Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/18294 
Autor:innen: 
Erscheinungsjahr: 
2004
Schriftenreihe/Nr.: 
DIW Discussion Papers No. 442
Verlag: 
Deutsches Institut für Wirtschaftsforschung (DIW), Berlin
Zusammenfassung: 
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.
Schlagwörter: 
missing data
incomplete data
non-response
multiple imputation
survey methodology
mixture regression models
vote choice
JEL: 
D80
D72
C81
Dokumentart: 
Working Paper

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