Quality and Quantity

, Volume 40, Issue 2, pp 225–244 | Cite as

Taking ‘Don’t Knows’ as Valid Responses: A Multiple Complete Random Imputation of Missing Data

  • Martin Kroh


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.


incomplete data missing data mixture regression models multiple imputation non-response survey methodology vote choice 


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Copyright information

© Springer 2006

Authors and Affiliations

  1. 1.German Institute for Economic Research, Socio-Economic Panel Study (SOEP), DIW BerlinGermanyBerlin

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