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- Stats in brief: 89-20-00062022004Description:
Gathering, exploring, analyzing and interpreting data are essential steps in producing information that benefits society, the economy and the environment. In this video, we will discuss the importance of considering data ethics throughout the process of producing statistical information.
As a pre-requisite to this video, make sure to watch the video titled “Data Ethics: An introduction” also available in Statistics Canada’s data literacy training catalogue.
Release date: 2022-10-17 - Stats in brief: 89-20-00062022001Description:
Gathering, exploring, analyzing and interpreting data are essential steps in producing information that benefits society, the economy and the environment. To properly conduct these processes, data ethics ethics must be upheld in order to ensure the appropriate use of data.
Release date: 2022-05-24 - Stats in brief: 89-20-00062022002Description:
This video will break down what it means to be FAIR in terms of data and metadata, and how each pillar of FAIR serves to guide data users and producers alike, as they navigate their way through the data journey, in order to gain maximum, long term value.
Release date: 2022-05-24 - Articles and reports: 11-522-X202100100014Description: Recent developments in questionnaire administration modes and data extraction have favored the use of nonprobability samples, which are often affected by selection bias that arises from the lack of a sample design or self-selection of the participants. This bias can be addressed by several adjustments, whose applicability depends on the type of auxiliary information available. Calibration weighting can be used when only population totals of auxiliary variables are available. If a reference survey that followed a probability sampling design is available, several methods can be applied, such as Propensity Score Adjustment, Statistical Matching or Mass Imputation, and doubly robust estimators. In the case where a complete census of the target population is available for some auxiliary covariates, estimators based in superpopulation models (often used in probability sampling) can be adapted to the nonprobability sampling case. We studied the combination of some of these methods in order to produce less biased and more efficient estimates, as well as the use of modern prediction techniques (such as Machine Learning classification and regression algorithms) in the modelling steps of the adjustments described. We also studied the use of variable selection techniques prior to the modelling step in Propensity Score Adjustment. Results show that adjustments based on the combination of several methods might improve the efficiency of the estimates, and the use of Machine Learning and variable selection techniques can contribute to reduce the bias and the variance of the estimators to a greater extent in several situations.
Key Words: nonprobability sampling; calibration; Propensity Score Adjustment; Matching.
Release date: 2021-10-15 - Articles and reports: 11-633-X2019003Description:
This report provides an overview of the definitions and competency frameworks of data literacy, as well as the assessment tools used to measure it. These are based on the existing literature and current practices around the world. Data literacy, or the ability to derive meaningful information from data, is a relatively new concept. However, it is gaining increasing recognition as a vital skillset in the information age. Existing approaches to measuring data literacy—from self-assessment tools to objective measures, and from individual to organizational assessments—are discussed in this report to inform the development of an assessment tool for data literacy in the Canadian public service.
Release date: 2019-08-14 - Articles and reports: 12-001-X201900200007Description:
When fitting an ordered categorical variable with L > 2 levels to a set of covariates onto complex survey data, it is common to assume that the elements of the population fit a simple cumulative logistic regression model (proportional-odds logistic-regression model). This means the probability that the categorical variable is at or below some level is a binary logistic function of the model covariates. Moreover, except for the intercept, the values of the logistic-regression parameters are the same at each level. The conventional “design-based” method used for fitting the proportional-odds model is based on pseudo-maximum likelihood. We compare estimates computed using pseudo-maximum likelihood with those computed by assuming an alternative design-sensitive robust model-based framework. We show with a simple numerical example how estimates using the two approaches can differ. The alternative approach is easily extended to fit a general cumulative logistic model, in which the parallel-lines assumption can fail. A test of that assumption easily follows.
