JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1228 10.6339/26-JDS1228 Data Science in Action Designing Accessible and Dependable Tools for Vocational Rehabilitation Data Analysis TaylorRuthruth.taylor@usu.edu1∗ BeanBrennan1 PhillipsBrian2 FlemingAllison3 Utah State University, Department of Mathematics and Statistics, Logan, UT, USA Utah State University, Department of Special Education and Rehabilitation Counseling, Logan, UT, USA Pennsylvania State University, Department of Educational Psychology, Counseling, and Special Education, University Park, PA, USA Corresponding author. Email: ruth.taylor@usu.edu. 202655202624237339316820252132026 2026 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2026 Open access article under the CC BY license.

The U.S. Rehabilitation Services Administration (RSA) has partnered with state vocational rehabilitation (VR) agencies since 1973 to improve employment outcomes for individuals with disabilities. A critical resource in this effort is the RSA-911 dataset, a quarterly collection of standardized participant data. However, its complex structure, including high rates of missing or ambiguous values, poses significant challenges for effective analysis. We address these challenges by developing an R package designed to streamline the cleaning and analysis of RSA-911 data, as well as the newly introduced Transition Readiness Toolkit (TRT) scores data (R Core Team, 2021). The TRT assesses participants’ improvement across services and offers a critical measure of VR program effectiveness. Using this R package, our work offers the first analysis of the relationship between TRT pre-post scores and RSA-911 demographic data, providing insights into program outcomes. Additionally, we deliver a user-friendly online dashboard, built with the shiny framework, to allow VR counselors and researchers to independently analyze RSA-911 and TRT data (Chang et al., 2024). This dashboard features intuitive visualizations and workflows, making it easier to generate reproducible analyses without requiring extensive technical expertise. By automating data preparation and providing accessible analysis tools, this project contributes to the field of vocational rehabilitation by facilitating more efficient research and empowering VR professionals with data-driven insights. The tools presented offer a framework for future studies, enhancing the consistency, flexibility, and reproducibility of VR data analysis.

 data dashboard data exploration messy data R package shiny References Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, ..., Borges B (2024). shiny: Web application framework for r. R package version 1.8.1.1. Commission UEEO (1973). Rehabilitation act of 1973 (Original text). US EEOC. https://www.eeoc.gov/rehabilitation-act-1973-original-text. Accessed 8-15-2025. Fleming AR, Phillips BN, Riesen T, Langone A (2024). Enhancing transition outcomes: A toolkit to facilitate data-driven pre-employment transition services. Journal of Vocational Rehabilitation, (Preprint), 1–13. R Core Team (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. RSA (2004). Reporting manual for the case service report (rsa-911). Example of documentation https://rsa.ed.gov/sites/default/files/subregulatory/pd-16-04.pdf. Accessed 8-15-2025. RSA (2024a). About RSA - Rehabilitation Services Administration. https://rsa.ed.gov/about. Accessed 8-15-2025. RSA (2024b). Case Service Report (RSA-911). Case Service Report (RSA-911) | Rehabilitation Services Administration. Retrieved from https://rsa.ed.gov/performance/rsa-911-policy-directive. Accessed 8-15-2025.