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  • 1. Treatment of nonresponse in cycle two of the National Population Health Survey Archived
    Articles and reports: 12-001-X19980024352
    Description:

    The National Population Health Survey (NPHS) is one of Statistics Canada's three major longitudinal household surveys providing an extensive coverage of the Canadian population. A panel of approximately 17,000 people are being followed up every two years for up to twenty years. The survey data are used for longitudinal analyses, although an important objective is the production of cross-sectional estimates. Each cycle panel respondents provide detailed health information (H) while, to augment the cross-sectional sample, general socio-demographic and health information (G) are collected from all members of their households. This particular collection strategy presents several observable response patterns for Panel Members after two cycles: GH-GH, GH-G*, GH-**, G*-GH, G*-G* and G*-**, where "*" denotes a missing portion of data. The article presents the methodology developed to deal with these types of longitudinal nonresponse as well as with nonresponse from a cross-sectional perspective. The use of weight adjustments for nonresponse and the creation of adjustment cells for weighting using a CHAID algorithm are discussed.

    Release date: 1999-01-14
  • 2. Model-based estimation of record linkage error rates Archived
    Articles and reports: 12-001-X199300214459
    Description:

    Record linkage is the matching of records containing data on individuals, businesses or dwellings when a unique identifier is not available. Methods used in practice involve classification of record pairs as links and non-links using an automated procedure based on the theoretical framework introduced by Fellegi and Sunter (1969). The estimation of classification error rates is an important issue. Fellegi and Sunter provide a method for calculation of classification error rate estimates as a direct by-product of linkage. These model-based estimates are easier to produce than the estimates based on manual matching of samples that are typically used in practice. Properties of model-based classification error rate estimates obtained using three estimators of model parameters are compared.

    Release date: 1993-12-15
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Articles and reports (2)

Articles and reports (2) ((2 results))

  • 1. Treatment of nonresponse in cycle two of the National Population Health Survey Archived
    Articles and reports: 12-001-X19980024352
    Description:

    The National Population Health Survey (NPHS) is one of Statistics Canada's three major longitudinal household surveys providing an extensive coverage of the Canadian population. A panel of approximately 17,000 people are being followed up every two years for up to twenty years. The survey data are used for longitudinal analyses, although an important objective is the production of cross-sectional estimates. Each cycle panel respondents provide detailed health information (H) while, to augment the cross-sectional sample, general socio-demographic and health information (G) are collected from all members of their households. This particular collection strategy presents several observable response patterns for Panel Members after two cycles: GH-GH, GH-G*, GH-**, G*-GH, G*-G* and G*-**, where "*" denotes a missing portion of data. The article presents the methodology developed to deal with these types of longitudinal nonresponse as well as with nonresponse from a cross-sectional perspective. The use of weight adjustments for nonresponse and the creation of adjustment cells for weighting using a CHAID algorithm are discussed.

    Release date: 1999-01-14
  • 2. Model-based estimation of record linkage error rates Archived
    Articles and reports: 12-001-X199300214459
    Description:

    Record linkage is the matching of records containing data on individuals, businesses or dwellings when a unique identifier is not available. Methods used in practice involve classification of record pairs as links and non-links using an automated procedure based on the theoretical framework introduced by Fellegi and Sunter (1969). The estimation of classification error rates is an important issue. Fellegi and Sunter provide a method for calculation of classification error rate estimates as a direct by-product of linkage. These model-based estimates are easier to produce than the estimates based on manual matching of samples that are typically used in practice. Properties of model-based classification error rate estimates obtained using three estimators of model parameters are compared.

    Release date: 1993-12-15
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