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- Articles and reports: 12-001-X200800110615Description:
We consider optimal sampling rates in element-sampling designs when the anticipated analysis is survey-weighted linear regression and the estimands of interest are linear combinations of regression coefficients from one or more models. Methods are first developed assuming that exact design information is available in the sampling frame and then generalized to situations in which some design variables are available only as aggregates for groups of potential subjects, or from inaccurate or old data. We also consider design for estimation of combinations of coefficients from more than one model. A further generalization allows for flexible combinations of coefficients chosen to improve estimation of one effect while controlling for another. Potential applications include estimation of means for several sets of overlapping domains, or improving estimates for subpopulations such as minority races by disproportionate sampling of geographic areas. In the motivating problem of designing a survey on care received by cancer patients (the CanCORS study), potential design information included block-level census data on race/ethnicity and poverty as well as individual-level data. In one study site, an unequal-probability sampling design using the subjectss residential addresses and census data would have reduced the variance of the estimator of an income effect by 25%, or by 38% if the subjects' races were also known. With flexible weighting of the income contrasts by race, the variance of the estimator would be reduced by 26% using residential addresses alone and by 52% using addresses and races. Our methods would be useful in studies in which geographic oversampling by race-ethnicity or socioeconomic characteristics is considered, or in any study in which characteristics available in sampling frames are measured with error.
Release date: 2008-06-26 - 2. A model for estimating and imputing nonrespondent census households under sampling for nonresponse follow-up ArchivedArticles and reports: 12-001-X20060019264Description:
Sampling for nonresponse follow-up (NRFU) was an innovation for U.S. Decennial Census methodology considered for the year 2000. Sampling for NRFU involves sending field enumerators to only a sample of the housing units that did not respond to the initial mailed questionnaire, thereby reducing costs but creating a major small-area estimation problem. We propose a model to impute the characteristics of the housing units that did not respond to the mailed questionnaire, to benefit from the large cost savings of NRFU sampling while still attaining acceptable levels of accuracy for small areas. Our strategy is to model household characteristics using low-dimensional covariates at detailed levels of geography and more detailed covariates at larger levels of geography. To do this, households are first classified into a small number of types. A hierarchical loglinear model then estimates the distribution of household types among the nonsample nonrespondent households in each block. This distribution depends on the characteristics of mailback respondents in the same block and sampled nonrespondents in nearby blocks. Nonsample nonrespondent households can then be imputed according to this estimated household type distribution. We evaluate the performance of our loglinear model through simulation. Results show that, when compared to estimates from alternative models, our loglinear model produces estimates with much smaller MSE in many cases and estimates with approximately the same size MSE in most other cases. Although sampling for NRFU was not used in the 2000 census, our estimation and imputation strategy can be used in any census or survey using sampling for NRFU where units are clustered such that the characteristics of nonrespondents are related to the characteristics of respondents in the same area and also related to the characteristics of sampled nonrespondents in nearby areas.
Release date: 2006-07-20 - Articles and reports: 12-001-X20050029052Description:
Estimates of a sampling variance-covariance matrix are required in many statistical analyses, particularly for multilevel analysis. In univariate problems, functions relating the variance to the mean have been used to obtain variance estimates, pooling information across units or variables. We present variance and correlation functions for multivariate means of ordinal survey items, both for complete data and for data with structured non-response. Methods are also developed for assessing model fit, and for computing composite estimators that combine direct and model-based predictions. Survey data from the Consumer Assessments of Health Plans Study (CAHPS®) illustrate the application of the methodology.
Release date: 2006-02-17 - Articles and reports: 12-001-X20050018088Description:
When administrative records are geographically linked to census block groups, local-area characteristics from the census can be used as contextual variables, which may be useful supplements to variables that are not directly observable from the administrative records. Often databases contain records that have insufficient address information to permit geographical links with census block groups; the contextual variables for these records are therefore unobserved. We propose a new method that uses information from "matched cases" and multivariate regression models to create multiple imputations for the unobserved variables. Our method outperformed alternative methods in simulation evaluations using census data, and was applied to the dataset for a study on treatment patterns for colorectal cancer patients.
Release date: 2005-07-21 - 5. Inferences for finite populations using multiple data sources with different reference times ArchivedArticles and reports: 11-522-X20020016733Description:
While censuses and surveys are often said to measure populations as they are, most reflect information about individuals as they were at the time of measurement, or even at some prior time point. Inferences from such data therefore should take into account change over time at both the population and individual levels. In this paper, we provide a unifying framework for such inference problems, illustrating it through a diverse series of examples including: (1) estimating residency status on Census Day using multiple administrative records, (2) combining administrative records for estimating the size of the US population, (3) using rolling averages from the American Community Survey, and (4) estimating the prevalence of human rights abuses.
