Tabellenauswertungen im Zensus unter Berücksichtigung fehlender Werte

Ralf Münnich (), Siegfried Gabler (), Christian Bruch, Jan Pablo Burgard (), Tobias Enderle, Jan-Philipp Kolb () and Thomas Zimmermann

AStA Wirtschafts- und Sozialstatistisches Archiv, 2015, vol. 9, issue 3, 269-304

Abstract: The European Statistics Code of Practice defines standards for the production of statistics, covering data quality aspects. As important items within the quality framework, sampling and non-sampling errors are covered including measuring the accuracy of statistics in the presence of missing values. In practice, missing values are often treated by using imputation methods, where two aspects should be considered. First, the plausibility of imputed values plays an important role in official statistics applications. This can be examined with editing methods. Second, measuring the accuracy e. g. via variance estimation must incorporate the randomness of the imputation process. Since all relevant methods to be considered are computer-intensive, a comparative study of the methodology must include their applicability in the presence of large surveys. The German register-assisted census 2011 has been conducted using a large sample. Accuracy goals for the census where given in the census law for the determination of the population size where imputation does not play any role. This aspect also holds for other variables in case of mandatory participation. However, in case of future censuses when some variables are based on voluntary participation, imputation has to be considered in the context of accuracy measurement as well. This paper presents the results of a feasibility study of variance or MSE estimation under imputation in large-scale surveys focusing on the register-assisted census. The main aim is to compare selected single and multiple methods considering the plausibility of imputed values. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Einfache Imputation; Multiple Imputation; Editing; Varianzschätzung; Single imputation; Multiple imputation; Editing; Variance estimation (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11943-015-0175-8

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