Blame Avoidance and Transgender Individuals' Attributions About Rape: Unpacking Gendered Assumptions in Defensive Attribution Research.

This research examines how binary gender (male or female) interacts with trans status (cis or trans) to determine rape myth acceptance (RMA), perceived similarity, victim empathy, and victim blaming. Utilizing vignette methodology with an acquaintance rape scenario with a heterosexual female victim, we analyze these variables using multivariate ordinary least squares (OLS) and ordered logit regression. We find support for the argument that both harm avoidance and blame avoidance are operating to predict similarity, empathy, and victim blaming experienced by cisgender and transgender individuals. We argue that this is based on in-group identification from both gender identity and perceived likelihood of future victimization. Specifically, we find that cisgender men have the lowest rates of perceived similarity. Transgender women have the highest rates of empathy whereas cisgender women and trans men have lower rates of victim blaming than cisgender men. Results suggest that the interaction between binary gender and trans status influence perceptions of rape myths and victimization.