Quality assurance

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  • Journals and periodicals: 75F0002M
    Description: This series provides detailed documentation on income developments, including survey design issues, data quality evaluation and exploratory research.
    Release date: 2024-02-22

  • Surveys and statistical programs – Documentation: 32-26-0007
    Description: Census of Agriculture data provide statistical information on farms and farm operators at fine geographic levels and for small subpopulations. Quality evaluation activities are essential to ensure that census data are reliable and that they meet user needs.

    This report provides data quality information pertaining to the Census of Agriculture, such as sources of error, error detection, disclosure control methods, data quality indicators, response rates and collection rates.
    Release date: 2024-02-06

  • Articles and reports: 13-604-M2024001
    Description: This documentation outlines the methodology used to develop the Distributions of household economic accounts published in January 2024 for the reference years 2010 to 2023. It describes the framework and the steps implemented to produce distributional information aligned with the National Balance Sheet Accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.
    Release date: 2024-01-22

  • Articles and reports: 13-604-M2023001
    Description: This documentation outlines the methodology used to develop the Distributions of household economic accounts published in March 2023 for the reference years 2010 to 2022. It describes the framework and the steps implemented to produce distributional information aligned with the National Balance Sheet Accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.
    Release date: 2023-03-31

  • Articles and reports: 13-604-M2022002
    Description:

    This documentation outlines the methodology used to develop the Distributions of household economic accounts published in August 2022 for the reference years 2010 to 2021. It describes the framework and the steps implemented to produce distributional information aligned with the National Balance Sheet Accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.

    Release date: 2022-08-03

  • 19-22-0009
    Description:

    Join us as Statistics Canada’s Quality Secretariat will give a presentation on the importance of data quality. We are living in an exciting time for data: sources are more abundant, they are being generated in innovative ways, and they are available quicker than ever. However, a data source is not only worthless if it does not meet basic quality standards – it can be misleading, and worse than having no data at all! Statistics Canada’s Quality Secretariat has a mandate to promote good quality practices within the agency, across the Government of Canada, and internationally. For quality to truly be present, it must be incorporated into each process (from design to analysis) and into the product itself – whether that product is a microdata file or estimates derived from it. We will address why data quality is important and how one can evaluate it in practice. We will cover some basic concepts in data quality (quality assurance vs. control, metadata, etc.), and present data quality as a multidimensional concept. Finally, we will show data quality in action by evaluating a data source together. All data quality literacy levels are welcome. After all, everybody plays a part in quality!

    https://www.statcan.gc.ca/en/services/webinars/19220009

    Release date: 2022-01-26

  • Articles and reports: 11-522-X202100100015
    Description: National statistical agencies such as Statistics Canada have a responsibility to convey the quality of statistical information to users. The methods traditionally used to do this are based on measures of sampling error. As a result, they are not adapted to the estimates produced using administrative data, for which the main sources of error are not due to sampling. A more suitable approach to reporting the quality of estimates presented in a multidimensional table is described in this paper. Quality indicators were derived for various post-acquisition processing steps, such as linkage, geocoding and imputation, by estimation domain. A clustering algorithm was then used to combine domains with similar quality levels for a given estimate. Ratings to inform users of the relative quality of estimates across domains were assigned to the groups created. This indicator, called the composite quality indicator (CQI), was developed and experimented with in the Canadian Housing Statistics Program (CHSP), which aims to produce official statistics on the residential housing sector in Canada using multiple administrative data sources.

    Keywords: Unsupervised machine learning, quality assurance, administrative data, data integration, clustering.

    Release date: 2021-10-22

  • Articles and reports: 11-522-X202100100023
    Description:

    Our increasingly digital society provides multiple opportunities to maximise our use of data for the public good – using a range of sources, data types and technologies to enable us to better inform the public about social and economic matters and contribute to the effective development and evaluation of public policy. Ensuring use of data in ethically appropriate ways is an important enabler for realising the potential to use data for public good research and statistics. Earlier this year the UK Statistics Authority launched the Centre for Applied Data Ethics to provide applied data ethics services, advice, training and guidance to the analytical community across the United Kingdom. The Centre has developed a framework and portfolio of services to empower analysts to consider the ethics of their research quickly and easily, at the research design phase thus promoting a culture of ethics by design. This paper will provide an overview of this framework, the accompanying user support services and the impact of this work.

