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- 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-X201300014261Description:
National statistical offices are subject to two requirements that are difficult to reconcile. On the one hand, they must provide increasingly precise information on specific subjects and hard-to-reach or minority populations, using innovative methods that make the measurement more objective or ensure its confidentiality, and so on. On the other hand, they must deal with budget restrictions in a context where households are increasingly difficult to contact. This twofold demand has an impact on survey quality in the broad sense, that is, not only in terms of precision, but also in terms of relevance, comparability, coherence, clarity and timeliness. Because the cost of Internet collection is low and a large proportion of the population has an Internet connection, statistical offices see this modern collection mode as a solution to their problems. Consequently, the development of Internet collection and, more generally, of multimode collection is supposedly the solution for maximizing survey quality, particularly in terms of total survey error, because it addresses the problems of coverage, sampling, non-response or measurement while respecting budget constraints. However, while Internet collection is an inexpensive mode, it presents serious methodological problems: coverage, self-selection or selection bias, non-response and non-response adjustment difficulties, ‘satisficing,’ and so on. As a result, before developing or generalizing the use of multimode collection, the National Institute of Statistics and Economic Studies (INSEE) launched a wide-ranging set of experiments to study the various methodological issues, and the initial results show that multimode collection is a source of both solutions and new methodological problems.
Release date: 2014-10-31 - Articles and reports: 11-522-X201300014268Description:
Information collection is critical for chronic-disease surveillance to measure the scope of diseases, assess the use of services, identify at-risk groups and track the course of diseases and risk factors over time with the goal of planning and implementing public-health programs for disease prevention. It is in this context that the Quebec Integrated Chronic Disease Surveillance System (QICDSS) was established. The QICDSS is a database created by linking administrative files covering the period from 1996 to 2013. It is an attractive alternative to survey data, since it covers the entire population, is not affected by recall bias and can track the population over time and space. In this presentation, we describe the relevance of using administrative data as an alternative to survey data, the methods selected to build the population cohort by linking various sources of raw data, and the processing applied to minimize bias. We will also discuss the advantages and limitations associated with the analysis of administrative files.
Release date: 2014-10-31 - 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: 11-522-X201300014286Description:
The Étude Longitudinale Française depuis l’Enfance (ELFE) [French longitudinal study from childhood on], which began in 2011, involves over 18,300 infants whose parents agreed to participate when they were in the maternity hospital. This cohort survey, which will track the children from birth to adulthood, covers the many aspects of their lives from the perspective of social science, health and environmental health. In randomly selected maternity hospitals, all infants in the target population, who were born on one of 25 days distributed across the four seasons, were chosen. This sample is the outcome of a non-standard sampling scheme that we call product sampling. In this survey, it takes the form of the cross-tabulation between two independent samples: a sampling of maternity hospitals and a sampling of days. While it is easy to imagine a cluster effect due to the sampling of maternity hospitals, one can also imagine a cluster effect due to the sampling of days. The scheme’s time dimension therefore cannot be ignored if the desired estimates are subject to daily or seasonal variation. While this non-standard scheme can be viewed as a particular kind of two-phase design, it needs to be defined within a more specific framework. Following a comparison of the product scheme with a conventional two-stage design, we propose variance estimators specially formulated for this sampling scheme. Our ideas are illustrated with a simulation study.
Release date: 2014-10-31 - Articles and reports: 82-003-X201401014098Geography: Province or territoryDescription:
This study compares registry and non-registry approaches to linking 2006 Census of Population data for Manitoba and Ontario to Hospital data from the Discharge Abstract Database.
Release date: 2014-10-15 - Articles and reports: 12-002-X201400111901Description:
This document is for analysts/researchers who are considering doing research with data from a survey where both survey weights and bootstrap weights are provided in the data files. This document gives directions, for some selected software packages, about how to get started in using survey weights and bootstrap weights for an analysis of survey data. We give brief directions for obtaining survey-weighted estimates, bootstrap variance estimates (and other desired error quantities) and some typical test statistics for each software package in turn. While these directions are provided just for the chosen examples, there will be information about the range of weighted and bootstrapped analyses that can be carried out by each software package.
