Data analysis
Filter results by
Search HelpKeyword(s)
Type
Results
All (22)
All (22) (0 to 10 of 22 results)
- Stats in brief: 89-20-00062023001Description: This course is intended for Government of Canada employees who would like to learn about evaluating the quality of data for a particular use. Whether you are a new employee interested in learning the basics, or an experienced subject matter expert looking to refresh your skills, this course is here to help.Release date: 2023-07-17
- 2. Linking the 2006 Census of Population to the Canadian Mortality Database: Descriptive and Validation Study ArchivedArticles and reports: 11-633-X2018014Description:
The Canadian Mortality Database (CMDB) is an administrative database that collects information on cause of death from all provincial and territorial vital statistics registries in Canada. The CMDB lacks subpopulation identifiers to examine mortality rates and disparities among groups such as First Nations, Métis, Inuit and members of visible minority groups. Linkage between the CMDB and the Census of Population is an approach to circumvent this limitation. This report describes a linkage between the CMDB (2006 to 2011) and the 2006 Census of Population, which was carried out using hierarchical deterministic exact matching, with a focus on methodology and validation.
Release date: 2018-02-14 - Articles and reports: 12-001-X201700254872Description:
This note discusses the theoretical foundations for the extension of the Wilson two-sided coverage interval to an estimated proportion computed from complex survey data. The interval is shown to be asymptotically equivalent to an interval derived from a logistic transformation. A mildly better version is discussed, but users may prefer constructing a one-sided interval already in the literature.
Release date: 2017-12-21 - Articles and reports: 12-001-X201700114822Description:
We use a Bayesian method to infer about a finite population proportion when binary data are collected using a two-fold sample design from small areas. The two-fold sample design has a two-stage cluster sample design within each area. A former hierarchical Bayesian model assumes that for each area the first stage binary responses are independent Bernoulli distributions, and the probabilities have beta distributions which are parameterized by a mean and a correlation coefficient. The means vary with areas but the correlation is the same over areas. However, to gain some flexibility we have now extended this model to accommodate different correlations. The means and the correlations have independent beta distributions. We call the former model a homogeneous model and the new model a heterogeneous model. All hyperparameters have proper noninformative priors. An additional complexity is that some of the parameters are weakly identified making it difficult to use a standard Gibbs sampler for computation. So we have used unimodal constraints for the beta prior distributions and a blocked Gibbs sampler to perform the computation. We have compared the heterogeneous and homogeneous models using an illustrative example and simulation study. As expected, the two-fold model with heterogeneous correlations is preferred.
Release date: 2017-06-22 - Articles and reports: 82-003-X201500614196Description:
This study investigates the feasibility and validity of using personal health insurance numbers to deterministically link the CCR and the Discharge Abstract Database to obtain hospitalization information about people with primary cancers.
Release date: 2015-06-17 - Articles and reports: 12-001-X201400114030Description:
The paper reports the results of a Monte Carlo simulation study that was conducted to compare the effectiveness of four different hierarchical Bayes small area models for producing state estimates of proportions based on data from stratified simple random samples from a fixed finite population. Two of the models adopted the commonly made assumptions that the survey weighted proportion for each sampled small area has a normal distribution and that the sampling variance of this proportion is known. One of these models used a linear linking model and the other used a logistic linking model. The other two models both employed logistic linking models and assumed that the sampling variance was unknown. One of these models assumed a normal distribution for the sampling model while the other assumed a beta distribution. The study found that for all four models the credible interval design-based coverage of the finite population state proportions deviated markedly from the 95 percent nominal level used in constructing the intervals.
Release date: 2014-06-27 - Articles and reports: 12-001-X201300111826Description:
It is routine practice for survey organizations to provide replication weights as part of survey data files. These replication weights are meant to produce valid and efficient variance estimates for a variety of estimators in a simple and systematic manner. Most existing methods for constructing replication weights, however, are only valid for specific sampling designs and typically require a very large number of replicates. In this paper we first show how to produce replication weights based on the method outlined in Fay (1984) such that the resulting replication variance estimator is algebraically equivalent to the fully efficient linearization variance estimator for any given sampling design. We then propose a novel weight-calibration method to simultaneously achieve efficiency and sparsity in the sense that a small number of sets of replication weights can produce valid and efficient replication variance estimators for key population parameters. Our proposed method can be used in conjunction with existing resampling techniques for large-scale complex surveys. Validity of the proposed methods and extensions to some balanced sampling designs are also discussed. Simulation results showed that our proposed variance estimators perform very well in tracking coverage probabilities of confidence intervals. Our proposed strategies will likely have impact on how public-use survey data files are produced and how these data sets are analyzed.
