Quality assurance

Skip to filters. View results.
View results
Sort Help
entries

Results

All (250)

All (250) (0 to 10 of 250 results)

  • 1. Income Research Paper Series
    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
  • 2. Census of Agriculture: Data quality report
    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
  • 3. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2023, technical methodology and quality report
    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
  • 4. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2022, technical methodology and quality report
    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
  • 5. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2021, technical methodology and quality report
    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
  • 6. The Importance of Data Quality
    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
  • 7. Creation of a composite quality indicator for administrative data-based estimates using clustering Archived
    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
  • 8. Empowering analysts to consider the ethics of their work: A case study of the UK Statistics Authority's data ethics framework Archived
    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
  • 9. Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2020, technical methodology and quality report
    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
  • 10. Data Quality in Six Dimensions Archived
    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
Data (0)

Data (0) (0 results)

No content available at this time.

Analysis (171)

Analysis (171) (20 to 30 of 171 results)

  • 21. Total cost-effectiveness of mammography screening strategies Archived
    Articles and reports: 82-003-X201501214295
    Description:

    Using the Wisconsin Cancer Intervention and Surveillance Monitoring Network breast cancer simulation model adapted to the Canadian context, costs and quality-adjusted life years were evaluated for 11 mammography screening strategies that varied by start/stop age and screening frequency for the general population. Incremental cost-effectiveness ratios are presented, and sensitivity analyses are used to assess the robustness of model conclusions.

    Release date: 2015-12-16
  • 22. A surveillance tool to assess diets according to Eating Well with Canada’s Food Guide Archived
    Articles and reports: 82-003-X201501114243
    Description:

    A surveillance tool was developed to assess dietary intake collected by surveys in relation to Eating Well with Canada’s Food Guide (CFG). The tool classifies foods in the Canadian Nutrient File (CNF) according to how closely they reflect CFG. This article describes the validation exercise conducted to ensure that CNF foods determined to be “in line with CFG” were appropriately classified.

    Release date: 2015-11-18
  • 23. Comparison of Physical Activity Adult Questionnaire results with accelerometer data Archived
    Articles and reports: 82-003-X201500714205
    Description:

    Discrepancies between self-reported and objectively measured physical activity are well-known. For the purpose of validation, this study compares a new self-reported physical activity questionnaire with an existing one and with accelerometer data.

    Release date: 2015-07-15
  • 24. The relevance of follow ups in data collection for the Quality Assurance system of the Portuguese Population and Housing Census Archived
    Articles and reports: 12-001-X201500114162
    Description:

    The operationalization of the Population and Housing Census in Portugal is managed by a hierarchical structure in which Statistics Portugal is at the top and local government institutions at the bottom. When the Census takes place every ten years, local governments are asked to collaborate with Statistics Portugal in the execution and monitoring of the fieldwork operations at the local level. During the Pilot Test stage of the 2011 Census, local governments were asked for additional collaboration: to answer the Perception of Risk survey, whose aim was to gather information to design a quality assurance instrument that could be used to monitor the Census operations. The response rate of the survey was desired to be 100%, however, by the deadline of data collection nearly a quarter of local governments had not responded to the survey and thus a decision was made to make a follow up mailing. In this paper, we examine whether the same conclusions could have been reached from survey without follow ups as with them and evaluate the influence of follow ups on the conception of the quality assurance instrument. Comparison of responses on a set of perception variables revealed that local governments answering previous or after the follow up did not differ. However the configuration of the quality assurance instrument changed when including follow up responses.

    Release date: 2015-06-29
  • 25. Assessment of the quality of the childhood physical abuse measure in the National Population Health Survey Archived
    Articles and reports: 82-003-X201500514170
    Description:

    All respondents to the National Population Health Survey aged 18 or older were asked a question about childhood physical abuse in cycles 1 (1994/1995), 7(2006/2007) and 8 (2008/2009). The reliability of this question was assessed over these periods. Associations between response patterns to the abuse item and health conditions related to childhood physical abuse were examined.

