JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1242 10.6339/26-JDS1242 Data Science in Action Predicting “Yes”: Machine Learning and Diverse Data to Boost Respondent Cooperation https://orcid.org/0009-0006-2926-7931 SalujaRashirashisaluja@westat.com1∗ SunHanyu1 KorkmazGizem1 CarleJill1 HubbardRyan1 EdwardsBrad1 DulaneyRick1 Westat, Bethesda, MD, United States Corresponding author. Email: rashisaluja@westat.com. 2026178202600114 Supplementary Material

R and Python code for sequence feature construction, clustering analysis, and multinomial logistic regression modeling.

16420261472026 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.

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.

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