Specifically, at the population level, the estimands of interest, such as the size or mean characteristics of a population, might be changing. At the same time, individual subjects might be moving in and out of the frame of the study or changing their characteristics. Such changes over time can affect statistical studies of government data that combine information from multiple data sources, including censuses, surveys and administrative records, an increasingly common practice. Inferences from the resulting merged databases often depend heavily on specific choices made in combining, editing and analysing the data that reflect assumptions about how populations of interest change or remain stable over time.
Release date: 2004-09-13 - 6. Between-state heterogeneity of undercount rates and surrogate variables in the 1990 U.S. census ArchivedArticles and reports: 12-001-X199500114410Description:
As part of the decision on adjustment of the 1990 Decennial Census, the U.S. Census Bureau investigated possible heterogeneity of undercount rates between parts of different states falling in the same adjustment cell or poststratum. Five “surrogate variables” believed to be associated with undercount were analyzed using a large extract from the census and significant heterogeneity was found. Analysis of Post Enumeration Survey on undercount rates showed that more variance was explained by poststratification variables than by state, supporting the decision to use the poststratum as the adjustment cell. Significant interstate heterogeneity was found in 19 out of 99 poststratum groups (mainly in nonurban areas), but there was little if any evidence that the poststratified estimator was biased against particular states after aggregating across poststrata. Nonetheless, this issue should be addressed in future coverage evaluation studies.
Release date: 1995-06-15 - Articles and reports: 12-001-X198800214588Description:
Suppose that undercount rates in a census have been estimated and that block-level estimates of the undercount have been computed. It may then be desirable to create a new roster of households incorporating the estimated omissions. It is proposed here that such a roster be created by weighting the enumerated households. The household weights are constrained by linear equations representing the desired total counts of persons in each estimation class and the desired total count of households. Weights are then calculated that satisfy the constraints while making the fitted table as close as possible to the raw data. The procedure may be regarded as an extension of the standard “raking” methodology to situations where the constraints do not refer to the margins of a contingency table. Continuous as well as discrete covariates may be used in the adjustment, and it is possible to check directly whether the constraints can be satisfied. Methods are proposed for the use of weighted data for various Census purposes, and for adjustment of covariate information on characteristics of omitted households, such as income, that are not directly considered in undercount estimation.
Release date: 1988-12-15
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Articles and reports (7)
Articles and reports (7) ((7 results))
- Articles and reports: 12-001-X200800110615Description:
We consider optimal sampling rates in element-sampling designs when the anticipated analysis is survey-weighted linear regression and the estimands of interest are linear combinations of regression coefficients from one or more models. Methods are first developed assuming that exact design information is available in the sampling frame and then generalized to situations in which some design variables are available only as aggregates for groups of potential subjects, or from inaccurate or old data. We also consider design for estimation of combinations of coefficients from more than one model. A further generalization allows for flexible combinations of coefficients chosen to improve estimation of one effect while controlling for another. Potential applications include estimation of means for several sets of overlapping domains, or improving estimates for subpopulations such as minority races by disproportionate sampling of geographic areas. In the motivating problem of designing a survey on care received by cancer patients (the CanCORS study), potential design information included block-level census data on race/ethnicity and poverty as well as individual-level data. In one study site, an unequal-probability sampling design using the subjectss residential addresses and census data would have reduced the variance of the estimator of an income effect by 25%, or by 38% if the subjects' races were also known. With flexible weighting of the income contrasts by race, the variance of the estimator would be reduced by 26% using residential addresses alone and by 52% using addresses and races. Our methods would be useful in studies in which geographic oversampling by race-ethnicity or socioeconomic characteristics is considered, or in any study in which characteristics available in sampling frames are measured with error.
Release date: 2008-06-26 - 2. A model for estimating and imputing nonrespondent census households under sampling for nonresponse follow-up ArchivedArticles and reports: 12-001-X20060019264Description:
Sampling for nonresponse follow-up (NRFU) was an innovation for U.S. Decennial Census methodology considered for the year 2000. Sampling for NRFU involves sending field enumerators to only a sample of the housing units that did not respond to the initial mailed questionnaire, thereby reducing costs but creating a major small-area estimation problem. We propose a model to impute the characteristics of the housing units that did not respond to the mailed questionnaire, to benefit from the large cost savings of NRFU sampling while still attaining acceptable levels of accuracy for small areas. Our strategy is to model household characteristics using low-dimensional covariates at detailed levels of geography and more detailed covariates at larger levels of geography. To do this, households are first classified into a small number of types. A hierarchical loglinear model then estimates the distribution of household types among the nonsample nonrespondent households in each block. This distribution depends on the characteristics of mailback respondents in the same block and sampled nonrespondents in nearby blocks. Nonsample nonrespondent households can then be imputed according to this estimated household type distribution. We evaluate the performance of our loglinear model through simulation. Results show that, when compared to estimates from alternative models, our loglinear model produces estimates with much smaller MSE in many cases and estimates with approximately the same size MSE in most other cases. Although sampling for NRFU was not used in the 2000 census, our estimation and imputation strategy can be used in any census or survey using sampling for NRFU where units are clustered such that the characteristics of nonrespondents are related to the characteristics of respondents in the same area and also related to the characteristics of sampled nonrespondents in nearby areas.
