart).

Research Question 1

Research question 1: Is there evidence to support the idea of an online or implicit bystander effect?

Hypothesis 1a: Decisions to intervene will be moderated by the number of perceived

bystanders involved in the situation; bystanders who perceive more people will be less

likely to intervene than those who perceive fewer bystanders.

Hypothesis 1b: Decisions to intervene will be moderated by participants’ BJW and IRI

scores; participants with high BJW will be less likely to intervene whereas participants

with a higher IRI score will be more likely to intervene.

To answer this question, each vignette was constructed with both a high (50) and low (5) number of bystanders but were otherwise identical. The purpose of this construction was to see if, similar to the bystander theory in a real-world context, the number of bystanders (high v. low) affected participants’ decisions to intervene. As an initial step, descriptive analyses were run of the likelihood of intervention for each vignette pair. 80

Descriptive analyses. Each vignette was followed by a series of questions, including a

question asking participants to rate how likely they were to intervene on a Likert-style scale

ranging from 1 (very unlikely) to 5 (very likely). For both social networking and texting

vignettes, the majority of participants rated themselves as ‘somewhat likely’ to intervene,

followed by ‘very likely’ (Table 38 and 39).

Table 38 Percentage of Participants’ Likelihood of Intervention for SN Vignettes Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette 1 2 3 4 7 8 11 12 13 14 Very 12.6 11.5 14.4 12.8 16.0 13.6 11.8 10.4 14.4 15.0 unlikely Somewhat 11.0 13.1 10.7 14.2 12.8 12.6 9.4 12.3 13.6 12.6 unlikely Neither 10.4 11.5 13.1 13.9 10.7 12.8 13.1 12.0 11.5 11.2 Somewhat 38.0 35.8 36.6 35.0 36.4 36.9 38.8 33.7 34.8 36.6 likely Very likely 28.1 28.1 25.1 24.1 24.1 24.1 27.0 31.6 25.7 24.6

Table 39 Percentage of Participants’ Likelihood of Intervention for TXT Vignettes Vignette 5 Vignette 6 Vignette 9 Vignette 10 Vignette 15 Vignette 15 Vignette 16 Very 11.2 10.4 9.9 10.2 11.8 10.7 11.2 unlikely Somewhat 7.0 8.6 9.4 7.5 10.7 9.1 7.0 unlikely Neither 8.0 8.0 7.2 7.2 8.3 10.7 8.0 Somewhat 35.8 33.7 34.8 36.9 34.2 37.7 35.8 likely Very likely 38.0 39.3 38.8 38.2 35.0 31.8 38.0

After the descriptive analyses were examined, paired samples t-tests were conducted on

the reported likelihood of intervention scores (1= low likelihood, 5= high likelihood) for each

social networking (SN) and texting (TXT) vignette pair. There were a total of 16 vignettes and

eight pairs, five SN and three TXT. 81

Paired t-tests. Results of the paired samples t-test show high correlations between pairs, as expected, but no significant difference in likelihood of intervention for any of the pairs (see

Table 40 for full pairwise comparisons). This is surprising and suggests that participants did not take the number of bystanders into account when making their decisions about intervention.

Table 40 Vignette Pairwise T-Test Comparisons for High/Low Bystander Pairs mean SD t p

Pair 1 .02139 .97671 .424 .672

Pair 2 .04011 .98296 .789 .431

Pair 3 -.00535 1.06867 -.097 .923

Pair 4 -.05615 .99033 -1.096 .274

Pair 5 -.02406 .92157 -.505 .614

Pair 6 -.03743 .91674 -.790 .430

Pair 7 .00267 .94486 .055 .956

Pair 8 -.00802 .95330 -.163 .871

Comparison of the means within each pair likelihood of intervention scores shows that, for the most part, participants were more likely to intervene in cases with a higher number of bystanders (Table 41). This trend, which also was seen in the pilot study, is counter to what most of the research on the bystander effect demonstrates. There are several reasons which might account for this counter trend to the bystander effect literature; one major reason might be the online environment. The visual cues used to trigger the bystander effect in a real world environment might not be strong enough in an online environment. Another reason also might be that, in a context without real world cues, the greater number of bystanders might impact bystanders’ perceptions of the situation; that is, situations with a higher number of bystanders 82 might be perceived as ‘more serious’ by other bystanders and, therefore, more worthy of intervention.

Table 41 Comparison of Means for Each Vignette Pair Vignette number Type of Vignette # of bystanders Mean SD

Vignette 1 Pair 1, SN High 3.5802 1.3353

Vignette 2 Pair 1, SN Low 3.5588 1.3284

Vignette 3 Pair 2, SN High 3.4733 1.3554

Vignette 4 Pair 2, SN Low 3.4332 1.3361

Vignette 5 Pair 3, TXT Low 3.8235 1.3123

Vignette 6 Pair 3, TXT High 3.8289 1.3151

Vignette 7 Pair 4, SN High 3.3957 1.3946

Vignette 8 Pair 4, SN Low 3.4519 1.3429

Vignette 9 Pair 5, TXT Low 3.8316 1.3041

Vignette 10 Pair 5, TXT High 3.8556 1.2853

Vignette 11 Pair 6, SN Low 3.5989 1.2953

Vignette 12 Pair 6, SN High 3.6364 1.3186

Vignette 13 Pair 7, SN Low 3.4358 1.3796

Vignette 14 Pair 7, SN High 3.4332 1.3756

Vignette 15 Pair 8, TXT High 3.7005 1.3546

Vignette 16 Pair 8, TXT Low 3.7086 1.2926

In addition to comparing the likelihood of intervention scores for each of the SN and

TXT vignettes high and low pairs, the aggregate likelihood of intervention scores for all of the 83 high and low SN and TXT vignettes were combined in order to see if an aggregate group differences occurred. The paired sample t-test result of the two pairs (high v. low SN and high v. low TXT) revealed no significant group mean differences which, again, supports the idea that the number of bystanders was not as important as other factors when participants were making their decisions about intervention.

General Linear Model. After descriptive analyses were conducted, a series of general linear models (GLM) were conducted. GLM was chosen as it is a more robust test, especially if there are any interaction effects among the variables.

The likelihood of intervention scores for each technology type, along with an overall likelihood of intervention score, were used to further examine how participants’ gender, BJW scores, and IRI scores might impact their decisions to intervene. The overall likelihood of intervention score was calculated by adding together the likelihood of intervention score for each vignette. Similarly, the overall SN and TXT likelihood of intervention scores were tabulated by adding together the SN vignettes likelihood scores and the TXT vignettes likelihood scores.

These three scores served as an indicator of the strength of the participants’ likelihood of intervention and, like the individual scores, a lower aggregate score was associated with a less likely chance of intervention on behalf of the cyberbullying victim.

BJW and IRI scores were aggregated based on the participants’ responses to the scale items. Once tabulated, these scores were then dichotomized as high or low according to the overall average score. Three separate GLM analyses were run; one with the likelihood of intervention for SN vignettes as the DV, one with the likelihood of intervention for the TXT vignettes as the DV, and one with the overall likelihood of intervention across all the vignettes as 84 the DV. Each model contained the participants’ BJW score, IRI score, and gender as the independent variables.

The first model examined the impact of the independent variables on the overall SN likelihood of intervention. Both gender and IRI were significant. Examination of the standardized coefficient estimates shows females reported lower likelihood scores than males and those with a higher IRI score were less likely to intervene than those with a lower score. This is an interesting finding as higher IRI is associated with a higher amount of empathy and it was speculated that a higher IRI would be associated with a higher likelihood of intervention. The second model examined the impact of the independent variables on the overall TXT likelihood of intervention. The results were similar to the SN results: gender and IRI were negatively related to likelihood of intervention with females and high scoring IRI participants reported a lower likelihood of intervention. The third model used the overall aggregate likelihood of interventions score across all vignettes as the dependent variable. Results show a similar pattern as the SN and

TXT overall models: females and IRI are negatively associated with a likelihood of intervention score (Table 42).

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Table 42 Parameter Estimates for Overall, SN, and TXT Likelihood of Intervention Factor β F p Social Newtorking GLM Gender -1.497 10.030 .002 IRI -5.259 35.816 .000 BJW 2.058 .58 .809 Gender*BJW -3.247 .654 .419 Gender*IRI -2.854 .423 .516 BJW*IRI -2.710 .351 .554 Gender*BJW*IRI 2.487 .250 .617 Texting GLM Gender -1.458 12.180 .001 IRI -3.909 34.474 .000 BJW .597 .020 .887 Gender*BJW -1.229 1.204 .273 Gender*IRI -.576 .423 .516 BJW*IRI .199 .014 .905 Gender*BJW*IRI -.744 .065 .799 Overall GLM Gender -2.955 12.085 .001 IRI -9.169 39.509 .000 BJW 2.656 .047 .829 Gender*BJW -4.476 .938 .333 Gender*IRI -3.430 .473 .492 BJW*IRI -2.511 .194 .659 Gender*BJW*IRI 1.737 .054 .816

The findings of the GLM present an interesting pattern, specifically that there was a negative relationship between above average IRI scores and likelihood of intervention across all three overall scores. This could mean that the IRI was not as accurate in evaluating the participants’ actual empathy or that empathy is not as strong a predictor of bystander intervention in cyberspace postulated. In addition, the lack of significance of BJW scores with any of the overall likelihood of intervention scores suggests that, while BJW might be a strong predictor for 86 some social attitudes, it is not a strong predictor for intervention in the cyberbullying vignettes of this study.

Multi-level model. One consideration for the current research is the impact that the vignettes could have on the total amount of error. One of the tenets of regression analysis, such as GLM, is that error in the model is uncorrelated or unrelated. By having participants read and score multiple vignettes, there arises the possibility of related error among the vignette scores.

This correlated error would violate the assumptions of regression models and therefore, in addition to GLM models, a multi-level model was run in SPSS.

Multi-level modeling allows the researcher to specify the possibility of related error across nested data. In this case, the vignettes were nested within participant (each participant read 16 vignettes), therefore the vignettes were the level-1 repeated data and participant number became the level-2 subject data. For both the null and full model, the dependent variable was the overall likelihood of intervention and the full model also contained the predictors of gender,

BJW, and IRI.

Results of the full multi-level model tests of fixed effects revealed that both gender and

IRI were significant, F (1, 5662) = 127.145, p<.001 for gender and F (1, 5662) = 401.170, p<.001 for IRI. As with the GLM, these were both negative relationships: females were less likely to intervene than males and those with above average IRI scores were less likely to intervene. Interestingly, there was a significant negative interaction effect for BJW and gender, F

(1, 5662) = 12.035, p<.001 even though BJW was not significant by itself. Parameter estimates show that above average BJW scores were related to lower likelihood of intervention scores for females. There was also a significant interaction effect between gender and IRI, F (1, 5662) = 87

5.053, p<.025. Parameter estimates reveal that females with above average IRI scores reported lower likelihood of intervention scores.

The multi-level model confirmed what the GLM regression models noted: gender and IRI were the strongest predictors of participants’ likelihood of intervention. Females were less likely to intervene than males and those with above average IRI scores also were less likely to intervene. Gender has often been noted as a factor for bystander effect research, although the findings are mixed. In cyberbullying, several studies have found no link between gender and cyberbullying (Beran & Li, 2007; Hinduja & Patchin, 2008; Li, 2007; Patchin & Hinduja, 2006) but a few have found gender as a significant predictor of victimization (Dehue et al., 2008;

Kowalski & Limber, 2007; Ybarra & Mitchell, 2008). The current results do not help to illuminate this debate in either direction.

Delivery mechanism. To examine the impact of the delivery mechanism (the cyberbullying method employed in the vignette) on intervention tendencies, likelihood of intervention scores for all SN and TXT vignettes were combined to form an overall SN and TXT likelihood of intervention score. Paired samples t-tests comparing these two scores revealed a significant difference; t (1, 373) = -7.994, p <.001. Analysis of the means revealed a higher mean for TXT (x = 22.7484) than for SN (20.9144) which indicates that participants were more likely to intervene on the texting vignettes versus the social networking vignettes. One possible reason for this finding might be that the relative lack of available information available on the texting vignette might have prompted bystanders to intervene because they felt that 1) there was less of a risk for them, relatively speaking, 2) the situation might be more serious than they knew, or 3) there was less of a chance of anonymity for texting due to the associated phone number. On the social networking vignettes, there is information about the individuals responding, along with 88 their responses, and a visual picture or avatar to mark their identity. These items are not present on a text exchange so participants might have felt that there was relatively little risk for them if they responded. Or, given the lack of information, participants might have felt that the texting situations were more serious as they were not able to ‘see’ what was happening as if they were in a social networking context, and therefore felt it necessary to intervene in the texting cyberbullying instances. Also, texting is more specific form of communication than social network messaging; a person is forced to specify the individuals or groups who will receive the text whereas in social networking a general message can be received by all friends.

