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• 1. Combining information from multiple complex surveys Archived

  Articles and reports: 12-001-X201400214089

  Description:

  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

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• 2. Towards the 2021 UK Census imputation strategy: Response mode as a matching variable in a donor-based approach? Archived

  Articles and reports: 11-522-X201300014275

  Description:

  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

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• 3. Bayesian multiple imputation for large-scale categorical data with structural zeros Archived

  Articles and reports: 12-001-X201400114002

  Description:

  We propose an approach for multiple imputation of items missing at random in large-scale surveys with exclusively categorical variables that have structural zeros. Our approach is to use mixtures of multinomial distributions as imputation engines, accounting for structural zeros by conceiving of the observed data as a truncated sample from a hypothetical population without structural zeros. This approach has several appealing features: imputations are generated from coherent, Bayesian joint models that automatically capture complex dependencies and readily scale to large numbers of variables. We outline a Gibbs sampling algorithm for implementing the approach, and we illustrate its potential with a repeated sampling study using public use census microdata from the state of New York, U.S.A.

  Release date: 2014-06-27

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Articles and reports (3) ((3 results))

• 1. Combining information from multiple complex surveys Archived

  Articles and reports: 12-001-X201400214089

  Description:

  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

  ---

• 2. Towards the 2021 UK Census imputation strategy: Response mode as a matching variable in a donor-based approach? Archived

  Articles and reports: 11-522-X201300014275

  Description:

  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

  ---

• 3. Bayesian multiple imputation for large-scale categorical data with structural zeros Archived

  Articles and reports: 12-001-X201400114002

  Description:

  We propose an approach for multiple imputation of items missing at random in large-scale surveys with exclusively categorical variables that have structural zeros. Our approach is to use mixtures of multinomial distributions as imputation engines, accounting for structural zeros by conceiving of the observed data as a truncated sample from a hypothetical population without structural zeros. This approach has several appealing features: imputations are generated from coherent, Bayesian joint models that automatically capture complex dependencies and readily scale to large numbers of variables. We outline a Gibbs sampling algorithm for implementing the approach, and we illustrate its potential with a repeated sampling study using public use census microdata from the state of New York, U.S.A.

  Release date: 2014-06-27

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