The Employment Register (ER) and the Labour Force Survey (LFS) measure the labour contract of Dutch citizens. However, both sources provide different results. One possible explanation is that both...Show moreThe Employment Register (ER) and the Labour Force Survey (LFS) measure the labour contract of Dutch citizens. However, both sources provide different results. One possible explanation is that both sources contain measurement error (ME). Previous research has used hidden Markov models (HMMs) to estimate and correct for ME in linked data from the ER and the LFS. The HMMs did, however, have some limitations. For example, the HMMs used a suboptimal approach to include covariates that were missing for observations for whom one particular contract type was observed by the ER. In this thesis, these covariates are referred to as missing covariates. To overcome the limitations of the HMMs, this thesis compared the performance of three different latent variable methods (LVMs), namely latent class (LC) analysis, latent class tree (LCT) analysis and tree-multiple imputation of latent classes (tree-MILC) analysis, to correct for ME in the ER and the LFS. For this purpose, two simulation studies were conducted: one without and one with missing covariates. For the second simulation study, a new approach was developed in which missing covariates were included using direct effects and parameter restrictions. In the end, LC and tree-MILC analysis was performed on real data from the ER and the LFS for respondents in the age of 25 to 55 in the first quarters of 2016, 2017 and 2018 to compare the estimates to the original HMM estimates. In the simulation studies, little differences were found between the methods. The results showed that all model-based estimators were often considerably biased in conditions with two indicators. Although the bias and the variance decreased when one or two missing covariates were added, the largest decreases in bias and variance were observed when a third indicator was added. Furthermore, the analyses of the real data showed that the LC estimates, the tree-MILC estimates, and the original HMM estimates were different from each other. Nevertheless, the differences were smaller than the original differences between the ER and the LFS. Future research that aims to correct for ME in the ER and the LFS could use the approach proposed in this study to include missing covariates. In addition, to enhance the accuracy of the estimates, the current findings suggest that it may be beneficial for Statistics Netherlands to find a third indicator measuring the contract types of Dutch citizens. Finally, LVMs could potentially be used for the production of official statistics. However, to implement these methods in practice, further research is needed on both a methodological and an organisational level.Show less
