Freshman College Students Perceptions of Gender and Cyberbullying Maria Duncan and Jessica Grobe Advised by: Susan Wolfgram, Ph.D. University of Wisconsin-Stout RESEARCH PROBLEM: Bullying is a national public health problem affecting millions of students which is now extending to the cyber-world and technology. (Juvonen & Gross, 2008) METHODS II Demographic Variables AGE (Age) VIC (I have been a victim of cyberbullying) PER (I have been a perpetrator of cyberbullying) Independent Variable GEN (Gender) Dependent Variables IMP (The effects of cyberbullying have negatively impacted my life) DEP (Depression is an effect of someone who has been cyberbullied) SUI (Cyberbullying can lead to suicidal behavior STR (Males are less likely to report incidences of cyberbullying) ASK (If someone is cyberbullied it is because they are “asking” for it REP (If I was aware of someone being cyberbullied, I would report the bullying to an adult AWR (Parents are not aware of what their teenagers are doing on social networks) RES (There should be more restrictions on social networking sites such as Facebook, Myspace, Twitter, to prevent cyberbullying) MON (Schools who block and/or monitor social networks are decreasing cyberbullying) NBD (Putting personal information on a social networking site is no big deal) RESEARCH QUESTION & HYPOTHESIS: Does Gender Play a Role in the way Freshman College Students Identify the Effects of Cyberbullying? Based on the literature and that no differences in gender were found we predicted that there would be no gender differences PURPOSE OF STUDY 1) To explore if there was a relationship between gender and perceptions 2) To develop a reliable survey instrument to measure those perceptions 3) To increase the publics’ awareness. LITERATURE REVIEW: Kite et al. (2010) found that 10% of the students they surveyed reported to being the victims of bullying online Sharples et al. (2009) results indicated that 74% of youth surveyed had used social networking sites . Juvonen and Gross (2008) 72% of respondents reported at least one online incident of bullying. Slonje and Smith (2008) found more girl then male involvement in indirect or relational bullying. Hinduja and Patchin (2007) found that sex and race were not significantly related to cyberbullying. Variables Males Females t df sig IMP 1.121 (.905) 2.62 (1.12) -3.377 38 *.002 REP 2.63 (.955) 3.42 (.926) -2.678 *.011 RES 2.58 3.52 (1.03) -2.777 *.008 NBD 2.26 (1.05) 1.57 2.108 *0.42 RESULTS SUMMARY Hypothesis: Based on the literature and that no differences in gender were found in the literature reviewed, we predicted that there would be no gender differences in the way that freshmen college males and females would identify the effects of cyberbullying. Based on our independent t-test and the fact that the four variables: IMP, REP, RES and NBD revealed significant differences in gender, our hypothesis was not supported. Reliability Analysis: Chronbach’s Alpha measures reliability and our survey items measured .290. (According to our Item-Total Statistics, if the variable NBD were deleted, our Chronbach’s Alpha would increase to .497. We feel this could be due to the interpretations of the words “no big deal” and feel that stronger, more understandable communication should have been used. THEORETICAL FRAMEWORK: Family Ecology Theory specifies four types of environmental systems and assumes that the family is influenced within and between four systems. (White & Klein 2008) When applied to our study this theory would predict that unrestricted access to technology and the resulting cyber bullying would negatively impact both males and females differently given the different gender roles in society. METHODS 1 Participants: Study was conducted at a mid-size Midwestern University. 40 freshmen undergraduate male and female students surveyed in two separate sections of a general education course. Research Design: 1) Non-random pilot study 2) Cross sectional 3) Purposive sampling design Data Collection Instrument: 1) IRB approved, informed by literature and theory, implied consent 2) Administered by researchers with one independent variable, three demographic variables, ten closed-ended questions based on a 1-5 Likert Scale, and one open-ended statement Procedure: 1) Questionnaires were administered to freshmen college students by researchers 2) Implied consent and confidentiality were explained Data Analysis Plan: 1) Cleaned and coded surveys 2) Analyzed data by using Statistical Package for the Social Sciences (SPSS) (Frequencies, mean comparisons, cross- tabulations, independent t-tests, and a Chronbach’s Alpha reliability analysis) IMPLICATIONS FOR PRACTIONERS The data demonstrated that gender does effect the way that freshmen college students identify the effects of cyberbullying Educate youth starting in junior high regarding definition, prevention and significance of cyberbullying Provide information to high school and college professors to help increase the awareness of cyberbullying *Significant @ p<=.05, two tailed. Standard deviations appear in parenthesis below means “I think this topic of cyberbullying is ridiculous. Bullying in person is what really gets to people. People don’t take cyber stuff as seriously” IMPLICATIONS FOR FUTURE RESEARCH Would benefit from a large, random national sample to generalize a worldwide view Wording the survey statement “putting personal contact information on a social networking site is no big deal” to be less vague and more explicit – suggest “dangerous” or “harmful” rather than “no big deal” Conduct qualitative interviews so respondents can elaborate on their responses REFERENCE LIST Hinduja , S., & Patchin, J.W. (2007). Offline Consequences of Online Victimization: School Violence and Delinquency. Journal of School Violence, 6 (3). 89-112. Juvon, J., & Gross, E.F. (2008). Extending the School Grounds? Bullying Experiences in Cyberspace. Journal of School Health, 78 (9). 496-505 Kite, S.L., Gable, R., & Filippelli, L. (2010). Assessing Middle School Students’ Knowledge of Conduct and Consequences and Their Behaviors Regarding the Use of Social Networking Sites. The Clearing House, 83 (5). 158-163 Sharples, M., Graber, R., Harrison, C., & Logan, K. (2009). E-Safety and Web 2.0 for Children Aged 11-16. Journal of Computer Assisted Learning, 25. 70-84 Slonje, R., & Smith, P.K. (2008). Cyberbullying: Another Main Type of Bullying? Scandinavian Journal of Psychology, 49. 147-154d CONCLUSION This pilot study sheds light that gender does play a role in the way freshmen college students identify the effects of cyberbullying. A high percentage of females strongly agreed that parents were not aware of what their teenagers were doing on social networks and responded much stronger than males that putting personal information on a site is a serious problem. Consequently, awareness and prevention can specifically target genders differently.