Research in statistics education has been supporting the use of simulation-based methods for teaching statistical inference and suggesting that this approach may lead to better student understanding of concepts in introductory courses, when compared to the "consensus" approach. Currently, there are a variety of simulation-based curricula being used in a wide range of statistics courses from undergraduate to graduate levels and serving a diverse body of students from statistics majors to social sciences majors. This paper examines students' statistical knowledge across three introductory statistics courses using different simulation-based curricula at the undergraduate level (social sciences and math/stat majors) and graduate level. Students were assessed at the end of the course using the REasoning and LIteracy (REALI) instrument, developed to assess statistical literacy and reasoning topics in introductory statistics courses. The texts used in each of the courses were: (1) Statistical Thinking - A Simulation Approach to Modeling Uncertainty (CATALST), (2) Investigating Statistical Concepts, Applications, and Methods (ISCAM), and (3) Statistics: Unlocking the Power of Data (LOCK5).