Asst. Prof. Yajuan Si to Work on Unified Theory for Survey Sampling

Congratulations to Asst. Prof. Yajuan Si, jointly appointed in Biostatistics and Medical Informatics and in Population Health Sciences, on the receipt of her first NSF grant!

The project aims to develop a unified framework for survey weighting through novel modifications of multilevel regression and poststratification (MrP) to incorporate design-based information into modeling.

Real-life survey data often are unrepresentative due to selection bias and non-response. Existing methods for adjusting for known differences between the sample and population from which the sample is drawn have some advantages but also practical limitations. Classical weights are subject to large variability and can result in unstable estimators, while regression approaches present computational and modeling challenges. The new framework developed by these investigators will allow adjustment for selection bias and non-response as well as improvements in design-respecting inference.

Using this approach, survey analysts will be able to properly account for unignorable design issues in the regression framework, and practitioners who conduct surveys in government, academic, commercial, and non-profit sectors will be able to construct statistically efficient survey weights in a routine manner. This new framework may be applicable to problems resulting from the newly emerging explosion of "big data", such as integration of surveys from multiple sources, analysis of streaming data, and respondent-driven sampling.

The project will develop software that can be accessed by the general research community.