Detecting and Statistically Correcting Sample Selection Bias

Researchers seldom realize 100% participation for any research study. If participants and non-participants are systematically different, substantive results may be biased in unknown ways, and external or internal validity may be compromised. Typically social work reGary Cuddeback, MSW, MPH, is Research Associate, Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, 101 Connor Drive, Suite 302, Chapel Hill, NC 27514. Elizabeth Wilson, MSW, is a Doctoral Student, John G. Orme, PhD, is Professor, and Terri Combs-Orme, PhD, is Associate Professor, University of Tennessee, College of Social Work, Children’s Mental Health Services Research Center, Henson Hall, Knoxville, TN 37996-3332. Address correspondence to: Gary S. Cuddeback, Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, 101 Connor Drive, Suite 302, Chapel Hill, NC 27514 or John G. Orme, University of Tennessee, College of Social Work, Children’s Mental Health Services Research Center, 128 Henson Hall, Knoxville, TN 37996-3332 (E-mail: Gary S. Cuddeback at [email protected]. unc.edu or John G. Orme at [email protected]). The authors would like to express their appreciation to Shenyang Guo for his suggestions on an earlier version of this paper and to Niki Le Prohn at Casey Family Programs for her support. This article is the result of a collaborative effort of the Casey Family Programs Foster Family Project at The University of Tennessee and Casey Family Programs, an operating foundation delivering foster care, adoption, and other permanency planning services in 14 states. You may review more information about Casey Family Programs at http://www.casey.research.org/research and more information about Casey Family Programs Foster Family Project at The University of Tennesee at http://www. utcmhsrc.csw.utk.edu/caseyproject/. 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_02 19 searchers use bivariate tests to detect selection bias (e.g., χ to compare the race of participants and non-participants). Occasionally multiple regression methods are used (e.g., logistic regression with participation/ non-participation as the dependent variable). Neither of these methods can be used to correct substantive results for selection bias. Sample selection models are a well-developed class of econometric models that can be used to detect and correct for selection bias, but these are rarely used in social work research. Sample selection models can help further social work research by providing researchers with methods of detecting and correcting sample selection bias. [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.]