Paired t-tests for likelihood/help follow-up questions. Although there were no significant differences found for participants’ personal likelihood of intervention scores for any of the high/low vignette pairs, the descriptive analysis of the follow-up questions, along with the significant bystander mechanism findings, suggest that there might be an effect, just one not strong enough to be revealed based on participants’ own likelihood scores. Therefore, the additional follow-up likelihood and help questions also were examined with paired t-tests in order to see if there were any significant group differences.

The first set of paired t-tests examined participants’ determination that somebody else would intervene via the question “Do you think somebody will intervene in the situation?” The results showed that there were significant differences between each vignette pair (Table 43).

These findings suggest that, although there were no differences for self-determination of intervention, part of the reason for this could be due to the fact that participants’ believed that someone else would intervene.

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Table 43 T-test Comparisons for ‘Somebody Intervene’ Follow-Up Question by High/Low Vignette Pair mean SD t p

Pair 1 .18356 1.14439 3.064 .002

Pair 2 .22715 1.02113 4.226 .000

Pair 3 -.19780 1.04678 -3.605 .000

Pair 4 .15217 1.05629 2.764 .006

Pair 5 -.27100 1.13600 -4.583 .000

Pair 6 -.20994 1.08891 -3.668 .000

Pair 7 -.22376 1.05115 -4.050 .000

Pair 8 .20219 1.15514 3.349 .001

Examination of the means for these pairs shows that the means for the high bystander vignettes were higher than the means for the low bystander vignettes (Table 44). This finding suggests that participants’ rated the likelihood of intervention as higher, or more likely, for those vignettes with a higher number of bystanders than those with a lower number of bystanders. This supports the findings that were found in the pilot study and the descriptive statistics; vignettes with a higher perceived number of bystanders were associated with a higher likelihood of intervention.

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Table 44 Mean Comparisons for ‘Somebody Intervene’ Follow-Up Question by High/Low Vignette Pair Vignette number Vignette type Mean SD

Pair 1 Vignette 1 high 3.6329 1.14930 Vignette 2 low 3.4493 1.16769 Pair 2 Vignette 3 high 3.6122 1.09761 Vignette 4 low 3.3850 1.10990 Pair 3 Vignette 5 low 3.6209 1.17326 Vignette 6 high 3.8187 1.08335 Pair 4 Vignette 7 high 3.5598 1.14207 Vignette 8 low 3.4076 1.18443 Pair 5 Vignette 9 low 3.5827 1.19069 Vignette 10 high 3.8537 1.11572 Pair 6 Vignette 11 low 3.5166 1.13665 Vignette 12 high 3.7265 1.13118 Pair 7 Vignette 13 low 3.2845 1.20674 Vignette 14 high 3.5083 1.15607 Pair 8 Vignette 15 high 3.7022 1.13541 Vignette 16 low 3.5000 1.20444

The second series of paired t-tests examined participants’ ratings for each high/low vignette pair for the question “Do you think a mutual friend will intervene and help?” Results show significant differences between each high/low pair (Table 45).

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Table 45 T-test Comparisons for ‘Mutual Friend’ Follow-Up Question by High/Low Vignette Pair Mean SD t p

Pair 1 .24185 1.17359 3.953 .000

Pair 2 .26630 1.12429 4.544 .000

Pair 3 -.23370 1.02543 -4.372 .000

Pair 4 .22372 1.11311 3.871 .000

Pair 5 -.21607 1.11697 -3.675 .000

Pair 6 -.29076 1.06963 -5.215 .000

Pair 7 -.25956 1.08330 -4.584 .000

Pair 8 .24528 1.02757 4.598 .000

Comparison of the means for each of the high/low pairs shows that, like the findings in the descriptive results and in the prior pair comparisons, the means for vignettes with the high number of bystanders are higher than the means with the low number of bystanders (Table 46).

These findings suggest that participants believe a mutual friend is more likely to intervene in cases with more bystanders.

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Table 46 Mean Comparisons for ‘Mutual Friend’ Follow-Up Question by High/Low Vignette Pair Vignette number Vignette type Mean SD

Pair 1 Vignette 1 high 3.6603 1.09810 Vignette 2 low 3.4185 1.16670 Pair 2 Vignette 3 high 3.6223 1.07295 Vignette 4 low 3.3560 1.13930 Pair 3 Vignette 5 low 3.5815 1.17600 Vignette 6 high 3.8152 1.01948 Pair 4 Vignette 7 high 3.5876 1.14833 Vignette 8 low 3.3639 1.14121 Pair 5 Vignette 9 low 3.5651 1.17464 Vignette 10 high 3.7812 1.09508 Pair 6 Vignette 11 low 3.4837 1.13635 Vignette 12 high 3.7745 1.07521 Pair 7 Vignette 13 low 3.2678 1.22280 Vignette 14 high 3.5273 1.14105 Pair 8 Vignette 15 high 3.7493 1.07513 Vignette 16 low 3.5040 1.15635

The third set of paired t-tests participants’ ratings of how likely they were to email or text a mutual friend in order to stop the situation (Table 47). Unlike the previous two likelihood/help questions, there were only three pairs with significant differences between the high/low pair.

These pairs were pair two (vignettes three and four), pair five (vignettes nine and ten), and pair seven (vignettes thirteen and fourteen). Pair two and seven were both social networking scenarios and pair five was a texting scenario, so there was no commonality in vignette type.

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Table 47 T-Test Comparisons for ‘Email or Text a Mutual Friend in Order to Stop the Situation’ Follow- up Question by High/Low Vignette Pair Mean SD t p

Pair 1 -.02981 .86394 -.663 .508

Pair 2 .15385 .84857 3.459 .001

Pair 3 -.06250 .94762 -1.265 .207

Pair 4 .02703 .95101 .547 .585

Pair 5 -.14674 .95409 -2.950 .003

Pair 6 -.06775 .91975 -1.415 .158

Pair 7 -.13587 .94134 -2.769 .006

Pair 8 .07945 1.03070 1.473 .142

Although there were no commonalities among the significant vignettes pair types, the three significant pairs did follow the common trend of higher means for the vignettes with the high number of bystanders (Table 48).

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Table 48 Mean Comparisons for ‘Email or Text a Mutual Friend in Order to Stop the Situation’ Follow- Up Question by High/Low Vignette Pair Vignette number Vignette type Mean SD

Pair 1 Vignette 1 high 3.2873 1.36886 Vignette 2 low 3.3171 1.38107 Pair 2 Vignette 3 high 3.3159 1.34294 Vignette 4 low 3.1621 1.34987 Pair 3 Vignette 5 low 3.6304 1.35693 Vignette 6 high 3.6929 1.29377 Pair 4 Vignette 7 high 3.2541 1.34961 Vignette 8 low 3.2270 1.36043 Pair 5 Vignette 9 low 3.5489 1.34014 Vignette 10 high 3.6957 1.33483 Pair 6 Vignette 11 low 3.3469 1.36294 Vignette 12 high 3.4146 1.41187 Pair 7 Vignette 13 low 3.1522 1.40792 Vignette 14 high 3.2880 1.37263 Pair 8 Vignette 15 high 3.5699 1.33781 Vignette 16 low 3.4904 1.33756

The fourth and final set of paired t-tests examined participants’ ratings of the likelihood that a mutual friend would text or email them. There were four pairs with significant differences between the high/low vignettes scenarios and four pairs with no significant differences. The pairs with significant differences were pair two (vignettes three and four), pair three (vignettes five and six), pair five (vignettes nine and ten), and pair seven (vignettes thirteen and fourteen). Two of the significant vignette pairs were social networking pairs and two were texting pairs. Of the 95 pairs in which there were no significant differences found, the p-value missed significance by a very small margin (Table 49).

Table 49 T-Test Comparisons for ‘Mutual Friend Email or Text You in Order to Stop the Situation’ Follow-up Question by High/Low Vignette Pair Mean SD t p

Pair 1 .08967 1.00413 1.713 .088

Pair 2 .11413 .92675 2.362 .019

Pair 3 -.12807 1.05450 -2.327 .021

Pair 4 .09434 .98324 1.848 .065

Pair 5 -.17166 .97536 -3.372 .001

Pair 6 -.05946 .96369 -1.187 .236

Pair 7 -.12129 .88462 -2.641 .009

Pair 8 .09434 .98324 1.848 .065

Examination of the means for this follow-up questions reveals that, similar to the ongoing trend, the means for the vignettes with a higher number of bystanders were higher than those with lower bystanders (Table 50). Also interesting to note is that, unlike the prior question in which the participant was asked to email or text, the increase in significant high/low pair differences suggests that participants, once again, believed that others would provide the intervention. This finding, along with the significant ‘someone else will intervene’ findings support the idea that participants, although not likely to intervene themselves, often believed that somebody else would.

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Table 50 Mean comparisons for ‘Mutual Friend Email or Text You in Order to Stop the Situation’ Follow- up Question by High/Low Vignette Pair Vignette number Vignette type Mean SD

Pair 1 Vignette 1 high 3.1114 1.30648 Vignette 2 low 3.0217 1.32244 Pair 2 Vignette 3 high 3.1168 1.28709 Vignette 4 low 3.0027 1.29871 Pair 3 Vignette 5 low 3.2807 1.33275 Vignette 6 high 3.4087 1.25743 Pair 4 Vignette 7 high 3.0809 1.31064 Vignette 8 low 2.9865 1.30584 Pair 5 Vignette 9 low 3.2371 1.29772 Vignette 10 high 3.4087 1.27040 Pair 6 Vignette 11 low 3.0946 1.29172 Vignette 12 high 3.1541 1.31308 Pair 7 Vignette 13 low 2.9434 1.31705 Vignette 14 high 3.0647 1.29703 Pair 8 Vignette 15 high 3.3154 1.30705 Vignette 16 low 3.2210 1.32530

Findings for Research Question 1

Overall, the findings do not support either hypothesis 1a or 1b; BJW was not significantly related to lower likelihood of intervention scores and above average IRI scores were significantly negatively related to likelihood of intervention scores. In addition, the paired samples t-tests revealed no significant differences in between-group means among all the high/low vignette pairs. According to these findings, and contrary to many of the real-world bystander effect findings, the average likelihood of intervention was not significantly related to the number of 97 perceived bystanders in the scenarios. Also contrary to many of the real-world bystander effect findings, the likelihood of intervention means were higher for high bystander frames.

Although the overall findings for the personal intervention question were not significant, examination of the descriptive statistics and paired t-tests of the intervention/help follow-up questions help provide an explanation for such findings. The paired t-tests for the ‘someone else will intervene’ question were significant between all high/low pairs, indicating that participants felt other people would intervene, especially in the high bystander frames. Additionally, there were two significant high/low pair differences for the ‘I will text or email a friend about the situation’ but four significant differences for the ‘a friend will text or email me about the situation’ indicating that, once again, participants felt it was more likely that a friend or somebody else would intervene in the high bystander frames.

Taken together, these findings suggest that the effect for personal intervention questions might have not been seen because the participants felt that they might intervene but other people were even more likely to intervene. This finding is consistent with real-world bystander effect findings and specifically with the findings on the bystander mechanism of diffusion of responsibility. The bystander mechanism findings will be discussed further under Research

Question 3 but it appears that the presence of bystander mechanisms may be affecting the current results.

The overall pattern of how likely participants are to intervene is interesting because it both demonstrates support for some of the bystander effect literature on diffusion of responsibility, but also conflicts with research on the likelihood of intervention or helping when more people are present. The general trend for participants in the current study appears to be that, as the number of bystanders increase, so does the likelihood of personal and other intervention, 98 as well as the chances for a bystander mechanism to occur. The means for likelihood of intervention, both personal and other, were higher for the high bystander frames, which conflicts with real-world bystander effect literature. However, the means for the bystander mechanism questions also were higher for the high bystander frame, which supports the real-world bystander effect literature on diffusion of responsibility.

Research Question 2

Research question 2: Do the kinds of attributions, personal v. situational, made about perpetrators and victims of cyberbullying impact bystanders’ decisions to intervene?

Hypothesis 2a: Participants who are more likely to make personal attributions about the

cyberbully and victim will be less likely to intervene.

