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Females in our study were more likely to report being victims or both-offenders-and-victims of cyber bullying as shown in Table 1. Note that in the category of pure-victim, 37% are males and 63% are females and for both-offender-and-victim 43% are males and 57% are females. Boys were more likely to be offenders and less likely to be victims. For the category of pure-offenders, 57% are males and 43% are females. These results are consistent with a study by Li (2007) that showed that boys were more likely to be cyber bullies only and girls were more likely to be cyber victims only (Li, 2007).
Measure This purpose of this study was to measure the perceived effectiveness of the 14 strategies presented in Table 2 by students who are offenders, victims, both offenders and victims, and neither offenders nor victims of cyber bullying. The strategies are numbered from one to fourteen in the first column of Table 2. The question number as it appeared in the survey is listed in the second column of Table 2. The abbreviated strategy listed in the third column of Table 2 provides a highlight of a strategy for the convenience of reference later in this paper. The full strategy as presented to the participants is listed in the fourth column of Table 2. These strategies were selected from research in the literature, state regulations, and programs in schools. Strategy 1, not allowing the offender to use the computer at home or school; strategy 3, parent taking away computer and cell phone; and strategy 6, offender not allowed to use social networking were based on the premise that teenagers did not want to tell their parents that they had experienced cyber bullying because they feared that they would have their computer privileges restricted (Juvonen & Gross, 2008) or taken away (Beran & Li, 2007; Keith & Martin, 2005; Patchin & Hinduja, 2006; Shariff, 2008).
Table 2 Summary of cyber bullying prevention strategies
The second strategy, sending the offender to an alternative school, was taken from Georgia’s state anti-bullying law that stated that the law “provides for the assignment of certain students to alternative schools” to prevent bullying (Georgia HB 81, 1999, section 2(D) (2)). Strategy 4, requiring the offender to find a job and pay the victim money from their wages, reflects the idea that a parent could be held financially liable for damages as a result of their teen’s behavior online (High, 2009; Tully, 2007; Williard, 2007a). Strategy 5, repeat offenders would not be allowed to go to a four year college their freshman year was based on the case in which Justin Layshock was denied admission to Pennsylvania State University (Associated Press, 2007) as a result of posting a phony online profile about his school’s principal (Layshock v. Hermitage School District, 2006). It is also based on the new rule on the Common Application used by 320 colleges and universities, that has added a discipline question to screen applicants who have committed a school violation leading to “probation, suspension, removal, dismissal or expulsion” (Pappano, 2007). Strategy 7, requiring the offender to attend netiquette classes on Saturday, follows Thorbahn’s research result that Saturday school programs were effective in changing student attitudes and helping them to understand the consequences of their behavior (Thorbahn, 1995). The SMART (Saturday Morning Alternative Reach and Teach) Program is a Saturday school program for students who have serious disciplinary violations that can include cyber bullying. The SMART program is used by the School District of Philadelphia and in the Chicago Public Schools (School District of Philadelphia, 2009; Chicago Public Schools, 2008). Saturday morning classes and community service are frequently used as a disciplinary measure in the SMART programs reflecting the ideas proposed strategies 7 and 8 (Chicago Public Schools, 2008; Bannester, 2000). Some schools punished students for cyber bullying by not allowing them to participate in extracurricular activities and to go on field trips (Associated Press, 2005). Such a strategy was addressed in strategy 9. Strategy 10 was from a case discussed in (Dickerson, 2005). Strategies presented in questions 11, 12, 13, and 14 are strategies are commonly used in schools for bullying/cyber bullying prevention (Willard, 2007 b).