Release date: 2006-07-20 - Articles and reports: 12-001-X20050029052Description:
Estimates of a sampling variance-covariance matrix are required in many statistical analyses, particularly for multilevel analysis. In univariate problems, functions relating the variance to the mean have been used to obtain variance estimates, pooling information across units or variables. We present variance and correlation functions for multivariate means of ordinal survey items, both for complete data and for data with structured non-response. Methods are also developed for assessing model fit, and for computing composite estimators that combine direct and model-based predictions. Survey data from the Consumer Assessments of Health Plans Study (CAHPS®) illustrate the application of the methodology.
Release date: 2006-02-17 - Articles and reports: 12-001-X20050018088Description:
When administrative records are geographically linked to census block groups, local-area characteristics from the census can be used as contextual variables, which may be useful supplements to variables that are not directly observable from the administrative records. Often databases contain records that have insufficient address information to permit geographical links with census block groups; the contextual variables for these records are therefore unobserved. We propose a new method that uses information from "matched cases" and multivariate regression models to create multiple imputations for the unobserved variables. Our method outperformed alternative methods in simulation evaluations using census data, and was applied to the dataset for a study on treatment patterns for colorectal cancer patients.
Release date: 2005-07-21 - 5. Inferences for finite populations using multiple data sources with different reference times ArchivedArticles and reports: 11-522-X20020016733Description:
While censuses and surveys are often said to measure populations as they are, most reflect information about individuals as they were at the time of measurement, or even at some prior time point. Inferences from such data therefore should take into account change over time at both the population and individual levels. In this paper, we provide a unifying framework for such inference problems, illustrating it through a diverse series of examples including: (1) estimating residency status on Census Day using multiple administrative records, (2) combining administrative records for estimating the size of the US population, (3) using rolling averages from the American Community Survey, and (4) estimating the prevalence of human rights abuses.
Specifically, at the population level, the estimands of interest, such as the size or mean characteristics of a population, might be changing. At the same time, individual subjects might be moving in and out of the frame of the study or changing their characteristics. Such changes over time can affect statistical studies of government data that combine information from multiple data sources, including censuses, surveys and administrative records, an increasingly common practice. Inferences from the resulting merged databases often depend heavily on specific choices made in combining, editing and analysing the data that reflect assumptions about how populations of interest change or remain stable over time.
Release date: 2004-09-13 - 6. Between-state heterogeneity of undercount rates and surrogate variables in the 1990 U.S. census ArchivedArticles and reports: 12-001-X199500114410Description:
As part of the decision on adjustment of the 1990 Decennial Census, the U.S. Census Bureau investigated possible heterogeneity of undercount rates between parts of different states falling in the same adjustment cell or poststratum. Five “surrogate variables” believed to be associated with undercount were analyzed using a large extract from the census and significant heterogeneity was found. Analysis of Post Enumeration Survey on undercount rates showed that more variance was explained by poststratification variables than by state, supporting the decision to use the poststratum as the adjustment cell. Significant interstate heterogeneity was found in 19 out of 99 poststratum groups (mainly in nonurban areas), but there was little if any evidence that the poststratified estimator was biased against particular states after aggregating across poststrata. Nonetheless, this issue should be addressed in future coverage evaluation studies.
Release date: 1995-06-15 - Articles and reports: 12-001-X198800214588Description:
Suppose that undercount rates in a census have been estimated and that block-level estimates of the undercount have been computed. It may then be desirable to create a new roster of households incorporating the estimated omissions. It is proposed here that such a roster be created by weighting the enumerated households. The household weights are constrained by linear equations representing the desired total counts of persons in each estimation class and the desired total count of households. Weights are then calculated that satisfy the constraints while making the fitted table as close as possible to the raw data. The procedure may be regarded as an extension of the standard “raking” methodology to situations where the constraints do not refer to the margins of a contingency table. Continuous as well as discrete covariates may be used in the adjustment, and it is possible to check directly whether the constraints can be satisfied. Methods are proposed for the use of weighted data for various Census purposes, and for adjustment of covariate information on characteristics of omitted households, such as income, that are not directly considered in undercount estimation.
Release date: 1988-12-15
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