What is the best way to analyze less frequent forms of violence? the case of sexual aggression

Objective: Most frequency data on violence are non-normally distributed, which can lead to faulty conclusions when not modeled appropriately. And, we can't prevent what we can't accurately predict. We therefore review a series of methods specifically suited to analyze frequency data, with specific reference to the psychological study of sexual aggression. In the process, we demonstrate a model comparison exercise using sample data on college men's sexual aggression. Method: We used a subset (n = 645) of a larger longitudinal dataset to demonstrate fitting and comparison of 6 analytic methods: OLS regression, OLS regression with a square-root-transformed outcome, Poisson regression, negative binomial regression, zero-inflated Poisson regression, and zero-inflated negative binomial regression. Risk and protective factors measured at Time 1 predicted frequency of sexual aggression at Time 2 (8 months later) within each model. Models were compared on overall fit, parsimony, and interpretability based upon previous findings and substantive theory. Results: As we predicted, OLS regression assumptions were untenable. Of the count-based regression models, the negative binomial model fit the data best; it fit the data better than the Poisson and zero-inflated Poisson models, and it was more parsimonious than the zero-inflated negative binomial model without a significant degradation in model fit. Conclusion: In addition to more accurately modeling violence frequency data, count-based models have clear interpretations that can be disseminated to a broad audience. We recommend analytic steps investigators can use when analyzing count outcomes as well as further avenues researchers can explore in working with non-normal data on violence.