Data Deidentification for Data Sharing in Education and Psychological Research - Importance, Barriers, and Techniques

Oct 15, 2026

Uploaded by Pentious

The article examines why data sharing matters in education and psychological research, situating the topic within historical practice and today’s open-science and replication-crisis context. It analyzes key barriers to sharing, especially concerns about failing to properly deidentify information or exposing private data. The work then outlines the rationale for deidentification and presents nonperturbative and perturbative techniques, offering recommendations by variable type, supported by a free Shiny interactive tutorial.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Open-Science Movement: Data Sharing

Data Deidentification: Importance and Barriers

Deidentification Techniques and Recommendations

AERA Open January-December 2025, Vol. 11, No. 1, pp. 1–13 DOI: 10. 1177/23328584251352814 Article reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)© The Author(s) 2025. [https://journals.sagepub.com/home/ero](https://journals.sagepub.com/home/ero) Data Deidentification for Data Sharing in Education and Psychological Research: Importance, Barriers, and Techniques Jeffrey. A. Shero Alexis E. Swanz Allyson L. Hanson Vanderbilt University Sara A. Hart University of Waterloo Jessica A. R. Logan Vanderbilt University 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. Keywords: collaboration, research utilization, psychology, secondary data analysis, data deidentification, data privacy, data sharing, open science, data anonymity Introduction Data sharing has become increasingly important in the field of education research as the demand for transparency, collaboration, and rigor in scientific inquiry has grown (Borgman, 2012; Institute for Education Sciences, 2024; National Institutes of Health, 2023) . Despite the growing recognition of the importance of data sharing for education research, many researchers still do not openly share their data (Fleming et al., 2024; Logan et al., 2025; Makel et al., 2021) . A lack of knowledge and resources regarding how and where to share data can contribute to this reluctance, with many researchers being uncertain on the appropriate repositories, platforms, or formats for sharing their data as well as how to ensure proper documentation and metadata for effective reuse (Kim & Adler, 2015; White et al., 2024) . To foster a culture of open science and support researchers in sharing their data, it is vital to provide researchers with adequate guidance, training, and support in data-sharing practices as well as addressing concerns related to privacy, intellectual property, and research integrity (Leonelli, 2014) . This article aims to provide an overview of the significance of quantitative data sharing in education research, the barriers to data sharing, and the importance of deidentifying data and provide a discussion of the basic techniques used to deidentify data while preserving their usefulness and integrity. Additionally, we highlight the limitations of work currently in the field and identify several areas of future direction that are needed to create standardized recommendations for data sharing in our field. As such, this paper serves as a comprehensive guide to deidentifying data for data sharing and a tutorial for implementing these techniques in the education field. We also contribute to creating more standardized practices for data deidentification and address the need for additional research on the topic. Open-Science Movement: Data Sharing The history of data sharing in education and psychological research dates back to the early days of the scientific Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ([https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4

FAQs

Why is data sharing important in education and psychological research?

The article explains that data sharing supports transparency, collaboration, and rigor, and aligns with open-science goals to strengthen reliability and reproducibility, particularly in the context of the replication crisis.

What are the main barriers researchers face when sharing data?

Key barriers include fear of incorrect deidentification and the accidental inclusion of private information, along with uncertainty about appropriate repositories, platforms, formats, and adequate documentation/metadata.

What techniques are presented for data deidentification?

The article presents nonperturbative and perturbative deidentification methods and recommends which techniques fit specific types of variables while preserving usefulness and integrity.

Oct 15, 2026

Uploaded by Pentious