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All (9) ((9 results))

  • Articles and reports: 12-001-X201300111823
    Description:

    Although weights are widely used in survey sampling their ultimate justification from the design perspective is often problematical. Here we will argue for a stepwise Bayes justification for weights that does not depend explicitly on the sampling design. This approach will make use of the standard kind of information present in auxiliary variables however it will not assume a model relating the auxiliary variables to the characteristic of interest. The resulting weight for a unit in the sample can be given the usual interpretation as the number of units in the population which it represents.

    Release date: 2013-06-28

  • Articles and reports: 12-001-X201300111824
    Description:

    In most surveys all sample units receive the same treatment and the same design features apply to all selected people and households. In this paper, it is explained how survey designs may be tailored to optimize quality given constraints on costs. Such designs are called adaptive survey designs. The basic ingredients of such designs are introduced, discussed and illustrated with various examples.

    Release date: 2013-06-28

  • Surveys and statistical programs – Documentation: 12-001-X201300111825
    Description:

    A considerable limitation of current methods for automatic data editing is that they treat all edits as hard constraints. That is to say, an edit failure is always attributed to an error in the data. In manual editing, however, subject-matter specialists also make extensive use of soft edits, i.e., constraints that identify (combinations of) values that are suspicious but not necessarily incorrect. The inability of automatic editing methods to handle soft edits partly explains why in practice many differences are found between manually edited and automatically edited data. The object of this article is to present a new formulation of the error localisation problem which can distinguish between hard and soft edits. Moreover, it is shown how this problem may be solved by an extension of the error localisation algorithm of De Waal and Quere (2003).

    Release date: 2013-06-28

  • Articles and reports: 12-001-X201300111827
    Description:

    SILC (Statistics on Income and Living Conditions) is an annual European survey that measures the population's income distribution, poverty and living conditions. It has been conducted in Switzerland since 2007, based on a four-panel rotation scheme that yields both cross-sectional and longitudinal estimates. This article examines the problem of estimating the variance of the cross-sectional poverty and social exclusion indicators selected by Eurostat. Our calculations take into account the non-linearity of the estimators, total non-response at different survey stages, indirect sampling and calibration. We adapt the method proposed by Lavallée (2002) for estimating variance in cases of non-response after weight sharing, and we obtain a variance estimator that is asymptotically unbiased and very easy to program.

    Release date: 2013-06-28

  • Surveys and statistical programs – Documentation: 12-001-X201300111828
    Description:

    A question that commonly arises in longitudinal surveys is the issue of how to combine differing cohorts of the survey. In this paper we present a novel method for combining different cohorts, and using all available data, in a longitudinal survey to estimate parameters of a semiparametric model, which relates the response variable to a set of covariates. The procedure builds upon the Weighted Generalized Estimation Equation method for handling missing waves in longitudinal studies. Our method is set up under a joint-randomization framework for estimation of model parameters, which takes into account the superpopulation model as well as the survey design randomization. We also propose a design-based, and a joint-randomization, variance estimation method. To illustrate the methodology we apply it to the Survey of Doctorate Recipients, conducted by the U.S. National Science Foundation.

    Release date: 2013-06-28

  • Articles and reports: 12-001-X201300111830
    Description:

    We consider two different self-benchmarking methods for the estimation of small area means based on the Fay-Herriot (FH) area level model: the method of You and Rao (2002) applied to the FH model and the method of Wang, Fuller and Qu (2008) based on augmented models. We derive an estimator of the mean squared prediction error (MSPE) of the You-Rao (YR) estimator of a small area mean that, under the true model, is correct to second-order terms. We report the results of a simulation study on the relative bias of the MSPE estimator of the YR estimator and the MSPE estimator of the Wang, Fuller and Qu (WFQ) estimator obtained under an augmented model. We also study the MSPE and the estimators of MSPE for the YR and WFQ estimators obtained under a misspecified model.

    Release date: 2013-06-28

  • Articles and reports: 12-001-X201300111831
    Description:

    We consider conservative variance estimation for the Horvitz-Thompson estimator of a population total in sampling designs with zero pairwise inclusion probabilities, known as "non-measurable" designs. We decompose the standard Horvitz-Thompson variance estimator under such designs and characterize the bias precisely. We develop a bias correction that is guaranteed to be weakly conservative (nonnegatively biased) regardless of the nature of the non-measurability. The analysis sheds light on conditions under which the standard Horvitz-Thompson variance estimator performs well despite non-measurability and where the conservative bias correction may outperform commonly-used approximations.

    Release date: 2013-06-28

  • Surveys and statistical programs – Documentation: 98-314-X2011051
    Description:

    Readers will find a complete analysis of factors affecting the comparability of Language results between the censuses in the Methodological Document on the 2011 Census Language Data.

    Release date: 2013-05-03

  • Articles and reports: 89-648-X2013002
    Geography: Canada
    Description:

    Data matching is a common practice used to reduce the response burden of respondents and to improve the quality of the information collected from respondents when the linkage method does not introduce bias. However, historical linkage, which consists in linking external records from previous years to the year of the initial wave of a survey, is relatively rare and, until now, had not been used at Statistics Canada. The present paper describes the method used to link the records from the Living in Canada Survey pilot to historical tax data on income and labour (T1 and T4 files). It presents the evolution of the linkage rate going back over time and compares earnings data collected from personal income tax returns with those collected from employers file. To illustrate the new possibilities of analysis offered by this type of linkage, the study concludes with an earnings profile by age and sex for different cohorts based on year of birth.

