Addressing nonresponse bias through adaptive survey design
A stratification method that uses predicted survey outcomes to identify groups for tailored data collection strategies.
Read paper ↗Yongchao Ma, Ph.D.
Social science methodologist and statistician
Better data.
Stronger social science.
I develop statistical and computational methods to improve how social data are collected. My research examines how to use limited resources to obtain survey responses that better reflect the population being studied, including through adaptive survey design and AI-assisted recruitment.
I earned my Ph.D. in Survey and Data Science from the University of Michigan in 2026. My work also spans health behavior, attitudes toward surveys, public understanding of polls, and open-source research software.
Survey design and participationComputational social scienceAI-assisted data collection
Selected work
Understanding cannabis use through daily web surveys
A pilot study of changing cannabis use motives and contexts, with implications for adaptive interventions.
Read paper ↗Machine learning for systematic reviews
Co-authored research introducing ASReview, an open-source framework for efficient and transparent systematic reviewing.
Read paper ↗Doctoral research
On effective and efficient survey data collection at the crossroads of statistics and artificial intelligence
Read dissertation · PDF ↗Research software
Open-source tools for active-learning-assisted reviewing and exploring text in review datasets.
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