Nezlek, J. B., & Zyzniewski, L. E. (1998). Using hierarchical linear modeling to analyze grouped data. Group Dynamics: Theory, Research, and Practice, 2, 313-320.
Research in which data are collected within groups present a challenge to the data analyst, whether the groups are created by the researcher or occur naturally. This article discusses how to use a random coefficient modeling technique known as hierarchical linear modeling to analyze data collected within groups. The article describes how to use this technique to examine group- and individual-level phenomena, including examination of how individual-level relationships vary as a function of group characteristics. A comparison of hierarchical linear modeling to more traditional, ordinary-least-squares techniques and a presentation of how to implement analyses to test specific hypotheses are included. This comparison and presentation include brief discussions of pertinent issues such as the impact of different centering options, the analysis of categorical variables, distinctions between random and fixed effects, and balanced and unbalanced designs.