Multilevel modeling (MLM) should be used when a researcher has collected hierarchical data. For example, when a researcher investigates an outcome variable (e.g., depression) with several clients drawn from different clinicians, the data set has a hierarchical structure. Herein, we describe the use of MLM in counseling research. The goals include the following: (a) to specify research contexts where MLM may be applied, (b) to describe how to conduct data analyses using MLM, and (c) to highlight key statistical and design issues encountered when analyzing hierarchical data. We also highlight how MLM can be used (a) to provide valid statistical inference in the presence of hierarchical data structure, (b) to separate the within-group effects from between-group effects for predictor variables, and (c) to study the interactions among predictor variables drawn from different levels (e.g., variables drawn from both clients and their clinicians).
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