Estimating Classification Errors Under Edit Restrictions in Composite Survey-Register Data Using Multiple Imputation Latent Class Modelling (MILC)

Boeschoten Laura (), Oberski Daniel () and Ton de Waal ()
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Boeschoten Laura: Tilburg UniversityTilburg School of Social and Behavioral Sciences – Methodology and Statistics, PO Box 90153, Tilburg5000 LE, Netherlands and Centraal Bureau voor de Statistiek – Process development and methodology Henri Faasdreef 312, Den Haag 2492 JP, The Netherlands.
Oberski Daniel: Universiteit Utrecht – Social and Behavioural Sciences, Utrecht, Utrecht, The Netherlands and Tilburg UniversityTilburg School of Social and Behavioral Sciences – Methodology and Statistics, Tilburg, The Netherlands.
Ton de Waal: Centraal Bureau voor de Statistiek – Process development and methodology Den Haag, The Netherlands and Tilburg UniversityTilburg School of Social and Behavioral Sciences – Methodology and Statistics, Tilburg, The Netherlands.

Journal of Official Statistics, 2017, vol. 33, issue 4, 921-962

Abstract: Both registers and surveys can contain classification errors. These errors can be estimated by making use of a composite data set. We propose a new method based on latent class modelling to estimate the number of classification errors across several sources while taking into account impossible combinations with scores on other variables. Furthermore, the latent class model, by multiply imputing a new variable, enhances the quality of statistics based on the composite data set. The performance of this method is investigated by a simulation study, which shows that whether or not the method can be applied depends on the entropy R2 of the latent class model and the type of analysis a researcher is planning to do. Finally, the method is applied to public data from Statistics Netherlands.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:33:y:2017:i:4:p:921-962:n:5

DOI: 10.1515/jos-2017-0044

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