Education researchers’ concerns on data and material sharing: A latent class analysis
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: Special Education and Education researchers tend to support data and material sharing in principle, but actual rates of sharing remain low (e.g., Logan et al., 2025; Cook et al., 2023). The current study used latent class analysis (LCA) to identify distinct groups of education researchers based on their concerns related to sharing data and study materials. We analyzed survey responses from 1,383 education researchers across four sub-samples: those who did and did not share data, and those who did and did not share materials in their most recent scholarly work. Among the respondents who did not share data (Data Non-Sharers; DNS), we found four classes (Unconvinced DNS, Unconcerned DNS, Protective and Possessive DNS, and Overwhelmed DNS). For the respondents who did not share materials (Material Non-Sharers; MNS), we found three classes (Unconcerned MNS, Logistically/Ethically Concerned MNS, and Unconvinced MNS). Class membership was associated with sharing attitudes, behaviors, and future intentions to share. Taken together, these findings suggest that barriers and concerns related to sharing cluster into distinct patterns, which may call for tailored supports.
Source: EdArXiv (OSF Preprints)
Institutions: Florida State University, University of Oxford
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7225581395