Inference under imputation for missing survey data

缺失调查数据的推论

• 批准号: 327048-2006
• 负责人: Haziza, David
• 金额: $ 0.8万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=397544
• 关键词: Inference; under; imputation; missing; survey

Despite the best efforts made by the survey staff to maximize response, it is almost certain that some degree of nonresponse will occur in large scale surveys. Recent studies suggest that participation in surveys is declining over time, resulting in appreciable nonresponse rates. Survey practitioners distinguish between unit and item nonresponse. Unit nonresponse occurs when no measurement was obtained on a sample unit. Item nonresponse occurs in a survey when a sampled element participates in the survey but fails to provide acceptable responses on one or more of the survey items. It is usually handled by some form of imputation which involves ''filling in'' missing values. Imputation may achieve an effective reduction of the nonresponse bias, provided suitable auxiliary information is available for all the sampled elements and appropriately incorporated in the imputation model and/or the nonresponse model. However, imputation presents some important difficulties: (i) it distorts the relationships between variables; (ii) treating the imputed values as if they were true values may lead to a substantial underestimation of the variance of the estimator, especially if the item nonresponse rate is appreciable and (iii) some imputation methods tend to distort the distribution of the items being imputed. Some aspects that address (i)-(iii) have been considered in the literature and some solutions have been proposed. However, several aspects remain unresolved.  The main goal of this research is to pursue the development of techniques in many important directions that have not been considered so far. Developing these new techniques should provide a better understanding of imputation as a method for treating item nonresponse and provide new methods that may be used in the course of a survey.

尽管调查人员尽了最大努力以最大限度地提高答复率，但几乎可以肯定的是，在大规模调查中会出现某种程度的无答复情况。最近的研究表明，随着时间的推移，调查参与率正在下降，导致不答复率显着上升。调查从业者区分单位不答复和项目不答复。当样本单元未获得测量结果时，就会发生单元无响应。当抽样元素参与调查但未能对一个或多个调查项目提供可接受的答复时，调查中就会出现项目无答复。它通常通过某种形式的插补来处理，其中涉及“填充”缺失值。假设合适的辅助信息可用于所有采样元素并适当地合并到插补模型和/或无响应模型中，则插补可以实现无响应偏差的有效减少。然而，插补存在一些重要的困难：（i）它扭曲了变量之间的关系； (ii) 将估算值视为真实值可能会导致估计量方差的大幅低估，特别是在项目无答复率相当大的情况下，以及 (iii) 某些估算方法往往会扭曲估算项目的分布。文献中已经考虑了解决（i）-（iii）的一些方面，并提出了一些解决方案。然而，仍有几个方面尚未解决。  这项研究的主要目标是追求迄今为止尚未考虑的许多重要方向的技术发展。开发这些新技术应该可以更好地理解插补作为处理项目无答复的方法，并提供可在调查过程中使用的新方法。