| Rape and acquaintance rape are not new, but scrutiny of colleges and their disclosure of crime statistics make analyzing the frequency of acquaintance rapes on campus and colleges' response strategies a priority. The struggle to maintain safe campuses is a concern to parents, college administrators, and students. The problem addressed in this study is the rape of college students by other college students and its impact on an organization's retention of students. Crime statistics show that college-age women are at a higher risk for sexual assault, and though the causes of sexual violence vary, college students' acceptance of rape myths has been associated with the incidence of sexual assaults. The purpose of this quantitative study was to examine the influence of a mock rape trial on college students' acceptance of rape myths and to assess the influence of students' knowledge of incidents of rape on retention of students. The study included a Likert scale in a pre-/post-test, utilizing the freshman class (N = 260) of a college with half attending a mock trial and half, the control group, not. Data analysis from the Illinois Rape Myth Acceptance Scale-SF was evaluated to determine the influence of the mock rape trial on the reduction of rape myth acceptance. Additionally, researcher-developed questions were used to assess the influence of students' knowledge of rape incidents on their intent to stay at that campus. The survey data were analyzed with descriptive statistics and Mann-Whitney U tests at an alpha level of .05. Conclusions were consistent with earlier research; males (n = 141; Md = 3.50; u = 5788; p = .000) showed more rape myth acceptance than females (n = 119; Md = 3.05) and athletes ( n = 145; Md = 3.4) more than non-athletes ( n = 114; Md = 3.0; u = 5925; p = .000). Key results were significant on knowledge of rape and transferring colleges, with females (Md = 4.0) more likely to transfer to another college than males (Md = 3.67). Recommendations for future research include examining various college population segments and employing multiple regression analysis to assess the influence of the variables simultaneously. |
