Multilevel models and unbiased tests for group based interventions: Examples from the safer choices study

Scott C. Carvajal, Elizabeth Baumler, Ronald B. Harrist, Guy S. Parcel

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)185-205
Number of pages21
JournalMultivariate Behavioral Research
Volume36
Issue number2
DOIs
StatePublished - Jan 1 2001
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

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