Brick J M, Kalton G
Westat Inc., Rockville, Maryland 20850-3129, USA.
Stat Methods Med Res. 1996 Sep;5(3):215-38. doi: 10.1177/096228029600500302.
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.
在调查研究中会出现缺失数据，原因包括目标总体中的某个元素未被纳入调查的抽样框（未涵盖）、某个被抽样元素未参与调查（完全无回应）以及某个回应的被抽样元素未能对一项或多项调查项目提供可接受的回答（项目无回应）。已经开发了多种方法来试图以通用方式补偿缺失的调查数据，使得能够在不考虑缺失数据的情况下对调查数据文件进行分析。加权调整通常用于补偿未涵盖和完全无回应的情况。为缺失回答赋值的插补方法用于补偿项目无回应的情况。本文描述了已开发的各种加权和插补方法，并讨论了它们的优点和局限性。
