Detecting and Statistically Correcting Sample Selection Bias

Gary Cuddeback, University of North Carolina at Chapel Hill
Elizabeth Wilson, University of Tennessee - Knoxville
John G. Orme, University of Tennessee - Knoxville
Terri Combs-Orme, University of Tennessee, Knoxville

Abstract

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 researchers use bivariate tests to detect selection bias (e.g., χ2 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.