Multilevel modeling provides several advantages over traditional ordinary least squares regression analysis; however, reporting results to stakeholders can be challenging. This article suggests some strategies for presenting complex, multilevel data and statistical results to institutional and higher education decision makers. The article is organized around two examples. The first example uses multilevel modeling to evaluate the importance of the sending high school in models of predicted first-year grades that can be used to guide admission decisions, and the focus is on how results can be reported to institutional decision makers. The second example examines the effects of student characteristics and academic disciplines on self-reports of critical thinking. The focus in this example is on how researchers or a national survey organization might present results showing the complex, contingent effects of student engagement and academic major on critical thinking outcomes. (Contains 3 tables and 1 figure.)