Quality assurance
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All (19) (0 to 10 of 19 results)
- Articles and reports: 11-522-X201700014711Description:
After the 2010 Census, the U.S. Census Bureau conducted two separate research projects matching survey data to databases. One study matched to the third-party database Accurint, and the other matched to U.S. Postal Service National Change of Address (NCOA) files. In both projects, we evaluated response error in reported move dates by comparing the self-reported move date to records in the database. We encountered similar challenges in the two projects. This paper discusses our experience using “big data” as a comparison source for survey data and our lessons learned for future projects similar to the ones we conducted.
Release date: 2016-03-24 - 2. 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 - Articles and reports: 11-522-X201300014264Description:
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 - Articles and reports: 11-522-X201300014284Description:
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 - Surveys and statistical programs – Documentation: 62F0026M2011001Description:
This report describes the quality indicators produced for the 2009 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2011-06-16 - Surveys and statistical programs – Documentation: 62F0026M2010004Description:
This report describes the quality indicators produced for the 2007 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-12-13 - Surveys and statistical programs – Documentation: 62F0026M2010005Description:
This report describes the quality indicators produced for the 2008 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-12-13 - Surveys and statistical programs – Documentation: 62F0026M2010001Description:
This report describes the quality indicators produced for the 2004 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-04-26 - Surveys and statistical programs – Documentation: 62F0026M2010002Description:
This report describes the quality indicators produced for the 2005 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-04-26 - Surveys and statistical programs – Documentation: 62F0026M2010003Description:
This report describes the quality indicators produced for the 2006 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-04-26
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- Articles and reports: 11-522-X201700014711Description:
After the 2010 Census, the U.S. Census Bureau conducted two separate research projects matching survey data to databases. One study matched to the third-party database Accurint, and the other matched to U.S. Postal Service National Change of Address (NCOA) files. In both projects, we evaluated response error in reported move dates by comparing the self-reported move date to records in the database. We encountered similar challenges in the two projects. This paper discusses our experience using “big data” as a comparison source for survey data and our lessons learned for future projects similar to the ones we conducted.
Release date: 2016-03-24 - Articles and reports: 11-522-X201300014264Description:
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 - Articles and reports: 11-522-X201300014284Description:
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 - Articles and reports: 11-522-X200800010968Description:
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 - Articles and reports: 62F0026M2005003Description:
The Food Expenditure Survey (FES) is a periodic survey collecting data from households on food spending habits. Data are collected mainly using weekly diaries of purchases that the respondents must fill in daily during two consecutive weeks.
The FES, like all surveys, is subject to error despite all the precautions taken at the various stages of the survey to control them. Although there is no exhaustive measure of a survey's data quality, certain quality measures taken at various stages of the survey can provide the user with relevant information to ensure sound data interpretation.
This paper presents, for the 2001 FES, the following quality indicators the coefficients of variation, the non-response rates, the vacancy rates, the slippage rates, the imputation rates as well the impacts of imputation on the estimates.
Release date: 2005-07-08 - Articles and reports: 11-522-X20010016235Description:
This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.
Police records collected by the Federal Bureau of Investigation (FBI) through the Uniform Crime Reporting (UCR) Program are the leading source of national crime statistics. Recently, audits to correct UCR records have raised concerns as to how to handle the errors discovered in these files. Concerns centre around the methodology used to detect errors and the procedures used to correct errors once they have been discovered. This paper explores these concerns, focusing on sampling methodology, establishment of a statistical-adjustment factor, and alternative solutions. The paper distinguishes the difference between sample adjustment and sample estimates of an agency's data, and recommends sample adjustment as the most accurate way of dealing with errors.
Release date: 2002-09-12 - Articles and reports: 11-522-X20010016289Description:
This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.
Increasing demand for electronic reporting in establishment surveys has placed additional emphasis on incorporating usability into electronic forms. We are just beginning to understand the implications surrounding electronic forms design. Cognitive interviewing and usability testing are analogous in that both types of testing have similar goals: to build an end instrument (paper or electronic) that reduces both respondent burden and measurement error. Cognitive testing has greatly influenced paper forms design and can also be applied towards the development of electronic forms. Usability testing expands on existing cognitive testing methodology to include examination of the interaction between the respondent and the electronic form.
The upcoming U.S. 2002 Economic Census will offer businesses the ability to report information using electronic forms. The U.S. Census Bureau is creating an electronic forms style guide outlining the design standards to be used in electronic form creation. The style guide's design standards are based on usability principles, usability and cognitive test results, and Graphical User Interface standards. This paper highlights the major electronic forms design issues raised during the preparation of the style guide and describes how usability testing and cognitive interviewing resolved these issues.
Release date: 2002-09-12
Reference (12)
Reference (12) (0 to 10 of 12 results)
- 1. 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 - Surveys and statistical programs – Documentation: 62F0026M2011001Description:
This report describes the quality indicators produced for the 2009 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2011-06-16 - Surveys and statistical programs – Documentation: 62F0026M2010004Description:
This report describes the quality indicators produced for the 2007 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-12-13 - Surveys and statistical programs – Documentation: 62F0026M2010005Description:
This report describes the quality indicators produced for the 2008 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-12-13 - Surveys and statistical programs – Documentation: 62F0026M2010001Description:
This report describes the quality indicators produced for the 2004 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-04-26 - Surveys and statistical programs – Documentation: 62F0026M2010002Description:
This report describes the quality indicators produced for the 2005 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-04-26 - Surveys and statistical programs – Documentation: 62F0026M2010003Description:
This report describes the quality indicators produced for the 2006 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2010-04-26 - Surveys and statistical programs – Documentation: 62F0026M2005006Description:
This report describes the quality indicators produced for the 2003 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2005-10-06 - Surveys and statistical programs – Documentation: 62F0026M2004001Description:
This report describes the quality indicators produced for the 2002 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.
Release date: 2004-09-15 - Surveys and statistical programs – Documentation: 62F0026M2002001Description:
This report describes the quality indicators produced for the 2000 Survey of Household Spending. It covers the usual quality indicators that help users interpret the data, such as coefficients of variation, non-response rates, slippage rates and imputation rates.
Release date: 2002-06-28
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