Recent criminological research has used latent class growth analysis (LCGA), a form of group-based trajectory analysis, to identify distinct terrorism trends and areas of high terrorism activity at the country-level. The current study contributes to the literature by assessing the robustness of recent findings generated by one type of group-based analysis, LCGA, to changes in measurement and statistical methodology. Using data from the Global Terrorism Database (GTD), we consider the challenges and advantages of applying group-based analysis to macro-level terrorism data. We summarize and classify country-level patterns of domestic and transnational terrorism using two types of group-based analyses, LCGA and an alternative yet similar modeling approach, general mixture modeling (GMM). We evaluate the results from each approach using both substantive and empirical criteria, highlighting the similarities and differences provided by both techniques. We conclude that both group-based models have utility for terrorism research, yet for the purposes of identifying hot spots of terrorist activity, LCGA results provide greater policy utility.