Survey Design A survey instrument was used to collect the data for the study. The survey was approved by Richard Stockton College of New Jersey’s IRB for Human Subjects committee. The format of the survey was designed by the authors with Zoomerang.com’s online survey tool. There were a total of 39 questions in the survey, of which two questions were used to obtain and verify parental consent, ten demographic questions, four about students’ roles as offenders and victims of cyber bullying, six about their roles as bystanders to cyber bullying, nine about their views on prevention strategies, three about why teens choose to or not to cyberbully, three about the cyber bullying problem and prevention measures at their school, one about how the respondent would prefer to report cyber bullying, and one about whose responsibility it was to prevent cyber bullying. Four of the 39 questions were open-ended. The others were multiple-choice questions.
Survey Administration A professional market research firm, Market Tools, Inc. was hired to recruit subjects and collect the data online. Market Tools, Inc. is a leading full-service provider of market research services. They are the parent company of www.zoomerang.com. This organization deploys surveys created with the Zoomerang survey tool through the Zoomerang website. The people who answer the surveys are part of the ZoomPanel that consists of over two million people (Market Tools, Inc, & Zoomerang.com). The profile of ZoomPanel members is based on United States census data to ensure accurate population (Market Tools, Inc, & Zoomerang.com). The ZoomPanel is used by multinational companies such as McDonalds, General Mills, KFC, Procter and Gamble and Microsoft (ZoomPanel.com, 2010). We chose to use the Market Tools because they use True Sample Technology to verify that participants are, unique, real, and engaged (Conklin, 2009). True Sample is a unique Market Research technology that systematically ensures data quality. To ensure that data was real, Market Tools verified that information about gender, zip code, and household income matched with the data in extensive databases with validated consumer demographics that are used by the financial services industries for each member of the ZoomPanel. Market Tools used digital fingerprinting technology to check that the respondents did not repeat surveys, and used TrueSample technology to identify fraudulent responders and removed them from the sample (Conklin, 2009). True Sample technology detects common markers of erroneous data that include respondents repeating surveys, taking the survey too quickly and not reading the questions, and fraudulent data. The time to take the survey was about 20 minutes. The survey was administered online from June 26, 2008 to July 8, 2008. Market Tools e-mailed the survey to the members of the ZoomPanel panel that have identified in their demographic profile that they have teenagers living in their home. The teenagers who live in homes with ZoomPanel members completed the survey for our study. The investigators did not have access to any personal identifiable information such as e-mail addresses. Since it impossible to personally obtain informed consent in most online surveys having implied consent from respondents is an accepted practice when using online survey research (Patchin & Hinduja, 2006; Walther, 2002). Respondents obtain implied consent by presenting participants with a consent letter before starting the survey (Patchin & Hinduja, 2006). The consent letter tells what specific actions must be performed before taking the survey (Patchin & Hinduja, 2006). Having the respondents click on an icon to start the survey and then click a submit button to return the data back implies consent (King, 1996). For this study the researchers presented participants with a consent letter. A participant under age 18 was asked to have his or her parents give them permission to participate in the survey. To give consent the parent entered his or her initials in a text box below the consent letter. This method is similar to the method Patchin and Hinduja (2006) used to obtain parental permission in their online survey. After parental permission was obtained the respondent clicked on an icon to start the survey. Because the survey is anonymous it is impossible to verify if parental consent was obtained. As a check the researchers asked the students in the first question if they had their parent’s permission to take the survey. If the student responded ‘no’, the survey was terminated. The Zoomerang survey tool has a quota feature that rejects respondents based on responses after the desired sample size for a category is achieved. The limit of respondents was set to 350 for cyber bullying offenders and 350 for non-offenders based on the funding available for the study. Once the number of completed surveys reached the quota for a group, the survey was closed to the filled group. There were 13 extra samples for non-offenders. The 713 samples of the survey were complete and valid based on our pre-set qualifications such as ages and parents’ permissions, and within the quota. This data is from a convenience sample in which participants decide to participate in the study. It is not possible to generalize convenience samples to a larger population (Couper, 2000), but the technique has been used for exploratory studies (Patchin & Hinduja, 2006) to obtain initial data for further research (Berson et al., 2002). There is limited empirical research about what methods students perceive to be effective to prevent cyber bullying. We believed that using a convenience sample would provide data that could lead to further study using more scientific methods.
