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

  Articles and reports: 12-001-X202200100008

  Description:

  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.

  Release date: 2022-06-21

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• 2. Measuring temporary employment. Do survey or register data tell the truth? Archived

  Articles and reports: 12-001-X201500114151

  Description:

  One of the main variables in the Dutch Labour Force Survey is the variable measuring whether a respondent has a permanent or a temporary job. The aim of our study is to determine the measurement error in this variable by matching the information obtained by the longitudinal part of this survey with unique register data from the Dutch Institute for Employee Insurance. Contrary to previous approaches confronting such datasets, we take into account that also register data are not error-free and that measurement error in these data is likely to be correlated over time. More specifically, we propose the estimation of the measurement error in these two sources using an extended hidden Markov model with two observed indicators for the type of contract. Our results indicate that none of the two sources should be considered as error-free. For both indicators, we find that workers in temporary contracts are often misclassified as having a permanent contract. Particularly for the register data, we find that measurement errors are strongly autocorrelated, as, if made, they tend to repeat themselves. In contrast, when the register is correct, the probability of an error at the next time period is almost zero. Finally, we find that temporary contracts are more widespread than the Labour Force Survey suggests, while transition rates between temporary to permanent contracts are much less common than both datasets suggest.

  Release date: 2015-06-29

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Articles and reports (2) ((2 results))

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

  Articles and reports: 12-001-X202200100008

  Description:

  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.

  Release date: 2022-06-21

  ---

• 2. Measuring temporary employment. Do survey or register data tell the truth? Archived

  Articles and reports: 12-001-X201500114151

  Description:

  One of the main variables in the Dutch Labour Force Survey is the variable measuring whether a respondent has a permanent or a temporary job. The aim of our study is to determine the measurement error in this variable by matching the information obtained by the longitudinal part of this survey with unique register data from the Dutch Institute for Employee Insurance. Contrary to previous approaches confronting such datasets, we take into account that also register data are not error-free and that measurement error in these data is likely to be correlated over time. More specifically, we propose the estimation of the measurement error in these two sources using an extended hidden Markov model with two observed indicators for the type of contract. Our results indicate that none of the two sources should be considered as error-free. For both indicators, we find that workers in temporary contracts are often misclassified as having a permanent contract. Particularly for the register data, we find that measurement errors are strongly autocorrelated, as, if made, they tend to repeat themselves. In contrast, when the register is correct, the probability of an error at the next time period is almost zero. Finally, we find that temporary contracts are more widespread than the Labour Force Survey suggests, while transition rates between temporary to permanent contracts are much less common than both datasets suggest.

  Release date: 2015-06-29

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