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  • 1. Investigating the Use of Blockchain to Authenticate Data from the Statistics Canada Website
    Articles and reports: 11-633-X2022007
    Description:

    This paper investigates how Statistics Canada can increase trust by giving users the ability to authenticate data from its website through digital signatures and blockchain technology.

    Release date: 2022-09-19
  • 2. An Approximate Bayesian Approach to Improving Probability Sample Estimators Using a Supplementary Non-Probability Sample Archived
    Articles and reports: 11-522-X202100100008
    Description:

    Non-probability samples are being increasingly explored by National Statistical Offices as a complement to probability samples. We consider the scenario where the variable of interest and auxiliary variables are observed in both a probability and non-probability sample. Our objective is to use data from the non-probability sample to improve the efficiency of survey-weighted estimates obtained from the probability sample. Recently, Sakshaug, Wisniowski, Ruiz and Blom (2019) and Wisniowski, Sakshaug, Ruiz and Blom (2020) proposed a Bayesian approach to integrating data from both samples for the estimation of model parameters. In their approach, non-probability sample data are used to determine the prior distribution of model parameters, and the posterior distribution is obtained under the assumption that the probability sampling design is ignorable (or not informative). We extend this Bayesian approach to the prediction of finite population parameters under non-ignorable (or informative) sampling by conditioning on appropriate survey-weighted statistics. We illustrate the properties of our predictor through a simulation study.

    Key Words: Bayesian prediction; Gibbs sampling; Non-ignorable sampling; Statistical data integration.

    Release date: 2021-10-29
  • 3. Administrative data for the estimation of population counts: Statistical learning from the first waves of Italian Permanent Population Census Archived
    Articles and reports: 11-522-X202100100005
    Description: The Permanent Census of Population and Housing is the new census strategy adopted in Italy in 2018: it is based on statistical registers combined with data collected through surveys specifically designed to improve registers quality and assure Census outputs. The register at the core of the Permanent Census is the Population Base Register (PBR), whose main administrative sources are the Local Population Registers. The population counts are determined correcting the PBR data with coefficients based on the coverage errors estimated with surveys data, but the need for additional administrative sources clearly emerged while processing the data collected with the first round of Permanent Census. The suspension of surveys due to global-pandemic emergency, together with a serious reduction in census budget for next years, makes more urgent a change in estimation process so to use administrative data as the main source. A thematic register has been set up to exploit all the additional administrative sources: knowledge discovery from this database is essential to extract relevant patterns and to build new dimensions called signs of life, useful for population estimation. The availability of the collected data of the two first waves of Census offers a unique and valuable set for statistical learning: association between surveys results and ‘signs of life’ could be used to build classification model to predict coverage errors in PBR. This paper present the results of the process to produce ‘signs of life’ that proved to be significant in population estimation.

    Key Words: Administrative data; Population Census; Statistical Registers; Knowledge discovery from databases.

    Release date: 2021-10-22
  • 4. Data Literacy: What It Is and How to Measure It in the Public Service
    Articles and reports: 11-633-X2019003
    Description:

    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
  • 5. The value of data in Canada: Experimental estimates
    Articles and reports: 13-605-X201900100009
    Description:

    In this paper a preliminary set of statistical estimates of the amounts invested in Canadian data, databases and data science in recent years are presented. The results indicate rapid growth in investment in data, databases and data science over the last three decades and a significant accumulation of these kinds of capital over time.

    Release date: 2019-07-10
  • 6. Measuring investment in data, databases and data science: Conceptual framework
    Articles and reports: 13-605-X201900100008
    Description:

    This paper aims to expand the current national accounting concepts and statistical methods for measuring data in order to shed light on some highly consequential changes in society that are related to the rising usage of data. The paper concludes by discussing possible methods that can be used to assign an economic value to the various elements in the information chain and tests these concepts and methods by presenting results for Canada as a first attempt to measure the value of data.

    Release date: 2019-06-24
  • 7. Adaptive survey designs to minimize survey mode effects – a case study on the Dutch Labor Force Survey Archived
    Articles and reports: 12-001-X201500214250
    Description:

    Assessing the impact of mode effects on survey estimates has become a crucial research objective due to the increasing use of mixed-mode designs. Despite the advantages of a mixed-mode design, such as lower costs and increased coverage, there is sufficient evidence that mode effects may be large relative to the precision of a survey. They may lead to incomparable statistics in time or over population subgroups and they may increase bias. Adaptive survey designs offer a flexible mathematical framework to obtain an optimal balance between survey quality and costs. In this paper, we employ adaptive designs in order to minimize mode effects. We illustrate our optimization model by means of a case-study on the Dutch Labor Force Survey. We focus on item-dependent mode effects and we evaluate the impact on survey quality by comparison to a gold standard.