Hypothesis 2b: Participant who are more likely to make situational attributions about the

cyberbully and victim will be more likely to intervene.

There were three situational attribution questions and three personal attribution questions

(Table 51). To examine if personal or situation attributions might have affected participants’ decisions to intervene, participants’ responses to the personal and situational attributions questions were added to form a total attribution score for personal attributions and for situational attributions. These scores then were dichotomized based on the average score in order to compare below and above average attribution groups on the variables of interest.

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Table 51 Attribution Questions about Cyberbullying Attribution focus Question

Personal Bullies are always bullies, no matter the situation

Personal Cyberbullying online or through text messages happens because some people are just naturally bullies Personal Cyberbullies bully others because of an internal or intrinsic trait

Situational Cyberbullying online or through text messages is a result of the situation

Situational Cyberbullies bully because of environmental causes

Situational People cyberbully other people because of reasons beyond their control

T-tests and aggregate GLM. In order to test hypotheses 2a and 2b, two independent samples t-tests were conducted. The overall likelihood of intervention scores, as well as likelihood of intervention scores for the SN and TXT vignettes, were the dependent variables for both the situational attributions and personal attributions t-tests. None of the t-tests were significant, which indicates that between-group mean attribution scores were not related to participants’ decisions about intervention. In addition to t-tests, GLM analyses were run with the three separate dependent variables. None of the GLM analyses were significant, supporting the t- test analyses and the conclusion that neither the participants’ aggregate personal nor situation attribution scores were related to their decisions to intervene.

Individual questions GLM. The aggregate attribution questions were not significantly related to likelihood of intervention, so the expanded personal and situation attribution questions were examined to see if they were related to likelihood of intervention. A series of three GLM analyses were run for each set of attribution questions, one with the overall likelihood of intervention as the dependent variable, one with the overall likelihood of intervention for SN as 100 the dependent variable, and one with the overall likelihood of intervention for TXT as the dependent variable. The three personal and three situation attribution questions were the independent variables for each series.

For the personal attribution questions, only the question “Bullies are always bullies, no matter the situation” was significantly related to the likelihood of intervention for overall, SN, and TXT likelihood scores (Table 52). Examination of the parameter estimates show that coefficients were negative, implying that the higher the score on this question, the lower the likelihood of help. This suggests that the stronger that participants agreed that bullies are bullies, the lower they rated the likelihood of help. None of the expanded situational attribution questions

(Table 53) were significantly related to the overall likelihood of intervention for any of the three dependent variables.

Table 52 Parameter Estimates for Personal Attributions GLM Factor F p Overall GLM

Bullies are always bullies 2.86 .011 Some people are just naturally bullies .922 .480 Because of an internal or intrinsic trait 1.502 .179 Social Networking GLM Bullies are always bullies 2.594 .019 Some people are just naturally bullies .924 .479 Because of an internal or intrinsic trait 1.577 .155 Texting GLM

Bullies are always bullies 2.790 .012 Some people are just naturally bullies .810 .563 Because of an internal or intrinsic trait 1.280 .268

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Table 53 Parameter Estimates for Situational Attributions GLM Factor F p Overall GLM

Result of the situation 1.369 .228 Environmental causes 1.051 .393 Reasons beyond their control 1.879 .085 Social Networking GLM Result of the situation 1.365 .230 Environmental causes .779 .587 Reasons beyond their control 1.824 .095 Texting GLM

Result of the situation 1.105 .360 Environmental causes 1.500 .179 Reasons beyond their control 1.759 .109

Further examination of the ‘bullies are always bullies’ was done utilizing age and gender.

ANOVA analysis revealed no significant group relationships between age and the personal attribution question, but ANOVA and independent samples t-test analyses revealed a significant relationship between gender and the personal attribution question, F (1, 372)=9.066, p<.003.

Higher means were reported for females, indicating that they were more likely to believe the attribution than males. This finding strengthens the idea that personal attributions made about those involved in cyberbullying situations can impact bystanders’ decisions to intervene.

Research Question 3

Research Question 3: Do the decision mechanisms associated with the bystander effect appear in an online environment and, if so, how do they impact participants? 102

As previously mentioned, the new bystander mechanism questions were constructed based on the definitions provided by Fischer et al. (2011) and Latane and Darley (1970) (Table

54).

Table 54 Bystander Mechanism Questions Bystander mechanism questions I am not likely to intervene because somebody else probably will (Diffusion of responsibility).

I am not likely to intervene because there are enough people (Diffusion of responsibility).

I am not likely to intervene because I might make a mistake (Evaluation apprehension).

I am not likely to intervene because nobody else is, so it’s probably no big deal (Evaluation apprehension).

I am not likely to intervene because I am not confident I know what is happening in the situation (Pluralistic ignorance).

I am not likely to intervene because nobody else is and they probably know something I don’t (Pluralistic ignorance).

Descriptive analyses. The mechanism questions were compared by each high/low vignette pair. Initial descriptive analyses revealed some interesting trends. Overall, the two mechanisms with the highest averages for each vignette were evaluation apprehension and pluralistic ignorance. Also, in keeping with a trend seen throughout the project, the vignettes with a higher number of bystanders also tended to have higher mean scores for each of the mechanism questions (Tables 55-64 for all comparisons).

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Table 55 Comparison of Bystander Mechanisms for Vignettes 1 and 2 Somebody There Might Probably Not Nobody else are make a no big confident else is & probably enough mistake deal what is know will people happening something I don’t Vignette 1 Mean 2.52 2.59 2.66 2.37 3.20 2.59 SD 1.18 1.22 1.30 1.15 1.33 1.18

Vignette 2 Mean 2.30 2.21 2.51 2.22 3.09 2.51 SD 1.12 1.03 1.25 1.09 1.35 1.21

Table 56 Comparison of Bystander Mechanisms for Vignettes 3 and 4 Somebody There Might Probably Not Nobody else are make a no big confident else is & probably enough mistake deal what is know will people happening something I don’t Vignette 3 Mean 2.53 2.52 2.68 2.27 3.19 2.63 SD 1.23 1.23 1.33 1.06 1.32 1.21

Vignette 4 Mean 2.50 2.35 2.64 2.35 3.30 2.70 SD 1.18 1.09 1.23 1.11 1.28 1.20

Table 57 Comparison of Bystander Mechanisms for Vignettes 5 and 6 Somebody There Might Probably Not Nobody else are make a no big confident else is & probably enough mistake deal what is know will people happening something I don’t Vignette 5 Mean 2.23 2.17 2.55 2.11 2.97 2.52 SD 1.17 1.06 1.30 1.09 1.39 1.32

Vignette 6 Mean 2.49 2.38 2.52 2.19 3.03 2.53 SD 1.23 1.15 1.27 1.11 1.37 1.19

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Table 58 Comparison of Bystander Mechanisms for Vignettes 7 and 8 Somebody There Might Probably Not Nobody else are make a no big confident else is & probably enough mistake deal what is know will people happening something I don’t Vignette 7 Mean 2.58 2.52 2.60 2.40 3.17 2.64 SD 1.22 1.13 1.25 1.15 1.31 1.20

Vignette 8 Mean 2.30 2.24 2.60 2.26 3.18 2.53 SD 1.12 1.07 1.27 1.12 1.30 1.18

Table 59 Comparison of Bystander Mechanisms for Vignettes 9 and 10 Somebody There Might Probably Not Nobody else are make a no big confident else is & probably enough mistake deal what is know will people happening something I don’t Vignette 9 Mean 2.23 2.17 2.61 2.22 2.97 2.45 SD 1.09 1.06 1.33 1.14 1.44 1.25

Vignette 10 Mean 2.51 2.42 2.60 2.22 2.91 2.56 SD 1.18 1.17 1.28 1.12 1.37 1.23

Table 60 Comparison of Bystander Mechanisms for Vignettes 11 and 12 Somebody There Might Probably Not Nobody else are make a no big confident else is & probably enough mistake deal what is know will people happening something I don’t Vignette 11 Mean 2.48 2.31 2.49 2.17 3.15 2.48 SD 2.47 2.49 2.54 2.13 3.07 2.50

Vignette 12 Mean 1.30 1.25 1.25 1.03 1.39 1.25 SD 2.48 2.31 2.49 2.17 3.15 2.48

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Table 61 Comparison of Bystander Mechanisms for Vignettes 13 and 14 Somebody There Might Probably Not Nobody else are make a no big confident else is & probably enough mistake deal what is know will people happening something I don’t Vignette 13 Mean 2.29 2.24 2.65 2.28 3.22 2.59 SD 1.11 1.07 1.29 1.16 1.37 1.18

Vignette 14 Mean 2.55 2.57 2.65 2.33 3.35 2.69 SD 1.20 1.18 1.31 1.11 1.29 1.19

Table 62 Comparison of Bystander Mechanisms for Vignettes 15 and 16 Somebody There Might Probably Not Nobody else are make a no big confident else is & probably enough mistake deal what is know will people happening something I don’t Vignette 15 Mean 2.62 2.49 2.51 2.26 3.23 2.60 SD 1.24 1.23 1.29 1.15 1.30 1.28

Vignette 16 Mean 2.50 2.45 2.71 2.35 3.19 2.61 SD 1.12 1.12 1.28 1.12 1.32 1.20

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Table 63 Comparison of Bystander Mechanisms for All High/Low SN Vignettes Somebod There Might Probabl Not Nobody y else are make a y no big confident else is & probably enough mistake deal what is know will people happenin somethin g g I don’t High SN Vignette 1 2.52 2.59 2.66 2.37 3.20 2.59 bystander Vignette 3 2.53 2.52 2.68 2.27 3.19 2.63 Vignette 7 2.58 2.52 2.60 2.40 3.17 2.64 Vignette 12 2.47 2.49 2.54 2.13 3.07 2.50 Vignette 14 2.55 2.57 2.65 2.33 3.35 2.69 Low SN Vignette 2 2.30 2.21 2.51 2.22 3.09 2.51 bystander Vignette 4 2.50 2.35 2.64 2.35 3.30 2.70 Vignette 8 2.30 2.24 2.60 2.26 3.18 2.53 Vignette 11 2.48 2.31 2.49 2.17 3.15 2.48 Vignette 13 2.29 2.24 2.65 2.28 3.22 2.59

Table 64 Comparison of Bystander Mechanisms for All High/Low TXT Vignettes Somebody There Might Probably Not Nobody else are make a no big confident else is & probably enough mistake deal what is know will people happening something I don’t High TXT Vignette 6 2.49 2.38 2.52 2.19 3.03 2.53 bystander Vignette 10 2.51 2.42 2.60 2.22 2.91 2.56 Vignette 15 2.62 2.49 2.51 2.26 3.23 2.60 Low TXT Vignette 5 2.23 2.17 2.55 2.11 2.97 2.52 bystander Vignette 9 2.23 2.17 2.61 2.22 2.97 2.45 vignette 16 2.50 2.45 2.71 2.35 3.19 2.61

Paired samples t-test. Paired samples t-test were conducted for each mechanism question between the high/low pairs in order to determine if there were any significant differences based on the number of perceived bystanders. In five of the eight vignette pairs, there were significant differences between the high and low vignette pairs on one of the two diffusion 107 of responsibility questions. Two vignette pairs had no significant differences on any of the follow-up mechanism questions, and one pair had a significant difference but with the evaluation apprehension question (see Table 65). These findings are especially interesting, given that, according to the descriptive findings, the mechanisms with the highest endorsed averages were pluralistic ignorance or evaluation apprehension. The high descriptive means for pluralistic ignorance and evaluation apprehension, combined with the significant t-test differences found for diffusion of responsibility, suggest that even if an overall bystander effect is not seen, the bystander mechanisms might still influence how or when people offer aid to others in a non- emergency situation.

According to Latane and Darley (1970) a bystander will go through several stages when ascertaining whether or not to offer help to a perceived victim. At each of these stages, one or more of the bystander mechanisms might occur and influence the bystander’s decision to offer help. First, the bystander must notice the situation and interpret it correctly. In the current study, the participants were given the vignettes, so they were aware that something was happening.

After interpreting the situation, the bystander must assume responsibility (Latane &

Darley, 1970). Sometimes, bystanders might not feel capable of assuming responsibility or think that somebody else will do something, thus demonstrating the diffusion of responsibility.

Evidence of diffusion of responsibility was found in the current study; there were significant group differences between the high/low vignette pairs on the ‘somebody else will intervene’ question.

