Multilevel Models and Unbiased Tests for Group Based Interventions: Examples from the Safer Choices Study

Multivariate Behavioral Research
S C CarvajalG S Parcel

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.

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Citations

Mar 5, 2004·American Journal of Public Health·David M MurrayJonathan L Blitstein
Mar 2, 2005·The Journal of Adolescent Health : Official Publication of the Society for Adolescent Medicine·Teresa GranilloScott C Carvajal
Oct 22, 2005·The British Journal of Educational Psychology·Christina SalmivalliMarinus Voeten
Oct 1, 2005·Multivariate Behavioral Research·Georges Van LandeghemJan Van Damme
Jan 1, 2004·Multivariate Behavioral Research·Mirjam Moerbeek
Oct 23, 2013·Sexual Abuse : a Journal of Research and Treatment·Benoit LeclercRichard Wortley

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