Handling missing data in survey research.
Handling missing data in survey research.
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DOI：
10.1177/096228029600500302
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发表时间：
1996-09-01
影响因子：
2.3
通讯作者：
Kalton, G
中科院分区：
文献类型：
--
作者：
Brick, J M;Kalton, G
缺失数据发生在调查研究中，因为目标人口中的一个元素不包括在调查的抽样框架中（未覆盖），因为抽样元素不参与调查（完全无应答），因为响应抽样元素未能提供可接受的答复一个或多个调查项目（项目无应答）。已经开发了各种方法，试图以通用方式补偿缺失的调查数据，使调查的数据文件能够在不考虑缺失数据的情况下进行分析。加权调整通常用于补偿未覆盖和完全未响应。为缺失的回答赋值的插补方法用于补偿项目无回答。本文介绍了各种加权和估算方法，已经开发，并讨论其优点和局限性。
Missing data occur in survey research because an element in the target population is not included on the survey's sampling frame (noncoverage), because a sampled element does not participate in the survey (total nonresponse) and because a responding sampled element fails to provide acceptable responses to one or more of the survey items (item nonresponse). A variety of methods have been developed to attempt to compensate for missing survey data in a general purpose way that enables the survey's data file to be analysed without regard for the missing data. Weighting adjustments are often used to compensate for noncoverage and total nonresponse. Imputation methods that assign values for missing responses are used to compensate for item nonresponses. This paper describes the various weighting and imputation methods that have been developed, and discusses their benefits and limitations.