    Release date: 2015-05-20
  • 26. Usability of the ACS Internet Instrument on Mobile Devices Archived
    Articles and reports: 11-522-X201300014256
    Description:

    The American Community Survey (ACS) added an Internet data collection mode as part of a sequential mode design in 2013. The ACS currently uses a single web application for all Internet respondents, regardless of whether they respond on a personal computer or on a mobile device. As market penetration of mobile devices increases, however, more survey respondents are using tablets and smartphones to take surveys that are designed for personal computers. Using mobile devices to complete these surveys may be more difficult for respondents and this difficulty may translate to reduced data quality if respondents become frustrated or cannot navigate around usability issues. This study uses several indicators to compare data quality across computers, tablets, and smartphones and also compares the demographic characteristics of respondents that use each type of device.

    Release date: 2014-10-31
  • 27. Making Use of Administrative, Big and Survey Data: An Assessment of the Quality of Canadian Wetland Databases Archived
    Articles and reports: 11-522-X201300014264
    Description:

    While wetlands represent only 6.4% of the world’s surface area, they are essential to the survival of terrestrial species. These ecosystems require special attention in Canada, since that is where nearly 25% of the world’s wetlands are found. Environment Canada (EC) has massive databases that contain all kinds of wetland information from various sources. Before the information in these databases could be used for any environmental initiative, it had to be classified and its quality had to be assessed. In this paper, we will give an overview of the joint pilot project carried out by EC and Statistics Canada to assess the quality of the information contained in these databases, which has characteristics specific to big data, administrative data and survey data.

    Release date: 2014-10-31
  • 28. Managing Quality in a Statistical Agency –A Rocky Road Archived
    Articles and reports: 11-522-X201300014267
    Description:

    Statistics Sweden has, like many other National Statistical Institutes (NSIs), a long history of working with quality. More recently, the agency decided to start using a number of frameworks to address organizational, process and product quality. It is important to consider all three levels, since we know that the way we do things, e.g., when asking questions, affects product quality and therefore process quality is an important part of the quality concept. Further, organizational quality, i.e., systematically managing aspects such as training of staff and leadership, is fundamental for achieving process quality. Statistics Sweden uses EFQM (European Foundation for Quality Management) as a framework for organizational quality and ISO 20252 for market, opinion and social research as a standard for process quality. In April 2014, as the first National Statistical Institute, Statistics Sweden was certified according to the ISO 20252. One challenge that Statistics Sweden faced in 2011 was to systematically measure and monitor changes in product quality and to clearly present them to stakeholders. Together with external consultants, Paul Biemer and Dennis Trewin, Statistics Sweden developed a tool for this called ASPIRE (A System for Product Improvement, Review and Evaluation). To assure that quality is maintained and improved, Statistics Sweden has also built an organization for quality comprising a quality manager, quality coaches, and internal and external quality auditors. In this paper I will present the components of Statistics Sweden’s quality management system and some of the challenges we have faced.

    Release date: 2014-10-31
  • 29. A Framework for the Evaluation of Administrative Data Archived
    Articles and reports: 11-522-X201300014284
    Description:

    The decline in response rates observed by several national statistical institutes, their desire to limit response burden and the significant budget pressures they face support greater use of administrative data to produce statistical information. The administrative data sources they must consider have to be evaluated according to several aspects to determine their fitness for use. Statistics Canada recently developed a process to evaluate administrative data sources for use as inputs to the statistical information production process. This evaluation is conducted in two phases. The initial phase requires access only to the metadata associated with the administrative data considered, whereas the second phase uses a version of data that can be evaluated. This article outlines the evaluation process and tool.

