Quality assurance
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- Journals and periodicals: 75F0002MDescription: 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: 32-26-0007Description: 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-M2024001Description: 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-M2023001Description: 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-M2022002Description:
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 - 19-22-0009Description:
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!
Release date: 2022-01-26 - 7. Creation of a composite quality indicator for administrative data-based estimates using clustering ArchivedArticles and reports: 11-522-X202100100015Description: 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-X202100100023Description:
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-M2021001Description:
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 ArchivedStats in brief: 89-20-00062020001Description:
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)
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- 51. Achieving data coherence for complex enterprises ArchivedArticles and reports: 11-522-X200800010985Description:
In Canada, although complex businesses represent less than 1% of the total number of businesses, they contribute more than 45% of the total revenue. Statistics Canada recognized that the quality of the data collected from them is of great importance and has adopted several initiatives to improve the quality. One of the initiatives is the evaluation of the coherence of the data collected from large, complex enterprises. The findings of these recent coherence analyses have been instrumental in identifying areas for improvement. These, once addressed and improved, would be increasing the quality of the data collected from the large, complex enterprises while reducing the response burden imposed on them.
Release date: 2009-12-03 - Articles and reports: 11-522-X200800010991Description:
In the evaluation of prospective survey designs, statistical agencies generally must consider a large number of design factors that may have a substantial impact on both survey costs and data quality. Assessments of trade-offs between cost and quality are often complicated by limitations on the amount of information available regarding fixed and marginal costs related to: instrument redesign and field testing; the number of primary sample units and sample elements included in the sample; assignment of instrument sections and collection modes to specific sample elements; and (for longitudinal surveys) the number and periodicity of interviews. Similarly, designers often have limited information on the impact of these design factors on data quality.
This paper extends standard design-optimization approaches to account for uncertainty in the abovementioned components of cost and quality. Special attention is directed toward the level of precision required for cost and quality information to provide useful input into the design process; sensitivity of cost-quality trade-offs to changes in assumptions regarding functional forms; and implications for preliminary work focused on collection of cost and quality information. In addition, the paper considers distinctions between cost and quality components encountered in field testing and production work, respectively; incorporation of production-level cost and quality information into adaptive design work; as well as costs and operational risks arising from the collection of detailed cost and quality data during production work. The proposed methods are motivated by, and applied to, work with partitioned redesign of the interview and diary components of the U.S. Consumer Expenditure Survey.
Release date: 2009-12-03 - 53. Methodological issues in anthropometry: Self-reported versus measured height and weight ArchivedArticles and reports: 11-522-X200800011002Description:
Based on a representative sample of the Canadian population, this article quantifies the bias resulting from the use of self-reported rather than directly measured height, weight and body mass index (BMI). Associations between BMI categories and selected health conditions are compared to see if the misclassification resulting from the use of self-reported data alters associations between obesity and obesity-related health conditions. The analysis is based on 4,567 respondents to the 2005 Canadian Community Health Survey (CCHS) who, during a face-to-face interview, provided self-reported values for height and weight and were then measured by trained interviewers. Based on self-reported data, a substantial proportion of individuals with excess body weight were erroneously placed in lower BMI categories. This misclassification resulted in elevated associations between overweight/obesity and morbidity.
Release date: 2009-12-03 - 54. Reducing the number of cognitive interviews by adding other cognitive methods of testing ArchivedArticles and reports: 11-522-X200800011007Description:
The Questionnaire Design Resource Centre (QDRC) is the focal point of expertise at Statistics Canada for questionnaire design and evaluation. As it stands now, cognitive interviewing to test questionnaires is most often done near the end of the questionnaire development process. By participating earlier in the questionnaire development process, the QDRC could test new survey topics using more adaptive cognitive methods for each step of the questionnaire development process. This would necessitate fewer participants for each phase of testing, thus reducing the cost and the recruitment challenge.
Based on a review of the literature and Statistics Canada's existing questionnaire evaluation projects, this paper will describe how the QDRC could help clients in making appropriate improvements to their questionnaire in a timely manner.
Release date: 2009-12-03 - Articles and reports: 11-522-X200800011014Description:
In many countries, improved quality of economic statistics is one of the most important goals of the 21st century. First and foremost, the quality of National Accounts is in focus, regarding both annual and quarterly accounts. To achieve this goal, data quality regarding the largest enterprises is of vital importance. To assure that the quality of data for the largest enterprises is good, coherence analysis is an important tool. Coherence means that data from different sources fit together and give a consistent view of the development within these enterprises. Working with coherence analysis in an efficient way is normally a work-intensive task consisting mainly of collecting data from different sources and comparing them in a structured manner. Over the last two years, Statistics Sweden has made great progress in improving the routines for coherence analysis. An IT tool that collects data for the largest enterprises from a large number of sources and presents it in a structured and logical matter has been built, and a systematic approach to analyse data for National Accounts on a quarterly basis has been developed. The paper describes the work in both these areas and gives an overview of the IT tool and the agreed routines.
