Keyword search
Filter results by
Search HelpKeyword(s)
Survey or statistical program
Results
All (5)
All (5) ((5 results))
- Articles and reports: 11-522-X20020016715Description:
This paper will describe the multiple imputation of income in the National Health Interview Survey and discuss the methodological issues involved. In addition, the paper will present empirical summaries of the imputations as well as results of a Monte Carlo evaluation of inferences based on multiply imputed income items.
Analysts of health data are often interested in studying relationships between income and health. The National Health Interview Survey, conducted by the National Center for Health Statistics of the U.S. Centers for Disease Control and Prevention, provides a rich source of data for studying such relationships. However, the nonresponse rates on two key income items, an individual's earned income and a family's total income, are over 20%. Moreover, these nonresponse rates appear to be increasing over time. A project is currently underway to multiply impute individual earnings and family income along with some other covariates for the National Health Interview Survey in 1997 and subsequent years.
There are many challenges in developing appropriate multiple imputations for such large-scale surveys. First, there are many variables of different types, with different skip patterns and logical relationships. Second, it is not known what types of associations will be investigated by the analysts of multiply imputed data. Finally, some variables, such as family income, are collected at the family level and others, such as earned income, are collected at the individual level. To make the imputations for both the family- and individual-level variables conditional on as many predictors as possible, and to simplify modelling, we are using a modified version of the sequential regression imputation method described in Raghunathan et al. ( Survey Methodology, 2001).
Besides issues related to the hierarchical nature of the imputations just described, there are other methodological issues of interest such as the use of transformations of the income variables, the imposition of restrictions on the values of variables, the general validity of sequential regression imputation and, even more generally, the validity of multiple-imputation inferences for surveys with complex sample designs.
Release date: 2004-09-13 - Articles and reports: 11-522-X20020016725Description:
In 1997, the US Office of Management and Budget issued revised standards for the collection of race information within the federal statistical system. One revision allows individuals to choose more than one race group when responding to federal surveys and other federal data collections. This change presents challenges for analyses that involve data collected under both the old and new race-reporting systems, since the data on race are not comparable. The following paper discusses the problems encountered by these changes and methods developed to overcome them.
Since most people under both systems report only a single race, a common proposed solution is to try to bridge the transition by assigning a single-race category to each multiple-race reporter under the new system, and to conduct analyses using just the observed and assigned single-race categories. Thus, the problem can be viewed as a missing-data problem, in which single-race responses are missing for multiple-race reporters and needing to be imputed.
The US Office of Management and Budget suggested several simple bridging methods to handle this missing-data problem. Schenker and Parker (Statistics in Medicine, forthcoming) analysed data from the National Health Interview Survey of the US National Center for Health Statistics, which allows multiple-race reporting but also asks multiple-race reporters to specify a primary race, and found that improved bridging methods could result from incorporating individual-level and contextual covariates into the bridging models.
While Schenker and Parker discussed only three large multiple-race groups, the current application requires predicting single-race categories for several small multiple-race groups as well. Thus, problems of sparse data arise in fitting the bridging models. We address these problems by building combined models for several multiple-race groups, thus borrowing strength across them. These and other methodological issues are discussed.
Release date: 2004-09-13 - Articles and reports: 12-001-X20040016999Description:
Combining response data from the Belgian Fertility and Family Survey with individual level and municipality level data from the 1991 Census for both nonrespondents and respondents, multilevel logistic regression models for contact and cooperation propensity are estimated. The covariates introduced are a selection of indirect features, all out of the researchers' direct control. Contrary to previous research, Socio Economic Status is found to be positively related to cooperation. Another unexpected result is the absence of any considerable impact of ecological correlates such as urbanity.
Release date: 2004-07-14 - 4. Proxy reporting of health information ArchivedArticles and reports: 82-003-X20030036847Geography: CanadaDescription:
This paper examines whether accepting proxy- instead of self-responses results in lower estimates of some health conditions. It analyses data from the National Population Health Survey and the Canadian Community Health Survey.
Release date: 2004-05-18 - Articles and reports: 12-001-X20030026781Description:
Census counts are known to be inexact based on comparisons of Census and Post Enumeration Survey (PES) figures. In Italy, the role of municipal administrations is crucial for both Census and PES field operations. In this paper we analyze the impact of municipality on Italian Census undercount rates by modeling data from the PES as well as from other sources using Poisson regression trees and hierarchical Poisson models. The Poisson regression trees cluster municipalities into homogeneous groups. The hierarchical Poisson models can be considered as tools for Small Area estimation.
Release date: 2004-01-27
Data (0)
Data (0) (0 results)
No content available at this time.
