Comparing the Ability of Regression Modeling and Bayesian Additive Regression Trees to Predict Costs in a Responsive Survey Design Context
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James Wagner [1] ; Brady T. West [1] ; Michael R. Elliott [2]
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[1] University of Michigan–Ann Arbor
University of Michigan–Ann Arbor
City of Ann Arbor, Estados Unidos
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[2] U.S. Census Bureau
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- Localización: Journal of official statistics, ISSN 0282-423X, Vol. 36, Nº. 4, 2020, págs. 907-931
- Idioma: inglés
- DOI: 10.2478/jos-2020-0043
- Enlaces
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Resumen
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).
