Modeling Survey Nonresponse Under a Cluster Sample Design: Classification and Regression Tree Methodologies Compared

• Activity Number: 285 - New Advances in Sample Design and Adjusting for Survey Nonresponse
• Type: Contributed
• Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Survey Research Methods Section
• Author(s): Michael Jones* and William Everett Cecere and Tien-Huan Lin and Jennifer Kali and Ismael Flores Cervantes
• Companies: Westat and Westat and Westat and Westat and Westat
• Keywords: classification trees; clustering; nonresponse bias; response propensities; survey weights; weighting class adjustments

Abstract

When computing survey weights for use during the analysis of complex sample survey data, an adjustment for nonresponse is often performed to reduce the bias of the estimates. Many algorithms and methodologies are available to analysts for modeling survey nonresponse for these adjustments. Lohr et al. (2015) discussed the benefits of using classification trees for estimating response propensities in surveys and how these methods could be used to reduce nonresponse bias. Cecere et al. (2020) extended their findings and recommendations using expanded simulations for more complex sample designs, such as a stratified design with equal sample size allocation. This paper will compare select algorithms when working with a complex cluster sample design. We evaluate the effect of the classification tree-based methods on the reduction of nonresponse bias in high response and low response settings, and investigate the performance of the methods when they are used to adjust survey weights. We discuss the benefits and limitations of using these methods for estimating response propensities in surveys that utilize a cluster sample.