Dissertation Research: An Investigation of the Nexus of Survey Nonresponse and Measurement Error

本项目将研究抽样调查中无答复和测量误差之间的关系。最近的研究还没有证明无应答率和无应答性偏见之间有很强的关系。尽管如此，最佳实践认为研究人员应该尝试最大限度地提高响应率。对于涉及减少无反应的做法，一种表达的担忧是，样本中不情愿的人通过说服努力成功地纳入受访者数据集，可能会提供充满测量误差的数据。然而，没有一项研究考察了受访者的倾向、对感兴趣的问题的真实价值以及受访者对该问题的测量误差特性之间的联系。这项研究将通过解决三个问题来填补这一空白。首先，在什么情况下，无回答倾向与感兴趣的调查变量有关？非接触或拒绝无反应更有可能导致无反应偏向？第二，无反应倾向、无反应偏差和测量误差之间的关系是什么？特别是，问题的性质和受访者的特征如何影响无回答偏差和测量误差之间的关系？非接触性的无反应倾向还是拒绝引起的无反应倾向更容易受到测量误差偏差的影响？第三，传统的统计调整或分析技术能解决这个问题吗？将对来自两个国家和两个区域调查的数据进行分析，以回答这些问题。这项研究将证明，当将资源投入无响应减少时，关键统计数据的测量误差增加会超过这一点。这项研究还将建议在实地收集期间或在调查后调整中进行更改，这些调整共同解释了两个误差源：无响应和测量误差。抽样调查是建立衡量国家福祉的关键指标以及检验个人和社会变化机制的机制。了解抽样调查的误差结构至关重要，因为从调查得出的结论可能是大错特错的。随着无回复率的持续上升，调查机构正在加大说服力度，试图保持一个有代表性的样本，以便做出推断。如果这些具有说服力的努力实际上降低了数据质量，那么用于这些努力的额外资金是被误导的。这项研究的结果将使调查实践者了解如何进行调查并获得更高质量的数据，以及使用调查了解何时调查得出的结果可能面临风险的科学界。作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立研究生涯。作为支持调查和统计方法研究的联合活动的一部分，这项研究得到了方法学、测量和统计计划和一个联邦统计机构联盟的支持。

This project will examine the nexus between nonresponse and measurement errors in sample surveys. Recent research has not demonstrated a strong relationship between nonresponse rates and nonresponse bias. Nonetheless, best practices argue that researchers should attempt to maximize response rates. One voiced concern about practices involving nonresponse reduction is that reluctant people in the sample, successfully brought into the respondent data set through persuasive efforts, may provide data filled with measurement error. However, no study has looked at links among the propensity to be a respondent, true values on a question of interest, and the respondent's measurement error properties for that question. The research will fill this gap by addressing three questions. First, under what circumstances is nonresponse propensity related to the survey variables of interest? Is noncontact or refusal nonresponse more likely to induce nonresponse bias? Second, what is the relationship between nonresponse propensity, nonresponse bias, and measurement error? In particular, how do properties of questions and characteristics of respondents affect the nexus between nonresponse bias and measurement error? Is nonresponse propensity arising from noncontact or that from refusal more susceptible to a correlation with measurement-error bias? Third, can traditional statistical adjustments or analytic techniques remedy the problem? Data from two national and two regional surveys will be analyzed to answer these questions. The research will demonstrate when placing resources into nonresponse reduction is outweighed by increases in measurement error on key statistics. The research also will suggest changes to be made during field collection or in postsurvey adjustments that jointly account for two error sources: nonresponse and measurement error. Sample surveys are the mechanism by which key indicators of the nation's well-being are created and through which mechanisms of personal and societal changes are tested. Understanding error structures of sample surveys - both in terms of when errors occur and the mechanism behind their occurrence - is critically important because conclusions made from surveys could be dramatically wrong. As nonresponse rates continue to rise, survey organizations are increasing their persuasive efforts in an attempt to maintain a representative sample on which inferences can be made. If these persuasive efforts actually lower the quality of data, then the additional funds for these efforts are misguided. The findings from this research will inform survey practitioners in how to conduct surveys and obtain better quality data and the scientific community that uses surveys to understand when survey-derived findings may be at risk. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career. The research is supported by the Methodology, Measurement, and Statistics Program and a consortium of federal statistical agencies as part of a joint activity to support research on survey and statistical methodology.