Journal of Data Science

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.