Not Asked and Not Answered: Multiple Imputation for Multiple Surveys
未提问与未回答：多项调查的多重插补AI 解读 ↓
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未提问与未回答：多项调查的多重插补
- 研究问题
- 在多次独立横截面调查中，部分问题整项未被询问、部分个体又未作答时，如何联合补齐信息并估计总体量。
- 方法与思路
- 作者在多重插补中加入个体与调查层面的层级回归，将抽样权重所依据的变量纳入分析，再对观测和插补响应重新加权。
- 材料陈述的贡献
- 摘要称，方法处理跨调查的缺题与个体缺答，并提出比较插补和非插补数据的拟合诊断，以选前民意调查展示应用。
- 阅读关注点
- 应核对两类缺失各自需要的假设、调查间信息共享的模型限制，以及权重处理和插补不确定性如何进入最终总体估计。
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原文摘要
We present a method of analyzing a series of independent cross-sectional surveys in which some questions are not answered in some surveys and some respondents do not answer some of the questions posed. The method is also applicable to a single survey in which different questions are asked or different sampling methods are used in different strata or clusters. Our method involves multiply imputing the missing items and questions by adding to existing methods of imputation designed for single surveys a hierarchical regression model that allows covariates at the individual and survey levels. Information from survey weights is exploited by including in the analysis the variables on which the weights were based, and then reweighting individual responses (observed and imputed) to estimate population quantities. We also develop diagnostics for checking the fit of the imputation model based on comparing imputed data to nonimputed data. We illustrate with the example that motivated this project: a study of pre-election public opinion polls in which not all the questions of interest are asked in all the surveys, so that it is infeasible to impute within each survey separately.