    Key words: Data ethics, data, research and statistics

    Release date: 2021-10-22

  • Articles and reports: 13-604-M2021001
    Description:

    This documentation outlines the methodology used to develop the Distributions of household economic accounts published in September 2021 for the reference years 2010 to 2020. It describes the framework and the steps implemented to produce distributional information aligned with the National Balance Sheet Accounts and other national accounts concepts. It also includes a report on the quality of the estimated distributions.

    Release date: 2021-09-07

  • Stats in brief: 89-20-00062020001
    Description:

    In this video, you will be introduced to the fundamentals of data quality, which can be summed up in six dimensions—or six different ways to think about quality. You will also learn how each dimension can be used to evaluate the quality of data.

    Release date: 2020-09-23
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  • Articles and reports: 11-522-X200600110421
    Description:

    In an effort to increase response rates and decrease costs, many survey operations have begun to use several modes to collect relevant data. While the National Health Interview Survey (NHIS), a multipurpose household health survey conducted annually by the National Center for Health Statistics, Centers for Disease Control and Prevention, is primarily a face-to-face survey, interviewers also rely on the telephone to complete some interviews. This has raised questions about the quality of resulting data. To address these questions, data from the 2005 NHIS are used to analyze the impact of mode on eight key health indicators.

    Release date: 2008-03-17

  • Articles and reports: 11-522-X200600110450
    Description:

    Using survey and contact attempt history data collected with the 2005 National Health Interview Survey (NHIS), a multi-purpose health survey conducted by the National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention (CDC), we set out to explore the impact of participant concerns/reluctance on data quality, as measured by rates of partially complete interviews and item nonresponse. Overall, results show that respondents from households where some type of concern or reluctance (e.g., "too busy," "not interested") was expressed produced higher rates of partially complete interviews and item nonresponse than respondents from households where concern/reluctance was not expressed. Differences by type of concern were also identified.

    Release date: 2008-03-17

  • Articles and reports: 75F0002M1992004
    Description:

    The accurate measurement of job search and unemployment has been a recurring problem in retrospective surveys. However, strategies to improve recall in such surveys have not been especially successful. Proposed solutions have included a) reducing the recall period and b) questioning whether the standard operationalization of labour force concepts is appropriate in a retrospective setting.

    One difficulty in arriving at an appropriate line of questioning is that there does not exist a reliable benchmark source indicating what sort of search patterns one should be observing over the year. Current notions of labour force dynamics have been heavily influenced by linked-record gross change data, which for various reasons cannot be considered a reliable source. These data show numerous changes in status from month-to-month and generally paint a picture of labour force participation that suggests little behavioural consistency.

    This study examines data from the Annual Work Patterns Survey (AWPS) and Labour Market Activity Survey (LMAS). It shows that the underreporting of job search in the AWPS (and to a lesser extent in the LMAS) is closely connected to the failure of respondents, in a significant number of cases, to report any job search prior to the start of a job, a problem for which there is a simple questionnaire solution.

    Release date: 2008-02-29

  • Articles and reports: 75F0002M1992008
    Description:

    This paper, as part of the design development process for the Survey of Labour and Income Dynamics (SLID), examines the implications for data quality of accepting proxy responses. While it is apparent that rules that minimize proxy responses add to data collection costs, a review of the existing evidence suggests the effects of proxy reporting on data quality are less clear. The general conclusions of this review are that proxy respondents tended to underestimate participation in government income support programs, be subject to higher item non-response rates and lower rates of personal and household interviews, offer more consistent responses to sensitive subject matter and have greater difficulty in reporting detail and events of short duration. As a result of shortcomings in the design of the research into this question, the evidence is not conclusive. While there is no firm basis for the rejection of proxy responses, the maintenance of some control over proxy responses would be a reasonable strategy.

