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).

中文翻译：

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比较回归建模和贝叶斯加性回归树在响应式调查设计环境中预测成本的能力。


响应式调查设计依赖于来自现场数据收集的传入数据来优化成本和质量权衡。为了实时做出这些决策，调查管理人员依靠生成成本和质量代理指标的监控工具。关于无反应偏差风险代理指标的文献正在不断涌现。然而，关于成本代理指标的研究很少，而且几乎没有任何旨在预测替代设计策略下的成本的研究。调查成本和代理误差指标的预测可用于实时优化调查设计。使用全国家庭成长调查的数据，我们评估了旨在预测调查成本（特别是访谈时间）的替代建模策略。这些模型包括多级回归（具有随机访谈者效应）和贝叶斯加性回归树 (BART)。