    Release date: 2014-10-31
  • 30. Estimation with Non-probability Surveys and the Question of External Validity Archived
    Articles and reports: 11-522-X201300014288
    Description:

    Probability-based surveys, those including with samples selected through a known randomization mechanism, are considered by many to be the gold standard in contrast to non-probability samples. Probability sampling theory was first developed in the early 1930’s and continues today to justify the estimation of population values from these data. Conversely, studies using non-probability samples have gained attention in recent years but they are not new. Touted as cheaper, faster (even better) than probability designs, these surveys capture participants through various “on the ground” methods (e.g., opt-in web survey). But, which type of survey is better? This paper is the first in a series on the quest for a quality framework under which all surveys, probability- and non-probability-based, may be measured on a more equal footing. First, we highlight a few frameworks currently in use, noting that “better” is almost always relative to a survey’s fit for purpose. Next, we focus on the question of validity, particularly external validity when population estimates are desired. Estimation techniques used to date for non-probability surveys are reviewed, along with a few comparative studies of these estimates against those from a probability-based sample. Finally, the next research steps in the quest are described, followed by a few parting comments.

    Release date: 2014-10-31
Reference (78)

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

  • 1. Census of Agriculture: Data quality report
    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
  • 2. Statistics Canada Quality Guidelines
    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
  • 3. Data Quality Toolkit
    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
  • 4. Comparison of Place of Residence between the T1 Family File and the Census: Evaluation using record linkage Archived
    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
  • 5. Statistics Canada's Quality Assurance Framework
    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
  • 6. Challenges and results in using Audit trail data to monitor Labour Force Survey data quality Archived
    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
  • 7. Comparing Survey Data to Administrative Sources: Immigration, Labour, and Demographic data from the Longitudinal and International Study of Adults Archived
    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
  • 8. Student Pathways and Graduate Outcomes Archived
    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
  • 9. Using data linkage to evaluate the consistency of place of residence between census data and tax data Archived
    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
  • 10. Estimating internal migration: Issues related to using tax data Archived
    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
Report a problem or mistake on this page
Date modified:

Search help

How do I use the filters and the search box?

  • You can initiate a search by entering keywords or selecting filters (e.g., under Subject, Geography, etc.) on the left side of the page.

  • Filters can be used together or in any combination. Each time you select a filter, the results on the page are updated.

  • To start a new search, click on the Clear all button above the search box or deselect each filter.

  • Specified keywords and filters appear above the search box. You can deselect any or all the tags to refine or clear your search.

How do I refine my search?

  • You can enter keywords in the search box. It is not necessary to use “+” or “, “ or “AND”.

  • You can delete some or all of the keywords from your search string.

  • Quotes around keywords limit the search to a specific phrase.

    • For example, “labour force survey” will limit results to products containing this phrase.

  • Use “or” between keywords to obtain results that contain one or more search terms.

    • For example, labour or­ force or survey will limit results to products containing any or all of these words.

How does the search work?

  • This search looks for results that have your query term in the title, description, subject, geography, product number or other information about the product.

    • For example, when searching the term ‘Diseases’, all results with the word ‘Diseases’ in the title, description or tagged the subject appear.

  • The text within an article or publication are not searched. For full text searching of articles, use the site search.

Sort help

How are the results ordered?

The Sort by feature provides three options:

  • By date – sorts results by product release date starting with the most recent.
  • By relevance is only needed or beneficial when you use the search box and submit a search query. Results are ordered by a combination of where the search term appears in the content, e.g., if it appears in the title, it likely means the product is more relevant to your search; how up to date or new the content in the product is; how popular the product is; and other factors.
  • By title – sorts results alphabetically.

Sort help

How are the results ordered?

The Sort by feature provides four options:

  • By date – sorts results by product release date starting with the most recent.
  • By most popular – gives priority to results that are most frequently selected by users, followed by the most recent.
  • By relevance is only needed or beneficial when you use the search box and submit a search query. Results are ordered by a combination of where the search term appears in the content, e.g., if it appears in the title, it likely means the product is more relevant to your search; how up to date or new the content in the product is; how popular the product is; and other factors.
  • By title – sorts results alphabetically.