Statistical techniques
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All (11)
All (11) (0 to 10 of 11 results)
- 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: 89-20-00062022005Description:
In this video, you will learn the answers to the following questions: What are the different types of error? What are the types of error that lead to statistical bias? Where during the data journey statistical bias can occur?
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 - Stats in brief: 89-20-00062022003Description:
By the end of this video you will understand what confidence intervals are, why we use them, and what factors have an impact on them.
Release date: 2022-05-24 - Articles and reports: 11-522-X202100100006Description:
In the context of its "admin-first" paradigm, Statistics Canada is prioritizing the use of non-survey sources to produce official statistics. This paradigm critically relies on non-survey sources that may have a nearly perfect coverage of some target populations, including administrative files or big data sources. Yet, this coverage must be measured, e.g., by applying the capture-recapture method, where they are compared to other sources with good coverage of the same populations, including a census. However, this is a challenging exercise in the presence of linkage errors, which arise inevitably when the linkage is based on quasi-identifiers, as is typically the case. To address the issue, a new methodology is described where the capture-recapture method is enhanced with a new error model that is based on the number of links adjacent to a given record. It is applied in an experiment with public census data.
Key Words: dual system estimation, data matching, record linkage, quality, data integration, big data.
Release date: 2021-10-22 - 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-X2016002Description:
Immigrants comprise an ever-increasing percentage of the Canadian population—at more than 20%, which is the highest percentage among the G8 countries (Statistics Canada 2013a). This figure is expected to rise to 25% to 28% by 2031, when at least one in four people living in Canada will be foreign-born (Statistics Canada 2010).
This report summarizes the linkage of the Immigrant Landing File (ILF) for all provinces and territories, excluding Quebec, to hospital data from the Discharge Abstract Database (DAD), a national database containing information about hospital inpatient and day-surgery events. A deterministic exact-matching approach was used to link data from the 1980-to-2006 ILF and from the DAD (2006/2007, 2007/2008 and 2008/2009) with the 2006 Census, which served as a “bridge” file. This was a secondary linkage in that it used linkage keys created in two previous projects (primary linkages) that separately linked the ILF and the DAD to the 2006 Census. The ILF–DAD linked data were validated by means of a representative sample of 2006 Census records containing immigrant information previously linked to the DAD.
Release date: 2016-08-17 - 9. Deterministic and probabilistic record linkage ArchivedArticles and reports: 11-522-X200600110404Description:
Pursuing reduction in cost and response burden in survey programs has led to increased use of information available in administrative databases. Linkages between these two data sources is a way to exploit their complementary nature and maximize their respective usefulness. This paper discusses the various ways we have performed record linkage between the Canadian Community Health Survey (CCHS) and the Health Person-Oriented Information (HPOI) databases. The files resulting from selected linkage methods are used in an analysis of risk factors for having been hospitalized for heart disease. The sensitivity of the analysis with respect to the various linkage approaches is investigated.
Release date: 2008-03-17 - Articles and reports: 11-522-X20050019477Description:
Using probabilistic data linkage, an integrated database of injuries is obtained by linking on some subset of various key variables or their derivatives: names (given names, surnames and alternative names), age, sex, birthdate, phone numbers, injury date, unique identification numbers, diagnosis. To assess the quality of the links produced, false positive rates and false negative rates are computed. These rates however do not give an indication of whether the databases used for linking have undercounted injuries (bias). It is of interest to an injury researcher moreover, to have some idea of the error margin for the figures generated from integrating various injury databases, similar to what one would get in a survey for instance.
Release date: 2007-03-02
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Analysis (11)
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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: 89-20-00062022005Description:
In this video, you will learn the answers to the following questions: What are the different types of error? What are the types of error that lead to statistical bias? Where during the data journey statistical bias can occur?
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 - Stats in brief: 89-20-00062022003Description:
By the end of this video you will understand what confidence intervals are, why we use them, and what factors have an impact on them.
Release date: 2022-05-24 - Articles and reports: 11-522-X202100100006Description:
In the context of its "admin-first" paradigm, Statistics Canada is prioritizing the use of non-survey sources to produce official statistics. This paradigm critically relies on non-survey sources that may have a nearly perfect coverage of some target populations, including administrative files or big data sources. Yet, this coverage must be measured, e.g., by applying the capture-recapture method, where they are compared to other sources with good coverage of the same populations, including a census. However, this is a challenging exercise in the presence of linkage errors, which arise inevitably when the linkage is based on quasi-identifiers, as is typically the case. To address the issue, a new methodology is described where the capture-recapture method is enhanced with a new error model that is based on the number of links adjacent to a given record. It is applied in an experiment with public census data.
Key Words: dual system estimation, data matching, record linkage, quality, data integration, big data.
Release date: 2021-10-22 - 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-X2016002Description:
Immigrants comprise an ever-increasing percentage of the Canadian population—at more than 20%, which is the highest percentage among the G8 countries (Statistics Canada 2013a). This figure is expected to rise to 25% to 28% by 2031, when at least one in four people living in Canada will be foreign-born (Statistics Canada 2010).
This report summarizes the linkage of the Immigrant Landing File (ILF) for all provinces and territories, excluding Quebec, to hospital data from the Discharge Abstract Database (DAD), a national database containing information about hospital inpatient and day-surgery events. A deterministic exact-matching approach was used to link data from the 1980-to-2006 ILF and from the DAD (2006/2007, 2007/2008 and 2008/2009) with the 2006 Census, which served as a “bridge” file. This was a secondary linkage in that it used linkage keys created in two previous projects (primary linkages) that separately linked the ILF and the DAD to the 2006 Census. The ILF–DAD linked data were validated by means of a representative sample of 2006 Census records containing immigrant information previously linked to the DAD.
Release date: 2016-08-17 - 9. Deterministic and probabilistic record linkage ArchivedArticles and reports: 11-522-X200600110404Description:
Pursuing reduction in cost and response burden in survey programs has led to increased use of information available in administrative databases. Linkages between these two data sources is a way to exploit their complementary nature and maximize their respective usefulness. This paper discusses the various ways we have performed record linkage between the Canadian Community Health Survey (CCHS) and the Health Person-Oriented Information (HPOI) databases. The files resulting from selected linkage methods are used in an analysis of risk factors for having been hospitalized for heart disease. The sensitivity of the analysis with respect to the various linkage approaches is investigated.
Release date: 2008-03-17 - Articles and reports: 11-522-X20050019477Description:
Using probabilistic data linkage, an integrated database of injuries is obtained by linking on some subset of various key variables or their derivatives: names (given names, surnames and alternative names), age, sex, birthdate, phone numbers, injury date, unique identification numbers, diagnosis. To assess the quality of the links produced, false positive rates and false negative rates are computed. These rates however do not give an indication of whether the databases used for linking have undercounted injuries (bias). It is of interest to an injury researcher moreover, to have some idea of the error margin for the figures generated from integrating various injury databases, similar to what one would get in a survey for instance.
Release date: 2007-03-02
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