Release date: 2009-12-03 - Articles and reports: 12-001-X200900110887Description:
Many survey organisations focus on the response rate as being the quality indicator for the impact of non-response bias. As a consequence, they implement a variety of measures to reduce non-response or to maintain response at some acceptable level. However, response rates alone are not good indicators of non-response bias. In general, higher response rates do not imply smaller non-response bias. The literature gives many examples of this (e.g., Groves and Peytcheva 2006, Keeter, Miller, Kohut, Groves and Presser 2000, Schouten 2004).
We introduce a number of concepts and an indicator to assess the similarity between the response and the sample of a survey. Such quality indicators, which we call R-indicators, may serve as counterparts to survey response rates and are primarily directed at evaluating the non-response bias. These indicators may facilitate analysis of survey response over time, between various fieldwork strategies or data collection modes. We apply the R-indicators to two practical examples.
Release date: 2009-06-22 - Articles and reports: 82-003-X200800410703Geography: CanadaDescription:
Data from 16,190 respondents to the 2004 Canadian Community Health Survey - Nutrition were used to estimate under-reporting of food intake for the population aged 12 or older in the 10 provinces.
Release date: 2008-10-15 - 58. The feasibility of establishing correction factors to adjust self-reported estimates of obesity ArchivedArticles and reports: 82-003-X200800310680Geography: CanadaDescription:
This study examines the feasibility of developing correction factors to adjust self-reported measures of body mass index to more closely approximate measured values. Data are from the 2005 Canadian Community Health Survey, in which respondents were asked to report their height and weight, and were subsequently measured.
Release date: 2008-09-17 - 59. Examining the Factorial Validity of Selected Modules from the Canadian Survey of Experiences with Primary Health Care ArchivedArticles and reports: 82-622-X2008001Geography: CanadaDescription:
In this study, I examine the factorial validity of selected modules from the Canadian Survey of Experiences with Primary Health Care (CSE-PHC), in order to determine the potential for combining the items within each module into summary indices representing global primary health care concepts. The modules examined were: Patient Assessment of Chronic Illness Care (PACIC), Patient Activation (PA), Managing Own Health Care (MOHC), and Confidence in the Health Care System (CHCS). Confirmatory factor analyses were conducted on each module to assess the degree to which multiple observed items reflected the presence of common latent factors. While a four-factor model was initially specified for the PACIC instrument on the basis of priory theory and research, it did not fit the data well; rather, a revised two-factor model was found to be most appropriate. These two factors were labelled: "Whole Person Care" and "Coordination of Care". The remaining modules studied here (i.e., PA, MOHC, and CHCS) were all well-represented by single-factor models. The results suggest that the original factor structure of the PACIC developed within studies using clinical samples does not hold in general populations, although the precise reasons for this are not clear. Further empirical investigation will be required to shed more light on this discrepancy. The two factors identified here for the PACIC, as well as the single factors produced for the PA, MOHC, and CHCS could be used as the basis of summary indices for use in further analyses with the CSE-PHC.
Release date: 2008-07-08 - 60. Measurement error in life history data ArchivedArticles and reports: 11-522-X200600110397Description:
In practice it often happens that some collected data are subject to measurement error. Sometimes covariates (or risk factors) of interest may be difficult to observe precisely due to physical location or cost. Sometimes it is impossible to measure covariates accurately due to the nature of the covariates. In other situations, a covariate may represent an average of a certain quantity over time, and any practical way of measuring such a quantity necessarily features measurement error. When carrying out statistical inference in such settings, it is important to account for the effects of mismeasured covariates; otherwise, erroneous or even misleading results may be produced. In this paper, we discuss several measurement error examples arising in distinct contexts. Specific attention is focused on survival data with covariates subject to measurement error. We discuss a simulation-extrapolation method for adjusting for measurement error effects. A simulation study is reported.
Release date: 2008-03-17
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Reference (78)
Reference (78) (0 to 10 of 78 results)
- Surveys and statistical programs – Documentation: 32-26-0007Description: 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-XDescription:
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-XDescription:
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 ArchivedSurveys and statistical programs – Documentation: 91F0015M2017013Description:
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-XDescription:
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 ArchivedSurveys and statistical programs – Documentation: 11-522-X201700014707Description:
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-X201700014716Description:
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 ArchivedSurveys and statistical programs – Documentation: 11-522-X201700014717Description:
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 ArchivedSurveys and statistical programs – Documentation: 11-522-X201700014725Description:
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-X201700014726Description:
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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