The Role of Multiple Imputation in Addressing Missing Data and Preserving Statistical Validity Across Studies

Posted: Oct 28, 2023

• Hazel Morris
• Hunter Barnes
• Isaac Long

Abstract

Missing data constitutes one of the most fundamental methodological challenges in empirical research, with implications that extend beyond individual studies to affect the cumulative nature of scientific knowledge. The conventional approach to multiple imputation, while statistically sound in principle, often fails to account for the complex interdependencies that characterize missing data patterns across different research contexts and study designs. This paper introduces a novel framework that reimagines multiple imputation not merely as a statistical correction technique but as an integrative methodology for preserving statistical validity across the entire research ecosystem. Our research addresses these limitations through the development of a quantum-inspired multiple imputation framework that integrates principles from information geometry and cross-study validation. This approach represents a paradigm shift from treating missing data as a statistical nuisance to recognizing it as an opportunity for enhancing methodological rigor across research domains.