Analyzing the Effect of Nonresponse Bias on Statistical Validity in Social and Health Survey Research

Posted: Apr 30, 2019

• Lucas Thomas
• Harper White
• Samuel Martin

Abstract

Survey research represents a cornerstone of social and health sciences, providing essential data for policy decisions, resource allocation, and scientific understanding of population trends. However, the increasing challenge of survey nonresponse threatens the validity and reliability of findings derived from these data collection efforts. Nonresponse bias occurs when individuals who do not participate in surveys systematically differ from those who do, leading to distorted estimates of population parameters. The problem has intensified in recent decades with declining response rates across all types of surveys, from government-sponsored health studies to academic social research. Traditional approaches to handling missing data, such as complete-case analysis or simple imputation methods, often fail to adequately address the complex mechanisms underlying nonresponse. These methods typically assume that data are missing at random, an assumption frequently violated in practice. When missingness is related to unobserved variables or the outcome of interest itself, conventional approaches can produce severely biased estimates. This research addresses these limitations by developing and validating a novel methodological framework that more accurately characterizes and corrects for nonresponse bias. Our study makes several distinctive contributions to the field of survey methodology. First, we introduce a hybrid approach that combines machine learning techniques with causal inference methods to model nonresponse mechanisms more flexibly than previous approaches. Second, we empirically demonstrate the magnitude of bias introduced by conventional methods across multiple health outcomes and demographic groups. Third, we identify specific factors that systematically predict nonresponse, providing actionable insights for survey design and implementation. Finally, we propose a practical framework for researchers to assess and adjust for nonresponse bias in their own studies.