Step 6
The aim of this research is to measure the effects cyberbullying has on young adults and using a quantitive numerical approach (Carter & Wilson, 2015). What factors contribute to cyberbullying will be addressed including; access, time spent on computer, and location of computer. Also to show that frequent access to technology increases a young adults chances of becoming a victim of cyberbullying.
Data Analysis (Step 10)
Data that was collected from participants were entered into a computer to be analyzed, the IBM-SPSS Ver. 21.0 (Carter & Wilson, 2015). To determine if demographic variables played a part in cyberbullying the logistic regression analysis was used. The demographic characteristics included details about the participants such as, age, gender, grade in school, ethnicity, and location. Ages ten to twelve frequency had a percentage of twenty one, ages thirteen to fifteen percentage of frequency was seventy two percent, and sixteen to eighteen percentage frequency was seven percent (Carter & Wilson, 2015). This sample shows ages thirteen to fifteen had more cyberbullying reports compared to other ages. Regarding gender, females had a higher frequency of cyberbully reports compared to male, forty nine percent and fifty percent. The grade characteristic showed students in the seventh and eighth grade had a fifty nine percent of reports, compared to fourth through sixth grade and ninth through twelfth grade (Carter & Wilson, 2015). Ethnicity characteristics showed African Americans had a whopping seventy seven percent of reports of being cyberbully victims. Another sample of how much access participants had to technology. Out of 367 participants, 337 participants had a computer, 288 had a cell phone, 322 reported to have an email account, 298 were either on MySpace or Facebook, 309 used text messaging, and 102 used Twitter (Carter & Wilson, 2015). The access to technology sample also revealed where participants computers were located, twenty...