    Release date: 2013-01-24
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Analysis (6)

Analysis (6) ((6 results))

  • Articles and reports: 12-001-X201300111823
    Description:

    Although weights are widely used in survey sampling their ultimate justification from the design perspective is often problematical. Here we will argue for a stepwise Bayes justification for weights that does not depend explicitly on the sampling design. This approach will make use of the standard kind of information present in auxiliary variables however it will not assume a model relating the auxiliary variables to the characteristic of interest. The resulting weight for a unit in the sample can be given the usual interpretation as the number of units in the population which it represents.

    Release date: 2013-06-28

  • Articles and reports: 12-001-X201300111824
    Description:

    In most surveys all sample units receive the same treatment and the same design features apply to all selected people and households. In this paper, it is explained how survey designs may be tailored to optimize quality given constraints on costs. Such designs are called adaptive survey designs. The basic ingredients of such designs are introduced, discussed and illustrated with various examples.

    Release date: 2013-06-28

  • Articles and reports: 12-001-X201300111827
    Description:

    SILC (Statistics on Income and Living Conditions) is an annual European survey that measures the population's income distribution, poverty and living conditions. It has been conducted in Switzerland since 2007, based on a four-panel rotation scheme that yields both cross-sectional and longitudinal estimates. This article examines the problem of estimating the variance of the cross-sectional poverty and social exclusion indicators selected by Eurostat. Our calculations take into account the non-linearity of the estimators, total non-response at different survey stages, indirect sampling and calibration. We adapt the method proposed by Lavallée (2002) for estimating variance in cases of non-response after weight sharing, and we obtain a variance estimator that is asymptotically unbiased and very easy to program.

    Release date: 2013-06-28

  • Articles and reports: 12-001-X201300111830
    Description:

    We consider two different self-benchmarking methods for the estimation of small area means based on the Fay-Herriot (FH) area level model: the method of You and Rao (2002) applied to the FH model and the method of Wang, Fuller and Qu (2008) based on augmented models. We derive an estimator of the mean squared prediction error (MSPE) of the You-Rao (YR) estimator of a small area mean that, under the true model, is correct to second-order terms. We report the results of a simulation study on the relative bias of the MSPE estimator of the YR estimator and the MSPE estimator of the Wang, Fuller and Qu (WFQ) estimator obtained under an augmented model. We also study the MSPE and the estimators of MSPE for the YR and WFQ estimators obtained under a misspecified model.

    Release date: 2013-06-28

  • Articles and reports: 12-001-X201300111831
    Description:

    We consider conservative variance estimation for the Horvitz-Thompson estimator of a population total in sampling designs with zero pairwise inclusion probabilities, known as "non-measurable" designs. We decompose the standard Horvitz-Thompson variance estimator under such designs and characterize the bias precisely. We develop a bias correction that is guaranteed to be weakly conservative (nonnegatively biased) regardless of the nature of the non-measurability. The analysis sheds light on conditions under which the standard Horvitz-Thompson variance estimator performs well despite non-measurability and where the conservative bias correction may outperform commonly-used approximations.

    Release date: 2013-06-28

  • Articles and reports: 89-648-X2013002
    Geography: Canada
    Description:

    Data matching is a common practice used to reduce the response burden of respondents and to improve the quality of the information collected from respondents when the linkage method does not introduce bias. However, historical linkage, which consists in linking external records from previous years to the year of the initial wave of a survey, is relatively rare and, until now, had not been used at Statistics Canada. The present paper describes the method used to link the records from the Living in Canada Survey pilot to historical tax data on income and labour (T1 and T4 files). It presents the evolution of the linkage rate going back over time and compares earnings data collected from personal income tax returns with those collected from employers file. To illustrate the new possibilities of analysis offered by this type of linkage, the study concludes with an earnings profile by age and sex for different cohorts based on year of birth.

    Release date: 2013-01-24
Reference (3)

Reference (3) ((3 results))

  • Surveys and statistical programs – Documentation: 12-001-X201300111825
    Description:

    A considerable limitation of current methods for automatic data editing is that they treat all edits as hard constraints. That is to say, an edit failure is always attributed to an error in the data. In manual editing, however, subject-matter specialists also make extensive use of soft edits, i.e., constraints that identify (combinations of) values that are suspicious but not necessarily incorrect. The inability of automatic editing methods to handle soft edits partly explains why in practice many differences are found between manually edited and automatically edited data. The object of this article is to present a new formulation of the error localisation problem which can distinguish between hard and soft edits. Moreover, it is shown how this problem may be solved by an extension of the error localisation algorithm of De Waal and Quere (2003).

    Release date: 2013-06-28

  • Surveys and statistical programs – Documentation: 12-001-X201300111828
    Description:

    A question that commonly arises in longitudinal surveys is the issue of how to combine differing cohorts of the survey. In this paper we present a novel method for combining different cohorts, and using all available data, in a longitudinal survey to estimate parameters of a semiparametric model, which relates the response variable to a set of covariates. The procedure builds upon the Weighted Generalized Estimation Equation method for handling missing waves in longitudinal studies. Our method is set up under a joint-randomization framework for estimation of model parameters, which takes into account the superpopulation model as well as the survey design randomization. We also propose a design-based, and a joint-randomization, variance estimation method. To illustrate the methodology we apply it to the Survey of Doctorate Recipients, conducted by the U.S. National Science Foundation.

    Release date: 2013-06-28

  • Surveys and statistical programs – Documentation: 98-314-X2011051
    Description:

    Readers will find a complete analysis of factors affecting the comparability of Language results between the censuses in the Methodological Document on the 2011 Census Language Data.

    Release date: 2013-05-03
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