Quality assurance

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  • 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: 2024-04-26
  • 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
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Analysis (171)

Analysis (171) (40 to 50 of 171 results)

  • 41. Quality control and data reduction procedures for accelerometry-derived measures of physical activity Archived
    Articles and reports: 82-003-X201000111066
    Geography: Canada
    Description:

    This article considers critical quality control and data reduction procedures that should be addressed before physical activity information is derived from accelerometry data.

    Release date: 2010-01-13
  • 42. Testing for the 2011 Census of Canada Archived
    Articles and reports: 11-522-X200800010950
    Description:

    The next census will be conducted in May 2011. Being a major survey, it presents a formidable challenge for Statistics Canada and requires a great deal of time and resources. Careful planning has been done to ensure that all deadlines are met. A number of steps have been planned in the questionnaire testing process. These tests apply to both census content and the proposed communications strategy. This paper presents an overview of the strategy, with a focus on combining qualitative studies with the 2008 quantitative study so that the results can be analyzed and the proposals properly evaluated.

    Release date: 2009-12-03
  • 43. Statistics Canada's computer-assisted personal interview monitoring program Archived
    Articles and reports: 11-522-X200800010954
    Description:

    Over the past year, Statistics Canada has been developing and testing a new way to monitor the performance of interviewers conducting computer-assisted personal interviews (CAPI). A formal process already exists for monitoring centralized telephone interviews. Monitors listen to telephone interviews as they take place to assess the interviewer's performance using pre-defined criteria and provide feedback to the interviewer on what was well done and what needs improvement. For the CAPI program, we have developed and are testing a pilot approach whereby interviews are digitally recorded and later a monitor listens to these recordings to assess the field interviewer's performance and provide feedback in order to help improve the quality of the data. In this paper, we will present an overview of the CAPI monitoring project at Statistics Canada by describing the CAPI monitoring methodology and the plans for implementation.

    Release date: 2009-12-03
  • 44. Computer audio-recorded interviewing (CARI): A tool for monitoring field interviewers and improving field data collection Archived
    Articles and reports: 11-522-X200800010955
    Description:

    Survey managers are still discovering the usefulness of digital audio recording for monitoring and managing field staff. Its value so far has been for confirming the authenticity of interviews, detecting curbstoning, offering a concrete basis for feedback on interviewing performance and giving data collection managers an intimate view of in-person interviews. In addition, computer audio-recorded interviewing (CARI) can improve other aspects of survey data quality, offering corroboration or correction of response coding by field staff. Audio recordings may replace or supplement in-field verbatim transcription of free responses, and speech-to-text technology might make this technique more efficient in the future.

    Release date: 2009-12-03
  • 45. CARI: A tool for improving data quality now and the next time Archived
    Articles and reports: 11-522-X200800010956
    Description:

    The use of Computer Audio-Recorded Interviewing (CARI) as a tool to identify interview falsification is quickly growing in survey research (Biemer, 2000, 2003; Thissen, 2007). Similarly, survey researchers are starting to expand the usefulness of CARI by combining recordings with coding to address data quality (Herget, 2001; Hansen, 2005; McGee, 2007). This paper presents results from a study included as part of the establishment-based National Center for Health Statistics' National Home and Hospice Care Survey (NHHCS) which used CARI behavior coding and CARI-specific paradata to: 1) identify and correct problematic interviewer behavior or question issues early in the data collection period before either negatively impact data quality, and; 2) identify ways to diminish measurement error in future implementations of the NHHCS. During the first 9 weeks of the 30-week field period, CARI recorded a subset of questions from the NHHCS application for all interviewers. Recordings were linked with the interview application and output and then coded in one of two modes: Code by Interviewer or Code by Question. The Code by Interviewer method provided visibility into problems specific to an interviewer as well as more generalized problems potentially applicable to all interviewers. The Code by Question method yielded data that spoke to understandability of the questions and other response problems. In this mode, coders coded multiple implementations of the same question across multiple interviewers. Using the Code by Question approach, researchers identified issues with three key survey questions in the first few weeks of data collection and provided guidance to interviewers in how to handle those questions as data collection continued. Results from coding the audio recordings (which were linked with the survey application and output) will inform question wording and interviewer training in the next implementation of the NHHCS, and guide future enhancement of CARI and the coding system.

