Empirical analyses of hierarchical data are important in various disciplines, but are most common to the social sciences. Until the 1980's, when the method of multilevel modeling was introduced, researchers dealt with the problem of nested data in a variety of ways, none of which was completely effective or accurate. The method of hierarchical modeling, and softwares such as HLM or MLwiN, provide the most appropriate available tools for dealing with the nested data. This article intends to introduce this strategy, as well as provide an empirical example to illustrate the relative advantages of using it to perform analysis.
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