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- Articles and reports: 12-001-X200800210760Description:
The design of a stratified simple random sample without replacement from a finite population deals with two main issues: the definition of a rule to partition the population into strata, and the allocation of sampling units in the selected strata. This article examines a tree-based strategy which plans to approach jointly these issues when the survey is multipurpose and multivariate information, quantitative or qualitative, is available. Strata are formed through a hierarchical divisive algorithm that selects finer and finer partitions by minimizing, at each step, the sample allocation required to achieve the precision levels set for each surveyed variable. In this way, large numbers of constraints can be satisfied without drastically increasing the sample size, and also without discarding variables selected for stratification or diminishing the number of their class intervals. Furthermore, the algorithm tends not to define empty or almost empty strata, thus avoiding the need for strata collapsing aggregations. The procedure was applied to redesign the Italian Farm Structure Survey. The results indicate that the gain in efficiency held using our strategy is nontrivial. For a given sample size, this procedure achieves the required precision by exploiting a number of strata which is usually a very small fraction of the number of strata available when combining all possible classes from any of the covariates.
Release date: 2008-12-23 - Articles and reports: 12-001-X200800210761Description:
Optimum stratification is the method of choosing the best boundaries that make strata internally homogeneous, given some sample allocation. In order to make the strata internally homogenous, the strata should be constructed in such a way that the strata variances for the characteristic under study be as small as possible. This could be achieved effectively by having the distribution of the main study variable known and create strata by cutting the range of the distribution at suitable points. If the frequency distribution of the study variable is unknown, it may be approximated from the past experience or some prior knowledge obtained at a recent study. In this paper the problem of finding Optimum Strata Boundaries (OSB) is considered as the problem of determining Optimum Strata Widths (OSW). The problem is formulated as a Mathematical Programming Problem (MPP), which minimizes the variance of the estimated population parameter under Neyman allocation subject to the restriction that sum of the widths of all the strata is equal to the total range of the distribution. The distributions of the study variable are considered as continuous with Triangular and Standard Normal density functions. The formulated MPPs, which turn out to be multistage decision problems, can then be solved using dynamic programming technique proposed by Bühler and Deutler (1975). Numerical examples are presented to illustrate the computational details. The results obtained are also compared with the method of Dalenius and Hodges (1959) with an example of normal distribution.
Release date: 2008-12-23 - Articles and reports: 12-001-X200800210762Description:
This paper considers the optimum allocation in multivariate stratified sampling as a nonlinear matrix optimisation of integers. As a particular case, a nonlinear problem of the multi-objective optimisation of integers is studied. A full detailed example including some of proposed techniques is provided at the end of the work.
Release date: 2008-12-23 - 4. A balanced sampling approach for multi-way stratification designs for small area estimation ArchivedArticles and reports: 12-001-X200800210763Description:
The present work illustrates a sampling strategy useful for obtaining planned sample size for domains belonging to different partitions of the population and in order to guarantee the sampling errors of domain estimates be lower than given thresholds. The sampling strategy that covers the multivariate multi-domain case is useful when the overall sample size is bounded and consequently the standard solution of using a stratified sample with the strata given by cross-classification of variables defining the different partitions is not feasible since the number of strata is larger than the overall sample size. The proposed sampling strategy is based on the use of balanced sampling selection technique and on a GREG-type estimation. The main advantages of the solution is the computational feasibility which allows one to easily implement an overall small area strategy considering jointly the sampling design and the estimator and improving the efficiency of the direct domain estimators. An empirical simulation on real population data and different domain estimators shows the empirical properties of the examined sample strategy.
