Investigation of sexual behavior involves many challenges, including how to assess sexual behavior and how to analyze the resulting data. Sexual behavior can be assessed using absolute frequency measures (also known as counts) or with relative frequency measures (e.g., rating scales that range from never to always). We discuss these 2 assessment approaches in the context of research on HIV risk behavior. We conclude that these 2 approaches yield nonredundant information and, more important, that only data yielding information about the absolute frequency of risk behavior have the potential to serve as valid indicators of HIV contraction risk. However, analyses of count data may be challenging because of non-normal distributions with many outliers. Therefore, we identify new and powerful data analytical solutions that have been developed recently to analyze count data and discuss limitations of a commonly applied method (viz., analysis of covariance using baseline scores as covariates).