Multilevel modeling (MLM), also referred to as hierarchical linear modeling (HLM) or mixed models, provides a powerful analytical framework through which to study colleges and universities and their impact on students. Due to the natural hierarchical structure of data obtained from students or faculty in colleges and universities, MLM offers many advantages to analysts and policy makers involved in institutional research (IR). This article introduces fundamental concepts of hierarchy and its statistical treatment specifically for data structures occurring in IR settings. It provides an introduction to multilevel modeling, including the impact of clustering and the intraclass correlation coefficient. Prototypical research questions in institutional research are examined, and an example is provided to illustrate the application and interpretation of multilevel models. (Contains 1 figure.)