Using Multilevel Statistical Models in Social Work Intervention Research

Statistical analyses of data from a classroom-based study illustrate the need to account for intra-class clustering in studies involving schools, classrooms, and other higher order units of analysis. Students were clustered in homerooms that were assigned to intervention and comparison conditions. Standard multiple linear regression analysis yielded a significant group effect but incorrectly ignored intra-cluster response correlations. A multilevel model appropriately accounting for the dependency among responses in the same cluster yielded a nonsignificant group effect. Implications for the analysis of intervention research data are discussed. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: Website: © 2004 by The Haworth Press, Inc. All rights reserved.] James K. Nash, PhD, is Assistant Professor, Graduate School of Social Work, Portland State University. Lawrence L. Kupper, PhD, is Alumni Distinguished Professor, Department of Biostatistics, School of Public Health, University of North Carolina at Chapel Hill. Mark W. Fraser, PhD, is Tate Distinguished Professor for Children in Need, School of Social Work, University of North Carolina at Chapel Hill. Address correspondence to: James K. Nash, Graduate School of Social Work, Portland State University, P.O. Box 751, Portland, OR 97207-0751 (E-mail: [email protected].). This project is supported by grants from the North Carolina State Division of Mental Health, Developmental Disabilities, and Substance Abuse Services, the Z. Smith Reynolds Foundation, and the UNC-CH Center for Injury Prevention. The authors thank the teachers, staff, and students who participated in this study. Journal of Social Service Research, Vol. 30(3) 2004 http://www.haworthpress.com/web/JSSR  2004 by The Haworth Press, Inc. All rights reserved. Digital Object Identifier: 10.1300/J079v30n03_03 35