    Release date: 2009-12-03
  • 46. Generalized quality control for optical data capture at Statistics Canada Archived
    Articles and reports: 11-522-X200800010968
    Description:

    Statistics Canada has embarked on a program of increasing and improving the usage of imaging technology for paper survey questionnaires. The goal is to make the process an efficient, reliable and cost effective method of capturing survey data. The objective is to continue using Optical Character Recognition (OCR) to capture the data from questionnaires, documents and faxes received whilst improving the process integration and Quality Assurance/Quality Control (QC) of the data capture process. These improvements are discussed in this paper.

    Release date: 2009-12-03
  • 47. iCADE the data capture system of the U.S. Census Bureau Archived
    Articles and reports: 11-522-X200800010969
    Description:

    In a multi-divisional initiative within the U. S. Census Bureau, a highly sophisticated and innovative system was developed and implemented for the capturing, tracking, and scanning of respondent data that implements Intelligent Character Recognition (ICR), Optical Character Recognition (OCR), Optical Mark Recognition (OMR), and keying technology with heavy emphasis on error detection and control. The system, known as the integrated Computer Assisted Data Entry (iCADE) System, provides digital imaging of respondent questionnaires which are then processed by a combination of imaging algorithms, sent through Optical Mark Recognition (OMR) to collect check box data, and automatically collect and send only write-in areas to data-keying staff for the data capture process. These capabilities have produced great efficiencies in the data capture process and have led to a novel and efficient approach to post-collection activities.

    Release date: 2009-12-03
  • 48. Writing and revising questionnaire design guidelines Archived
    Articles and reports: 11-522-X200800010974
    Description:

    This paper will focus on establishment survey questionnaire design guidelines. More specifically, it will discuss the process involved in transitioning a set of guidelines written for a broad, survey methodological audience to a more narrow, agency-specific audience of survey managers and analysts. The process involved the work of a team comprised of individuals from across the Census Bureau's Economic Directorate, working in a cooperative and collaborative manner. The team decided what needed to be added, modified, and deleted from the broad starting point, and determined how much of the theory and experimental evidence found in the literature was necessary to include in the guidelines. In addition to discussing the process, the paper will also describe the end result: a set of questionnaire design guidelines for the Economic Directorate.

    Release date: 2009-12-03
  • 49. Multi-mode data collection: What can still be expected? Archived
    Articles and reports: 11-522-X200800010975
    Description:

    A major issue in official statistics is the availability of objective measures supporting the based-on-fact decision process. Istat has developed an Information System to assess survey quality. Among other standard quality indicators, nonresponse rates are systematically computed and stored for all surveys. Such a rich information base permits analysis over time and comparisons among surveys. The paper focuses on the analysis of interrelationships between data collection mode and other survey characteristics on total nonresponse. Particular attention is devoted to the extent to which multi-mode data collection improves response rates.

    Release date: 2009-12-03
  • 50. Indicators for the representativeness of survey response Archived
    Articles and reports: 11-522-X200800010976
    Description:

    Many survey organizations use the response rate as an indicator for the quality of survey data. As a consequence, a variety of measures are implemented to reduce non-response or to maintain response at an acceptable level. However, the response rate is not necessarily a good indicator of non-response bias. A higher response rate does not imply smaller non-response bias. What matters is how the composition of the response differs from the composition of the sample as a whole. This paper describes the concept of R-indicators to assess potential differences between the sample and the response. Such indicators may facilitate analysis of survey response over time, between various fieldwork strategies or data collection modes. Some practical examples are given.

    Release date: 2009-12-03
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
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