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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: 2024-04-26
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) (50 to 60 of 171 results)

51. Achieving data coherence for complex enterprises Archived
Articles and reports: 11-522-X200800010985
Description:
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
52. Accounting for uncertainty in the evaluation of data collection costs and data quality under partitioned designs for the U.S. Consumer Expenditure Surveys Archived
Articles and reports: 11-522-X200800010991
Description:
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 Archived
Articles and reports: 11-522-X200800011002
Description:
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 Archived
Articles and reports: 11-522-X200800011007
Description:
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
55. Coherence analysis: Improved quality of National Accounts Archived
Articles and reports: 11-522-X200800011014
Description:
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
56. Indicators for the representativeness of survey response Archived
Articles and reports: 12-001-X200900110887
Description:
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
57. Under-reporting of energy intake in the Canadian Community Health Survey Archived
Articles and reports: 82-003-X200800410703
Geography: Canada
Description:
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 Archived
Articles and reports: 82-003-X200800310680
Geography: Canada
Description:
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 Archived
Articles and reports: 82-622-X2008001
Geography: Canada
Description:
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 Archived
Articles and reports: 11-522-X200600110397
Description:
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

Reference (78)

Reference (78) (70 to 80 of 78 results)

71. An increment-decrement model of secondary school progression for Canadian provinces Archived
Surveys and statistical programs – Documentation: 11-522-X19980015034
Description:
A model of secondary school progression has been estimated using data from the 1991 School Leavers Survey conducted by Statistics Canada. The data on which the school progression model was based comprised current educational status and responses to retrospective questions on the timing of schooling events. These data were sufficient for approximate reconstruction of educational event histories of each respondent. The school progression model was designed to be included in a larger, continuous time micro-simulation model. Its main features involve estimation -- by age, month of birth and season for both sexes in each province -- of rates of graduation, of dropout, of return and of dropout graduation. Estimation was reinforced with auxiliary 1991 Census and administative data.
Release date: 1999-10-22
72. Estimation with partial overlap longitudinal samples Archived
Surveys and statistical programs – Documentation: 11-522-X19980015035
Description:
In a longitudinal survey conducted for k periods some units may be observed for less than k of the periods. Examples include, surveys designed with partially overlapping subsamples, a pure panel survey with nonresponse, and a panel survey supplemented with additional samples for some of the time periods. Estimators of the regression type are exhibited for such surveys. An application to special studies associated with the National Resources Inventory is discussed.
Release date: 1999-10-22
73. Multivariate logistic regression for data from complex surveys Archived
Surveys and statistical programs – Documentation: 11-522-X19980015036
Description:
Multivariate logistic regression, introduced by Glonek and McCullagh (1995) as a generalisation of logistic regression, is useful in the analysis of longitudinal data as it allows for dependent repeated observations of a categorical variable and for incomplete response profiles. We show how the method can be extended to deal with data from complex surveys and we illustrate it on data from the Swiss Labour Force Survey. The effect of the sampling weights on the parameter estimates and their standard errors is considered.
Release date: 1999-10-22
74. Parameter estimation for a finite mixture of distributions for dichotomous longitudinal data: Comparing algorithms Archived
Surveys and statistical programs – Documentation: 11-522-X19980015037
Description:
For longitudinal data, mixed models are often used, since they allow analysts to take account of the correlation between different observations from the same individual. The finite mixture model may be considered as a special case of a mixed model. In this document, attention will be given to the maximum likelihood method. The maximization of the likelihood function for a finite mixture of distributions is generally more difficult than in the usual case of a single distribution and can require considerable time. The objective of this project was therefore primarily to identify the one or more algorithms that best meet the criteria of run time and of efficiency in finding the solution. To achieve this objective, a simulation study was carried out. Only the situation in which the dependent variable is dichotomous was considered. This situation is very useful in practice, since among other things it can be used to model discrete durations, such as the length of time in "low income" status.
Release date: 1999-10-22
75. Qualitative Aspects of the Survey of Labour and Income Dynamics (SLID) Test 3A Data Collection Archived
Surveys and statistical programs – Documentation: 75F0002M1993007
Description:
This report presents a summary evaluation of the quality of the data collected during the Survey of Labour and Income Dynamics (SLID) field test of labour market activity data, held in January and February 1993.
Release date: 1995-12-30
76. Qualitative Aspects of the Survey of Labour and Income Dynamics (SLID) Test 3B Data Collection Archived
Surveys and statistical programs – Documentation: 75F0002M1993011
Description:
This report presents a summary evaluation of the quality of the data collected during the Survey of Labour and Income Dynamics (SLID) field test of income and wealth, held in April and May 1993.
Release date: 1995-12-30
77. Data Quality of Income Data Using Computer-assisted Interviewing: SLID Experience Archived
Surveys and statistical programs – Documentation: 75F0002M1994015
Description:
This paper describes how the computer-assisted interviewing (CAI) income application was programmed for a Survey of Labour and Income Dynamics (SLID) test conducted in 1993.
Release date: 1995-12-30
78. Some Effects of Computer-assisted Interviewing on the Data Quality of the Survey of Labour and Income Dynamics Archived
Surveys and statistical programs – Documentation: 75F0002M1995007
Description:
This paper describes the impact of computer-assisted interviewing (CAI) on the quality of the Survey of Labour and Income Dynamics (SLID) data in three content areas: labour force activity, respondent-sensitive sources of income, and household relationships.
Release date: 1995-12-30

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Date modified:: 2024-09-10