Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/10419/18294
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dc.creatorKroh, Martin-
dc.date2004-
dc.date.accessioned2013-10-16T06:59:23Z-
dc.date.available2013-10-16T06:59:23Z-
dc.date.issued2013-10-16-
dc.identifierhttp://hdl.handle.net/10419/18294-
dc.identifierppn:396403204-
dc.identifier.urihttp://koha.mediu.edu.my:8181/xmlui/handle/10419/18294-
dc.descriptionIncomplete 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.-
dc.languageeng-
dc.publisherDeutsches Institut für Wirtschaftsforschung (DIW) Berlin-
dc.relationDIW-Diskussionspapiere 442-
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.subjectD80-
dc.subjectD72-
dc.subjectC81-
dc.subjectddc:330-
dc.subjectmissing data-
dc.subjectincomplete data-
dc.subjectnon-response-
dc.subjectmultiple imputation-
dc.subjectsurvey methodology-
dc.subjectmixture regression models-
dc.subjectvote choice-
dc.titleTaking ?don?t knows? as valid responses: A complete random imputation of missing data-
dc.typedoc-type:workingPaper-
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