Finally, if bystanders assume responsibility, they must then decide what to do (Latane and Darley (1970). In this stage, evaluation inhibition or audience inhibition might occur because the bystander might not feel capable or that they might make a mistake. In the current study, the 108 mechanism question “I am not likely to intervene because I might make a mistake” was endorsed with high means for almost all the vignettes, so there is evidence that this mechanism might be occurring.

Of all the bystander mechanisms, diffusion of responsibility was the mechanism with the most reported significant differences between groups. However, participants also endorsed two mechanisms, pluralistic ignorance and evaluation inhibition, in higher numbers in the follow-up questions for all the vignettes. These findings suggest that, much like real-life encounters, online bystanders also are influenced by the mechanisms of the Latane and Darley (1970) model of offering help.

Table 65 Comparison of Follow-Up Mechanism Questions Vignette Pair Mechanism t df p

Pair 1 Diffusion of responsibility 2.72 98.00 .008

Pair 2 None - - -

Pair 3 Diffusion of responsibility -2.318 90 .023

Pair 4 Diffusion of responsibility 2.723 107 .008

Pair 5 Diffusion of responsibility -1.992 92 0.49

Pair 6 None - - -

Pair 7 Diffusion of responsibility -2.144 112 .034 Diffusion of responsibility -2.562 114 .013 Pair 8 Evaluation apprehension -2.022 89 .046

Results summary

The overall results of the dissertation study are mixed. There were no significant differences on the overall likelihood of intervention for any of the high/low eight vignette pairs.

Because the vignettes were identical in all aspects except for the number of bystanders, this 109 finding suggests that number of bystanders was not related to participants’ decisions about intervention in the cyberbullying scenarios depicted in the study vignettes.

However, examination of the follow-up questions revealed significant differences between the high/low pairs on the likelihood of someone else intervening, which suggests that although participants did not rate themselves as intervening, they believed that it was likely others would in the high bystander frames. In addition to the personal and other likelihood findings, there were significant differences found between some of the high/low pairs on the “I will text/email a friend’ and ‘friend email or text me’ options. This finding supports the idea that participants believe that another bystander is more likely to do something than they are.

There also were significant differences found between the high/low bystander pairs on some of the bystander mechanism components. The most common mechanism with significant between group differences was the diffusion of responsibility. An additional significant difference between groups on the evaluation apprehension also was found for one of the pairings.

These findings are especially interesting given that the descriptive findings show that the mechanisms with the highest averages were pluralistic ignorance and evaluation apprehension. In addition, all of the mechanism questions follow the trend seen throughout the study with higher likelihood and means reported for high bystander frames, meaning that participants believed others would intervene in the frames with the higher number of bystanders.

The descriptive findings suggest that pluralistic ignorance and evaluation apprehension were the highest rated mechanisms but the significance between pairs on diffusion of responsibility reveals that diffusion of responsibility was the mechanism with the largest effect size. Taken together, these findings suggest that the effects for someone else intervening and the 110 bystander mechanisms were robust and consistent, and may be related to the lack of individual intervention effects found.

Finally, the only attribution question significantly related to likelihood of intervention was the personal attribution question “Bullies are always bullies”. This question was negatively associated with likelihood of intervention for all intervention scales: overall, social networking, and texting. This suggests that people are less likely to offer help when they believe that the aggressor is a bully.

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Chapter 7--Discussion

The results from the pilot and dissertation study reflect some interesting and, in some cases, counterintuitive trends. Although the hypotheses for Research Question 1 and Research

Question 2 were not fully supported, analysis of the follow-up questions, along with analysis of the bystander mechanism questions for Research Question 3 provide a more complete picture of all the processes occurring in the study.

Vignette pair findings

One consistent trend, seen in the pilot as well as the dissertation study, was that the vignettes with the higher number of bystanders also were the vignettes with the higher average likelihood of help from participants. This was seen for both social networking and texting vignettes. This is counterintuitive because much of the bystander effect research demonstrates that, in real-life circumstances, more people involved in a situation is usually associated with lower help outcomes (Darley & Latane, 1968; Latane & Darley, 1970; Latane & Nida, 1981).

One reason for this trend and the lack of support for hypothesis 1a might be due to the online medium. Prior bystander effect research has usually been conducted in a real-life environment, that is, where the bystanders can physically see or hear the other bystanders in order to make a determination of the number of bystander present in the situation (Fischer et al.,

2011). Even Garcia, Weaver, Moskowitz and Darley (2002) utilized priming cues when examining the implicit bystander effect. In that instance, the researchers told participants to imagine that they were in the company of a certain amount of people.

Because the current study was conducted online, there was no way for participants to physically see or hear the other bystanders. Nor did the current research utilize explicit priming cues to direct participants to think of a social group. Instead pictures and a line of text stating the 112 number of people online or in the text chain was employed to act as the indicator of the number of other bystanders. This was done in order to closely mimic those social networking and texting contexts in which one might see cases of cyberbullying. The lack of an explicit prime or cue, as with Garcia et al. (2002), or any physical evidence of other bystanders might have made it difficult for participants to visualize the other bystanders and therefore difficult to prompt the bystander effect.

The unexpected and reversed findings also might be due to participants’ perception of the situation. In order to mimic a real-world bystander effect process as closely as possible, participants were not given any leading or outside information about the people in the vignettes.

Instead, participants were given social networking and texting vignettes that closely resembled a cyberbullying situation as it might have been occurring in real-time, where there is often very little or no information available, except for the context of the cyberbullying event. The participants’ perceptions of the events might have contributed to their decisions about intervention. For example, if participants perceived the vignettes with a higher numbers of bystanders to be a dangerous or an emergency situation, then they might have decided to intervene. Both Harari, Harari, and White (1985) and Fischer et al. (2006) found that in emergency situations, the bystander effect was not as strong. This might help explain why there was non-significant findings, and, in most cases, higher average likelihood of help for the vignettes with higher number of bystanders as those with lower number of bystanders.

Participants might have believed that because there were more bystanders involved, the situation might have been more dangerous for the person being cyberbullied and were therefore more likely to intervene. Participants also might have believed that someone else was likely to intervene when there were more bystanders involved. 113

Bystander mechanisms findings

Although the results for the personal likelihood of intervention questions (i.e. “How likely are you to intervene?”) revealed no significant differences between each of the matching high/low vignette pairs, there were significant differences between these pairs on some of the follow-up questions, including the ‘somebody else intervene’ items (i.e. “Do you think somebody will intervene?”) and the mutual friend intervene (i.e. “Do you think a mutual friend will intervene and help?”). These findings are very interesting in the context of the bystander effect because they exemplify one of the bystander effect mechanisms. That is, participants themselves were not likely to intervene but believed that somebody else would. This is the basic definition of the bystander effect mechanism of diffusion of responsibility.

The bystander effect mechanisms questions were part of Research Question 3 and added after the pilot study. Examination of the means of the bystander effect mechanism questions for each high/low vignette pair reveals that the highest average mechanism rating by participants was for the pluralistic ignorance and evaluation inhibition questions. Paired sample t-tests disclosed, however, the significant differences between vignette pairs was only found in the diffusion of responsibility mechanism.

The findings here suggest that the mechanisms commonly associated with the bystander effect also can operate in an online context. And, although the main effect of an individual likelihood to intervene decision was not found, there were significant differences between the identical high and low vignettes for each type of cyberbullying incident for the ‘somebody else’ and ‘mutual friend’ will intervene options. One explanation for this might be found in the somewhat vast ‘space’ associated with the Internet and texting. That is to say, it might be hard for people to visualize specific groups of other people (e.g. ‘friends’ or ‘bystanders’) because the 114 concept of ‘online’ or the ‘Internet’ might already come with the built-in connotation of a vast

‘everybody is online’ sort of mentality that makes it easier for ‘real world’ bystander effects to become salient.

Taken together, these findings give weight to the idea that the implicit bystander effect can occur in the absence of explicit primes for participants. It also is interesting to note that, even in the bystander mechanism questions, often the vignette with the higher number of bystanders had the higher reported means for intervention. Certainly there seems something about the online context which might be affecting how the processes of the bystander effect operate. It might be that the contexts of the Internet and texting exacerbate diffusion of responsibility to inhibit a personal sense of responsibility. One reason for this might be that the participants assumed that there were always others to do something and the personal sense of intervening was subsumed under this idea. There is a certain time constraint that comes with real-world bullying encounters and, eventually, the incident will end. This time constraint is not as evident online or with texting so participants might not have felt as pressured to do anything because they believed that, given time, another person would intervene.

BJW, IRI, and Gender findings

Another interesting finding was the relationship between participants likelihood of intervention, gender, and IRI score. Hypothesis 1b examined how participants BJW and IRI scores impacted their decisions to intervene. Overall, there was only a partial support for this hypothesis. BJW scores were not related to participants’ likelihood of intervention and IRI scores were negatively related to likelihood of intervention. Gender also was negatively related to likelihood of intervention as females were less likely to intervene than males. These findings are 115 interesting because they, once again, contradict what is seen in the literature from studies examining ‘real word’ and cyberbullying situations.

The IRI, or Interpersonal Reactivity Index, is a measure of empathy (Davis, 1980). In this study, only the empathic concern subscale was used as this scale assesses the feelings of concern and sympathy for unfortunate others. In this case, the unfortunate others were the cyberbully victims. The IRI and its subscales has been utilized to measure empathy in a variety of settings, including physicians (Yarnold, Bryant, Nightingale & Martin, 1996) to adult prisoners

(Lauterbach & Hosser, 2007). A higher score on the IRI is associated with higher amounts of empathy (Davis, 1980).

In the present study, the relationship between empathy and likelihood of intervention for participants was negative, meaning a higher IRI score was associated with a lower likelihood of intervention. Although the IRI has not been utilized (to the best of the researcher’s knowledge) with cyberbullying acts, the response expected in the study was the opposite: the IRI was hypothesized to be related to a higher likelihood of intervention on behalf of cyberbullying victims. There are several explanations for why the current research found the opposite. Empathy is a complicated construct, in both definition and in measurement, so one of the most basic reasons for the findings here might be that the subscale did not capture participants’ actual empathetic profile. Another explanation is that the participants did not feel empathy for the cyberbullying victims because they did not ‘buy into’ the vignette scenarios. One of the weaknesses of vignette research can be an inability to fully capture the scenario in a real enough fashion (Wallander, 2009). Because the participants were only reading each scenario within a study context, they might not have felt an empathetic response because they knew that the scenarios were not real. 116

Along this line, a third reason for the finding could be due to the lack of information presented in the vignettes. It was noted in the pilot study that the participants might have had different reactions if they had known what the relationship was between themselves and the purported cyberbully victim. This information was purposefully kept from participants in order to make the vignettes mimic bystander effect research in which the participants are naïve to the situation and to the victims and must make decisions based on the number of other bystanders.

This decision could have impacted the participants’ feelings of empathy for the victim because there was no designation of a relationship so the participants felt less empathy for the victims.

A fourth reason for this finding also can be found in the relative distance participants might have felt due to the incident occurring on the Internet or via text message, and not in closer physical proximity. The Internet and text messages represent a form of distance between the bystander and the incident and it is a simple matter to just close the browser or turn off the computer or cell-phone entirely. This idea is supported by the findings in the immediate action steps. In the social networking vignettes, one of the most common choices by participants was to

‘check back later’, meaning the participant was not going to take any action and instead, see what happened as the incident progressed. The lack of a feeling of connectedness to the incident, along with those involved in the incident, might explain why participants did not feel empathy for the victims in the incidents.

A final explanation for the inverse empathy finding also might be that those who were highly empathetic might have been afraid to worsen the situation. There were high means seen across all vignettes for the evaluation apprehension bystander mechanism. This could denote that participants were not likely to intervene because they did not understand the situation. The relative lack of information might have contributed to participants’ empathetic inhibition, as they 117 might have feared that somehow they would inflame the situation, so instead they decided to just keep silent or leave the situation.

The negative relationship between likelihood of intervention and participant gender and

IRI score also were very interesting findings. There are mixed findings in regards to gender effects in bullying within the literature, although males are often associated with higher incidents of traditional or face-to-face bullying and females are more often associated with indirect or relational bullying (Underwood, 2003). In cyberbullying, Li (2006) found that males were more likely to be victims of bullies and cyberbullies than females. Similarly, there are mixed findings regarding gender and the bystander effect, although in a meta-analysis of gender and helping behavior, Eagly and Crowley (1986) found that men were more likely to help and women were more likely to receive help. The findings in the current study reveal that females report lower likelihood of intervention than males. This finding did not vary across the type of cyberbullying

(e.g. social networking, texting, or overall), so it is not a product of the delivery mechanism. One explanation for this finding might be due to a lack of connection that female participants felt with the cyberbully victims or due to a belief that somebody else would intervene. As previously mentioned, gender is a mixed factor in bullying/cyberbullying research and, unfortunately, this current research does not provide clarity on this issue.

