Taking "Don't Knows" as Valid Responses: A Complete Random Imputation of Missing Data

Martin Kroh

No 442, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research

Abstract: Incomplete data is a common problem of survey research. Recent work on multiple imputation techniques has increased analysts' awareness of the biasing effects of missing data and has also provided a convenient solution. Imputation methods replace non-response with estimates of the unobserved scores. In many instances, however, non-response to a stimulus does not result from measurement problems that inhibit accurate surveying of empirical reality, but from the inapplicability of the survey question. In such cases, existing imputation techniques replace valid non-response with counterfactual estimates of a situation in which the stimulus is applicable to all respondents. This paper suggests an alternative imputation procedure for incomplete data for which no true score exists: multiple complete random imputation, which overcomes the biasing effects of missing data and allows analysts to model respondents' valid "I don't know" answers.

Keywords: Missing data; Incomplete data; Non-response; Multiple imputation; Survey methodology; Mixture regression models; Vote choice (search for similar items in EconPapers)
JEL-codes: C81 D72 D80 (search for similar items in EconPapers)
Pages: 22 p.
Date: 2004
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