    Release date: 2008-02-29

  • Articles and reports: 75F0002M2007003
    Description:

    The Survey of Labour and Income Dynamics (SLID) is a longitudinal survey initiated in 1993. The survey was designed to measure changes in the economic well-being of Canadians as well as the factors affecting these changes.

    Sample surveys are subject to errors. As with all surveys conducted at Statistics Canada, considerable time and effort is taken to control such errors at every stage of the Survey of Labour and Income Dynamics. Nonetheless errors do occur. It is the policy at Statistics Canada to furnish users with measures of data quality so that the user is able to interpret the data properly. This report summarizes a set of quality measures that has been produced in an attempt to describe the overall quality of SLID data. Among the measures included in the report are sample composition and attrition rates, sampling errors, coverage errors in the form of slippage rates, response rates, tax permission and tax linkage rates, and imputation rates.

    Release date: 2007-05-10

  • Articles and reports: 11-522-X20050019434
    Description:

    Traditional methods for statistical disclosure limitation in tabular data are cell suppression, data rounding and data perturbation. Because the suppression mechanism is not describable in probabilistic terms, suppressed tables are not amenable to statistical methods such as imputation. Data quality characteristics of suppressed tables are consequently poor.

    Release date: 2007-03-02

  • Articles and reports: 11-522-X20050019435
    Description:

    Data swapping introduces noise in a dataset to improve the protection of statistical confidentiality. We demonstrate in this article that this technique introduces a bias in the estimates.

    Release date: 2007-03-02

  • Articles and reports: 11-522-X20050019436
    Description:

    Regardless of the specifics of any given metadata scheme, there are common metadata constructs used to describe statistical data. This paper will give an overview of the different approaches taken to achieve the common goal of providing consistent information.

    Release date: 2007-03-02

  • Articles and reports: 11-522-X20050019441
    Description:

    The Survey of Principals collects information on various topics related to the work of school principals. This article presents a brief description of this survey 's challenges and provides examples to illustrate the problems observed. The steps taken to resolve the challenges are described, and their impact on data quality is analyzed.

    Release date: 2007-03-02

  • Articles and reports: 11-522-X20050019444
    Description:

    There are several ways to improve data quality. One of them is to re-design and test questionnaires for ongoing surveys. The benefits of questionnaire re-design and testing include improving the accuracy by ensuring the questions collect the required data, as well as decreased response burden.

    Release date: 2007-03-02
Reference (78)

Reference (78) (0 to 10 of 78 results)

  • Surveys and statistical programs – Documentation: 32-26-0007
    Description: Census of Agriculture data provide statistical information on farms and farm operators at fine geographic levels and for small subpopulations. Quality evaluation activities are essential to ensure that census data are reliable and that they meet user needs.

    This report provides data quality information pertaining to the Census of Agriculture, such as sources of error, error detection, disclosure control methods, data quality indicators, response rates and collection rates.
    Release date: 2024-02-06

  • Surveys and statistical programs – Documentation: 12-539-X
    Description:

    This document brings together guidelines and checklists on many issues that need to be considered in the pursuit of quality objectives in the execution of statistical activities. Its focus is on how to assure quality through effective and appropriate design or redesign of a statistical project or program from inception through to data evaluation, dissemination and documentation. These guidelines draw on the collective knowledge and experience of many Statistics Canada employees. It is expected that Quality Guidelines will be useful to staff engaged in the planning and design of surveys and other statistical projects, as well as to those who evaluate and analyze the outputs of these projects.

    Release date: 2019-12-04

  • Surveys and statistical programs – Documentation: 12-606-X
    Description:

    This is a toolkit intended to aid data producers and data users external to Statistics Canada.

    Release date: 2017-09-27

  • Surveys and statistical programs – Documentation: 91F0015M2017013
    Description:

    Using records linkage, this article compares the place of residence in the 2011 Census to that of the 2010 T1 Family File (T1FF). The main result is that although the overall level of consistency in the place of residence is relatively high, it decreases, sometimes substantially, for some segments of the population.

