Studies utilizing large data sets that characterize reproductive isolation (the ability to cross and produce hybrids) across species with varying degrees of relatedness have been extremely influential in the study of speciation. However several limitations have made it difficult to test specific hypothesis about factors that predict the evolution of reproductive isolation. In particular, the statistical methods typically used are limited in their ability to test complex hypotheses involving more than one predictor variable; at least one method, the Mantel Test, has also been found to be unreliable. In this paper I describe a framework to determine which factors contribute to the evolution of reproductive isolation using phylogenetic linear mixed models. Phylogenetic linear mixed models do not suffer from the same statistical limitations as other methods and I demonstrate the flexibility of this framework to analyze data collected at different evolutionary scales, to test both categorical and continuous predictor variables, and to test the effect of multiple predictors simultaneously, all of which cannot be achieved using any other single statistical method. I do so by re-analyzing several classic data sets and explicitly testing hypotheses that had previously been untested directly, including differences in accumulation of reproductive isolation between sympatric and allopatric species pairs.