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反应与测量误差之间的关系。最近的研究并没有证明无反应率和无反应偏差之间有很强的关系。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。关于减少不回应的做法的一个担忧是，样本中不情愿的人，通过说服的努力成功地进入了被调查者的数据集，可能会提供充满测量误差的数据。然而，没有研究关注被调查者的倾向、对感兴趣的问题的真实价值以及被调查者对该问题的测量误差属性之间的联系。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，无反应倾向与感兴趣的调查变量相关？不接触或拒绝不反应更容易引起不反应偏见吗？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，问题的性质和被调查者的特征如何影响非反应偏差和测量误差之间的关系？不接触引起的不反应倾向还是拒绝引起的不反应倾向更容易与测量误差偏差相关？第三，传统的统计调整或分析技术能解决这个问题吗？将分析来自两个国家和两个区域调查的数据来回答这些问题。该研究将证明，在关键统计数据的测量误差增加的情况下，将资源投入到减少非响应上是不重要的。该研究还将建议在实地收集或调查后调整期间进行更改，这两种错误来源共同考虑：无响应和测量误差。抽样调查是建立国家福祉关键指标的机制，也是检验个人和社会变化机制的机制。了解抽样调查的错误结构——包括错误发生的时间和错误发生背后的机制——是至关重要的，因为从调查中得出的结论可能是严重错误的。由于不回复率持续上升，调查机构正在增加他们的说服努力，试图保持一个有代表性的样本，可以做出推断。如果这些有说服力的努力实际上降低了数据的质量，那么为这些努力提供的额外资金就是错误的。这项研究的结果将告知调查从业者如何进行调查并获得更高质量的数据，并告知使用调查的科学界何时可能面临调查结果的风险。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，该研究得到了方法、测量和统计计划和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.