Best Practices in Handling Missing Data in Psychological Research

Whether it stems from participant attrition, nonresponse, unwillingness to disclose information, technical errors, or flawed collection methods, incomplete data pose significant challenges to researchers in psychology. Although a rich methodological literature exists, applied researchers often lack clear guidance for aligning missing-data methods with study design, assumptions, and analytic goals. In this article, I provide a practical, assumption-aware framework for reasoning about missing data in psychology, emphasizing how missingness operates as a selection process and how method choice depends on the underlying data-generating structure. I review commonly used approaches, including likelihood-based estimation, multiple imputation, Bayesian data augmentation, and pattern-mixture models, highlighting their assumptions, strengths, and limitations. To support implementation and pedagogy, I introduce DataPatch, an interactive tool that allows users to simulate missing-data mechanisms, apply alternative handling strategies, and examine their consequences for estimation and interpretation (davidmoreau.shinyapps.io/DataPatch/). Together, the conceptual framework and accompanying tool aim to promote more transparent, principled, and informed handling of missing data in psychological research.