Release date: 2019-06-27 - 7. Canadian Cancer Treatment Linkage Project ArchivedArticles and reports: 11-633-X2018016Description:
Record linkage has been identified as a potential mechanism to add treatment information to the Canadian Cancer Registry (CCR). The purpose of the Canadian Cancer Treatment Linkage Project (CCTLP) pilot is to add surgical treatment data to the CCR. The Discharge Abstract Database (DAD) and the National Ambulatory Care Reporting System (NACRS) were linked to the CCR, and surgical treatment data were extracted. The project was funded through the Cancer Data Development Initiative (CDDI) of the Canadian Partnership Against Cancer (CPAC).
The CCTLP was developed as a feasibility study in which patient records from the CCR would be linked to surgical treatment records in the DAD and NACRS databases, maintained by the Canadian Institute for Health Information. The target cohort to whom surgical treatment data would be linked was patients aged 19 or older registered on the CCR (2010 through 2012). The linkage was completed in Statistics Canada’s Social Data Linkage Environment (SDLE).
Release date: 2018-03-27 - Articles and reports: 11-633-X2017005Description:
Hospitalization rates are among commonly reported statistics related to health-care service use. The variety of methods for calculating confidence intervals for these and other health-related rates suggests a need to classify, compare and evaluate these methods. Zeno is a tool developed to calculate confidence intervals of rates based on several formulas available in the literature. This report describes the contents of the main sheet of the Zeno Tool and indicates which formulas are appropriate, based on users’ assumptions and scope of analysis.
Release date: 2017-01-19 - Articles and reports: 11-633-X2016003Description:
Large national mortality cohorts are used to estimate mortality rates for different socioeconomic and population groups, and to conduct research on environmental health. In 2008, Statistics Canada created a cohort linking the 1991 Census to mortality. The present study describes a linkage of the 2001 Census long-form questionnaire respondents aged 19 years and older to the T1 Personal Master File and the Amalgamated Mortality Database. The linkage tracks all deaths over a 10.6-year period (until the end of 2011, to date).
Release date: 2016-10-26 - Articles and reports: 82-003-X201300611796Geography: CanadaDescription:
The study assesses the feasibility of using statistical modelling techniques to fill information gaps related to risk factors, specifically, smoking status, in linked long-form census data.
Release date: 2013-06-19
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- Stats in brief: 89-20-00062022004Description:
Gathering, exploring, analyzing and interpreting data are essential steps in producing information that benefits society, the economy and the environment. In this video, we will discuss the importance of considering data ethics throughout the process of producing statistical information.
As a pre-requisite to this video, make sure to watch the video titled “Data Ethics: An introduction” also available in Statistics Canada’s data literacy training catalogue.
Release date: 2022-10-17 - Stats in brief: 89-20-00062022001Description:
Gathering, exploring, analyzing and interpreting data are essential steps in producing information that benefits society, the economy and the environment. To properly conduct these processes, data ethics ethics must be upheld in order to ensure the appropriate use of data.
Release date: 2022-05-24 - Stats in brief: 89-20-00062022002Description:
This video will break down what it means to be FAIR in terms of data and metadata, and how each pillar of FAIR serves to guide data users and producers alike, as they navigate their way through the data journey, in order to gain maximum, long term value.
Release date: 2022-05-24 - Articles and reports: 11-522-X202100100014Description: Recent developments in questionnaire administration modes and data extraction have favored the use of nonprobability samples, which are often affected by selection bias that arises from the lack of a sample design or self-selection of the participants. This bias can be addressed by several adjustments, whose applicability depends on the type of auxiliary information available. Calibration weighting can be used when only population totals of auxiliary variables are available. If a reference survey that followed a probability sampling design is available, several methods can be applied, such as Propensity Score Adjustment, Statistical Matching or Mass Imputation, and doubly robust estimators. In the case where a complete census of the target population is available for some auxiliary covariates, estimators based in superpopulation models (often used in probability sampling) can be adapted to the nonprobability sampling case. We studied the combination of some of these methods in order to produce less biased and more efficient estimates, as well as the use of modern prediction techniques (such as Machine Learning classification and regression algorithms) in the modelling steps of the adjustments described. We also studied the use of variable selection techniques prior to the modelling step in Propensity Score Adjustment. Results show that adjustments based on the combination of several methods might improve the efficiency of the estimates, and the use of Machine Learning and variable selection techniques can contribute to reduce the bias and the variance of the estimators to a greater extent in several situations.