Results and Discussion This study compares the perspectives of pure-victims, both-offenders-and-victims, pure- offenders, and neither-offenders-nor-victims, and explains correlations between a student’s role in cyber bullying and his or her views of the effectiveness of various cyber bullying prevention strategies. The results of the data analyses presented in this section are organized into answers to the inquiries about cyber bullying prevention strategies that school administrators, researchers, and legislators may be concerned about.
Students’ Roles in Cyber bullying vs. Their Views
Inquiry 1. Are there statistically significant differences among the four categories in their views on the effectiveness of each of the 14 cyber bullying prevention strategies? If so, how do the four categories correlate to the views of effectiveness of the strategies?
To answer this inquiry, one needs to investigate (1) whether the students in each of the four categories have significantly different views on the effectiveness of the prevention strategies, and (2) how they are correlated. For that purpose, the regression method was selected to be used in the analysis. By using this method the researchers could tell the significance of a correlation, as well as whether the correlation was positive or negative. Considering possible colinearity existing among the 14 strategies, single regression, instead of multiple regressions, was used. Each single regression model takes the effectiveness of one strategy as the independent variable X, and the category of the respondent as the dependent variable Y. There are five possible values for X, one through five, representing very ineffective to very effective. There are four values for Y, 1 through 4, each representing a category of a student’s role in cyber bullying. Since different ways of numbering categories may have different results in regression, twenty four (permutation of four) single regression models were run for every strategy. Each model is associated with a way of category numbering. The results showed that the most significant differences always occur when numbering the categories as 1 for pure-offender, 2 for both-offender-and-victim, 3 for neither- offender-nor-victim, and 4 for pure-victim. Table 3 lists the eight strategies that students’ views of effectiveness are significantly different among the four categories. The eight strategies are sorted according to their p-values for statistical correlation with the smallest (most significant) on top. The table does not include those strategies that are not significantly correlated with the categories such as p > 0.056. The plus or minus sign in the parentheses after a p-value gives the correlation direction telling whether the correlation between X and Y is positive or negative. For example, 0.005(+) for strategy 3 means that the chance of making an error is 0.005 by asserting that different categories of students would have different views of the effectiveness of this strategy, and the correlation is positive. In the regression models that derived the results in Table 3, the meanings of values of dependent variable Y are 1 for pure-offender, 2 for both-offender-and-victim, 3 for neither-offender-nor-victim, and 4 for pure-victim. Since independent variable X’s values are 1 through 5 representing very ineffective through very effective, a positive correlation between a strategy and four categories implies that students who have had experience cyber bullying others (pure-offender and both-offender-and-victim) tend to rate a prevention strategy’s effectiveness lower than those who have never bullied others. Also, with positive correlations students who have been victims of cyber bullying tend to rate the effectiveness of a particular strategy higher than the offenders. When there are positive correlations pure-victims give a prevention strategy the highest ratings while pure-offenders give the same prevention strategy the lowest ratings. The reverse is true when negative correlations exist. When there are negative correlations pure victims give a strategy the lowest ratings while pure offenders give the same strategy the highest ratings. A high rating for a strategy means that a student views that strategy as effective. Hence, offenders view strategies with negative correlations as effective since negative correlations mean that offenders gave that strategy a high rating.
