European Survey Research Association Annual Meeting

Using Machine Learning to Correct for Survey Nonresponse Bias
Dr Antje Kirchner (University of Nebraska- Lincoln)
Dr Curtis Signorino (University of Rochester)

Abstract: We compare survey nonresponse bias corrections using recent machine learning techniques to model response propensity. We apply these to the German ‘Labor Market and Social Security’ survey, using administrative data for both respondents as well as nonrespondents. We compare the nonresponse bias correction when using Adaptive LASSO with a polynomial expansion of regressors to existing techniques: logistic regression, neural nets, classification trees, and random forests