Release date: 2008-12-23 - Articles and reports: 11-522-X200600110424Description:
The International Tobacco Control (ITC) Policy Evaluation China Survey uses a multi-stage unequal probability sampling design with upper level clusters selected by the randomized systematic PPS sampling method. A difficulty arises in the execution of the survey: several selected upper level clusters refuse to participate in the survey and have to be replaced by substitute units, selected from units not included in the initial sample and once again using the randomized systematic PPS sampling method. Under such a scenario the first order inclusion probabilities of the final selected units are very difficult to calculate and the second order inclusion probabilities become virtually intractable. In this paper we develop a simulation-based approach for computing the first and the second order inclusion probabilities when direct calculation is prohibitive or impossible. The efficiency and feasibility of the proposed approach are demonstrated through both theoretical considerations and numerical examples. Several R/S-PLUS functions and codes for the proposed procedure are included. The approach can be extended to handle more complex refusal/substitution scenarios one may encounter in practice.
Release date: 2008-06-26 - 6. A noninformative Bayesian approach to finite population sampling using auxiliary variables ArchivedArticles and reports: 12-001-X200800110611Description:
In finite population sampling prior information is often available in the form of partial knowledge about an auxiliary variable, for example its mean may be known. In such cases, the ratio estimator and the regression estimator are often used for estimating the population mean of the characteristic of interest. The Polya posterior has been developed as a noninformative Bayesian approach to survey sampling. It is appropriate when little or no prior information about the population is available. Here we show that it can be extended to incorporate types of partial prior information about auxiliary variables. We will see that it typically yields procedures with good frequentist properties even in some problems where standard frequentist methods are difficult to apply.
Release date: 2008-06-26 - Articles and reports: 12-001-X200800110613Description:
The International Tobacco Control (ITC) Policy Evaluation Survey of China uses a multi-stage unequal probability sampling design with upper level clusters selected by the randomized systematic PPS sampling method. A difficulty arises in the execution of the survey: several selected upper level clusters refuse to participate in the survey and have to be replaced by substitute units, selected from units not included in the initial sample and once again using the randomized systematic PPS sampling method. Under such a scenario the first order inclusion probabilities of the final selected units are very difficult to calculate and the second order inclusion probabilities become virtually intractable. In this paper we develop a simulation-based approach for computing the first and the second order inclusion probabilities when direct calculation is prohibitive or impossible. The efficiency and feasibility of the proposed approach are demonstrated through both theoretical considerations and numerical examples. Several R/S-PLUS functions and codes for the proposed procedure are included. The approach can be extended to handle more complex refusal/substitution scenarios one may encounter in practice.
Release date: 2008-06-26 - 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 - Articles and reports: 12-001-X200800110618Description:
The National Health and Nutrition Examination Survey (NHANES) is one of a series of health-related programs sponsored by the United States National Center for Health Statistics. A unique feature of NHANES is the administration of a complete medical examination for each respondent in the sample. To standardize administration, these examinations are carried out in mobile examination centers. The examination includes physical measurements, tests such as eye and dental examinations, and the collection of blood and urine specimens for laboratory testing. NHANES is an ongoing annual health survey of the noninstitutionalized civilian population of the United States. The major analytic goals of NHANES include estimating the number and percentage of persons in the U.S. population and in designated subgroups with selected diseases and risk factors. The sample design for NHANES must create a balance between the requirements for efficient annual and multiyear samples and the flexibility that allows changes in key design parameters to make the survey more responsive to the needs of the research and health policy communities. This paper discusses the challenges involved in designing and implementing a sample selection process that satisfies the goals of NHANES.
Release date: 2008-06-26 - 10. The General Social Survey: New Data Overview ArchivedSurveys and statistical programs – Documentation: 89-631-XDescription:
This report highlights the latest developments and rationale behind recent cycles of the General Social Survey (GSS). Starting with an overview of the GSS mandate and historic cycle topics, we then focus on two recent cycles related to families in Canada: Family Transitions (2006) and Family, Social Support and Retirement (2007). Finally, we give a summary of what is to come in the 2008 GSS on Social Networks, and describe a special project to mark 'Twenty Years of GSS'.
