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All (8) ((8 results))

  • Articles and reports: 12-001-X202000200004
    Description:

    This article proposes a weight scaling method for Firth’s penalized likelihood for proportional hazards regression models. The method derives a relationship between the penalized likelihood that uses scaled weights and the penalized likelihood that uses unscaled weights, and it shows that the penalized likelihood that uses scaled weights have some desirable properties. A simulation study indicates that the penalized likelihood using scaled weights produces smaller biases in point estimates and standard errors than the biases produced by the penalized likelihood using unscaled weights. The weighted penalized likelihood is applied to estimate hazard rates for heart attacks by using a public-use data set from the National Health and Epidemiology Followup Study (NHEFS). SAS® statements to estimate hazard rates using data from complex surveys are given in the appendix.

    Release date: 2020-12-15

  • Articles and reports: 89-648-X2020004
    Description:

    This technical report is intended to validate the Longitudinal and International Study of Adults (LISA) Wave 4 (2018) Food Security (FSC) module and provide recommendations for analytical use. Section 2 of this report provides an overview of the LISA data. Section 3 provides some background information of food security measures in national surveys and why it is significant in today's literature. Section 4 analyzes FSC data by presenting key descriptive statistics and logic checks using LISA methodology as well as outside researcher information. In section 5, certification validation was done by comparing other Canadian national surveys that have used the FSC module to the one used by LISA. Finally in section 6, key findings and their implications with regard to LISA are outlined.

    Release date: 2020-11-02

  • Articles and reports: 11-633-X2020004
    Description:

    Recent advances in artificial intelligence have rekindled ancient fears that robots will replace humans in the economy. Previous waves of automation changed but did not reduce labour’s role, but robots’ human-like flexibility could make this time different. Whether or not it will is an empirical question that has lacked suitable data to answer. This paper describes the creation of a dataset to fill the evidence gap in Canada. Robots! is firm-level panel data on robot adoption created using Canadian import data. The data identify a substantial amount of the robot investment in the Canadian economy from 1996 to 2017. Although many robots are imported by robotics wholesalers or programmers for resale, the majority of them can be attributed to their final (direct) adopting firm. The data can be used to study the impact of robot adoption at the economic region, industry or firm-level.

    Release date: 2020-11-02

  • Stats in brief: 89-20-00062020009
    Description:

    By the end of this video, you will learn about the basic concepts of the analytical process: the guiding principles of analysis, the steps of the analytical process, and planning your analysis.

    Release date: 2020-09-23

  • Stats in brief: 89-20-00062020010
    Description:

    In this video, you will learn how to implement your analytical plan. The key steps in implementing your plan include: preparing and checking your data, performing your analysis, and documenting your analytical decisions.

    Release date: 2020-09-23

  • Stats in brief: 89-20-00062020011
    Description:

    In this video, you will learn how to summarize and interpret your data and share your findings. The key elements to communicating your findings are as follows: select your essential findings, summarize and interpret the results, organize and assess your reviews, and prepare for dissemination.

    Release date: 2020-09-23

  • Stats in brief: 89-20-00062020012
    Description:

    In this video, we will review the steps of the analytical process and you will obtain a better understanding of how analysts apply each step of the analytical process by walking through an example. The example that we will discuss is a project that examined the relationship between walkability in neighbourhoods, meaning how well they support physical activity, and actual physical activity for Canadians.

    Release date: 2020-09-23

  • Notices and consultations: 98-26-0001
    Description:

    This white paper presents Statistics Canada’s planned approach to the 2021 Census of Population and provides a clear explanation of the processes behind the census program, touching on historical, legal, operational and content aspects. Statistics Canada recognizes that it is important to not only successfully conduct the census, but also to be transparent and informative about the way in which those efforts are accomplished. Painting a Portrait of Canada: The 2021 Census of Population gives readers an exclusive, detailed look at how census data is collected, analyzed and given back to Canadians, in the form of high-quality statistical information, used to make evidence-based decisions in Canadian society.

