Taking don't knows as valid responses: a multiple complete random imputation of missing data

Martin Kroh

Research output: Contribution to journalArticleAcademic

11 Citations (Scopus)


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.
Original languageUndefined
Pages (from-to)225-244
JournalQuality and quantity
Issue number2
Publication statusPublished - 2006
Externally publishedYes


  • IR-61247

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