Quality assurance

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All (5) ((5 results))

  • Articles and reports: 11-522-X201300014256
    Description:

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

    Release date: 2014-10-31

  • Articles and reports: 11-522-X201300014264
    Description:

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

    Release date: 2014-10-31

  • Articles and reports: 11-522-X201300014267
    Description:

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

    Release date: 2014-10-31

  • Articles and reports: 11-522-X201300014284
    Description:

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

    Release date: 2014-10-31

  • Articles and reports: 11-522-X201300014288
    Description:

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

    Release date: 2014-10-31
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  • Articles and reports: 11-522-X201300014256
    Description:

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

    Release date: 2014-10-31

  • Articles and reports: 11-522-X201300014264
    Description:

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

    Release date: 2014-10-31

  • Articles and reports: 11-522-X201300014267
    Description:

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

    Release date: 2014-10-31

  • Articles and reports: 11-522-X201300014284
    Description:

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

    Release date: 2014-10-31

  • Articles and reports: 11-522-X201300014288
    Description:

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

    Release date: 2014-10-31
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