Table 3 Strategies in which participants had significantly different views based on their roles in cyberbullying
The results in Table 3 provide important references for school administrators and educators who are setting up cyber bullying prevention strategies. The student’s view on the effectiveness of a strategy is a determinant of its effectiveness in implementation. However. students’ views typically differ on a strategy, depending on their roles in cyber bullying. When deciding to use a strategy, one should not only consider the strategy’s overall effectiveness, but also to whom it is most effective and to whom it is least effective. For example, if a policy is to prevent cyber bullying by penalizing offenders, a negative correlation would be better than a positive one, since such a policy is mainly for the offenders and a negative correlation shows that the pure-offenders tend to view the strategy as effective. On the other hand, if the goal is to prevent ordinary students from cyber bullying others, a positive correlation would be better, since such a strategy is mainly for non-offenders. In this case a positive correlation shows that the non-offenders tend to view the strategy as effective. The top three strategies listed in Table 3, strategy 9, no extracurricular activities for offender; strategy 10, offender doing a presentation about cyber bullying; and strategy 7, offender attending Netiquette classes on Saturday, are relatively “mild” prevention strategies. The extremely low p-values show that students’ views are significantly different. Considering the fact that the three strategies are all penalties to offenders or something against the wills of offenders, the effectiveness of implementing these strategies would be most dependent on offenders’ views. However, the correlation directions are all positive, which suggests that offenders tend to view these strategies as ineffective compared to non-offenders. Offenders often have low school commitment (Patchin, 2006) and may not care if they are not allowed to participate in extracurricular activities as they may not want to participate in these activities in the first place. Hence, strategy 9, revoking the privilege to participate in extracurricular activities would not be an effective strategy to deter offenders from cyber bullying. Offenders may not perceive cyber bullying to be wrong and would not be embarrassed to give a presentation about it making strategy 10 ineffective. They may not believe that strategy 7, netiquette classes, are necessary and may perceive taking the classes as being told what to do online (Moessner, 2007). Hence, they may view strategy 7 as ineffective because it is a strategy in which adults at school tell them what to do online. There are two strategies in Table 3 with negative correlations. They are “setting clear rules and consequences” and “having ongoing cyber bullying prevention programs.” The negative correlation is interpreted as that offenders tend to view the strategies as being more effective than victims. Students who have had experience cyber bullying others tend to view “clear rules” and “ongoing programs” as being more effective than those who have never cyber bullied others. Compared to strategies 7, 9, and 10 that have positive correlations, strategies, 12 and 14, are general and not specific. It suggests that although cyber bullying offenders may not view the three specific and mild strategies as being effective, they would not consider any rule to be ineffective. Clear rules with penalties enforced and ongoing cyber bullying prevention programs are in general viewed as effective by the offenders. So, when a specific cyber bullying prevention strategy is established, its effectiveness should be accessed carefully and individually. Students’ views on effectiveness of the strategies other than the above eight were not significantly different, and they are not shown in Table 3. Those six strategies are: strategy 2 (sending offender to another school), strategy 5 (one year delay to 4-year college for offender), strategy 6 (no access to social networking sites for offender), strategy 8 (20 hours of community service for offender), strategy 11 (telling in class what to do as a victim), and strategy 13 (having written policy on zero toleration about bullying).
Top-Five Effective Prevention Strategies
Inquiry 2. Which strategies are considered most effective from the students’ point of view, for all as a whole and for each of the four categories? If you were to pick five strategies to adopt based on the data, which would they be?