Release date: 2013-06-28 - 8. Combining cohorts in longitudinal surveys ArchivedArticles and reports: 12-001-X201300111828Description:
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 - 9. Using a Trend-cycle Approach to Estimate Changes in Southern Canada's Water Yield from 1971 to 2004 ArchivedSurveys and statistical programs – Documentation: 16-001-M2010014Description: Quantifying how Canada's water yield has changed over time is an important component of the water accounts maintained by Statistics Canada. This study evaluates the movement in the series of annual water yield estimates for Southern Canada from 1971 to 2004. We estimated the movement in the series using a trend-cycle approach and found that water yield for southern Canada has generally decreased over the period of observation.Release date: 2010-09-13
- 10. Geovisualization of health and social capital data derived from Statistics Canada surveys ArchivedArticles and reports: 11-522-X200600110447Description:
The classification and identification of locations where persons report to be more or less healthy or have more or less social capital, within a specific area such as a health region, is tremendously helpful for understanding place and health associations. The objective of the proposed study is to classify and map areas within the Zone 6 Health Region (Figure 1) of Nova Scotia (Halifax Regional Municipality and Annapolis Valley regions) according to health status (Dimension 1) and social capital (Dimension 2). We abstracted responses to questions about self-reported health status, mental health, and social capital from the master files of the Canadian Community Health Survey (Cycles 1.1, 1.2 and 2.1), National Population Health Survey (Cycle 5), and the General Social Survey (Cycles 13, 14, 17, and 18). Responses were geocoded using the Statistics Canada Postal Code Conversion File (PCCF+) and imported into a geographical information system (GIS) so that the postal code associated with the response will be assigned to a latitude and longitude within the Nova Scotia Zone 6 health region. Kernel density estimators and additional spatial interpolators were used to develop statistically-smoothed surfaces of the distribution of respondent values for each question. The smoothing process eliminates the possibility of revealing individual respondent location and confidential Statistics Canada sampling frame information. Using responses from similar questions across multiple surveys improves the likelihood of detecting heterogeneity among the responses within the health region area, as well as the accuracy of the smoothed map classification.
Release date: 2008-03-17
Data (0)
Data (0) (0 results)
No content available at this time.
Analysis (21)
Analysis (21) (0 to 10 of 21 results)
- Stats in brief: 89-20-00062023001Description: This course is intended for Government of Canada employees who would like to learn about evaluating the quality of data for a particular use. Whether you are a new employee interested in learning the basics, or an experienced subject matter expert looking to refresh your skills, this course is here to help.Release date: 2023-07-17
- 2. Linking the 2006 Census of Population to the Canadian Mortality Database: Descriptive and Validation Study ArchivedArticles and reports: 11-633-X2018014Description:
The Canadian Mortality Database (CMDB) is an administrative database that collects information on cause of death from all provincial and territorial vital statistics registries in Canada. The CMDB lacks subpopulation identifiers to examine mortality rates and disparities among groups such as First Nations, Métis, Inuit and members of visible minority groups. Linkage between the CMDB and the Census of Population is an approach to circumvent this limitation. This report describes a linkage between the CMDB (2006 to 2011) and the 2006 Census of Population, which was carried out using hierarchical deterministic exact matching, with a focus on methodology and validation.
Release date: 2018-02-14 - Articles and reports: 12-001-X201700254872Description:
This note discusses the theoretical foundations for the extension of the Wilson two-sided coverage interval to an estimated proportion computed from complex survey data. The interval is shown to be asymptotically equivalent to an interval derived from a logistic transformation. A mildly better version is discussed, but users may prefer constructing a one-sided interval already in the literature.
Release date: 2017-12-21 - Articles and reports: 12-001-X201700114822Description:
We use a Bayesian method to infer about a finite population proportion when binary data are collected using a two-fold sample design from small areas. The two-fold sample design has a two-stage cluster sample design within each area. A former hierarchical Bayesian model assumes that for each area the first stage binary responses are independent Bernoulli distributions, and the probabilities have beta distributions which are parameterized by a mean and a correlation coefficient. The means vary with areas but the correlation is the same over areas. However, to gain some flexibility we have now extended this model to accommodate different correlations. The means and the correlations have independent beta distributions. We call the former model a homogeneous model and the new model a heterogeneous model. All hyperparameters have proper noninformative priors. An additional complexity is that some of the parameters are weakly identified making it difficult to use a standard Gibbs sampler for computation. So we have used unimodal constraints for the beta prior distributions and a blocked Gibbs sampler to perform the computation. We have compared the heterogeneous and homogeneous models using an illustrative example and simulation study. As expected, the two-fold model with heterogeneous correlations is preferred.
Release date: 2017-06-22 - Articles and reports: 82-003-X201500614196Description:
This study investigates the feasibility and validity of using personal health insurance numbers to deterministically link the CCR and the Discharge Abstract Database to obtain hospitalization information about people with primary cancers.
