We believe IS researchers can and should do a better of job of improving (assuring) the validity of their findings by minimizing nonresponse error. To demonstrate that there is, in fact, a problem, we first present the response rates reported in six well-regarded IS journals and summarize how nonresponse error was estimated and handled in published IS research. To illustrate how nonresponse error may bias findings in IS research, we calculate its impact on confidence intervals. After

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... ng the impact of nonresponse on research findings, we discuss three post hoc remedies and three preventative measures for the IS researcher to consider. The paper concludes with a general discussion about nonresponse and its implications for IS research practice. In our delimitations section, we suggest directions for further exploring external validity. is a Research Psychologist and an Associate Professor of Educational Research at the University of Central Florida teaching structural equation modeling, multivariate statistics, survey research methodology, and advanced measurement theory. He is co-author of an internationally distributed book on Monte Carlo Simulation Studies using SAS software, and he is published research in many journals including Structural Equation Modeling, Multivariate Behavioral Research, Psychological Reports, and the Journal of Experimental Education. His research primarily centers on integrating psychometric and econometric models for panel data and studying fit index and power issues in SEM. Since 2000, he has served on more than 85 doctoral dissertation committees either chairing or assisting students with statistical/methodological issues.