Exploring the Impact of Informal Rape Myth Education in a Nonstudent Sample

Leah Noelle Reddy, Portland State University
Christopher Campbell, Portland State University
Amber L. Morczek, Nevada State College at Henderson

Copyright © 2020 by Sage Publications


Sexual assault has come to the forefront in terms of prevention and education for many social institutions such as college campuses. However, with a growing body of research highlighting the importance and effectiveness of interventions, research examining the impact of sexual assault education (SAE) on altering rape myth acceptance (RMA) among nonstudent populations is severely lacking. This is particularly problematic when considering that the issue of sexual assault extends well beyond academia in the United States. Accordingly, this study aims to fill this gap by employing an experimental design with repeated measures. Using a sample of 137 nonstudents surveyed via Amazon’s Mechanical Turk, participants were randomly assigned to a treatment (rape myth intervention) or control (unrelated video content) group. To detect changes in RMA after a short rape myth education intervention, pretest and posttest RMA scores were generated for all participants using an altered version of the Updated Illinois Rape Myth Adherence scale. The treatment video was roughly 10 min in length and constructed by the lead author for the current research. All participants were presented with common rape myths, and then, the treatment group was provided with information (e.g., available research and statistics) to “debunk” these myths, and ultimately decrease acceptance of myths. Analyses indicate support for a significant change in RMA scores from pretest to posttest in the treatment group, finding support for the use of informal rape myth education in altering immediate RMA scores of a nonstudent sample. The RMA difference scores were also examined through a demographic lens to determine if the inclusion could further explain score changes. Demographics were not deemed significant predictors. Limitations and implications are discussed.