    Release date: 2020-07-20
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Analysis (7)

Analysis (7) ((7 results))

  • Articles and reports: 12-001-X202000200004
    Description:

    This article proposes a weight scaling method for Firth’s penalized likelihood for proportional hazards regression models. The method derives a relationship between the penalized likelihood that uses scaled weights and the penalized likelihood that uses unscaled weights, and it shows that the penalized likelihood that uses scaled weights have some desirable properties. A simulation study indicates that the penalized likelihood using scaled weights produces smaller biases in point estimates and standard errors than the biases produced by the penalized likelihood using unscaled weights. The weighted penalized likelihood is applied to estimate hazard rates for heart attacks by using a public-use data set from the National Health and Epidemiology Followup Study (NHEFS). SAS® statements to estimate hazard rates using data from complex surveys are given in the appendix.

    Release date: 2020-12-15

  • Articles and reports: 89-648-X2020004
    Description:

    This technical report is intended to validate the Longitudinal and International Study of Adults (LISA) Wave 4 (2018) Food Security (FSC) module and provide recommendations for analytical use. Section 2 of this report provides an overview of the LISA data. Section 3 provides some background information of food security measures in national surveys and why it is significant in today's literature. Section 4 analyzes FSC data by presenting key descriptive statistics and logic checks using LISA methodology as well as outside researcher information. In section 5, certification validation was done by comparing other Canadian national surveys that have used the FSC module to the one used by LISA. Finally in section 6, key findings and their implications with regard to LISA are outlined.

    Release date: 2020-11-02

  • Articles and reports: 11-633-X2020004
    Description:

    Recent advances in artificial intelligence have rekindled ancient fears that robots will replace humans in the economy. Previous waves of automation changed but did not reduce labour’s role, but robots’ human-like flexibility could make this time different. Whether or not it will is an empirical question that has lacked suitable data to answer. This paper describes the creation of a dataset to fill the evidence gap in Canada. Robots! is firm-level panel data on robot adoption created using Canadian import data. The data identify a substantial amount of the robot investment in the Canadian economy from 1996 to 2017. Although many robots are imported by robotics wholesalers or programmers for resale, the majority of them can be attributed to their final (direct) adopting firm. The data can be used to study the impact of robot adoption at the economic region, industry or firm-level.

    Release date: 2020-11-02

  • Stats in brief: 89-20-00062020009
    Description:

    By the end of this video, you will learn about the basic concepts of the analytical process: the guiding principles of analysis, the steps of the analytical process, and planning your analysis.

    Release date: 2020-09-23

  • Stats in brief: 89-20-00062020010
    Description:

    In this video, you will learn how to implement your analytical plan. The key steps in implementing your plan include: preparing and checking your data, performing your analysis, and documenting your analytical decisions.

    Release date: 2020-09-23

  • Stats in brief: 89-20-00062020011
    Description:

    In this video, you will learn how to summarize and interpret your data and share your findings. The key elements to communicating your findings are as follows: select your essential findings, summarize and interpret the results, organize and assess your reviews, and prepare for dissemination.

    Release date: 2020-09-23

  • Stats in brief: 89-20-00062020012
    Description:

    In this video, we will review the steps of the analytical process and you will obtain a better understanding of how analysts apply each step of the analytical process by walking through an example. The example that we will discuss is a project that examined the relationship between walkability in neighbourhoods, meaning how well they support physical activity, and actual physical activity for Canadians.

    Release date: 2020-09-23
Reference (1)

Reference (1) ((1 result))

  • Notices and consultations: 98-26-0001
    Description:

    This white paper presents Statistics Canada’s planned approach to the 2021 Census of Population and provides a clear explanation of the processes behind the census program, touching on historical, legal, operational and content aspects. Statistics Canada recognizes that it is important to not only successfully conduct the census, but also to be transparent and informative about the way in which those efforts are accomplished. Painting a Portrait of Canada: The 2021 Census of Population gives readers an exclusive, detailed look at how census data is collected, analyzed and given back to Canadians, in the form of high-quality statistical information, used to make evidence-based decisions in Canadian society.

    Release date: 2020-07-20
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