The survey collects data over a twelve month period from the population living in private households in the 10 provinces. For all cycles except Cycles 16 and 21, the population aged 15 and older has been sampled. Cycles 16 and 21 sampled persons aged 45 and older.
Cycle 20 (GSS 2006) is the fourth cycle of the GSS to collect data on families (the first three cycles on the family were in 1990, 1995 and 2001). Cycle 20 covers much the same content as previous cycles on families with some sections revised and expanded. The data enable analysts to measure conjugal and fertility history (chronology of marriages, common-law unions, and children), family origins, children's home leaving, fertility intentions, child custody as well as work history and other socioeconomic characteristics. Questions on financial support agreements or arrangements (for children and the ex-spouse or ex-partner) for separated and divorced families have been modified. Also, sections on social networks, well-being and housing characteristics have been added.
Release date: 2008-05-27
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- Articles and reports: 12-001-X200800210760Description:
The design of a stratified simple random sample without replacement from a finite population deals with two main issues: the definition of a rule to partition the population into strata, and the allocation of sampling units in the selected strata. This article examines a tree-based strategy which plans to approach jointly these issues when the survey is multipurpose and multivariate information, quantitative or qualitative, is available. Strata are formed through a hierarchical divisive algorithm that selects finer and finer partitions by minimizing, at each step, the sample allocation required to achieve the precision levels set for each surveyed variable. In this way, large numbers of constraints can be satisfied without drastically increasing the sample size, and also without discarding variables selected for stratification or diminishing the number of their class intervals. Furthermore, the algorithm tends not to define empty or almost empty strata, thus avoiding the need for strata collapsing aggregations. The procedure was applied to redesign the Italian Farm Structure Survey. The results indicate that the gain in efficiency held using our strategy is nontrivial. For a given sample size, this procedure achieves the required precision by exploiting a number of strata which is usually a very small fraction of the number of strata available when combining all possible classes from any of the covariates.
Release date: 2008-12-23 - Articles and reports: 12-001-X200800210761Description:
Optimum stratification is the method of choosing the best boundaries that make strata internally homogeneous, given some sample allocation. In order to make the strata internally homogenous, the strata should be constructed in such a way that the strata variances for the characteristic under study be as small as possible. This could be achieved effectively by having the distribution of the main study variable known and create strata by cutting the range of the distribution at suitable points. If the frequency distribution of the study variable is unknown, it may be approximated from the past experience or some prior knowledge obtained at a recent study. In this paper the problem of finding Optimum Strata Boundaries (OSB) is considered as the problem of determining Optimum Strata Widths (OSW). The problem is formulated as a Mathematical Programming Problem (MPP), which minimizes the variance of the estimated population parameter under Neyman allocation subject to the restriction that sum of the widths of all the strata is equal to the total range of the distribution. The distributions of the study variable are considered as continuous with Triangular and Standard Normal density functions. The formulated MPPs, which turn out to be multistage decision problems, can then be solved using dynamic programming technique proposed by Bühler and Deutler (1975). Numerical examples are presented to illustrate the computational details. The results obtained are also compared with the method of Dalenius and Hodges (1959) with an example of normal distribution.
Release date: 2008-12-23 - Articles and reports: 12-001-X200800210762Description:
This paper considers the optimum allocation in multivariate stratified sampling as a nonlinear matrix optimisation of integers. As a particular case, a nonlinear problem of the multi-objective optimisation of integers is studied. A full detailed example including some of proposed techniques is provided at the end of the work.
Release date: 2008-12-23 - 4. A balanced sampling approach for multi-way stratification designs for small area estimation ArchivedArticles and reports: 12-001-X200800210763Description:
The present work illustrates a sampling strategy useful for obtaining planned sample size for domains belonging to different partitions of the population and in order to guarantee the sampling errors of domain estimates be lower than given thresholds. The sampling strategy that covers the multivariate multi-domain case is useful when the overall sample size is bounded and consequently the standard solution of using a stratified sample with the strata given by cross-classification of variables defining the different partitions is not feasible since the number of strata is larger than the overall sample size. The proposed sampling strategy is based on the use of balanced sampling selection technique and on a GREG-type estimation. The main advantages of the solution is the computational feasibility which allows one to easily implement an overall small area strategy considering jointly the sampling design and the estimator and improving the efficiency of the direct domain estimators. An empirical simulation on real population data and different domain estimators shows the empirical properties of the examined sample strategy.
