Using state achievement data that are openly accessible, this paper demonstrates the application of hierarchical linear modeling within the context of career technical education research. Three prominent approaches to analyzing clustered data (i.e., modeling aggregated data, modeling disaggregated data, modeling hierarchical data) are discussed and demonstrated. Syntax to replicate the analyses are included which may help break the learning curve associated with conducting and interpreting the results of this advanced statistical technique. (Contains 5 tables, 1 figure and 1 note.)