Release date: 2014-08-07 - 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 - 9. A nonparametric method to generate synthetic populations to adjust for complex sampling design features ArchivedArticles and reports: 12-001-X201400114003Description:
Outside of the survey sampling literature, samples are often assumed to be generated by simple random sampling process that produces independent and identically distributed (IID) samples. Many statistical methods are developed largely in this IID world. Application of these methods to data from complex sample surveys without making allowance for the survey design features can lead to erroneous inferences. Hence, much time and effort have been devoted to develop the statistical methods to analyze complex survey data and account for the sample design. This issue is particularly important when generating synthetic populations using finite population Bayesian inference, as is often done in missing data or disclosure risk settings, or when combining data from multiple surveys. By extending previous work in finite population Bayesian bootstrap literature, we propose a method to generate synthetic populations from a posterior predictive distribution in a fashion inverts the complex sampling design features and generates simple random samples from a superpopulation point of view, making adjustment on the complex data so that they can be analyzed as simple random samples. We consider a simulation study with a stratified, clustered unequal-probability of selection sample design, and use the proposed nonparametric method to generate synthetic populations for the 2006 National Health Interview Survey (NHIS), and the Medical Expenditure Panel Survey (MEPS), which are stratified, clustered unequal-probability of selection sample designs.
Release date: 2014-06-27 - 10. A weighted composite likelihood approach to inference for two-level models from survey data ArchivedArticles and reports: 12-001-X201300211887Description:
Multi-level models are extensively used for analyzing survey data with the design hierarchy matching the model hierarchy. We propose a unified approach, based on a design-weighted log composite likelihood, for two-level models that leads to design-model consistent estimators of the model parameters even when the within cluster sample sizes are small provided the number of sample clusters is large. This method can handle both linear and generalized linear two-level models and it requires level 2 and level 1 inclusion probabilities and level 1 joint inclusion probabilities, where level 2 represents a cluster and level 1 an element within a cluster. Results of a simulation study demonstrating superior performance of the proposed method relative to existing methods under informative sampling are also reported.
Release date: 2014-01-15
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Articles and reports (10)
Articles and reports (10) ((10 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 - Articles and reports: 11-522-X201300014261Description:
National statistical offices are subject to two requirements that are difficult to reconcile. On the one hand, they must provide increasingly precise information on specific subjects and hard-to-reach or minority populations, using innovative methods that make the measurement more objective or ensure its confidentiality, and so on. On the other hand, they must deal with budget restrictions in a context where households are increasingly difficult to contact. This twofold demand has an impact on survey quality in the broad sense, that is, not only in terms of precision, but also in terms of relevance, comparability, coherence, clarity and timeliness. Because the cost of Internet collection is low and a large proportion of the population has an Internet connection, statistical offices see this modern collection mode as a solution to their problems. Consequently, the development of Internet collection and, more generally, of multimode collection is supposedly the solution for maximizing survey quality, particularly in terms of total survey error, because it addresses the problems of coverage, sampling, non-response or measurement while respecting budget constraints. However, while Internet collection is an inexpensive mode, it presents serious methodological problems: coverage, self-selection or selection bias, non-response and non-response adjustment difficulties, ‘satisficing,’ and so on. As a result, before developing or generalizing the use of multimode collection, the National Institute of Statistics and Economic Studies (INSEE) launched a wide-ranging set of experiments to study the various methodological issues, and the initial results show that multimode collection is a source of both solutions and new methodological problems.
Release date: 2014-10-31 - Articles and reports: 11-522-X201300014268Description:
Information collection is critical for chronic-disease surveillance to measure the scope of diseases, assess the use of services, identify at-risk groups and track the course of diseases and risk factors over time with the goal of planning and implementing public-health programs for disease prevention. It is in this context that the Quebec Integrated Chronic Disease Surveillance System (QICDSS) was established. The QICDSS is a database created by linking administrative files covering the period from 1996 to 2013. It is an attractive alternative to survey data, since it covers the entire population, is not affected by recall bias and can track the population over time and space. In this presentation, we describe the relevance of using administrative data as an alternative to survey data, the methods selected to build the population cohort by linking various sources of raw data, and the processing applied to minimize bias. We will also discuss the advantages and limitations associated with the analysis of administrative files.
