title: What is the Best Way to Analyze Less Frequent Forms of Violence? The Case of Sexual Aggression.
abstract: 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.
  
  
  METHODS
  We used a subset (n=645) of a larger longitudinal dataset to demonstrate fitting and comparison of six 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 SA 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Psychology, Georgia State University.
authors.forename: Kevin M
authors.initials: KM
authors.lastname: Swartout
authors.affiliation: Department of Public Health Sciences, Clemson University.
authors.forename: Martie P
authors.initials: MP
authors.lastname: Thompson
authors.affiliation: Health Promotion Sciences Division, Mel and Enid Zuckerman College of Public Health, University of Arizona.
authors.forename: Mary P
authors.initials: MP
authors.lastname: Koss
authors.affiliation: Department of Mathematical Sciences, Clemson University.
authors.forename: Nan
authors.initials: N
authors.lastname: Su
keywords: Poisson; count data; frequency data; measurement; negative binomial; non-normal data; sexual aggression; violence; zero-inflated models