Analysis

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• 1. Cohort profile: The Canadian Census Health and Environment Cohorts (CanCHECs)

  Articles and reports: 82-003-X201901200003

  Description:

  This article provides a description of the Canadian Census Health and Environment Cohorts (CanCHECs), a population-based linked datasets of the household population at the time of census collection. The CanCHEC datasets are rich national data resources that can be used to measure and examine health inequalities across socioeconomic and ethnocultural dimensions for different periods and locations. These datasets can also be used to examine the effects of exposure to environmental factors on human health.

  Release date: 2019-12-18

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• 2. A note on propensity score weighting method using paradata in survey sampling

  Articles and reports: 12-001-X201900300002

  Description:

  Paradata is often collected during the survey process to monitor the quality of the survey response. One such paradata is a respondent behavior, which can be used to construct response models. The propensity score weight using the respondent behavior information can be applied to the final analysis to reduce the nonresponse bias. However, including the surrogate variable in the propensity score weighting does not always guarantee the efficiency gain. We show that the surrogate variable is useful only when it is correlated with the study variable. Results from a limited simulation study confirm the finding. A real data application using the Korean Workplace Panel Survey data is also presented.

  Release date: 2019-12-17

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• 3. A grouping genetic algorithm for joint stratification and sample allocation designs

  Articles and reports: 12-001-X201900300007

  Description:

  Finding the optimal stratification and sample size in univariate and multivariate sample design is hard when the population frame is large. There are alternative ways of modelling and solving this problem, and one of the most natural uses genetic algorithms (GA) combined with the Bethel-Chromy evaluation algorithm. The GA iteratively searches for the minimum sample size necessary to meet precision constraints in partitionings of atomic strata created by the Cartesian product of auxiliary variables. We point out a drawback with classical GAs when applied to the grouping problem, and propose a new GA approach using “grouping” genetic operators instead of traditional operators. Experiments show a significant improvement in solution quality for similar computational effort.

  Release date: 2019-12-17

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Articles and reports (3) ((3 results))

• 1. Cohort profile: The Canadian Census Health and Environment Cohorts (CanCHECs)

  Articles and reports: 82-003-X201901200003

  Description:

  This article provides a description of the Canadian Census Health and Environment Cohorts (CanCHECs), a population-based linked datasets of the household population at the time of census collection. The CanCHEC datasets are rich national data resources that can be used to measure and examine health inequalities across socioeconomic and ethnocultural dimensions for different periods and locations. These datasets can also be used to examine the effects of exposure to environmental factors on human health.

  Release date: 2019-12-18

  ---

• 2. A note on propensity score weighting method using paradata in survey sampling

  Articles and reports: 12-001-X201900300002

  Description:

  Paradata is often collected during the survey process to monitor the quality of the survey response. One such paradata is a respondent behavior, which can be used to construct response models. The propensity score weight using the respondent behavior information can be applied to the final analysis to reduce the nonresponse bias. However, including the surrogate variable in the propensity score weighting does not always guarantee the efficiency gain. We show that the surrogate variable is useful only when it is correlated with the study variable. Results from a limited simulation study confirm the finding. A real data application using the Korean Workplace Panel Survey data is also presented.

  Release date: 2019-12-17

  ---

• 3. A grouping genetic algorithm for joint stratification and sample allocation designs

  Articles and reports: 12-001-X201900300007

  Description:

  Finding the optimal stratification and sample size in univariate and multivariate sample design is hard when the population frame is large. There are alternative ways of modelling and solving this problem, and one of the most natural uses genetic algorithms (GA) combined with the Bethel-Chromy evaluation algorithm. The GA iteratively searches for the minimum sample size necessary to meet precision constraints in partitionings of atomic strata created by the Cartesian product of auxiliary variables. We point out a drawback with classical GAs when applied to the grouping problem, and propose a new GA approach using “grouping” genetic operators instead of traditional operators. Experiments show a significant improvement in solution quality for similar computational effort.

  Release date: 2019-12-17

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