Personal attributions findings

Although only one of the attributions questions ‘bullies are always bullies’ was significantly related to intervention, this relationship supports the idea that characteristics of the bully and the victim can impact bystanders’ decisions about intervention. This finding also supports the research proposed by Levine and Crowther (2008) who found that group membership, when salient, can prompt bystanders to intervene for friends. Females reported 118 higher means for this question than males, indicating that they were more likely to agree with the attribution than males. This finding, in addition to the finding that females in the current study were less likely to intervene than males, suggests that gender also might be a factor which affects not only the attributions that bystanders make about cyberbullies and victims, but also how these attributions can affect their behavior.

Strengths and Limitations

The current research project attempted to explore how the theory of the bystander effect might impact the decisions of bystanders to offer help in a cyberbullying situations. As with any research project, there are strengths and limitations.

One strength of the current project was the attempt to extend a real-world theory in an online environment as a way to examine cyberbullying incidents. The theory of the bystander effect has been used to explain why people might not offer help to others in emergency and non- emergency situations in the real-world, and therefore might also help explain a lack of help in an online context as well. Evidence for an implicit bystander effect was found by Garcia, Weaver,

Moskowitz, and Darley (2002) and the current study attempted to extend this research by removing the explicit primes to social groups in order to see if cues in an online or texting situation would be enough to trigger the bystander effect.

Another strength of the current research was the attempt to address literature gaps in previous research by offering a cohesive and concise definition of cyberbullying. This has been an ongoing issue in the literature, as found by Tokunaga (2010). The current study utilized definitions offered by both prior cyberbullying and bullying research in order to provide a comprehensive definition of cyberbullying, as well as what constitutes a cyberbully, a cybervictim, and a cyberbystander. The current study also attempted to address literature gaps by 119 extending the research on cyberbullying into a new age group beyond middle-school and high- school student samples, using a mixed sample of college-aged and older participants in order to see how conceptions of cyberbullying might be different with a sample older than school-aged children. In the overall sample for the study, 32% of SONA participants had experienced cyberbullying and 31% of M-Turk participants reported that they had experienced cyberbullying.

Participants’ self-reported experiences with cyberbullying numbered from approximately one or two times to over one hundred, demonstrating that older populations also are familiar with cyberbullying.

Finally, the current research attempted to address cyberbullying from the perspective of the bystander, which is an understudied population in the bullying and cyberbullying literature.

Often, the focus of research in bullying and cyberbullying is on the bully/victim dyad, and does not take into account the other potential participants in the encounter. These other bystanders can sometimes impact the situation by aiding either the bully or the victim, therefore they should be considered when examining the situation in its entirety.

One limitation of the current study is the use factorial design vignettes. Having a nested model in which all participants read all vignettes increases the familywise error rate and therefore the chances of committing a Type I error. In fact, a Bonferroni correction revealed an adjusted alpha value of p<.02 instead of p<.05. The number of vignettes employed in the current study also could have contributed to an increased likelihood of participant fatigue, which might have been a factor with the large attrition rate. In addition, the current study used vignettes as a proxy for actual cyberbullying situations and these vignettes might not have had the same impact as an actual cyberbullying situation, which could have influenced the validity of the study.

Finally, some of the other measures in the study, including the vignettes, were research based 120 and pilot tested, but have not been utilized in a cyberbullying context before which also could have affected their validity.

Another limitation for the current research is that the study was entirely online, therefore there was no way to ensure that participants completed the survey in one sitting free from any outside distractions. In addition, participants were given incentives to encourage participation, these incentives might have led to an increase in researcher bias for participants.

Methodologically, another limitation was that the research was quasi-experimental and therefore there was no random assignment of participants into a control or experimental group.

Also, the vignettes were focused on a single type of cyberbullying, flaming, and there might have been issues in correctly translating the construct into the parameters of the vignettes for participants.

Finally, there were several variables, including different types of cyberbullying and intervention strategies for cyberbullying, which were not included in the current study. These limitations affects the generalizability of the research for possible interventions beyond those geared towards overcoming the bystander effect for victims of online flaming.

Implications and Future Research

Current findings have implications for cyberbullying research as well as theoretical implications for the bystander effect, and attribution theory. Overall, the current study did not find effects linked to the number of bystanders. However, the evidence found in support of the bystander effect mechanisms, especially diffusion of responsibility, supports the idea that this real-world theory can be applied in an online context. The implications from this finding suggests that, similarly to real-world contexts, people are not inclined to offer help in situations in which they believe help may be forthcoming from other people. Future research should build 121 from this finding by determining how closely the bystander mechanisms studied in the current research follow the Latane and Darley (1970) pathway to providing help. If more information can be found regarding what triggers the bystander effect and these inhibitory mechanisms, then this information could be used to overcome these inhibiting mechanisms and motivate people to help others when they see cyberbullying occurring. Future research also should consider longitudinal study designs to understand the complete developmental course and resolution opportunities of a cyberbullying event. Future research also should build from these findings and the findings of Garcia, Weaver, Moskowitz, and Darley (2002) in order to determine which natural primes might occur online. In addition, new theoretical research should examine the different levels of bystanders needed to trigger the online bystander effect at a personal intervention level as well as how personal and situational attributions can affect how or if people offer aid in a cyberbullying situation. Recently, Desmet et al. (2012) conducted focus groups with children ages 12-16 to understand defending behavior for cyberbullying victims. They found the most common themes that emerged regarding defending the cyberbully victim were that the victim was considered to be a member of an in-group, and that the bystander was popular. Moral disengagement, however, was linked to decreases in defending behavior.

Desmert et al. (2012) also found that offering comfort to the cyberbullying victim was the most expected and considered the easiest way to defend a cyberbully victim. In the current study, posting a comment or text for the victim, which might be a way of offering comfort, also was one of the most popular follow-up steps among participants.

Another reason that bystanders do not intervene could be due to the idea of competitive altruism. According to Hardy and Van Vugt (2006), individuals will behave in an altruistic manner to enhance their reputation and receive status or social benefits. In the context of 122 cyberbullying, this would explain why bystanders might not intervene right away when they witness a cyberbullying incident. In order to gain the biggest social reward, (e.g. enhanced social status among online social ‘friends’) individuals might wait until the incident has garnered enough bystanders before they decide to intervene, therefore ensuring that their defense of the victim is seen by enough people so that they receive the increased social status benefit. The exact tipping point for when individuals might decide that enough people were present to witness their intervention is still unknown but this concept provides an alternate idea into why people might wait to intervene in cyberbullying. Future research could examine previous cases of cyberbullying to determine what, if any, social reward is associated with defending behavior and the number of witnesses or bystanders that need to be present for this social reward to occur.

A second implication from the current research is that the concept of empathy for others may be expressed differently online than in the real-world. The inverse findings here might suggest that, given a lack of full information and real-world cues on how to proceed, many individuals might believe that the most helpful or empathetic thing they can do is simply do nothing so as to not prolong or inflame the situation. It seems clear that doing nothing online is substantially different than doing nothing in a real-world emergency situation. This also could be related to anxiety reduction; when people do not know how best to help they get anxious and one strategy to decrease this anxiety is to close the browser or delete the text. Anxiety reduction might also be related to the significant personal attribution finding. If a person witnesses a cyberbullying incident, believing something like ‘bullies are always bullies’ allows them to reduce their perceived duty to intervene because they might believe there is nothing they can do to change or alter the personal attribution, therefore they do not intervene and their anxiety about the situation is also reduced. The fact that females were more likely to agree with this ‘bullies are 123 always bullies’ attribution than males, and that females were less likely to intervene overall than males, suggests that gender can be a contributing factor in cyberbullying bystander interventions.

A third implication from the current research is that helping behavior might be expressed differently online than it is in a real-world situation. The extent to which one person will offer aid to someone being bullied relies on a variety of factors, including factors which are not readily available in an online environment. These factors include how the victim appears to be responding, the behaviors and social cues of the other bystanders (if present), contextual cues surrounding the situation (e.g. location, time of day, relationship between victim and bully), and even the actions and suspected motivations of the bully. Traditionally, this helping behavior might include interceding on behalf of a victim or finding a way to give the victim aid (i.e. involving a teacher or authority figure to end the incident) but online, as with the differences in visible situation cues, the concept of helping a victim might be different. The timing of the incident can mean that there is no authority figure available to offer aid and therefore it would be the responsibility of the bystander. With a lack of situational cues, the bystander might conclude that attempting to intercede or offer a rebuttal to a cyberbully could inflame and prolong the situation, thus making it worse for the victim rather than better. Therefore, helping behavior in this case, would be to not become directly involved in the situation but to offer a different form of help to a victim. This idea is somewhat supported by the current research; in the follow-up actions for most of the vignettes, often the actions with the highest reported averages were for the “feel sorry for the victim’ choice and the ‘check back later to see if the incident had stopped’ choice. In addition, participants reported that it would more likely that they would text or email a mutual friend about the situation rather than have a mutual friend email or text them. These results might indicate that, at least online, helping behavior is enacted in a different way than in 124 real-life. Future research should attempt to find out what, exactly, is ‘helping’ behavior in an online context because it may differ from real-world contexts. Asking people to define what it means to help others in these kinds of situations or to rate the perceived level of concern for cyberbullying victims via qualitative studies and interviews might be one way to determine how empathy and anxiety might impact online decisions to help.

There are implications in the current research that also extend beyond the current age group. The mean age for the current study was 30 years old with a range from 18 to 66 years of age. If the effects of the bystander mechanisms were found with an older sample of bystanders, it is very possible that these kinds of effects, especially diffusion of responsibility, would be found in a younger sample. Adolescent youth are developmentally susceptible to a wider range of social cues and peer pressure than most people who have reached age 30, it is logical to suspect that, given the importance adolescent’s often place on their peer group and their relative position with their peers, they would be even less likely to intervene on behalf of a cyberbullying victim.

Further research with younger samples would be needed but the current study provides a basis for beginning such research.

The current study employed one specific type of cyberbullying: flaming, and two different technological mechanisms: social networking and texting. Future research should expand on both of these areas to see how different cyberbullying acts and different technological mediums might affect how or when bystanders offer help to a cyberbully victim. According to

Willard (2007), there are several different kinds of cyberbullying including: flaming, online harassment, cyberstalking, masquerade, outing, and exclusion. These different acts might require different approaches for bystander offering help; one area of future research might be to see how bystanders rate each of these acts and how their helping behaviors might change for each of 125 them. Additionally, there are online contexts that were not in the current study, message boards and chat rooms, for example. Future research might investigate how cyberbullying occurs in these different mediums and how or if bystanders approach intervention in a chat room differently than in a message board or forum.

Future research also should examine the impact that gender and culture might have on bystanders in a cyberbullying situation. In the current study, males were more likely to intervene than females. Examination of the full extent of the relationship between gender and cyberbullying was beyond the scope of the current project but the future research should examine how gender might impact bystanders’ decisions to intervene. As mentioned, Desmet et al. (2012) found that in-group membership was linked to increases in bystander defending behavior and according to Eagly (1986), females are more likely to receive help and men are more likely to offer help. Taken in context with the current findings, it would be beneficial to study how gender and attributions made about gender might interact to affect if and how people offer help in cyberbullying situations.

Future research also needs to examine how culture might impact bystanders’ decisions to offer help. Cross-cultural studies of cyberbullying are limited in the research. Li (2008) examined differences in cyberbullying and found that Canadian students cyberbullied others at a higher rate than Chinese students and highlights the role of culture in the design of effective prevention programs. This is an important observation because reasons for bullying or cyberbullying can vary across cultures. Bishop et al. (2004) suggests that higher social status in

American schools is linked to athletic prowess whereas academic achievement is what is considered important in Taiwan. Understanding the factors which can prompt cyberbullying 126 through further examination of collectivistic and individualistic cultures might be a key component to developing the appropriate interventions.

Conclusion

The current study investigated if the bystander effect could occur online in the absence of explicit primes and, if so, what factors impacted bystander’s decisions to intervene. Although there was no main effect for personal intervention, there was a significant effect for believing other people would intervene. This effect occurred for both of the delivery mechanisms, social networking and texting, as well as the overall likelihood of intervention. In addition, the likelihood of intervention by others was higher for the higher bystander frames. This finding supports the idea that bystander mechanisms, specifically diffusion of responsibility, can occur in an online environment.

Although diffusion of responsibility was the only bystander mechanism with significant group differences, participants endorsed both pluralistic ignorance as well as evaluation apprehension in greater numbers for all of the vignettes, meaning that they also found these mechanisms to be relevant explanations for why they chose not to intervene. Overall, the presence of these bystander mechanisms demonstrates that, much like real-world research, online bystanders also encounter the barriers in the pathway to providing help, as suggested by Latane and Darley (1970).

In addition to the bystander mechanisms, the current research found that the follow-up actions most endorsed by participants were avoidant or passive actions, like closing the browser or checking back later. These actions, along with the inverse findings for empathy and likelihood of intervention, together suggest that participants were unsure of how to approach the cyberbullying situation and might have felt that the best or most empathetic course of action was 127 to not say anything at all, rather than risk making the situation worse. This idea also is supported by the high number of participants who endorsed the evaluation apprehension bystander effect mechanism, which is related to the concept of not intervening because the person is unsure of the situation or believes that somebody else might know more or be in a better position to provided aid.

Overall, the current project provides a solid basis for future research to examine cyberbullying and the factors which can impact cyberbullying situations, including the often overlooked role of the bystander. In addition, the current project provides support for the idea that real-world theories, like the bystander effect, can occur in an online environment and therefore might help provide explanations for online behavior. Technology is rapidly evolving, and the ways that people interact and communicate with each other are no longer limited to real- world circumstances. Taking social science research into the online realm can provide an entirely new pathway of research on human interaction and help to reveal how those ideas and theories that traditionally apply in the real world also might apply in the virtual world.

128

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Appendix A

An exploratory examination of cyberbullying.

Interview Question Guide

[read to participant]. In this part of the study, I am going to ask you a series of questions about the survey you just completed. I want to know what you thought of it, whether there were any questions or directions you found confusing, and how you felt when you completed it. You can say “pass” at any time to any question. You can also choose not to participate in this part if you do not want to. I’m going to give you a blank copy of the survey you just finished so we can discuss it.

Before we begin though, I’d like to know a little more about you. What kinds of things do you like to do here in Reno and at UNR? Great, thanks for that. Now I’m going to begin taping and ask you some questions about the survey you just completed.

Proceed to questions below. 143

Reaction to Vignettes

QUESTION 1: “What was your initial reaction towards the hypothetical cyberbullying scenarios that you read?”

Probe 1: What did you think about the content of the scenarios?

Probe 2: Did the scenarios seem believable?

Probe 3: Did it make you feel uncomfortable to read the scenarios? Was the

language understandable?

Probe 4: Were the scenarios easy to visually see and read?

Reaction to Survey

QUESTION 2: “What was your initial reaction to the survey and the questions asked?”

Probe 1: What did you think of the survey questions? Were there too few or too many? Probe 2: Did the questions make sense? Probe 3: Could you read and understand the language of the questions?

Content of Scenarios and Survey: Prior Experience with Cyberbullying

QUESTION 3: “Have you or anyone you know ever experienced anything like you read in the scenarios?”

Probe 1: Did the scenarios seem believable?

Probe 2: Did the responses seem appropriate?

Probe 3: Were there responses or reactions you think were left out that should be

added?

Content of Scenarios and Survey: Attributions of Blame 144

QUESTION 4: “Do you think anybody is to blame in the scenarios?”

Probe 1: How would you describe the people making the comments/texts?

Probe 2: How would you describe Matt/Jennifer (the recipient of the

comments/texts)?

Probe 3: If you ever saw something like these scenarios, would you say

something to someone? If so, who?

Probe 4: What might make you intervene in similar situations? What kinds of

comments or texts do you think would prompt you to say something to someone?

Probe 5: If you were prompted to say something, what would you say?

Probe 6: Who do you think should be responsible for saying something to

somebody about the scenarios? Matt/Jennifer or somebody else?

Content of Scenarios and Survey: Number of Bystanders

QUESTION 5: “Did you notice the number of bystanders at the end of the scenarios?”

Probe 1: Did the number of bystanders seem believable? Probe 2: If you didn’t notice the number of bystanders, how do you think you would have noticed them? Probe 3: Did the presence of bystanders affect your answers on the survey questions? Probe 4: Did the presence of bystanders make you think of your own friends or social group Probe 5: Do you think of your friends or family when you’re online or texting on your phone? If so, what makes you think of them?

[Also read to participant]

Thank you so much for helping me out with these questions. I really appreciate it. Do you have any questions for me? 145

Appendix B

An exploratory examination

of cyberbullying

146

Title: An exploratory examination of cyberbullying dissertation study

Researcher: Megan Armstrong, MA; William P. Evans, PhD

Estimated time commitment: 30-45 minutes

Description: The role of the Internet and new technology has greatly influenced the way in which people communicate. Part of the explosive growth of the Internet has included social networking websites, which have brought with them a new form of bullying. The current exploratory study is aimed at understanding ‘cyberbullying’ and how people react when they see it happening. The survey is comprised of eight short cyberbullying vignettes followed by a series of questions. Please read the vignettes and answer the questions honestly. If you feel uncomfortable with any of the questions or content, you many quit at any time. The estimated time commitment is around 30 to 45 minutes.

Thank you very much for you participation.

147

You are on a popular online social networking site and you see this ongoing comment thread on your friend Matthew’s page after one of his updates: Sharon is online

View 26 more comments Philip is online

Amy is online James ha ur such a bitch August 27 at 10:37am · Like Jose is online

Malcolm is online Kevin stop with the drama Matt. your being stupid August 27 at 10:38am · Like Julia is online

Hank matt maybe you should go jump off a cliff Vera is online August 28 at 9:08pm · Like ·

Matthew quit being such jerks you guys! Calum is online September 2 at 3:42pm · Like ** You are online

* ...plus 42 others!

According to the sidebar ticker, you and Matt currently have 50 friends-in-common online right now.

Please read the statements below and pick the rating which best fits the likelihood that you

would respond in a similar way to each statement. 148

Would you... Very Somewhat Neither Somewhat Very

unlikely unlikely (3) likely likely

(1) (2) (4) (5)

1. Tease or make fun of

Matt

2. Feel sorry for Matt

3. Just close the browser

4. Check later to see if the incident had stopped

5. Post a comment in support

6. Post a comment telling people to knock it off

7. I’d do something else. 149

Write in the space here:

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you

1. Do you think somebody will intervene in the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake. 150

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal.

1 2 3 4 5 Very untrue Neutral Very true

5. I am not likely to intervene because I am not confident I know what is happening in the situation.

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

151

3. How likely are you to intervene?

Not likely Somewhat likely Very likely

1 2 3 4 5

4. How likely are you to email or text a mutual friend in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

152

You are on a popular online social networking site and you see this ongoing comment thread on

your friend Matthew’s page after one of his updates: Sharon is online

View 2 more comments Philip is online

James ha ur such a bitch Amy is online August 27 at 10:37am · Like

Jose is online

Kevin stop with the drama Matt. your being stupid August 27 at 10:38am · Like Malcolm is online

**You are online

Hank matt maybe you should go jump off a cliff August 28 at 9:08pm · Like ·

Matthew quit being such jerks you guys! September 2 at 3:42pm · Like

According to the sidebar ticker, you and Matt currently have 5 friends-in-common online right now.

Please read the statements below and pick the rating which best fits the likelihood that you

would respond in a similar way to each statement.

Would you... Very Somewhat Neither Somewhat Very

unlikely unlikely (3) likely likely

(1) (2) (4) (5)

1. Tease or

make fun of

Matt

2. Feel sorry for

Matt 153

3. Just close the browser

4. Check later to see if the incident had stopped

5. Post a comment in support

6. Post a comment telling people to knock it off

7. I’d do something else.

Write in the space here:

154

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you

1. Do you think somebody will intervene in the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake.

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal. 155

1 2 3 4 5 Very untrue Neutral Very true

5. I am not likely to intervene because I am not confident I know what is happening in the situation.

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

3. How likely are you to intervene?

Not likely Somewhat likely Very likely 156

1 2 3 4 5

4. How likely are you to email or text a mutual friend in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

157

You are on a popular online social networking site and you see this ongoing comment thread on

your friend Jennifer’s page after one of her updates: Sharon is online

View 26 more comments Philip is online

Hillary God Jennifer, give it a rest! Amy is online August 27 at 10:37am · Like

Jose is online

Bryce why don’t you whine about it someplace else stupid! August 27 at 10:38am · Like Malcolm is online

Calum Such. A. Bitch. Grow up and get over it! Julia is online August 30 at 11:59am · Like Vera is online Jennifer: quit being such jerks you guys! August 31 at 5:33pm · Like Calum is online

** You are online

* ...plus 42 others!

According to the sidebar ticker, you and Jennifer currently have 50 friends-in-common online right now.

Please read the statements below and pick the rating which best fits the likelihood that you

would respond in a similar way to each statement.

Would you... Very Somewhat Neither Somewhat Very

unlikely unlikely (3) likely likely

(1) (2) (4) (5)

1. Tease or

make fun of

Jennifer 158

2. Feel sorry for

Jennifer

3. Just close the browser

4. Check later to see if the incident had stopped

5. Post a comment in support

6. Post a comment telling people to knock it off

7. I’d do something else.

Write in the space here:

159

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you

1. Do you think somebody will intervene in the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake.

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal.

1 2 3 4 5 160

Very untrue Neutral Very true

5. I am not likely to intervene because I am not confident I know what is happening in the situation.

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

3. How likely are you to intervene?

Not likely Somewhat likely Very likely

1 2 3 4 5 161

4. How likely are you to email or text a mutual friend in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

162

You are on a popular online social networking site and you see this ongoing comment thread on your friend Jennifer’s page after one of her updates: Sharon is online View 2 more comments

Philip is online Hillary God Jennifer, give it a rest! August 27 at 10:37am · Like Amy is online

Jose is online Allie spare us from you dumb shit August 27 at 10:38am · Like Malcolm is online

Calum Such. A. Bitch. Grow up and get over it! **You are online August 30 at 11:59am · Like

Jennifer quit being such jerks you guys! August 31 at 5:33pm · Like

According to the sidebar ticker, you and Jennifer currently have 5 friends-in-common online right

now.

163

Please read the statements below and pick the rating which best fits the likelihood that you would respond in a similar way to each statement.

Would you... Very Somewhat Neither Somewhat Very

unlikely unlikely (3) likely likely

(1) (2) (4) (5)

1. Tease or make fun of

Jennifer

2. Feel sorry for

Jennifer

3. Just close the browser

4. Check later to see if the incident had stopped

5. Post a comment in support

6. Post a comment telling people to knock it off 164

7. I’d do something else.

Write in the space here:

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you

1. Do you think somebody will intervene in the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 165

Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake.

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal.

1 2 3 4 5 Very untrue Neutral Very true

5. I am not likely to intervene because I am not confident I know what is happening in the situation.

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true 166

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

3. How likely are you to intervene?

Not likely Somewhat likely Very likely

1 2 3 4 5

4. How likely are you to email or text a mutual friend in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5 167

You receive the following group MMS text message: 168

matthew sucks and i hope he dies

Sep 25, 2012 1:53 PM Seconds later you receive the following message from Matthew:

U guys r such jerks! knock it off!

Sep 25, 2012 1:53 PM Several more texts follow with people insulting Matthew. The original message was sent to 5 people total and also is being recirculated by the same phone number.

Please read the statements below and pick the rating which best fits the likelihood that you would respond in a similar way to each statement.

Would you.. Very Somewhat Neither Somewhat Very likely

unlikely unlikely (3) likely (5)

(1) (2) (4)

1. Just put my phone away

2. Tease or make fun of

Matt 169

3. Ignore or delete the text

4. Send a text telling people to knock it off

5. Send a text scolding the original texter

6. I’d do something else. Write in the space here:

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you

1. Do you think somebody will intervene in the situation? 170

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake.

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal.

1 2 3 4 5 Very untrue Neutral Very true

5. I am not likely to intervene because I am not confident I know what is happening in the situation. 171

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

3. How likely are you to intervene?

Not likely Somewhat likely Very likely

1 2 3 4 5

4. How likely are you to email or text a mutual friend in order to stop the situation?

172

Not likely Somewhat likely Very likely

1 2 3 4 5

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

173

You receive the following group MMS text message:

matthew sucks and i hope he dies

Sep 25, 2012 1:53 PM Seconds later you receive the following message from Matthew:

U guys r such jerks! knock it off!

