Editing and imputation
Results
All (7)
All (7) ((7 results))
- 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 - 2. Fractional hot deck imputation for robust inference under item nonresponse in survey sampling ArchivedArticles and reports: 12-001-X201400214091Description:
Parametric fractional imputation (PFI), proposed by Kim (2011), is a tool for general purpose parameter estimation under missing data. We propose a fractional hot deck imputation (FHDI) which is more robust than PFI or multiple imputation. In the proposed method, the imputed values are chosen from the set of respondents and assigned proper fractional weights. The weights are then adjusted to meet certain calibration conditions, which makes the resulting FHDI estimator efficient. Two simulation studies are presented to compare the proposed method with existing methods.
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 - 4. On Bias Adjustments for Web Surveys ArchivedArticles and reports: 11-522-X201300014281Description:
Web surveys exclude the entire non-internet population and often have low response rates. Therefore, statistical inference based on Web survey samples will require availability of additional information about the non-covered population, careful choice of survey methods to account for potential biases, and caution with interpretation and generalization of the results to a target population. In this paper, we focus on non-coverage bias, and explore the use of weighted estimators and hot-deck imputation estimators for bias adjustment under the ideal scenario where covariate information was obtained for a simple random sample of individuals from the non-covered population. We illustrate empirically the performance of the proposed estimators under this scenario. Possible extensions of these approaches to more realistic scenarios are discussed.
Release date: 2014-10-31 - 5. Automatic Coding of Occupations ArchivedArticles and reports: 11-522-X201300014291Description:
Occupational coding in Germany is mostly done using dictionary approaches with subsequent manual revision of cases which could not be coded. Since manual coding is expensive, it is desirable to assign a higher number of codes automatically. At the same time the quality of the automatic coding must at least reach that of the manual coding. As a possible solution we employ different machine learning algorithms for the task using a substantial amount of manually coded occuptions available from recent studies as training data. We asses the feasibility of these methods of evaluating performance and quality of the algorithms.
Release date: 2014-10-31 - 6. The influence of sampling method and interviewers on sample realization in the European Social Survey ArchivedArticles and reports: 12-001-X201400114001Description:
This article addresses the impact of different sampling procedures on realised sample quality in the case of probability samples. This impact was expected to result from varying degrees of freedom on the part of interviewers to interview easily available or cooperative individuals (thus producing substitutions). The analysis was conducted in a cross-cultural context using data from the first four rounds of the European Social Survey (ESS). Substitutions are measured as deviations from a 50/50 gender ratio in subsamples with heterosexual couples. Significant deviations were found in numerous countries of the ESS. They were also found to be lowest in cases of samples with official registers of residents as sample frame (individual person register samples) if one partner was more difficult to contact than the other. This scope of substitutions did not differ across the ESS rounds and it was weakly correlated with payment and control procedures. It can be concluded from the results that individual person register samples are associated with higher sample quality.
Release date: 2014-06-27 - Articles and reports: 12-001-X201400114002Description:
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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Analysis (7)
Analysis (7) ((7 results))
- 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 - 2. Fractional hot deck imputation for robust inference under item nonresponse in survey sampling ArchivedArticles and reports: 12-001-X201400214091Description:
Parametric fractional imputation (PFI), proposed by Kim (2011), is a tool for general purpose parameter estimation under missing data. We propose a fractional hot deck imputation (FHDI) which is more robust than PFI or multiple imputation. In the proposed method, the imputed values are chosen from the set of respondents and assigned proper fractional weights. The weights are then adjusted to meet certain calibration conditions, which makes the resulting FHDI estimator efficient. Two simulation studies are presented to compare the proposed method with existing methods.
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 - 4. On Bias Adjustments for Web Surveys ArchivedArticles and reports: 11-522-X201300014281Description:
Web surveys exclude the entire non-internet population and often have low response rates. Therefore, statistical inference based on Web survey samples will require availability of additional information about the non-covered population, careful choice of survey methods to account for potential biases, and caution with interpretation and generalization of the results to a target population. In this paper, we focus on non-coverage bias, and explore the use of weighted estimators and hot-deck imputation estimators for bias adjustment under the ideal scenario where covariate information was obtained for a simple random sample of individuals from the non-covered population. We illustrate empirically the performance of the proposed estimators under this scenario. Possible extensions of these approaches to more realistic scenarios are discussed.
Release date: 2014-10-31 - 5. Automatic Coding of Occupations ArchivedArticles and reports: 11-522-X201300014291Description:
Occupational coding in Germany is mostly done using dictionary approaches with subsequent manual revision of cases which could not be coded. Since manual coding is expensive, it is desirable to assign a higher number of codes automatically. At the same time the quality of the automatic coding must at least reach that of the manual coding. As a possible solution we employ different machine learning algorithms for the task using a substantial amount of manually coded occuptions available from recent studies as training data. We asses the feasibility of these methods of evaluating performance and quality of the algorithms.
Release date: 2014-10-31 - 6. The influence of sampling method and interviewers on sample realization in the European Social Survey ArchivedArticles and reports: 12-001-X201400114001Description:
This article addresses the impact of different sampling procedures on realised sample quality in the case of probability samples. This impact was expected to result from varying degrees of freedom on the part of interviewers to interview easily available or cooperative individuals (thus producing substitutions). The analysis was conducted in a cross-cultural context using data from the first four rounds of the European Social Survey (ESS). Substitutions are measured as deviations from a 50/50 gender ratio in subsamples with heterosexual couples. Significant deviations were found in numerous countries of the ESS. They were also found to be lowest in cases of samples with official registers of residents as sample frame (individual person register samples) if one partner was more difficult to contact than the other. This scope of substitutions did not differ across the ESS rounds and it was weakly correlated with payment and control procedures. It can be concluded from the results that individual person register samples are associated with higher sample quality.
Release date: 2014-06-27 - Articles and reports: 12-001-X201400114002Description:
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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