For many large-scale behavioral interventions, random assignment to intervention condition occurs at the group level. Data analytic models that ignore potential non-independence of observations provide inefficient parameter estimates and often produce biased test statistics. For studies in which individuals are randomized by groups to treatment condition, multilevel models (MLMs) provide a flexible approach to statistically evaluating program effects. This article presents an explanation of the need for MLM's for such nested designs and uses data from the Safer Choices study to illustrate the application of MLMs for both continuous and dichotomous outcomes. When designing studies, researchers who are considering group-randomized interventions should also consider the features of the multilevel analytic models they might employ.
ASJC Scopus subject areas
- Statistics and Probability
- Experimental and Cognitive Psychology
- Arts and Humanities (miscellaneous)