Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/18294
Authors: 
Kroh, Martin
Year of Publication: 
2004
Series/Report no.: 
DIW Discussion Papers 442
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.
Subjects: 
missing data
incomplete data
non-response
multiple imputation
survey methodology
mixture regression models
vote choice
JEL: 
D80
D72
C81
Document Type: 
Working Paper

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