This special issue of SMR is about the analysis of data collected at different levels of observation, such as groups and individuals within these groups, and about the methodological problems that are present when natural experimentation and observations nested within existing social groups are the object of study. The methodological problems are summarized in the term multilevel problems. A multilevel problem is a problem that inquires into the relationships between a set of variables that are measured at a number of different levels of a hierarchy. This article discusses some traditional approaches to the analysis of multilevel data and their statistical shortcomings. The random coefficient linear model is presented, which resolves many of these problems, and the currently available software is discussed. Next, some more general developments in multilevel modeling are discussed. The authors end with an overview of this special issue.
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