Chapter 15: Multilevel Analysis: An Overview and Some Contemporary Issues

Jee Seon Kim

Multilevel analysis: An overview and some contemporary issues Human behavior occurs in context, and that context often matters in understanding and interpreting behavior. Multilevel models provide tools for statistical analysis in the presence of clustered or hierarchical data structures, as are common in the social sciences, and can be used to study (or alternatively control for) such structures. Examples of hierarchical structures include data in which students are nested within schools, patients are nested within clinics, or employees are nested within organizations. Multilevel models are also commonly referred to as hierarchical linear models or mixed effects models (Goldstein, 2003; Hox, 2002; Raudenbush and Bryk, 2002; Snijders and Bosker, 1999). The multilevel approach allows researchers to examine hypothesized relationships that incorporate many different ‘units of analysis’ in a statistically appropriate way, thus permitting more accurate modeling of complex systems. In many applications, the variability and dependency associated with nesting are not ...

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