    Release date: 2015-12-17
  • 8. Linking 2006 Census and hospital data in Canada Archived
    Articles and reports: 82-003-X201501014228
    Description:

    This study presents the results of a hierarchical exact matching approach to link the 2006 Census of Population with hospital data for all provinces and territories (excluding Quebec) to the 2006/2007-to-2008/2009 Discharge Abstract Database. The purpose is to determine if the Census–DAD linkage performed similarly in different jurisdictions, and if linkage and coverage rates declined as time passed since the census.

    Release date: 2015-10-21
  • 9. Measuring temporary employment. Do survey or register data tell the truth? Archived
    Articles and reports: 12-001-X201500114151
    Description:

    One of the main variables in the Dutch Labour Force Survey is the variable measuring whether a respondent has a permanent or a temporary job. The aim of our study is to determine the measurement error in this variable by matching the information obtained by the longitudinal part of this survey with unique register data from the Dutch Institute for Employee Insurance. Contrary to previous approaches confronting such datasets, we take into account that also register data are not error-free and that measurement error in these data is likely to be correlated over time. More specifically, we propose the estimation of the measurement error in these two sources using an extended hidden Markov model with two observed indicators for the type of contract. Our results indicate that none of the two sources should be considered as error-free. For both indicators, we find that workers in temporary contracts are often misclassified as having a permanent contract. Particularly for the register data, we find that measurement errors are strongly autocorrelated, as, if made, they tend to repeat themselves. In contrast, when the register is correct, the probability of an error at the next time period is almost zero. Finally, we find that temporary contracts are more widespread than the Labour Force Survey suggests, while transition rates between temporary to permanent contracts are much less common than both datasets suggest.

    Release date: 2015-06-29
  • 10. Optimal adjustments for inconsistency in imputed data Archived
    Articles and reports: 12-001-X201500114193
    Description:

    Imputed micro data often contain conflicting information. The situation may e.g., arise from partial imputation, where one part of the imputed record consists of the observed values of the original record and the other the imputed values. Edit-rules that involve variables from both parts of the record will often be violated. Or, inconsistency may be caused by adjustment for errors in the observed data, also referred to as imputation in Editing. Under the assumption that the remaining inconsistency is not due to systematic errors, we propose to make adjustments to the micro data such that all constraints are simultaneously satisfied and the adjustments are minimal according to a chosen distance metric. Different approaches to the distance metric are considered, as well as several extensions of the basic situation, including the treatment of categorical data, unit imputation and macro-level benchmarking. The properties and interpretations of the proposed methods are illustrated using business-economic data.

    Release date: 2015-06-29
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Analysis (163)

Analysis (163) (0 to 10 of 163 results)

  • 1. Investigating the Use of Blockchain to Authenticate Data from the Statistics Canada Website
    Articles and reports: 11-633-X2022007
    Description:

    This paper investigates how Statistics Canada can increase trust by giving users the ability to authenticate data from its website through digital signatures and blockchain technology.

    Release date: 2022-09-19
  • 2. An Approximate Bayesian Approach to Improving Probability Sample Estimators Using a Supplementary Non-Probability Sample Archived
    Articles and reports: 11-522-X202100100008
    Description:

    Non-probability samples are being increasingly explored by National Statistical Offices as a complement to probability samples. We consider the scenario where the variable of interest and auxiliary variables are observed in both a probability and non-probability sample. Our objective is to use data from the non-probability sample to improve the efficiency of survey-weighted estimates obtained from the probability sample. Recently, Sakshaug, Wisniowski, Ruiz and Blom (2019) and Wisniowski, Sakshaug, Ruiz and Blom (2020) proposed a Bayesian approach to integrating data from both samples for the estimation of model parameters. In their approach, non-probability sample data are used to determine the prior distribution of model parameters, and the posterior distribution is obtained under the assumption that the probability sampling design is ignorable (or not informative). We extend this Bayesian approach to the prediction of finite population parameters under non-ignorable (or informative) sampling by conditioning on appropriate survey-weighted statistics. We illustrate the properties of our predictor through a simulation study.

    Key Words: Bayesian prediction; Gibbs sampling; Non-ignorable sampling; Statistical data integration.

    Release date: 2021-10-29
  • 3. Administrative data for the estimation of population counts: Statistical learning from the first waves of Italian Permanent Population Census Archived
    Articles and reports: 11-522-X202100100005
    Description: The Permanent Census of Population and Housing is the new census strategy adopted in Italy in 2018: it is based on statistical registers combined with data collected through surveys specifically designed to improve registers quality and assure Census outputs. The register at the core of the Permanent Census is the Population Base Register (PBR), whose main administrative sources are the Local Population Registers. The population counts are determined correcting the PBR data with coefficients based on the coverage errors estimated with surveys data, but the need for additional administrative sources clearly emerged while processing the data collected with the first round of Permanent Census. The suspension of surveys due to global-pandemic emergency, together with a serious reduction in census budget for next years, makes more urgent a change in estimation process so to use administrative data as the main source. A thematic register has been set up to exploit all the additional administrative sources: knowledge discovery from this database is essential to extract relevant patterns and to build new dimensions called signs of life, useful for population estimation. The availability of the collected data of the two first waves of Census offers a unique and valuable set for statistical learning: association between surveys results and ‘signs of life’ could be used to build classification model to predict coverage errors in PBR. This paper present the results of the process to produce ‘signs of life’ that proved to be significant in population estimation.