Sep 25, 2012 1:53 PM Several more texts follow with people insulting Matthew. The original message was sent to 50 people total and also is being recirculated by the same phone number.

Please read the statements below and pick the rating which best fits the likelihood that you would respond in a similar way to each statement.

Would you.. Very Somewhat Neither Somewhat Very likely

unlikely unlikely (3) likely (5)

(1) (2) (4)

1. Just put my phone away

2. Tease or make fun of

Matt 174

3. Ignore or delete the text

4. Send a text telling people to knock it off

5. Send a text scolding the original texter

6. I’d do something else. Write in the space here:

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you

1. Do you think somebody will intervene in the situation? 175

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake.

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal.

1 2 3 4 5 Very untrue Neutral Very true

5. I am not likely to intervene because I am not confident I know what is happening in the situation. 176

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

3. How likely are you to intervene?

Not likely Somewhat likely Very likely

1 2 3 4 5

4. How likely are you to email or text a mutual friend in order to stop the situation?

177

Not likely Somewhat likely Very likely

1 2 3 4 5

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

178

You are on a popular online social networking site and you see this ongoing comment thread on your friend Jennifer’s page after one of her updates: Sharon is online

View 26 more comments Philip is online

Lisa Jen you are such a drama queen! Amy is online August 27 at 10:37am · Like

Jose is online

Aaron thats so stupid. jen ur such a retard! Malcolm is online August 27 at 10:38am · Like Julia is online Wes Nobody cares about how stupid you are. Quit trying 2 be an attention whore August 28 at 9:08pm · Like · Vera is online

Jennifer quit being such jerks you guys!! August 31 at 5:33pm · Like Calum is online

** You are online

According to the sidebar ticker, you and Jennifer currently have 50 friends-in-common online * right ...plus 42 others! now. Please read the statements below and pick the rating which best fits the likelihood that you

would respond in a similar way to each statement.

Would you... Very Somewhat Neither Somewhat Very likely

unlikely unlikely (3) likely (5)

(1) (2) (4)

1. Tease or

make fun of

Jennifer

2. Feel bad

about the act 179

3. Just close the browser

4. Check later to see if the incident had stopped

5. Post a comment for

Jennifer

6. Post a comment telling people to knock it off

7. I’d do something else. Write in the space here:

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you

1. Do you think somebody will intervene in the situation? 180

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake.

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal.

1 2 3 4 5 Very untrue Neutral Very true

5. I am not likely to intervene because I am not confident I know what is happening in the situation. 181

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

3. How likely are you to intervene?

Not likely Somewhat likely Very likely

1 2 3 4 5

4. How likely are you to email or text a mutual friend in order to stop the situation?

182

Not likely Somewhat likely Very likely

1 2 3 4 5

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

183

You are on a popular online social networking site and you see this ongoing comment thread on your friend Jennifer’s page after one of her updates: Sharon is online

View 2 more comments Philip is online

Lisa Jen you are such a drama queen! Amy is online August 27 at 10:37am · Like Jose is online

Aaron thats so stupid. jen ur such a retard! August 27 at 10:38am · Like Malcolm is online

**You are online Wes Nobody cares about how stupid you are. Quit trying 2 be an attention whore August 28 at 9:08pm · Like ·

Jennifer: quit being such jerks you guys! August 31 at 5:33pm · Like

According to the sidebar ticker, you and Jennifer currently have 5 friends-in-common online right now. Please read the statements below and pick the rating which best fits the likelihood that you

would respond in a similar way to each statement.

Would you... Very Somewhat Neither Somewhat Very likely

unlikely unlikely (3) likely (5)

(1) (2) (4)

1. Tease or

make fun of

Jennifer

2. Feel bad

about the act

3. Just close

the browser 184

4. Check later to see if the incident had stopped

5. Post a comment for

Jennifer

6. Post a comment telling people to knock it off

7. I’d do something else. Write in the space here:

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you 185

1. Do you think somebody will intervene in the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake.

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal.

1 2 3 4 5 Very untrue Neutral Very true 186

5. I am not likely to intervene because I am not confident I know what is happening in the situation.

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

3. How likely are you to intervene?

Not likely Somewhat likely Very likely

1 2 3 4 5

4. How likely are you to email or text a mutual friend in order to stop the situation?

187

Not likely Somewhat likely Very likely

1 2 3 4 5

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

188

You receive the following group MMS text message:

matthew is a nark and should just kill himself Sep 25, 2012 1:53 PM Seconds later you receive the following message from Matthew:

U guys r such jerks! knock it off!

The message was sent to 5 people total and is being recirculated by the same phone number.

Matthew has not responded back.

Please read the statements below and pick the rating which best fits the likelihood that you would respond in a similar way to each statement.

Would you.. Very Somewhat Neither Somewhat Very likely

unlikely unlikely (3) likely (5)

(1) (2) (4)

1. Just put my phone away 189

2. Tease or make fun of

Matt

3. Ignore or delete the text

4. Send a text telling people to knock it off

5. Send a text scolding the original texter

6. I’d do something else. Write in the space here:

190

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you

1. Do you think somebody will intervene in the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake.

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal.

1 2 3 4 5 191

Very untrue Neutral Very true

5. I am not likely to intervene because I am not confident I know what is happening in the situation.

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

3. How likely are you to intervene?

Not likely Somewhat likely Very likely

1 2 3 4 5 192

4. How likely are you to email or text a mutual friend in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

193

You receive the following group MMS text message:

matthew is a nark and should just kill himself Sep 25, 2012 1:53 PM Seconds later you receive the following message from Matthew:

U guys r such jerks! knock it off!

The message was sent to 50 people total and is being recirculated by the same phone number.

Matthew has not responded back.

Please read the statements below and pick the rating which best fits the likelihood that you would respond in a similar way to each statement.

Would you.. Very Somewhat Neither Somewhat Very likely

unlikely unlikely (3) likely (5)

(1) (2) (4)

1. Just put my phone away

2. Tease or make fun of

Matt 194

3. Ignore or delete the text

4. Send a text telling people to knock it off

5. Send a text scolding the original texter

6. I’d do something else. Write in the space here:

195

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you

1. Do you think somebody will intervene in the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake.

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal.

1 2 3 4 5 196

Very untrue Neutral Very true

5. I am not likely to intervene because I am not confident I know what is happening in the situation.

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

3. How likely are you to intervene?

197

Not likely Somewhat likely Very likely

1 2 3 4 5

4. How likely are you to email or text a mutual friend in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

198

You are on a popular online social networking site and you see this ongoing comment thread on your friend Jennifer’s page after one of her updates: Sharon is online View 2 more comments Philip is online

Callie ha ur such a stupid idiot Amy is online August 27 at 10:37am · Like

Jose is online

Jennifer: quit being such a bunch of jerks you guys! Rob thats so stupid. jen ur such a retard! Malcolm is online August 27 at 10:38am · Like

**You are online

Ashley yah. jen likes to screw. the footbaall team says so August 28 at 9:08pm · Like ·

Jennifer: quit being such a bunch of jerks you guys! August 31 at 5:33pm · Like

199

According to the sidebar ticker, you and Jennifer currently have 5 friends-in-common online right now.

Please read the statements below and pick the rating which best fits the likelihood that you

would respond in a similar way to each statement.

Would you... Very Somewhat Neither Somewhat Very likely

unlikely unlikely (3) likely (5)

(1) (2) (4)

1. Tease or

make fun of

Jennifer

2. Feel bad

about the act

3. Just close

the browser

4. Check

later to see if

the incident

had stopped

5. Post a

comment for

Jennifer 200

6. Post a comment telling people to knock it off

7. I’d do something else. Write in the space here:

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you

1. Do you think somebody will intervene in the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 201

Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake.

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal.

1 2 3 4 5 Very untrue Neutral Very true

5. I am not likely to intervene because I am not confident I know what is happening in the situation.

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true 202

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

3. How likely are you to intervene?

Not likely Somewhat likely Very likely

1 2 3 4 5

4. How likely are you to email or text a mutual friend in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely 203

1 2 3 4 5

204

You are on a popular online social networking site and you see this ongoing Sharon is online comment thread on your friend Jennifer’s page after one of her updates: Philip is online View 26 more comments

Like · Comment Amy is online

Jose is online Callie ha ur such a stupid idiot August 27 at 10:37am · Like Malcolm is online

Julia is online Jamie quit lookin for drama jenny. nobody cares about ur stupid life August 27 at 10:38am · Like Vera is online Ashley yah. jen likes to screw. the footbaall team says so August 28 at 9:08pm · Like · Calum is online

Jennifer: quit being such a bunch of jerks you guys! ** You are online August 31 at 5:33pm · Like

* ...plus 42 others!

According to the sidebar ticker, you and Jennifer currently have 50 friends-in-common online right now. Please read the statements below and pick the rating which best fits the likelihood that you

would respond in a similar way to each statement.

Would you... Very Somewhat Neither Somewhat Very likely

unlikely unlikely (3) likely (5)

(1) (2) (4)

1. Tease or

make fun of

Jennifer

2. Feel bad

about the act 205

3. Just close the browser

4. Check later to see if the incident had stopped

5. Post a comment for

Jennifer

6. Post a comment telling people to knock it off

7. I’d do something else. Write in the space here:

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you

1. Do you think somebody will intervene in the situation? 206

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake.

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal.

1 2 3 4 5 Very untrue Neutral Very true

5. I am not likely to intervene because I am not confident I know what is happening in the situation. 207

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

3. How likely are you to intervene?

Not likely Somewhat likely Very likely

1 2 3 4 5

4. How likely are you to email or text a mutual friend in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5 208

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

209

You are on a popular online social networking site and you see this ongoing comment thread on your friend Matthew’s page after one of his updates: Sharon is online

View 2 more comments Philip is online

Henry quit being such a pussy Matt Amy is online August 27 at 10:37am · Like

Jose is online Calum Such. A. Bitch. Grow up and get over it! August 30 at 11:59am · Like Malcolm is online

Preston U r such a whiny little jerk. Why don’t u man up n-stead of posting dumb shit **You are online August 28 at 9:08pm · Like ·

Matthew quit being such a bunch of jerks you guys! August 30 at 11:59am · Like

According to the sidebar ticker, you and Matthew currently have 5 friends-in-common online right now.

Please read the statements below and pick the rating which best fits the likelihood that you

would respond in a similar way to each statement.

Would you... Very Somewhat Neither Somewhat Very likely

unlikely unlikely (3) likely (5)

(1) (2) (4)

1. Tease or

make fun of

Matt

2. Feel bad

about the act 210

3. Just close the browser

4. Check later to see if the incident had stopped

5. Post a comment for

Matt

6. Post a comment telling people to knock it off

7. I’d do something else. Write in the space here:

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you 211

1. Do you think somebody will intervene in the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake.

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal.

1 2 3 4 5 Very untrue Neutral Very true 212

5. I am not likely to intervene because I am not confident I know what is happening in the situation.

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

3. How likely are you to intervene?

Not likely Somewhat likely Very likely

1 2 3 4 5

213

4. How likely are you to email or text a mutual friend in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

214

You are on a popular online social networking site and you see this ongoing comment thread on your friend Matthew’s page after one of his updates: Sharon is online

View 26 more comments Philip is online

Henry quit being such a pussy Matt Amy is online August 27 at 10:37am · Like

Jose is online Calum Such. A. Bitch. Grow up and get over it! August 30 at 11:59am · Like Malcolm is online

Julia is online Preston U r such a whiny little jerk. Why don’t u man up n-stead of posting dumb shit August 28 at 9:08pm · Like · Vera is online

Matthew quit being such a bunch of jerks you guys! Calum is online August 30 at 11:59am · Like

** You are online

* ...plus 42 others! According to the sidebar ticker, you and Matthew currently have 50 friends-in-common online right now. Please read the statements below and pick the rating which best fits the likelihood that you

would respond in a similar way to each statement.

215

Would you... Very Somewhat Neither Somewhat Very likely

unlikely unlikely (3) likely (5)

(1) (2) (4)

1. Tease or make fun of

Matt

2. Feel bad about the act

3. Just close the browser

4. Check later to see if the incident had stopped

5. Post a comment for

Matt

6. Post a comment telling people to knock it off 216

7. I’d do something else. Write in the space here:

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you

1. Do you think somebody will intervene in the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake. 217

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal.

