Editing and imputation
Results
All (5)
All (5) ((5 results))
- Articles and reports: 12-001-X202200100008Description:
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 - Articles and reports: 12-001-X201400214089Description:
This manuscript describes the use of multiple imputation to combine information from multiple surveys of the same underlying population. We use a newly developed method to generate synthetic populations nonparametrically using a finite population Bayesian bootstrap that automatically accounts for complex sample designs. We then analyze each synthetic population with standard complete-data software for simple random samples and obtain valid inference by combining the point and variance estimates using extensions of existing combining rules for synthetic data. We illustrate the approach by combining data from the 2006 National Health Interview Survey (NHIS) and the 2006 Medical Expenditure Panel Survey (MEPS).
Release date: 2014-12-19 - Articles and reports: 11-522-X201300014275Description:
Since July 2014, the Office for National Statistics has committed to a predominantly online 2021 UK Census. Item-level imputation will play an important role in adjusting the 2021 Census database. Research indicates that the internet may yield cleaner data than paper based capture and attract people with particular characteristics. Here, we provide preliminary results from research directed at understanding how we might manage these features in a 2021 UK Census imputation strategy. Our findings suggest that if using a donor-based imputation method, it may need to consider including response mode as a matching variable in the underlying imputation model.
Release date: 2014-10-31 - Articles and reports: 12-001-X20060019260Description:
This paper considers the use of imputation and weighting to correct for measurement error in the estimation of a distribution function. The paper is motivated by the problem of estimating the distribution of hourly pay in the United Kingdom, using data from the Labour Force Survey. Errors in measurement lead to bias and the aim is to use auxiliary data, measured accurately for a subsample, to correct for this bias. Alternative point estimators are considered, based upon a variety of imputation and weighting approaches, including fractional imputation, nearest neighbour imputation, predictive mean matching and propensity score weighting. Properties of these point estimators are then compared both theoretically and by simulation. A fractional predictive mean matching imputation approach is advocated. It performs similarly to propensity score weighting, but displays slight advantages of robustness and efficiency.
Release date: 2006-07-20 - Articles and reports: 12-001-X20050018087Description:
In Official Statistics, data editing process plays an important role in terms of timeliness, data accuracy, and survey costs. Techniques introduced to identify and eliminate errors from data are essentially required to consider all of these aspects simultaneously. Among others, a frequent and pervasive systematic error appearing in surveys collecting numerical data, is the unity measure error. It highly affects timeliness, data accuracy and costs of the editing and imputation phase. In this paper we propose a probabilistic formalisation of the problem based on finite mixture models. This setting allows us to deal with the problem in a multivariate context, and provides also a number of useful diagnostics for prioritising cases to be more deeply investigated through a clerical review. Prioritising units is important in order to increase data accuracy while avoiding waste of time due to the follow up of non-really critical units.
Release date: 2005-07-21
Data (0)
Data (0) (0 results)
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Analysis (5)
Analysis (5) ((5 results))
- Articles and reports: 12-001-X202200100008Description:
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 - Articles and reports: 12-001-X201400214089Description:
This manuscript describes the use of multiple imputation to combine information from multiple surveys of the same underlying population. We use a newly developed method to generate synthetic populations nonparametrically using a finite population Bayesian bootstrap that automatically accounts for complex sample designs. We then analyze each synthetic population with standard complete-data software for simple random samples and obtain valid inference by combining the point and variance estimates using extensions of existing combining rules for synthetic data. We illustrate the approach by combining data from the 2006 National Health Interview Survey (NHIS) and the 2006 Medical Expenditure Panel Survey (MEPS).
Release date: 2014-12-19 - Articles and reports: 11-522-X201300014275Description:
Since July 2014, the Office for National Statistics has committed to a predominantly online 2021 UK Census. Item-level imputation will play an important role in adjusting the 2021 Census database. Research indicates that the internet may yield cleaner data than paper based capture and attract people with particular characteristics. Here, we provide preliminary results from research directed at understanding how we might manage these features in a 2021 UK Census imputation strategy. Our findings suggest that if using a donor-based imputation method, it may need to consider including response mode as a matching variable in the underlying imputation model.
Release date: 2014-10-31 - Articles and reports: 12-001-X20060019260Description:
This paper considers the use of imputation and weighting to correct for measurement error in the estimation of a distribution function. The paper is motivated by the problem of estimating the distribution of hourly pay in the United Kingdom, using data from the Labour Force Survey. Errors in measurement lead to bias and the aim is to use auxiliary data, measured accurately for a subsample, to correct for this bias. Alternative point estimators are considered, based upon a variety of imputation and weighting approaches, including fractional imputation, nearest neighbour imputation, predictive mean matching and propensity score weighting. Properties of these point estimators are then compared both theoretically and by simulation. A fractional predictive mean matching imputation approach is advocated. It performs similarly to propensity score weighting, but displays slight advantages of robustness and efficiency.
Release date: 2006-07-20 - Articles and reports: 12-001-X20050018087Description:
In Official Statistics, data editing process plays an important role in terms of timeliness, data accuracy, and survey costs. Techniques introduced to identify and eliminate errors from data are essentially required to consider all of these aspects simultaneously. Among others, a frequent and pervasive systematic error appearing in surveys collecting numerical data, is the unity measure error. It highly affects timeliness, data accuracy and costs of the editing and imputation phase. In this paper we propose a probabilistic formalisation of the problem based on finite mixture models. This setting allows us to deal with the problem in a multivariate context, and provides also a number of useful diagnostics for prioritising cases to be more deeply investigated through a clerical review. Prioritising units is important in order to increase data accuracy while avoiding waste of time due to the follow up of non-really critical units.
Release date: 2005-07-21
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