    Key Words: Administrative data; Population Census; Statistical Registers; Knowledge discovery from databases.

    Release date: 2021-10-22
  • 4. Data Literacy: What It Is and How to Measure It in the Public Service
    Articles and reports: 11-633-X2019003
    Description:

    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
  • 5. The value of data in Canada: Experimental estimates
    Articles and reports: 13-605-X201900100009
    Description:

    In this paper a preliminary set of statistical estimates of the amounts invested in Canadian data, databases and data science in recent years are presented. The results indicate rapid growth in investment in data, databases and data science over the last three decades and a significant accumulation of these kinds of capital over time.

    Release date: 2019-07-10
  • 6. Measuring investment in data, databases and data science: Conceptual framework
    Articles and reports: 13-605-X201900100008
    Description:

    This paper aims to expand the current national accounting concepts and statistical methods for measuring data in order to shed light on some highly consequential changes in society that are related to the rising usage of data. The paper concludes by discussing possible methods that can be used to assign an economic value to the various elements in the information chain and tests these concepts and methods by presenting results for Canada as a first attempt to measure the value of data.

    Release date: 2019-06-24
  • 7. Adaptive survey designs to minimize survey mode effects – a case study on the Dutch Labor Force Survey Archived
    Articles and reports: 12-001-X201500214250
    Description:

    Assessing the impact of mode effects on survey estimates has become a crucial research objective due to the increasing use of mixed-mode designs. Despite the advantages of a mixed-mode design, such as lower costs and increased coverage, there is sufficient evidence that mode effects may be large relative to the precision of a survey. They may lead to incomparable statistics in time or over population subgroups and they may increase bias. Adaptive survey designs offer a flexible mathematical framework to obtain an optimal balance between survey quality and costs. In this paper, we employ adaptive designs in order to minimize mode effects. We illustrate our optimization model by means of a case-study on the Dutch Labor Force Survey. We focus on item-dependent mode effects and we evaluate the impact on survey quality by comparison to a gold standard.

    Release date: 2015-12-17
  • 8. Linking 2006 Census and hospital data in Canada Archived
    Articles and reports: 82-003-X201501014228
    Description:

    This study presents the results of a hierarchical exact matching approach to link the 2006 Census of Population with hospital data for all provinces and territories (excluding Quebec) to the 2006/2007-to-2008/2009 Discharge Abstract Database. The purpose is to determine if the Census–DAD linkage performed similarly in different jurisdictions, and if linkage and coverage rates declined as time passed since the census.

    Release date: 2015-10-21
  • 9. Measuring temporary employment. Do survey or register data tell the truth? Archived
    Articles and reports: 12-001-X201500114151
    Description:

    One of the main variables in the Dutch Labour Force Survey is the variable measuring whether a respondent has a permanent or a temporary job. The aim of our study is to determine the measurement error in this variable by matching the information obtained by the longitudinal part of this survey with unique register data from the Dutch Institute for Employee Insurance. Contrary to previous approaches confronting such datasets, we take into account that also register data are not error-free and that measurement error in these data is likely to be correlated over time. More specifically, we propose the estimation of the measurement error in these two sources using an extended hidden Markov model with two observed indicators for the type of contract. Our results indicate that none of the two sources should be considered as error-free. For both indicators, we find that workers in temporary contracts are often misclassified as having a permanent contract. Particularly for the register data, we find that measurement errors are strongly autocorrelated, as, if made, they tend to repeat themselves. In contrast, when the register is correct, the probability of an error at the next time period is almost zero. Finally, we find that temporary contracts are more widespread than the Labour Force Survey suggests, while transition rates between temporary to permanent contracts are much less common than both datasets suggest.

    Release date: 2015-06-29
  • 10. Optimal adjustments for inconsistency in imputed data Archived
    Articles and reports: 12-001-X201500114193
    Description:

    Imputed micro data often contain conflicting information. The situation may e.g., arise from partial imputation, where one part of the imputed record consists of the observed values of the original record and the other the imputed values. Edit-rules that involve variables from both parts of the record will often be violated. Or, inconsistency may be caused by adjustment for errors in the observed data, also referred to as imputation in Editing. Under the assumption that the remaining inconsistency is not due to systematic errors, we propose to make adjustments to the micro data such that all constraints are simultaneously satisfied and the adjustments are minimal according to a chosen distance metric. Different approaches to the distance metric are considered, as well as several extensions of the basic situation, including the treatment of categorical data, unit imputation and macro-level benchmarking. The properties and interpretations of the proposed methods are illustrated using business-economic data.

    Release date: 2015-06-29
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