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

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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 University Tilburg School of Social and Behavioral Sciences – Methodology and Statistics, Tilburg, The Netherlands.)

  • de Waal Ton

    (Centraal Bureau voor de Statistiek – Process development and methodology Den Haag, The Netherlands and Tilburg University Tilburg School of Social and Behavioral Sciences – Methodology and Statistics, Tilburg, The Netherlands.)

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.

Suggested Citation

  • Boeschoten Laura & Oberski Daniel & de Waal Ton, 2017. "Estimating Classification Errors Under Edit Restrictions in Composite Survey-Register Data Using Multiple Imputation Latent Class Modelling (MILC)," Journal of Official Statistics, Sciendo, vol. 33(4), pages 921-962, December.
    • Handle: RePEc:vrs:offsta:v:33:y:2017:i:4:p:921-962:n:5
    DOI: 10.1515/jos-2017-0044
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    References listed on IDEAS

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    1. Forcina, Antonio, 2008. "Identifiability of extended latent class models with individual covariates," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5263-5268, August.
    2. Li‐Chun Zhang, 2012. "Topics of statistical theory for register‐based statistics and data integration," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(1), pages 41-63, February.
    3. Daniël W. Palm & L. Andries Ark & Jeroen K. Vermunt, 2016. "Divisive Latent Class Modeling as a Density Estimation Method for Categorical Data," Journal of Classification, Springer;The Classification Society, vol. 33(1), pages 52-72, April.
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    Cited by:

    1. L. Boeschoten & M. A. Croon & D. L. Oberski, 2019. "A Note on Applying the BCH Method Under Linear Equality and Inequality Constraints," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 566-575, October.
    2. Peter G. M. van der Heijden & Maarten Cruyff & Paul A. Smith & Christine Bycroft & Patrick Graham & Nathaniel Matheson‐Dunning, 2022. "Multiple system estimation using covariates having missing values and measurement error: Estimating the size of the Māori population in New Zealand," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 156-177, January.
    3. Alexandru Cernat & Daniel L. Oberski, 2022. "Estimating stochastic survey response errors using the multitrait‐multierror model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 134-155, January.
    4. Ton de Waal & Arnout van Delden & Sander Scholtus, 2020. "Multi‐source Statistics: Basic Situations and Methods," International Statistical Review, International Statistical Institute, vol. 88(1), pages 203-228, April.

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