Release date: 2008-12-23 - Articles and reports: 11-522-X200600110424Description:
The International Tobacco Control (ITC) Policy Evaluation China Survey uses a multi-stage unequal probability sampling design with upper level clusters selected by the randomized systematic PPS sampling method. A difficulty arises in the execution of the survey: several selected upper level clusters refuse to participate in the survey and have to be replaced by substitute units, selected from units not included in the initial sample and once again using the randomized systematic PPS sampling method. Under such a scenario the first order inclusion probabilities of the final selected units are very difficult to calculate and the second order inclusion probabilities become virtually intractable. In this paper we develop a simulation-based approach for computing the first and the second order inclusion probabilities when direct calculation is prohibitive or impossible. The efficiency and feasibility of the proposed approach are demonstrated through both theoretical considerations and numerical examples. Several R/S-PLUS functions and codes for the proposed procedure are included. The approach can be extended to handle more complex refusal/substitution scenarios one may encounter in practice.
Release date: 2008-06-26 - 6. A noninformative Bayesian approach to finite population sampling using auxiliary variables ArchivedArticles and reports: 12-001-X200800110611Description:
In finite population sampling prior information is often available in the form of partial knowledge about an auxiliary variable, for example its mean may be known. In such cases, the ratio estimator and the regression estimator are often used for estimating the population mean of the characteristic of interest. The Polya posterior has been developed as a noninformative Bayesian approach to survey sampling. It is appropriate when little or no prior information about the population is available. Here we show that it can be extended to incorporate types of partial prior information about auxiliary variables. We will see that it typically yields procedures with good frequentist properties even in some problems where standard frequentist methods are difficult to apply.
Release date: 2008-06-26 - Articles and reports: 12-001-X200800110613Description:
The International Tobacco Control (ITC) Policy Evaluation Survey of China uses a multi-stage unequal probability sampling design with upper level clusters selected by the randomized systematic PPS sampling method. A difficulty arises in the execution of the survey: several selected upper level clusters refuse to participate in the survey and have to be replaced by substitute units, selected from units not included in the initial sample and once again using the randomized systematic PPS sampling method. Under such a scenario the first order inclusion probabilities of the final selected units are very difficult to calculate and the second order inclusion probabilities become virtually intractable. In this paper we develop a simulation-based approach for computing the first and the second order inclusion probabilities when direct calculation is prohibitive or impossible. The efficiency and feasibility of the proposed approach are demonstrated through both theoretical considerations and numerical examples. Several R/S-PLUS functions and codes for the proposed procedure are included. The approach can be extended to handle more complex refusal/substitution scenarios one may encounter in practice.
Release date: 2008-06-26 - 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 - Articles and reports: 12-001-X200800110618Description:
The National Health and Nutrition Examination Survey (NHANES) is one of a series of health-related programs sponsored by the United States National Center for Health Statistics. A unique feature of NHANES is the administration of a complete medical examination for each respondent in the sample. To standardize administration, these examinations are carried out in mobile examination centers. The examination includes physical measurements, tests such as eye and dental examinations, and the collection of blood and urine specimens for laboratory testing. NHANES is an ongoing annual health survey of the noninstitutionalized civilian population of the United States. The major analytic goals of NHANES include estimating the number and percentage of persons in the U.S. population and in designated subgroups with selected diseases and risk factors. The sample design for NHANES must create a balance between the requirements for efficient annual and multiyear samples and the flexibility that allows changes in key design parameters to make the survey more responsive to the needs of the research and health policy communities. This paper discusses the challenges involved in designing and implementing a sample selection process that satisfies the goals of NHANES.