Release date: 2015-06-17 - Articles and reports: 12-001-X201400114030Description:
The paper reports the results of a Monte Carlo simulation study that was conducted to compare the effectiveness of four different hierarchical Bayes small area models for producing state estimates of proportions based on data from stratified simple random samples from a fixed finite population. Two of the models adopted the commonly made assumptions that the survey weighted proportion for each sampled small area has a normal distribution and that the sampling variance of this proportion is known. One of these models used a linear linking model and the other used a logistic linking model. The other two models both employed logistic linking models and assumed that the sampling variance was unknown. One of these models assumed a normal distribution for the sampling model while the other assumed a beta distribution. The study found that for all four models the credible interval design-based coverage of the finite population state proportions deviated markedly from the 95 percent nominal level used in constructing the intervals.
Release date: 2014-06-27 - Articles and reports: 12-001-X201300111826Description:
It is routine practice for survey organizations to provide replication weights as part of survey data files. These replication weights are meant to produce valid and efficient variance estimates for a variety of estimators in a simple and systematic manner. Most existing methods for constructing replication weights, however, are only valid for specific sampling designs and typically require a very large number of replicates. In this paper we first show how to produce replication weights based on the method outlined in Fay (1984) such that the resulting replication variance estimator is algebraically equivalent to the fully efficient linearization variance estimator for any given sampling design. We then propose a novel weight-calibration method to simultaneously achieve efficiency and sparsity in the sense that a small number of sets of replication weights can produce valid and efficient replication variance estimators for key population parameters. Our proposed method can be used in conjunction with existing resampling techniques for large-scale complex surveys. Validity of the proposed methods and extensions to some balanced sampling designs are also discussed. Simulation results showed that our proposed variance estimators perform very well in tracking coverage probabilities of confidence intervals. Our proposed strategies will likely have impact on how public-use survey data files are produced and how these data sets are analyzed.
Release date: 2013-06-28 - 8. Combining cohorts in longitudinal surveys ArchivedArticles and reports: 12-001-X201300111828Description:
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 - 9. Geovisualization of health and social capital data derived from Statistics Canada surveys ArchivedArticles and reports: 11-522-X200600110447Description:
The classification and identification of locations where persons report to be more or less healthy or have more or less social capital, within a specific area such as a health region, is tremendously helpful for understanding place and health associations. The objective of the proposed study is to classify and map areas within the Zone 6 Health Region (Figure 1) of Nova Scotia (Halifax Regional Municipality and Annapolis Valley regions) according to health status (Dimension 1) and social capital (Dimension 2). We abstracted responses to questions about self-reported health status, mental health, and social capital from the master files of the Canadian Community Health Survey (Cycles 1.1, 1.2 and 2.1), National Population Health Survey (Cycle 5), and the General Social Survey (Cycles 13, 14, 17, and 18). Responses were geocoded using the Statistics Canada Postal Code Conversion File (PCCF+) and imported into a geographical information system (GIS) so that the postal code associated with the response will be assigned to a latitude and longitude within the Nova Scotia Zone 6 health region. Kernel density estimators and additional spatial interpolators were used to develop statistically-smoothed surfaces of the distribution of respondent values for each question. The smoothing process eliminates the possibility of revealing individual respondent location and confidential Statistics Canada sampling frame information. Using responses from similar questions across multiple surveys improves the likelihood of detecting heterogeneity among the responses within the health region area, as well as the accuracy of the smoothed map classification.
Release date: 2008-03-17 - Articles and reports: 12-001-X20070019848Description:
We investigate some modifications of the classical single-spell Cox model in order to handle multiple spells from the same individual when the data are collected in a longitudinal survey based on a complex sample design. One modification is the use of a design-based approach for the estimation of the model coefficients and their variances; in the variance estimation each individual is treated as a cluster of spells, bringing an extra stage of clustering into the survey design. Other modifications to the model allow a flexible specification of the baseline hazard to account for possibly differential dependence of hazard on the order and duration of successive spells, and also allow for differential effects of the covariates on the spells of different orders. These approaches are illustrated using data from the Canadian Survey of Labour and Income Dynamics (SLID).
Release date: 2007-06-28
Reference (1)
Reference (1) ((1 result))
- 1. Using a Trend-cycle Approach to Estimate Changes in Southern Canada's Water Yield from 1971 to 2004 ArchivedSurveys and statistical programs – Documentation: 16-001-M2010014Description: Quantifying how Canada's water yield has changed over time is an important component of the water accounts maintained by Statistics Canada. This study evaluates the movement in the series of annual water yield estimates for Southern Canada from 1971 to 2004. We estimated the movement in the series using a trend-cycle approach and found that water yield for southern Canada has generally decreased over the period of observation.Release date: 2010-09-13
- Date modified: