Assessing the Impact of Nonresponse Adjustment Techniques on Survey Sampling Error and Population Inference

Posted: May 02, 2011

• Gavin Russell
• Grace Brooks
• Hannah Turner

Abstract

Survey research represents a cornerstone of empirical investigation across social, health, and behavioral sciences, yet the persistent challenge of nonresponse threatens the validity of inferences drawn from sample data. While substantial literature has addressed the biasing effects of nonresponse and developed various adjustment techniques to mitigate these effects, a critical gap remains in understanding how these adjustments influence sampling error and, consequently, population inference accuracy. The prevailing paradigm in nonresponse research has predominantly emphasized bias reduction, often treating variance inflation as a secondary concern or assuming it to be negligible. This research challenges that paradigm by systematically examining the dual impact of nonresponse adjustment on both bias and variance components of error, thereby providing a more nuanced understanding of how adjustment techniques affect overall inference quality. Our investigation is motivated by the observation that as survey response rates continue to decline across research domains, the reliance on sophisticated adjustment techniques increases correspondingly. However, the mathematical properties of these techniques suggest that they may introduce substantial variability in estimates, particularly when applied to complex sample designs or heterogeneous populations. The central research question guiding this study asks: How do different nonresponse adjustment techniques affect the trade-off between bias reduction and variance inflation, and under what conditions does this trade-off optimize or compromise population inference?