Nonresponse arises frequently in surveys and follow-ups are routinely made to increase the response rate. In order to monitor the follow-up process, callback data have been used in social sciences and survey studies for decades. In modern surveys, the availability of callback data is increasing because the response rate is decreasing and follow-ups are essential to collect maximum information. Although callback data are helpful to reduce the bias in surveys, such data have not been widely used in statistical analysis until recently. We propose a stableness of resistance assumption for nonresponse adjustment with callback data. We establish the identification and the semiparametric efficiency theory under this assumption, and propose a suite of semiparametric estimation methods including a doubly robust one, which generalize existing parametric approaches for callback data analysis. We apply the approach to a Consumer Expenditure Survey dataset. The results suggest an association between nonresponse and high housing expenditures.

翻译：无应答在调查中频繁出现，通常通过回访来提高应答率。为监控回访过程，社会学和调查研究中数十年来一直使用回调数据。在现代调查中，由于应答率下降且回访对于收集最大信息至关重要，回调数据的可用性正在增加。尽管回调数据有助于减少调查偏差，但直到最近这类数据才被广泛用于统计分析。我们提出了一种基于回调数据的无应答调整的稳定性抗性假设。在该假设下，我们建立了识别性和半参数效率理论，并提出了一套半参数估计方法（包括一种双重稳健方法），这些方法推广了现有的回调数据分析参数方法。我们将该方法应用于消费者支出调查数据集，结果表明无应答与高住房支出之间存在关联。