Reduction of Nonresponse Bias in Surveys through Case Prioritization
Reduction of Nonresponse Bias in Surveys through Case Prioritization
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通过案例优先排序减少调查中的不答复偏差
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
M. Lindblad
中科院分区：
文献类型：
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作者：
Andy Peytchev;Sarah F. Riley;J. Rosen;Joseph Murphy;M. Lindblad
如何提高反应率可以决定估计中剩余的无反应偏差。研究通常针对最有可能被采访的样本成员，以最大限度地提高回复率。相反，我们建议从研究开始就针对可能的非应答者使用不同的方案，以尽量减少无反应偏差。为了确定样本成员的目标，可以利用各种信息来源：采访者收集的资料、前几轮人口统计和实质性调查数据以及行政数据。使用这些数据，可以估计任何样本成员成为非回答者的可能性，并且在那些最不可能回应的样本案例中，可以采用更有效（通常更昂贵）的调查方案来获得回答者的合作。本文描述了这种减少非反应偏差的方法的两个组成部分。我们展示了基于响应倾向模型的案例优先级分配，并展示了使用不同协议优先案例的经验结果。在现场数据收集中，随机选取一半低响应倾向的病例，给予更高的优先级和更多的资源。对高优先级案件的资源分配作为采访者的奖励。我们发现，在调查之前，我们在预测响应结果方面相对成功，并强调需要测试干预措施，以便从病例优先排序中受益。
How response rates are increased can determine the remaining nonresponse bias in estimates. Studies often target sample members that are most likely to be interviewed to maximize response rates. Instead, we suggest targeting likely nonrespondents from the onset of a study with a different protocol to minimize nonresponse bias. To inform the targeting of sample members, various sources of information can be utilized: paradata collected by interviewers, demographic and substantive survey data from prior waves, and administrative data. Using these data, the likelihood of any sample member becoming a nonrespondent is estimated and on those sample cases least likely to respond, a more effective, often more costly, survey protocol can be employed to gain respondent cooperation. This paper describes the two components of this approach to reducing nonresponse bias. We demonstrate assignment of case priority based on response propensity models, and present empirical results from the use of a different protocol for prioritized cases. In a field data collection, a random half of cases with low response propensity received higher priority and increased resources. Resources for high-priority cases were allocated as interviewer incentives. We find that we were relatively successful in predicting response outcome prior to the survey and stress the need to test interventions in order to benefit from case prioritization.