Release date: 2014-10-31 - 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: 11-522-X201300014286Description:
The Étude Longitudinale Française depuis l’Enfance (ELFE) [French longitudinal study from childhood on], which began in 2011, involves over 18,300 infants whose parents agreed to participate when they were in the maternity hospital. This cohort survey, which will track the children from birth to adulthood, covers the many aspects of their lives from the perspective of social science, health and environmental health. In randomly selected maternity hospitals, all infants in the target population, who were born on one of 25 days distributed across the four seasons, were chosen. This sample is the outcome of a non-standard sampling scheme that we call product sampling. In this survey, it takes the form of the cross-tabulation between two independent samples: a sampling of maternity hospitals and a sampling of days. While it is easy to imagine a cluster effect due to the sampling of maternity hospitals, one can also imagine a cluster effect due to the sampling of days. The scheme’s time dimension therefore cannot be ignored if the desired estimates are subject to daily or seasonal variation. While this non-standard scheme can be viewed as a particular kind of two-phase design, it needs to be defined within a more specific framework. Following a comparison of the product scheme with a conventional two-stage design, we propose variance estimators specially formulated for this sampling scheme. Our ideas are illustrated with a simulation study.
Release date: 2014-10-31 - Articles and reports: 82-003-X201401014098Geography: Province or territoryDescription:
This study compares registry and non-registry approaches to linking 2006 Census of Population data for Manitoba and Ontario to Hospital data from the Discharge Abstract Database.
Release date: 2014-10-15 - Articles and reports: 12-002-X201400111901Description:
This document is for analysts/researchers who are considering doing research with data from a survey where both survey weights and bootstrap weights are provided in the data files. This document gives directions, for some selected software packages, about how to get started in using survey weights and bootstrap weights for an analysis of survey data. We give brief directions for obtaining survey-weighted estimates, bootstrap variance estimates (and other desired error quantities) and some typical test statistics for each software package in turn. While these directions are provided just for the chosen examples, there will be information about the range of weighted and bootstrapped analyses that can be carried out by each software package.
Release date: 2014-08-07 - 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 - 9. A nonparametric method to generate synthetic populations to adjust for complex sampling design features ArchivedArticles and reports: 12-001-X201400114003Description:
Outside of the survey sampling literature, samples are often assumed to be generated by simple random sampling process that produces independent and identically distributed (IID) samples. Many statistical methods are developed largely in this IID world. Application of these methods to data from complex sample surveys without making allowance for the survey design features can lead to erroneous inferences. Hence, much time and effort have been devoted to develop the statistical methods to analyze complex survey data and account for the sample design. This issue is particularly important when generating synthetic populations using finite population Bayesian inference, as is often done in missing data or disclosure risk settings, or when combining data from multiple surveys. By extending previous work in finite population Bayesian bootstrap literature, we propose a method to generate synthetic populations from a posterior predictive distribution in a fashion inverts the complex sampling design features and generates simple random samples from a superpopulation point of view, making adjustment on the complex data so that they can be analyzed as simple random samples. We consider a simulation study with a stratified, clustered unequal-probability of selection sample design, and use the proposed nonparametric method to generate synthetic populations for the 2006 National Health Interview Survey (NHIS), and the Medical Expenditure Panel Survey (MEPS), which are stratified, clustered unequal-probability of selection sample designs.
Release date: 2014-06-27 - 10. A weighted composite likelihood approach to inference for two-level models from survey data ArchivedArticles and reports: 12-001-X201300211887Description:
Multi-level models are extensively used for analyzing survey data with the design hierarchy matching the model hierarchy. We propose a unified approach, based on a design-weighted log composite likelihood, for two-level models that leads to design-model consistent estimators of the model parameters even when the within cluster sample sizes are small provided the number of sample clusters is large. This method can handle both linear and generalized linear two-level models and it requires level 2 and level 1 inclusion probabilities and level 1 joint inclusion probabilities, where level 2 represents a cluster and level 1 an element within a cluster. Results of a simulation study demonstrating superior performance of the proposed method relative to existing methods under informative sampling are also reported.
Release date: 2014-01-15
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