In education, most naturally occurring data are clustered within contexts. Students are clustered within classrooms, classrooms are clustered within schools, and schools are clustered within districts. When people are clustered within naturally occurring organizational units such as schools, classrooms, or districts, the responses of people from the same cluster are likely to exhibit some degree of relatedness with each other. The use of hierarchical linear modeling allows researchers to adjust for and model this non-independence. Furthermore, it may be of great substantive interest to try to understand the degree to which people from the same cluster are similar to each other and then to try to identify variables that help us to understand differences both within and across clusters. In HLM, we endeavor to understand and explain between- and within-cluster variability of an outcome variable of interest. We can also use predictors at both the individual level (level 1), and the contextual level (level 2) to explain the variance in the dependent variable. This article presents a simple example using a real data set and walk through the interpretation of a simple hierarchical linear model to illustrate the utility of the technique. (Contains 1 table and 1 note.) [For Part I, see EJ875269.]