Release date: 2008-06-26 - 10. Variable selection models for weight trimming ArchivedArticles and reports: 11-522-X200600110409Description:
In unequal-probability-of-selection sample, correlations between the probability of selection and the sampled data can induce bias. Weights equal to the inverse of the probability of selection are often used to counteract this bias. Highly disproportional sample designs have large weights, which can introduce unnecessary variability in statistics such as the population mean estimate. Weight trimming reduces large weights to a fixed cutpoint value and adjusts weights below this value to maintain the untrimmed weight sum. This reduces variability at the cost of introducing some bias. Standard approaches are not "data-driven": they do not use the data to make the appropriate bias-variance tradeoff, or else do so in a highly inefficient fashion. This presentation develops Bayesian variable selection methods for weight trimming to supplement standard, ad-hoc design-based methods in disproportional probability-of-inclusion designs where variances due to sample weights exceeds bias correction. These methods are used to estimate linear and generalized linear regression model population parameters in the context of stratified and poststratified known-probability sample designs. Applications will be considered in the context of traffic injury survey data, in which highly disproportional sample designs are often utilized.
Release date: 2008-03-17
Reference (3)
Reference (3) ((3 results))
- 1. The General Social Survey: New Data Overview ArchivedSurveys and statistical programs – Documentation: 89-631-XDescription:
This report highlights the latest developments and rationale behind recent cycles of the General Social Survey (GSS). Starting with an overview of the GSS mandate and historic cycle topics, we then focus on two recent cycles related to families in Canada: Family Transitions (2006) and Family, Social Support and Retirement (2007). Finally, we give a summary of what is to come in the 2008 GSS on Social Networks, and describe a special project to mark 'Twenty Years of GSS'.
The survey collects data over a twelve month period from the population living in private households in the 10 provinces. For all cycles except Cycles 16 and 21, the population aged 15 and older has been sampled. Cycles 16 and 21 sampled persons aged 45 and older.
Cycle 20 (GSS 2006) is the fourth cycle of the GSS to collect data on families (the first three cycles on the family were in 1990, 1995 and 2001). Cycle 20 covers much the same content as previous cycles on families with some sections revised and expanded. The data enable analysts to measure conjugal and fertility history (chronology of marriages, common-law unions, and children), family origins, children's home leaving, fertility intentions, child custody as well as work history and other socioeconomic characteristics. Questions on financial support agreements or arrangements (for children and the ex-spouse or ex-partner) for separated and divorced families have been modified. Also, sections on social networks, well-being and housing characteristics have been added.
Release date: 2008-05-27 - Surveys and statistical programs – Documentation: 75F0002M1992001Description:
Starting in 1994, the Survey of Labour and Income Dynamics (SLID) will follow individuals and families for at least six years, tracking their labour market experiences, changes in income and family circumstances. An initial proposal for the content of SLID, entitled "Content of the Survey of Labour and Income Dynamics : Discussion Paper", was distributed in February 1992.
That paper served as a background document for consultation with and a review by interested users. The content underwent significant change during this process. Based upon the revised content, a large-scale test of SLID will be conducted in February and May 1993.
The present document outlines the income and wealth content to be tested in May 1993. This document is really a continuation of SLID Research Paper Series 92-01A, which outlines the demographic and labour content used in the January /February 1993 test.
Release date: 2008-02-29 - Surveys and statistical programs – Documentation: 75F0002M1992007Description:
A Preliminary Interview will be conducted on the first panel of SLID, in January 1993, as a supplement to the Labour Force Survey. The first panel is made up of about 20,000 households that are rotating out of the Labour Force Survey in January and February, 1993.
The purpose of this document is to provide a description of the purpose of the SLID Preliminary Interview and the question wordings to be used.
Release date: 2008-02-29
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