Key Words: nonprobability sampling; calibration; Propensity Score Adjustment; Matching.
Release date: 2021-10-15 - Articles and reports: 11-633-X2019003Description:
This report provides an overview of the definitions and competency frameworks of data literacy, as well as the assessment tools used to measure it. These are based on the existing literature and current practices around the world. Data literacy, or the ability to derive meaningful information from data, is a relatively new concept. However, it is gaining increasing recognition as a vital skillset in the information age. Existing approaches to measuring data literacy—from self-assessment tools to objective measures, and from individual to organizational assessments—are discussed in this report to inform the development of an assessment tool for data literacy in the Canadian public service.
Release date: 2019-08-14 - Articles and reports: 12-001-X201900200007Description:
When fitting an ordered categorical variable with L > 2 levels to a set of covariates onto complex survey data, it is common to assume that the elements of the population fit a simple cumulative logistic regression model (proportional-odds logistic-regression model). This means the probability that the categorical variable is at or below some level is a binary logistic function of the model covariates. Moreover, except for the intercept, the values of the logistic-regression parameters are the same at each level. The conventional “design-based” method used for fitting the proportional-odds model is based on pseudo-maximum likelihood. We compare estimates computed using pseudo-maximum likelihood with those computed by assuming an alternative design-sensitive robust model-based framework. We show with a simple numerical example how estimates using the two approaches can differ. The alternative approach is easily extended to fit a general cumulative logistic model, in which the parallel-lines assumption can fail. A test of that assumption easily follows.
Release date: 2019-06-27 - 7. Canadian Cancer Treatment Linkage Project ArchivedArticles and reports: 11-633-X2018016Description:
Record linkage has been identified as a potential mechanism to add treatment information to the Canadian Cancer Registry (CCR). The purpose of the Canadian Cancer Treatment Linkage Project (CCTLP) pilot is to add surgical treatment data to the CCR. The Discharge Abstract Database (DAD) and the National Ambulatory Care Reporting System (NACRS) were linked to the CCR, and surgical treatment data were extracted. The project was funded through the Cancer Data Development Initiative (CDDI) of the Canadian Partnership Against Cancer (CPAC).
The CCTLP was developed as a feasibility study in which patient records from the CCR would be linked to surgical treatment records in the DAD and NACRS databases, maintained by the Canadian Institute for Health Information. The target cohort to whom surgical treatment data would be linked was patients aged 19 or older registered on the CCR (2010 through 2012). The linkage was completed in Statistics Canada’s Social Data Linkage Environment (SDLE).
Release date: 2018-03-27 - Articles and reports: 11-633-X2017005Description:
Hospitalization rates are among commonly reported statistics related to health-care service use. The variety of methods for calculating confidence intervals for these and other health-related rates suggests a need to classify, compare and evaluate these methods. Zeno is a tool developed to calculate confidence intervals of rates based on several formulas available in the literature. This report describes the contents of the main sheet of the Zeno Tool and indicates which formulas are appropriate, based on users’ assumptions and scope of analysis.
Release date: 2017-01-19 - Articles and reports: 11-633-X2016003Description:
Large national mortality cohorts are used to estimate mortality rates for different socioeconomic and population groups, and to conduct research on environmental health. In 2008, Statistics Canada created a cohort linking the 1991 Census to mortality. The present study describes a linkage of the 2001 Census long-form questionnaire respondents aged 19 years and older to the T1 Personal Master File and the Amalgamated Mortality Database. The linkage tracks all deaths over a 10.6-year period (until the end of 2011, to date).
Release date: 2016-10-26 - Articles and reports: 82-003-X201300611796Geography: CanadaDescription:
The study assesses the feasibility of using statistical modelling techniques to fill information gaps related to risk factors, specifically, smoking status, in linked long-form census data.
Release date: 2013-06-19
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