Shiyu Zhang

Shiyu Zhang is a PhD candidate at the Michigan Program in Survey and Data Science. Before arriving at Michigan, she received master's degrees in immigration study, sociology and data science, and a bachelor's degree in psychology. Shiyu's dissertation focuses on the effect of adaptive survey design on estimates. She is also interested in collecting and using neighborhood features as auxiliary variables.

The Additional Effects of Adaptive Survey Design Beyond Post-Survey Adjustment: An Experimental Evaluation

Adaptive survey design refers to using targeted procedures to recruit different sampled cases. This technique strives to reduce bias and variance of survey estimates by trying to recruit a larger and more balanced set of respondents. However, it is not well understood how adaptive design can improve data and survey estimates beyond the well-established post-survey adjustment. This paper reports the results of an experiment that evaluated the additional effect of adaptive design to post-survey adjustments. The experiment was conducted in the Detroit Metro Area Communities Study in 2021. We evaluated the adaptive design in five outcomes: 1) response rates, 2) demographic composition of respondents, 3) bias and variance of key survey estimates, 4) changes in coefficients of regression model results, and 5) costs. The most significant benefit of the adaptive design was its ability to generate more efficient survey estimates with smaller variances and smaller design effects.