    Release date: 2017-09-26

  • Surveys and statistical programs – Documentation: 12-586-X
    Description:

    The Quality Assurance Framework (QAF) serves as the highest-level governance tool for quality management at Statistics Canada. The QAF gives an overview of the quality management and risk mitigation strategies used by the Agency’s program areas. The QAF is used in conjunction with Statistics Canada management practices, such as those described in the Quality Guidelines.

    Release date: 2017-04-21

  • Surveys and statistical programs – Documentation: 11-522-X201700014707
    Description:

    The Labour Force Survey (LFS) is a monthly household survey of about 56,000 households that provides information on the Canadian labour market. Audit Trail is a Blaise programming option, for surveys like LFS with Computer Assisted Interviewing (CAI), which creates files containing every keystroke and edit and timestamp of every data collection attempt on all households. Combining such a large survey with such a complete source of paradata opens the door to in-depth data quality analysis but also quickly leads to Big Data challenges. How can meaningful information be extracted from this large set of keystrokes and timestamps? How can it help assess the quality of LFS data collection? The presentation will describe some of the challenges that were encountered, solutions that were used to address them, and results of the analysis on data quality.

    Release date: 2016-03-24

  • Surveys and statistical programs – Documentation: 11-522-X201700014716
    Description:

    Administrative data, depending on its source and original purpose, can be considered a more reliable source of information than survey-collected data. It does not require a respondent to be present and understand question wording, and it is not limited by the respondent’s ability to recall events retrospectively. This paper compares selected survey data, such as demographic variables, from the Longitudinal and International Study of Adults (LISA) to various administrative sources for which LISA has linkage agreements in place. The agreement between data sources, and some factors that might affect it, are analyzed for various aspects of the survey.

    Release date: 2016-03-24

  • Surveys and statistical programs – Documentation: 11-522-X201700014717
    Description:

    Files with linked data from the Statistics Canada, Postsecondary Student Information System (PSIS) and tax data can be used to examine the trajectories of students who pursue postsecondary education (PSE) programs and their post-schooling labour market outcomes. On one hand, administrative data on students linked longitudinally can provide aggregate information on student pathways during postsecondary studies such as persistence rates, graduation rates, mobility, etc. On the other hand, the tax data could supplement the PSIS data to provide information on employment outcomes such as average and median earnings or earnings progress by employment sector (industry), field of study, education level and/or other demographic information, year over year after graduation. Two longitudinal pilot studies have been done using administrative data on postsecondary students of Maritimes institutions which have been longitudinally linked and linked to Statistics Canada Ttx data (the T1 Family File) for relevant years. This article first focuses on the quality of information in the administrative data and the methodology used to conduct these longitudinal studies and derive indicators. Second, it will focus on some limitations when using administrative data, rather than a survey, to define some concepts.

    Release date: 2016-03-24

  • Surveys and statistical programs – Documentation: 11-522-X201700014725
    Description:

    Tax data are being used more and more to measure and analyze the population and its characteristics. One of the issues raised by the growing use of these type of data relates to the definition of the concept of place of residence. While the census uses the traditional concept of place of residence, tax data provide information based on the mailing address of tax filers. Using record linkage between the census, the National Household Survey and tax data from the T1 Family File, this study examines the consistency level of the place of residence of these two sources and its associated characteristics.

    Release date: 2016-03-24

  • Surveys and statistical programs – Documentation: 11-522-X201700014726
    Description:

    Internal migration is one of the components of population growth estimated at Statistics Canada. It is estimated by comparing individuals’ addresses at the beginning and end of a given period. The Canada Child Tax Benefit and T1 Family File are the primary data sources used. Address quality and coverage of more mobile subpopulations are crucial to producing high-quality estimates. The purpose of this article is to present the results of evaluations of these elements using access to more tax data sources at Statistics Canada.

    Release date: 2016-03-24
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