<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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).</p></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).

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

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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...

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

Online veröffentlicht: 10. Dez. 2020

Seitenbereich: 907 - 931

Eingereicht: 01. Aug. 2019

Akzeptiert: 01. Mai 2020

DOI: https://doi.org/10.2478/jos-2020-0043

Schlüsselwörter
Survey cost models, machine learning

© 2020 James Wagner et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Online veröffentlicht: 10. Dez. 2020

Seitenbereich: 907 - 931

Eingereicht: 01. Aug. 2019

Akzeptiert: 01. Mai 2020

DOI: https://doi.org/10.2478/jos-2020-0043

SchlüsselwörterSurvey cost models, machine learning

© 2020 James Wagner et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Survey cost models, machine learning