Weighting and estimation

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  • 1. Comparisons of collection follow-up score functions Archived
    Articles and reports: 11-522-X200800010959
    Description:

    The Unified Enterprise Survey (UES) at Statistics Canada is an annual business survey that unifies more than 60 surveys from different industries. Two types of collection follow-up score functions are currently used in the UES data collection. The objective of using a score function is to maximize the economically weighted response rates of the survey in terms of the primary variables of interest, under the constraint of a limited follow-up budget. Since the two types of score functions are based on different methodologies, they could have different impacts on the final estimates.

    This study generally compares the two types of score functions based on the collection data obtained from the two recent years. For comparison purposes, this study applies each score function method to the same data respectively and computes various estimates of the published financial and commodity variables, their deviation from the true pseudo value and their mean square deviation, based on each method. These estimates of deviation and mean square deviation based on each method are then used to measure the impact of each score function on the final estimates of the financial and commodity variables.

    Release date: 2009-12-03
  • 2. Single stage cluster sampling in prevalence-incidence surveys: Some issues suggested by the Shanghai Survey of Alzheimer’s Disease and Dementia Archived
    Articles and reports: 12-001-X199200114492
    Description:

    The scenario considered here is that of a sample survey having the following two major objectives: (1) identification for future follow up studies of n^* subjects in each of H subdomains, and (2) estimation as of this time of conduct of the survey of the level of some characteristic in each of these subdomains. An additional constraint imposed here is that the sample design is restricted to single stage cluster sampling. A variation of single stage cluster sampling called telescopic single stage cluster sampling (TSSCS) had been proposed in an earlier paper (Levy et al. 1989) as a cost effective method of identifying n^* individuals in each sub domain and, in this article, we investigate the statistical properties of TSSCS in crossectional estimation of the level of a population characteristic. In particular, TSSCS is compared to ordinary single stage cluster sampling (OSSCS) with respect to the reliability of estimates at fixed cost. Motivation for this investigation comes from problems faced during the statistical design of the Shanghai Survey of Alzheimer’s Disease and Dementia (SSADD), an epidemiological study of the prevalence and incidence of Alzheimer’s disease and dementia.

    Release date: 1992-06-15
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  • 1. Comparisons of collection follow-up score functions Archived
    Articles and reports: 11-522-X200800010959
    Description:

    The Unified Enterprise Survey (UES) at Statistics Canada is an annual business survey that unifies more than 60 surveys from different industries. Two types of collection follow-up score functions are currently used in the UES data collection. The objective of using a score function is to maximize the economically weighted response rates of the survey in terms of the primary variables of interest, under the constraint of a limited follow-up budget. Since the two types of score functions are based on different methodologies, they could have different impacts on the final estimates.

    This study generally compares the two types of score functions based on the collection data obtained from the two recent years. For comparison purposes, this study applies each score function method to the same data respectively and computes various estimates of the published financial and commodity variables, their deviation from the true pseudo value and their mean square deviation, based on each method. These estimates of deviation and mean square deviation based on each method are then used to measure the impact of each score function on the final estimates of the financial and commodity variables.

    Release date: 2009-12-03
  • 2. Single stage cluster sampling in prevalence-incidence surveys: Some issues suggested by the Shanghai Survey of Alzheimer’s Disease and Dementia Archived
    Articles and reports: 12-001-X199200114492
    Description:

    The scenario considered here is that of a sample survey having the following two major objectives: (1) identification for future follow up studies of n^* subjects in each of H subdomains, and (2) estimation as of this time of conduct of the survey of the level of some characteristic in each of these subdomains. An additional constraint imposed here is that the sample design is restricted to single stage cluster sampling. A variation of single stage cluster sampling called telescopic single stage cluster sampling (TSSCS) had been proposed in an earlier paper (Levy et al. 1989) as a cost effective method of identifying n^* individuals in each sub domain and, in this article, we investigate the statistical properties of TSSCS in crossectional estimation of the level of a population characteristic. In particular, TSSCS is compared to ordinary single stage cluster sampling (OSSCS) with respect to the reliability of estimates at fixed cost. Motivation for this investigation comes from problems faced during the statistical design of the Shanghai Survey of Alzheimer’s Disease and Dementia (SSADD), an epidemiological study of the prevalence and incidence of Alzheimer’s disease and dementia.

    Release date: 1992-06-15
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