Table 4 Average effectiveness of 14 strategies for each category of participants (1=very ineffective, through 5=very effective)
Table 5 Strategies Ranked on Effectiveness with Four Categories
Each of the fourteen cyber bullying preventing strategies had five choices in the survey for the students to pick. Five numbers were assigned to the five choices with 1 for very ineffective and 5 for very effective. Table 4 summarizes the statistics of the effectiveness of 14 strategies with respect to the four categories. For each strategy, Table 4 shows its category mean in each category. A larger mean indicates a higher average rating of effectiveness given by the students. To identify the strategies that are effective from the perspective of students, the fourteen strategies are sorted in descending order of the means within each of the four categories. Table 5 shows the results of the sorting, which tells for each category which strategy is viewed as most effective and which strategy is viewed as the least effective. From Table 5 it would be convenient to pick the top-five or top-three most effective strategies viewed by students in a category. Note the difference between category means in Table 4 and the correlations in Table 3. A strategy whose correlation with categories is statistically significant, as those shown in Table 3, does not have to have a high mean as in Table 4. Moreover, a strategy that has a low mean in Table 4 may show a significant correlation in Table 3. Of the eight strategies in Table 4, three strategies, 1, 3, and 12, are among the top nine most effective strategies from the perspectives of students in any of the four categories as in Table 5. Actually, a ranking under a category in Table 5 is derived by comparing means of the fourteen strategies within a category, while Table 3 is derived by comparing students’ views on a strategy among categories. For three categories, both-offender-and-victim, pure-victim, and neither-offender-nor-victim, the top five most effective cyber bullying preventing strategies are as follows, albeit their orders in categories are different. (1) Strategy 6: No access to social networking sites for offender. (2) Strategy 3: Parent taking away offender’s computers and cell phones. (3) Strategy 12: Setting clear rules and enforcing penalties on offender. (4) Strategy 8: 20 hours of community service for offender. (5) Strategy 1: No computer use in school and home for offender. The top-five most effective strategies for category pure-offender are somewhat different from the other categories: (1) Strategy 6: No access to social networking sites for offender. (2) Strategy 3: Parent taking away offender’s computer and cell phone. (3) Strategy 1: No computer use in school and home for offender. (4) Strategy 11: Telling in class what to do as a victim. (5) Strategy 13: Having written policy on zero toleration about cyber bullying. If the top-three most effective strategies are selected, then two strategies will be picked by every category. These strategies are strategy 3 (taking away offender’s computers and cell phones) and strategy 6 (no access to social networking sites for offender). Students, no matter whether they are offenders or victims, believed that “not allowing offenders to access to social networking sites” and “parent taking away offenders computers and cell phones” are among the top-three strategies that could most effectively prevent cyber bullying. That is a strong message for school administrators and parents. The finding is consistent with research that teenagers use technology as a link to their friends and social life (Keith & Martin, 2005). They fear losing their technology privileges (Beran & Li, 2007; Keith & Martin, 2005; Patchin & Hinduja, 2006; Shariff, 2008). Without the Internet and cell phone they feel disconnected from their peer group. No student, regardless of whether they are a pure-offender, pure-victim, both-offender-and-victim, or neither-offender-nor-victim, would want to be isolated from their social network online. In contrast with strategies 3 and 6, strategy 5, “one year delay to 4-year-college for offenders”, is rated as least effective consistently by all the four categories. This is another strong message for school administrators when considering avoiding ineffective cyber bullying prevention policies. The disciplinary question on the application for colleges will most likely not be a deterrent for cyber bullying. Strategy 11 (telling in class what to do as a victim) and strategy 14 (having written policy on zero toleration about bullying) are in pure-offenders’ top five picks. However, the two strategies are ranked very low in the other categories. It seems that pure-offenders tend to rate these two strategies as more effective than the students in the other categories as strategy 11 does not penalize them. Note the six strategies that offenders rated lower than any other categories. These strategies are strategy 4 (offender paying victim money), strategy 7 (offender attending netiquette classes), strategy 8 (20 hours community service for offender), strategy 10(offender doing presentation about cyber bullying), and strategy 12 (setting clear rules and enforcing penalties on offender). For those strategies that restrict technology access, pure-offenders tended to view strategy 1, (No computer use in school and home for offender), strategy 3 (Taking away offender’s computers and cell phones), and strategy 6 (No access to social networking sites for offender) as effective. Penalties that would consume an offender’s time or money were viewed to be ineffective. That is a message from the pure-offenders that they are deterred by the penalties restricting their access to technology rather than consuming their time or money.
