Not Asked and Not Answered: Multiple Imputation for Multiple Surveys
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未问与未答：多项调查的多重插补
- 研究问题
- 多项独立横截面调查中，部分问题整项未问、部分受访者未答时，如何联合利用调查信息估计总体数量？
- 方法与思路
- 作者以包含个体与调查层协变量的分层回归进行多重插补，纳入权重构造变量，再对观测及插补回答重新加权。
- 材料陈述的贡献
- 摘要提出跨调查插补与总体估计程序，并通过比较插补和未插补数据开发拟合诊断，以选前民意调查展示应用。
- 阅读关注点
- 应核对跨调查信息共享的模型条件、未问与未答的处理区别、权重及插补不确定性的传播，以及诊断的检验能力。
- 阅读边界
- 仅据稿件版本未注明的 OpenAlex 摘要；缺失机制和不确定性计算未说明，独立横截面调查不能据此视为面板。
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.