Analysis (5)
Analysis (5) ((5 results))
- Articles and reports: 11-522-X20020016715Description:
This paper will describe the multiple imputation of income in the National Health Interview Survey and discuss the methodological issues involved. In addition, the paper will present empirical summaries of the imputations as well as results of a Monte Carlo evaluation of inferences based on multiply imputed income items.
Analysts of health data are often interested in studying relationships between income and health. The National Health Interview Survey, conducted by the National Center for Health Statistics of the U.S. Centers for Disease Control and Prevention, provides a rich source of data for studying such relationships. However, the nonresponse rates on two key income items, an individual's earned income and a family's total income, are over 20%. Moreover, these nonresponse rates appear to be increasing over time. A project is currently underway to multiply impute individual earnings and family income along with some other covariates for the National Health Interview Survey in 1997 and subsequent years.
There are many challenges in developing appropriate multiple imputations for such large-scale surveys. First, there are many variables of different types, with different skip patterns and logical relationships. Second, it is not known what types of associations will be investigated by the analysts of multiply imputed data. Finally, some variables, such as family income, are collected at the family level and others, such as earned income, are collected at the individual level. To make the imputations for both the family- and individual-level variables conditional on as many predictors as possible, and to simplify modelling, we are using a modified version of the sequential regression imputation method described in Raghunathan et al. ( Survey Methodology, 2001).
Besides issues related to the hierarchical nature of the imputations just described, there are other methodological issues of interest such as the use of transformations of the income variables, the imposition of restrictions on the values of variables, the general validity of sequential regression imputation and, even more generally, the validity of multiple-imputation inferences for surveys with complex sample designs.
Release date: 2004-09-13 - Articles and reports: 11-522-X20020016725Description:
In 1997, the US Office of Management and Budget issued revised standards for the collection of race information within the federal statistical system. One revision allows individuals to choose more than one race group when responding to federal surveys and other federal data collections. This change presents challenges for analyses that involve data collected under both the old and new race-reporting systems, since the data on race are not comparable. The following paper discusses the problems encountered by these changes and methods developed to overcome them.
Since most people under both systems report only a single race, a common proposed solution is to try to bridge the transition by assigning a single-race category to each multiple-race reporter under the new system, and to conduct analyses using just the observed and assigned single-race categories. Thus, the problem can be viewed as a missing-data problem, in which single-race responses are missing for multiple-race reporters and needing to be imputed.
The US Office of Management and Budget suggested several simple bridging methods to handle this missing-data problem. Schenker and Parker (Statistics in Medicine, forthcoming) analysed data from the National Health Interview Survey of the US National Center for Health Statistics, which allows multiple-race reporting but also asks multiple-race reporters to specify a primary race, and found that improved bridging methods could result from incorporating individual-level and contextual covariates into the bridging models.
While Schenker and Parker discussed only three large multiple-race groups, the current application requires predicting single-race categories for several small multiple-race groups as well. Thus, problems of sparse data arise in fitting the bridging models. We address these problems by building combined models for several multiple-race groups, thus borrowing strength across them. These and other methodological issues are discussed.
Release date: 2004-09-13 - Articles and reports: 12-001-X20040016999Description:
Combining response data from the Belgian Fertility and Family Survey with individual level and municipality level data from the 1991 Census for both nonrespondents and respondents, multilevel logistic regression models for contact and cooperation propensity are estimated. The covariates introduced are a selection of indirect features, all out of the researchers' direct control. Contrary to previous research, Socio Economic Status is found to be positively related to cooperation. Another unexpected result is the absence of any considerable impact of ecological correlates such as urbanity.
Release date: 2004-07-14 - 4. Proxy reporting of health information ArchivedArticles and reports: 82-003-X20030036847Geography: CanadaDescription:
This paper examines whether accepting proxy- instead of self-responses results in lower estimates of some health conditions. It analyses data from the National Population Health Survey and the Canadian Community Health Survey.
Release date: 2004-05-18 - Articles and reports: 12-001-X20030026781Description:
Census counts are known to be inexact based on comparisons of Census and Post Enumeration Survey (PES) figures. In Italy, the role of municipal administrations is crucial for both Census and PES field operations. In this paper we analyze the impact of municipality on Italian Census undercount rates by modeling data from the PES as well as from other sources using Poisson regression trees and hierarchical Poisson models. The Poisson regression trees cluster municipalities into homogeneous groups. The hierarchical Poisson models can be considered as tools for Small Area estimation.
Release date: 2004-01-27
Reference (0)
Reference (0) (0 results)
No content available at this time.
- Date modified: