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

Boeschoten, Laura; Oberski, Daniel L.; Waal, Ton de

doi:10.1515/jos-2017-0044

Cited by 18 publications

(29 citation statements)

References 33 publications

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“…This is in practice also not often the case, although this is commonly assumed by many researchers. Recently, imputation methods have been developed to take misclassification in combined data sets into account, for example by assuming that a certain proportion of the data is misclassified (Manrique-Vallier and Reiter 2016) or by estimating the number of misclassified units by using information from multiple sources (Boeschoten et al 2016).…”

Section: Discussionmentioning

confidence: 99%

How to Obtain Valid Inference under Unit Nonresponse?

Boeschoten

Vink

Hox

2017

Journal of Official Statistics

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Weighting methods are commonly used in situations of unit nonresponse with linked register data. However, several arguments in terms of valid inference and practical usability can be made against the use of weighting methods in these situations. Imputation methods such as sample and mass imputation may be suitable alternatives, as they lead to valid inference in situations of item nonresponse and have some practical advantages. In a simulation study, sample and mass imputation were compared to traditional weighting when dealing with unit nonresponse in linked register data. Methods were compared on their bias and coverage in different scenarios. Both, sample and mass imputation, had better coverage than traditional weighting in all scenarios.Imputation methods can therefore be recommended over weighting as they also have practical advantages, such as that estimates outside the observed data distribution can be created and that many auxiliary variables can be taken into account. The use of sample or mass imputation depends on the specific data structure.

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Section: Discussionmentioning

confidence: 99%

How to Obtain Valid Inference under Unit Nonresponse?

Boeschoten

Vink

Hox

2017

Journal of Official Statistics

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“…Boeschoten et al . () also use an LC model to model the true value of a variable that is observed (with measurement error) in multiple sources. We sketch their approach.…”

Section: Basic Situations and Their Methodsmentioning

confidence: 99%

“…The method proposed by Boeschoten et al . () starts with the original combined data set and then proceeds with five steps. Select m bootstrap samples from the original combined data set. Create an LC model for every bootstrap sample. Multiply impute latent ‘true' variable X for each bootstrap sample.…”

Section: Basic Situations and Their Methodsmentioning

confidence: 99%

Multi‐source Statistics: Basic Situations and Methods

Waal

Delden

Scholtus

2019

Int Statistical Rev

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Summary Many National Statistical Institutes (NSIs), especially in Europe, are moving from single‐source statistics to multi‐source statistics. By combining data sources, NSIs can produce more detailed and more timely statistics and respond more quickly to events in society. By combining survey data with already available administrative data and Big Data, NSIs can save data collection and processing costs and reduce the burden on respondents. However, multi‐source statistics come with new problems that need to be overcome before the resulting output quality is sufficiently high and before those statistics can be produced efficiently. What complicates the production of multi‐source statistics is that they come in many different varieties as data sets can be combined in many different ways. Given the rapidly increasing importance of producing multi‐source statistics in Official Statistics, there has been considerable research activity in this area over the last few years, and some frameworks have been developed for multi‐source statistics. Useful as these frameworks are, they generally do not give guidelines to which method could be applied in a certain situation arising in practice. In this paper, we aim to fill that gap, structure the world of multi‐source statistics and its problems and provide some guidance to suitable methods for these problems.

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“…Boeschoten et al . () showed that the performance of the MILC method is closely related to the entropy R 2 of the corresponding latent class model.…”

Section: Applying the Extended Multiple Imputation Of Latent Classes mentioning

confidence: 99%

“…Boeschoten et al . () introduced the MILC method and evaluated the method under a range of conditions in terms of data quality. In addition, Boeschoten et al .…”

Section: Applying the Extended Multiple Imputation Of Latent Classes mentioning

confidence: 99%

Estimating the Number of Serious Road Injuries Per Vehicle Type in the Netherlands by using Multiple Imputation of Latent Classes

Boeschoten

Waal

Vermunt

2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Statistics that are published by official agencies are often generated by using population registries, which are likely to contain classification errors and missing values. A method that simultaneously handles classification errors and missing values is multiple imputation of latent classes (MILC). We apply the MILC method to estimate the number of serious road injuries per vehicle type in the Netherlands and to stratify the number of serious road injuries per vehicle type into relevant subgroups by using data from two registries. For this specific application, the MILC method is extended to handle the large number of missing values in the stratification variable ‘region of accident’ and to include more stratification covariates. After applying the extended MILC method, a multiply imputed data set is generated that can be used to create statistical figures in a straightforward manner, and that incorporates uncertainty due to classification errors and missing values in the estimate of the total variance.

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

Cited by 18 publications

References 33 publications

How to Obtain Valid Inference under Unit Nonresponse?

How to Obtain Valid Inference under Unit Nonresponse?

Multi‐source Statistics: Basic Situations and Methods

Estimating the Number of Serious Road Injuries Per Vehicle Type in the Netherlands by using Multiple Imputation of Latent Classes

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