Journal of Data Science

In the evolving field of survey research, leveraging machine learning to predict response behavior has transformative potential for the efficiency of survey operations. Integrating multiple data sources may improve response prediction by providing more nuanced insights for household outreach. This study presents a model-driven approach to enhancing respondent cooperation in the Medical Expenditure Panel Survey (MEPS) by combining features from disparate data sources. MEPS is a longitudinal household survey with 5 rounds of interviewing over 2.5 years. Its sample is derived prior National Health Interview Survey (NHIS) participants. MEPS Round 1 response rates are critical for sustaining representativeness throughout each panel. In this study, we constructed a multimodal machine learning model to predict (1) the likelihood of a positive response for an upcoming contact attempt and (2) the likelihood that new panel households complete a Round 1 interview. The model integrates tract-level data from the American Community Survey (ACS), outcomes from the Advance Call Records (ACR) made prior to MEPS Round 1, and paradata from the early contact period. We also explored the relative contributions of these sources to model performance. Our model aims to help manage field labor by identifying complex cases needing specialized support. It can also assist in determining the optimal mode for the next contact to increase the chance of a completed interview. Beyond improving MEPS operations, this study offers a roadmap for incorporating additional data sources to support fieldwork.

This paper presents an in-depth analysis of patterns and trends in the open-source software (OSS) contributions by the U.S. federal government agencies. OSS is a unique category of computer software notable for its publicly accessible source code and the rights it provides for modification and distribution for any purpose. Prompted by the Federal Source Code Policy (USCIO, 2016), Code.gov was established as a platform to facilitate the sharing of custom-developed software across various federal government agencies. This study leverages data from Code.gov, which catalogs OSS projects developed and shared by government agencies, and enhances this data with detailed development and contributor information from GitHub. By adopting a cost estimation methodology that is consistent with the U.S. national accounting framework for software investment proposed in Korkmaz et al. (2024), this research provides annual estimates of investment in OSS by government agencies for the 2009–2021 period. The findings indicate a significant investment by the federal government in OSS, with the 2021 investment estimated at around $407 million. This study not only sheds light on the government’s role in fostering OSS development but also offers a valuable framework for assessing the scope and value of OSS initiatives within the public sector.