Conclusion: Implications for Preventing Cyber bullying First, the category of students that prevention strategies must deter is the offenders. The data revealed that the specific strategy that offenders’ viewed as effective was the penalty that restricted his or her Internet and technology use. In general, clear rules with enforced penalties and ongoing prevention programs were perceived as effective by the offenders. Secondly, teens perceived the theme of taking away the offender’s access to technology as an effective prevention measure, regardless of their roles in cyber bullying. This finding makes sense as all teenagers, regardless of their roles in cyber bullying, are part of a generation that engages in cyber immersion (Brown, Jackson, & Cassidy, 2006). Cyber immersion means that the Internet serves as the primary way that they communicate for relationships, commerce, and recreation (Brown et al., 2006). By taking away access to Internet and technology teenagers would lose their primary means of communication and feel isolated. Teenagers view having their own cell phone and computer as prize possessions, regardless of their roles in cyber bullying. They use these items everyday and would miss them if they were taken away. Having these personal possessions taken away by their parents, even for a short period of time, would cause them to lose their social status within their peer group. So even though the argument could be made that if a teen’s cell phone or computer were taken away they could use a friend’s technology or go to a public library, losing the technological convenience and the stigma would indeed serve as a punishment. Parents, schools, and social networking sites all are important stakeholders in preventing cyber bullying (Ybarra el al., 2007). Hence their cooperation is needed to for that purpose. Policies need to be in place at school, and the school should involve parents when a cyber bullying issue arises. Meanwhile, parents have a responsibility to establish and enforce rules at home. Such a joint work is necessary for effectively resolving the problem of cyber bullying.
Limitations of the study A limitation of this anonymous online study is that the researchers do not really know who the respondents are or if they are completely honest in their answers. This survey is a convenience survey. It is possible that the responses are not representative of larger population of teenagers or online teenagers, although the study does have value as it provides insights into how teenagers view cyber bullying prevention strategies. Furthermore, online demographic groups may not be the same as real world demographic groups (Witte, Amoroso, & Howard, 2000) as people on the Internet are more educated and have higher incomes (Berson et al, 2002). However, there is evidence that Web study populations “are more representative of the public than samples from more traditional lab experiments” (Berson et al., p. 56). The market research firm, Market Tools, estimated that the margin of error was 5-6% for the survey. This estimate was based on the profile of people that have 13-17 year olds in their database, incidence of cyber bullying, total completes; total screenouts, total partials, and accesses are used to calculate the margin of error rate. We estimated an incidence rate of 25% based on studies in the literature (Kraft, 2006). By using True Sample technologies Market Tools took technological measures to eliminate common markers of fraudulent data. Common markers of fraudulent data are respondents who take the same survey multiple times, people who do not exist, respondents who provide answers to open ended questions such as “dsflkjdagsfi”, and respondents who do not read survey questions (Conklin, 2009) True sample technology correlates survey-taking time and response patterns to identify fraudulent behavior and remove the fraudulent data from the sample (Conklin, 2009). In this study the investigators differentiated pure-offenders, non-offenders, pure-victims, and non-victims, according to whether they reported at least one incident of cyber bullying or not. We did not take the severity and frequency of the incidents into account. There is no universal precise definition of cyber bullying (Szoka &Thierer, 2009). Cyber bullying is conceptualized as being repeated incidents (Belsey, 2010; Hinduja and Patchin, 2006; Shariff, 2008) of online bullying. However, Aftab notes that “Cyber bullying is usually not a one time communication, unless it involves a death threat or a credible threat of serious bodily harm” (Aftab, 2010c). Beran and Li (2007) found that most participants in their study reported being victims or offenders of cyber bullying only once or twice. Our definition of cyber bullying did not have the word repeated in it. We needed 350 students to admit to cyber bullying someone. We also did not want to exclude students who may have had one traumatic incident online such as receiving a death threat. Since students tend to report cyber bullying once or twice, we decided to ask if the respondent had been cyber bullied or bullied someone online at least once.
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[1] Assistant Professor of Business Studies, Richard Stockton College of New Jersey, Box 195 Jimmie Leeds Road, Pomona, NJ, USA. E-mail: krafte@stockton.edu [2] Professor of Business Studies, Richard Stockton College of New Jersey, Box 195 Jimmie Leeds Road, Pomona, NJ, USA. E-mail: jinchang.wang@stockton.edu
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