Using Multiple Imputation of Latent Classes to construct population census tables with data from multiple sources

The Multiple Imputation of Latent Classes (MILC) method combines multiple imputation and latent class analysis to correct for misclassification in combined datasets. Furthermore, MILC generates a multiply imputed dataset which can be used to estimate different statistics in a straightforward manner,...

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Bibliographic Details
Published in:Survey methodology 2022-06, Vol.48 (1), p.119
Main Authors: Boeschoten, Laura, Scholtus, Sander, Daalmans, Jacco, Vermunt, Jeroen K, de Waa, Ton
Format: Article
Language:English
Subjects:
Censuses
Latent class analysis
Multiple imputation
Simulation
Uncertainty
Online Access:Get full text
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Summary:The Multiple Imputation of Latent Classes (MILC) method combines multiple imputation and latent class analysis to correct for misclassification in combined datasets. Furthermore, MILC generates a multiply imputed dataset which can be used to estimate different statistics in a straightforward manner, ensuring that uncertainty due to misclassification is incorporated when estimating the total variance. In this paper, it is investigated how the MILC method can be adjusted to be applied for census purposes. More specifically, it is investigated how the MILC method deals with a finite and complete population register, how the MILC method can simultaneously correct misclassification in multiple latent variables and how multiple edit restrictions can be incorporated. A simulation study shows that the MILC method is in general able to reproduce cell frequencies in both low- and high-dimensional tables with low amounts of bias. In addition, variance can also be estimated appropriately, although variance is overestimated when cell frequencies are small.
ISSN:0714-0045
1492-0921