This chapter introduces a statistical approach for analyzing nested data structures that both accounts for the dependence of observations due to hierarchical arrangements and allows for testing hypotheses at multiple levels. The most common application of multilevel models is for analyses of objects (e.g., people) nested within groups or clusters of some sort. Multilevel models can also be applied to longitudinal data analyses such that the “levels” do not refer to objects nested within groups but instead refer to multiple measurements (e.g., measures made at different occasions/time points) nested within individuals. The chapter illustrates some of the major considerations and basic steps for performing multilevel analyses so that the reader can begin to imagine how to apply this technique to the reader’s own research questions.
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