Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 453 - Recursive Partitioning for Modeling Survey Data and Randomized Trials
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 11:50 AM
Sponsor: Survey Research Methods Section
Abstract #309861
Title: A Comparison of Classification and Regression Tree Methodologies When Modeling Survey Nonresponse
Author(s): William Cecere* and Amy Lin and Jennifer Kali and Michael Jones
Companies: Westat and Westat and Westat and Westat
Keywords: classification trees; nonresponse bias; response propensities; survey weights; weighting class adjustments

When computing survey weights for use during analysis of complex sample survey data, an adjustment for nonresponse is often performed to reduce bias in estimates. Many algorithms and methodologies are available to analysts for modeling survey nonresponse. Lohr et al. (2015) discussed possible benefits of using regression trees for estimating response propensities in surveys and how these methods might be used to reduce nonresponse bias. In this paper we extend their findings and recommendations. Using expanded simulations we evaluate the effect of the methods on the reduction of nonresponse bias and further investigate the sensitivity of the methods when using survey weights. We discuss the benefits of using these methods for estimating response propensities in surveys.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program