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Comparing the Ability of Regression Modeling and Bayesian Additive Regression Trees to Predict Costs in a Responsive Survey Design Context

Author

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  • Wagner James
  • West Brady T.
  • Elliott Michael R.

    (University of Michigan, 4053 ISR, 426 Thompson St., Ann Arbor, MI 48104, U.S.A.)

  • Coffey Stephanie

    (U.S. Census Bureau, 4600 Silver Hill Road, Suitland, MD 20746, U.S.A.)

Abstract

Responsive survey designs rely upon incoming data from the field data collection to optimize cost and quality tradeoffs. In order to make these decisions in real-time, survey managers rely upon monitoring tools that generate proxy indicators for cost and quality. There is a developing literature on proxy indicators for the risk of nonresponse bias. However, there is very little research on proxy indicators for costs and almost none aimed at predicting costs under alternative design strategies. Predictions of survey costs and proxy error indicators can be used to optimize survey designs in real time. Using data from the National Survey of Family Growth, we evaluate alternative modeling strategies aimed at predicting survey costs (specifically, interviewer hours). The models include multilevel regression (with random interviewer effects) and Bayesian Additive Regression Trees (BART).

Suggested Citation

  • Wagner James & West Brady T. & Elliott Michael R. & Coffey Stephanie, 2020. "Comparing the Ability of Regression Modeling and Bayesian Additive Regression Trees to Predict Costs in a Responsive Survey Design Context," Journal of Official Statistics, Sciendo, vol. 36(4), pages 907-931, December.
    • Handle: RePEc:vrs:offsta:v:36:y:2020:i:4:p:907-931:n:8
    DOI: 10.2478/jos-2020-0043
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    References listed on IDEAS

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    Keywords

    Survey cost models; machine learning;

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