1 2 3 4 5 Very untrue Neutral Very true

5. I am not likely to intervene because I am not confident I know what is happening in the situation.

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

218

3. How likely are you to intervene?

Not likely Somewhat likely Very likely

1 2 3 4 5

4. How likely are you to email or text a mutual friend in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

219

You receive the following group MMS text message:

jennifer is a giant slut!

Sep 25, 2012 1:53 PM Seconds later you receive the following message from Jennifer:

U guys r such jerks! knock it off!

Sep 25, 2012 1:53 PM 220

Several more texts follow with people insulting Jennifer. The original message was sent to 50 people total and also is being recirculated by the same phone number.

Please read the statements below and pick the rating which best fits the likelihood that you would respond in a similar way to each statement.

Would you… Very Somewhat Neither Somewhat Very likely

unlikely unlikely (3) likely (5)

(1) (2) (4)

1. Just put my phone away

2. Tease or make fun of

Jennifer

3. Ignore or delete the text

4. Send a text telling people to knock it off 221

5. Send a text scolding the original texter

6. I’d do something else. Write in the space here:

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you

1. Do you think somebody will intervene in the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 222

Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake.

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal.

1 2 3 4 5 Very untrue Neutral Very true

5. I am not likely to intervene because I am not confident I know what is happening in the situation.

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true 223

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

3. How likely are you to intervene?

Not likely Somewhat likely Very likely

1 2 3 4 5

4. How likely are you to email or text a mutual friend in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely 224

1 2 3 4 5

You receive the following group MMS text message: 225

jennifer is a giant slut!

Sep 25, 2012 1:53 PM Seconds later you receive the following message from Jennifer:

U guys r such jerks! knock it off!

Sep 25, 2012 1:53 PM Several more texts follow with people insulting Jennifer. The original message was sent to 5 people total and also is being recirculated by the same phone number.

Please read the statements below and pick the rating which best fits the likelihood that you would respond in a similar way to each statement.

Would you.. Very Somewhat Neither Somewhat Very likely

unlikely unlikely (3) likely (5)

(1) (2) (4)

1. Just put my phone away

2. Tease or make fun of

Jennifer 226

3. Ignore or delete the text

4. Send a text telling people to knock it off

5. Send a text scolding the original texter

6. I’d do something else. Write in the space here:

The following questions are based on the scenario you just read. Please read the questions and pick the answer that is the best fit for you

1. Do you think somebody will intervene in the situation? 227

Not likely Somewhat likely Very likely

1 2 3 4 5

Branch if they answer 1 or 2:

1. I am not likely to intervene because somebody else probably will.

1 2 3 4 5 Very untrue Neutral Very true 2. I am not likely to intervene because there are enough people.

1 2 3 4 5 Very untrue Neutral Very true

3. I am not likely to intervene because I might make a mistake.

1 2 3 4 5 Very untrue Neutral Very true

4. I am not likely to intervene because nobody else is, so it’s probably no big deal.

1 2 3 4 5 Very untrue Neutral Very true

5. I am not likely to intervene because I am not confident I know what is happening in the situation. 228

1 2 3 4 5 Very untrue Neutral Very true

6. I am not likely to intervene because nobody else is and they probably know something I don’t.

1 2 3 4 5 Very untrue Neutral Very true

2. Do you think a mutual friend will intervene and help?

Not likely Somewhat likely Very likely

1 2 3 4 5

3. How likely are you to intervene?

Not likely Somewhat likely Very likely

1 2 3 4 5

4. How likely are you to email or text a mutual friend in order to stop the situation?

229

Not likely Somewhat likely Very likely

1 2 3 4 5

5. How likely is it that a mutual friend will text or email you in order to stop the situation?

Not likely Somewhat likely Very likely

1 2 3 4 5

230

Great job! Now on to some questions...

People sometimes make and use attributions in order to explain other people’s behavior and motivations. Please rate how much you disagree (1) or agree (7) with each statement.

81. Bullies are always bullies, no matter the situation.

Completely disagree Neither disagree nor agree Completely agree

1 2 3 4 5 6 7

82. Bullying online is the same as bullying in person.

Completely disagree Neither disagree nor agree Completely agree

1 2 3 4 5 6 7

83. Bullying through text messages is the same as bullying in person.

231

Completely disagree Neither disagree nor agree Completely agree

1 2 3 4 5 6 7

84. Cyberbullying online or through text messages happens because some people are just naturally bullies.

Completely disagree Neither disagree nor agree Completely agree

1 2 3 4 5 6 7

85. Cyberbullying online or through text messages is a result of the situation.

Completely disagree Neither disagree nor agree Completely agree

1 2 3 4 5 6 7

86. People who are cyberbullied online or through text messages should just shut the device off.

Completely disagree Neither disagree nor agree Completely agree

1 2 3 4 5 6 7

232

87. Cyberbullies bully others because of an internal or intrinsic trait.

Completely disagree Neither disagree nor agree Completely agree

1 2 3 4 5 6 7

88. People who are cyberbullied should just ignore the bullies.

Completely disagree Neither disagree nor agree Completely agree

1 2 3 4 5 6 7

89. Cyberbullies bully because of environmental causes.

Completely disagree Neither disagree nor agree Completely agree

1 2 3 4 5 6 7

90. People cyberbully other people because of reasons beyond their control.

Completely disagree Neither disagree nor agree Completely agree

1 2 3 4 5 6 7

233

Strongly disagree Disagree Neither Agree Strongly Agree

(1) (2) (3) (4) (5)

91. I feel that the world treats people fairly

92. I feel that people get what they deserve

93. I feel that people treat each other fairly in life

94. I feel that the people earn the rewards and punishments they get

95. I feel that people treat each other with the respect they deserve 234

96. I fell that people get what they are entitled to have

97. I feel that a person’s efforts are noticed and rewarded

98. I feel that when people meet with misfortune, they have brought it upon themselves

Please rate the following statements based on how much you disagree or agree with them.

235

Please indicate if each statement is true for you or false for you.

99. I never hesitate to go out of my way to help someone in trouble.

A. True B. False

100. I have never intensely disliked anyone.

A. True B. False

101. When I don't know something I don't at all mind admitting it. A. True B. False

102. I am always courteous, even to people who are disagreeable. A. True B. False

236

103. I would never think of letting someone else be punished for my wrong doings. A. True B. False

104. I sometimes feel resentful when I don't get my way. A. True B. False

105. There have been times when I felt like rebelling against people in authority even though I knew they were right. A. True B. False

106. I can remember "playing sick" to get out of something. A. True B. False

107. There have been times when I was quite jealous of the good fortune of others. A. True B. False

108. I am sometimes irritated by people who ask favors of me. A. True B. False

Super! You're almost done!

The following statements inquire about your thoughts and feelings in a variety of situations. For each item, indicate how well it describes you by choosing the appropriate number on the scale

109. I often have tender, concerned feelings for people less fortunate than me

Does not describe me well Describes me very well

1 2 3 4 5

110. Sometimes I don't feel very sorry for other people when they are having problems. 237

Does not describe me well Describes me very well

1 2 3 4 5

111. When I see someone being taken advantage of, I feel kind of protective towards them.

Does not describe me well Describes me very well

1 2 3 4 5

112. Other people's misfortunes do not usually disturb me a great deal.

Does not describe me well Describes me very well

1 2 3 4 5

113. I am often quite touched by things that I see happen.

Does not describe me well Describes me very well

1 2 3 4 5

114. I would describe myself as a pretty soft-hearted person. 238

Does not describe me well Describes me very well

1 2 3 4 5

Please tell us a little about yourself.

115. How old are you? ______

116. Are you:

 Male  Female

117. Have you ever experienced cyberbullying before?

 Yes  No

118. If yes, approximately how many times? ______

119. Have any of your close friends or family experienced cyberbullying before?

 Yes  No

120. If yes, approximately how many times? ______

239

121. Has there ever been a time when you did not intervene when you saw a case of cyberbullying?

 Yes  No

122. If you could now, would you go back and intervene?

 Yes  No

You're done! Thanks so much!

240

Appendix C Frequencies of the follow-up questions for social networking vignettes

Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette 1 2 3 4 7 8 11 12 13 14 1. Tease Very 338 327 329 340 333 331 332 333 335 328 or unlikely make Somewha 19 26 22 20 19 27 22 24 17 21 fun of t unlikely victim Neither 6 10 12 7 7 8 6 6 7 11

Somewha 7 4 5 4 7 4 4 7 7 4 t likely Very 2 2 1 1 5 1 4 5 likely Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette 1 2 3 4 7 8 11 12 13 14 2. Feel Very 36 41 39 34 42 37 50 49 58 49 sorry unlikely for Somewha 18 22 23 28 18 23 19 21 18 20 victim t unlikely Neither 21 20 30 27 20 21 28 22 33 28

Somewha 124 128 115 126 130 139 124 116 127 124 t likely Very 172 159 160 152 156 150 145 163 132 148 likely Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette 1 2 3 4 7 8 11 12 13 14 3. Just Very 109 106 93 97 103 106 104 111 97 99 close unlikely the Somewha 84 82 90 84 77 87 92 83 88 83 browse t unlikely r Neither 56 62 64 60 59 59 52 55 61 64

Somewha 71 69 66 77 72 63 71 65 69 64 t likely Very 48 50 56 51 55 55 48 57 52 57 likely Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette 1 2 3 4 7 8 11 12 13 14 4. Very 37 41 44 40 45 43 35 34 39 41 Check unlikely later to Somewha 24 21 23 25 22 27 24 24 27 24 see if t unlikely the incident Neither 26 34 25 34 33 33 30 25 36 26 had stopped Somewha 165 166 160 161 149 161 165 146 161 160 t likely Very 117 106 119 111 117 107 112 139 106 117 likely Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette 1 2 3 4 7 8 11 12 13 14 5. Post Very 70 65 64 59 69 67 58 59 59 61 a unlikely comme Somewha 41 44 47 44 50 49 42 41 49 50 nt in t unlikely support Neither 54 57 66 76 57 68 45 47 49 42

Somewha 107 109 97 109 105 105 119 114 114 114 t likely 241

Very 98 92 96 83 85 80 105 109 98 103 likely Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette Vignette 1 2 3 4 7 8 11 12 13 14 6. Post Very 58 56 53 59 61 56 50 52 57 63 a unlikely comme Somewha 39 46 48 41 41 48 36 39 49 49 nt t unlikely telling people Neither 45 46 62 62 55 55 49 43 55 39 to knock it Somewha 110 109 103 106 101 105 117 110 98 104 off t likely Very 118 114 105 104 108 108 118 129 112 113 likely

242

Appendix D Frequencies of the follow-up questions for texting vignettes

Vignette 5 Vignette 6 Vignette 9 Vignette 10 Vignette 15 Vignette 16

1. Put my Very unlikely 174 169 176 189 175 164 phone away Somewhat 79 76 63 64 57 70 unlikely Neither 27 27 26 24 29 21

Somewhat 47 58 60 50 59 59 likely Very likely 42 40 42 43 50 57

Vignette 5 Vignette 6 Vignette 9 Vignette 10 Vignette 15 Vignette 16

2. Tease or Very unlikely 326 335 326 335 328 332 make fun of victim Somewhat 18 19 25 19 22 17 unlikely Neither 14 7 7 7 10 13

Somewhat 6 8 6 6 7 6 likely Very likely 5 1 1 2 3 2

Vignette 5 Vignette 6 Vignette 9 Vignette 10 Vignette 15 Vignette 16

3. Ignore or Very unlikely 143 134 144 146 137 118 delete text Somewhat 77 71 68 72 56 75 unlikely Neither 41 45 40 38 49 43

Somewhat 59 60 65 59 65 67 likely Very likely 48 55 50 53 62 63

Vignette 5 Vignette 6 Vignette 9 Vignette 10 Vignette 15 Vignette 16

4. Send a Very unlikely 48 42 44 45 46 41 text telling people to Somewhat 30 41 43 38 51 38 knock it off unlikely Neither 33 27 39 31 33 48

Somewhat 115 116 100 112 101 116 likely Very likely 145 146 142 145 140 129

Vignette 5 Vignette 6 Vignette 9 Vignette 10 Vignette 15 Vignette 16

5. Send a Very unlikely 51 46 48 53 56 50 text scolding the original Somewhat 37 37 42 36 45 49 texter unlikely Neither 36 35 35 38 36 46

Somewhat 95 104 103 92 91 92 likely 243

Very likely 151 149 142 153 142 134