Data Deidentification for Data Sharing in Education and Psychological Research: Importance, Barriers, and Techniques - Jeffrey. A. Shero, Alexis E. Swanz, Allyson L. Hanson, Sara A. Hart, Jessica A. R. Logan, 2025

Abstract:  In this article we discuss the importance of data sharing in education and psychological research, emphasizing the historical context of data sharing, the current open-science movement, and the so-called replication crisis. We additionally explore the barriers to data sharing, particularly the fear of incorrectly deidentifying data or accidentally including private information. We then highlight the importance of deidentifying data for data sharing. Finally, we present specific techniques for data deidentification, namely nonperturbative and perturbative methods, and make recommendations for which techniques are relevant for specific types of variables. To assist readers in implementing the material from this study, we have additionally created an interactive tutorial as a Shiny web application that is publicly available and free to use.