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