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- Articles and reports: 12-001-X202200100006Description:
In the last two decades, survey response rates have been steadily falling. In that context, it has become increasingly important for statistical agencies to develop and use methods that reduce the adverse effects of non-response on the accuracy of survey estimates. Follow-up of non-respondents may be an effective, albeit time and resource-intensive, remedy for non-response bias. We conducted a simulation study using real business survey data to shed some light on several questions about non-response follow-up. For instance, assuming a fixed non-response follow-up budget, what is the best way to select non-responding units to be followed up? How much effort should be dedicated to repeatedly following up non-respondents until a response is received? Should they all be followed up or a sample of them? If a sample is followed up, how should it be selected? We compared Monte Carlo relative biases and relative root mean square errors under different follow-up sampling designs, sample sizes and non-response scenarios. We also determined an expression for the minimum follow-up sample size required to expend the budget, on average, and showed that it maximizes the expected response rate. A main conclusion of our simulation experiment is that this sample size also appears to approximately minimize the bias and mean square error of the estimates.
Release date: 2022-06-21 - Articles and reports: 46-28-0001202200100001Description:
When a survey publishes statistics with a quality indicator, it is usually derived from measures based on sampling theory. The production of quality indicators is a significant challenge when statistics are produced using alternative sources for which no sampling is done. This paper describes a new method used to create a quality indicator that combines indicators obtained at different stages of data processing. An example of the application of the method in the Canadian Housing Statistics Program is provided in the Appendix.
Release date: 2022-01-06 - Articles and reports: 11-522-X202100100005Description: The Permanent Census of Population and Housing is the new census strategy adopted in Italy in 2018: it is based on statistical registers combined with data collected through surveys specifically designed to improve registers quality and assure Census outputs. The register at the core of the Permanent Census is the Population Base Register (PBR), whose main administrative sources are the Local Population Registers. The population counts are determined correcting the PBR data with coefficients based on the coverage errors estimated with surveys data, but the need for additional administrative sources clearly emerged while processing the data collected with the first round of Permanent Census. The suspension of surveys due to global-pandemic emergency, together with a serious reduction in census budget for next years, makes more urgent a change in estimation process so to use administrative data as the main source. A thematic register has been set up to exploit all the additional administrative sources: knowledge discovery from this database is essential to extract relevant patterns and to build new dimensions called signs of life, useful for population estimation. The availability of the collected data of the two first waves of Census offers a unique and valuable set for statistical learning: association between surveys results and ‘signs of life’ could be used to build classification model to predict coverage errors in PBR. This paper present the results of the process to produce ‘signs of life’ that proved to be significant in population estimation.
Key Words: Administrative data; Population Census; Statistical Registers; Knowledge discovery from databases.
Release date: 2021-10-22 - 4. 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: 82-003-X201901200003Description:
This article provides a description of the Canadian Census Health and Environment Cohorts (CanCHECs), a population-based linked datasets of the household population at the time of census collection. The CanCHEC datasets are rich national data resources that can be used to measure and examine health inequalities across socioeconomic and ethnocultural dimensions for different periods and locations. These datasets can also be used to examine the effects of exposure to environmental factors on human health.
Release date: 2019-12-18 - 6. Canadian Cancer Treatment Linkage Project ArchivedArticles and reports: 11-633-X2018016Description:
Record linkage has been identified as a potential mechanism to add treatment information to the Canadian Cancer Registry (CCR). The purpose of the Canadian Cancer Treatment Linkage Project (CCTLP) pilot is to add surgical treatment data to the CCR. The Discharge Abstract Database (DAD) and the National Ambulatory Care Reporting System (NACRS) were linked to the CCR, and surgical treatment data were extracted. The project was funded through the Cancer Data Development Initiative (CDDI) of the Canadian Partnership Against Cancer (CPAC).
The CCTLP was developed as a feasibility study in which patient records from the CCR would be linked to surgical treatment records in the DAD and NACRS databases, maintained by the Canadian Institute for Health Information. The target cohort to whom surgical treatment data would be linked was patients aged 19 or older registered on the CCR (2010 through 2012). The linkage was completed in Statistics Canada’s Social Data Linkage Environment (SDLE).
Release date: 2018-03-27 - 7. Social media as a data source for official statistics; the Dutch Consumer Confidence Index ArchivedArticles and reports: 12-001-X201700254871Description:
In this paper the question is addressed how alternative data sources, such as administrative and social media data, can be used in the production of official statistics. Since most surveys at national statistical institutes are conducted repeatedly over time, a multivariate structural time series modelling approach is proposed to model the series observed by a repeated surveys with related series obtained from such alternative data sources. Generally, this improves the precision of the direct survey estimates by using sample information observed in preceding periods and information from related auxiliary series. This model also makes it possible to utilize the higher frequency of the social media to produce more precise estimates for the sample survey in real time at the moment that statistics for the social media become available but the sample data are not yet available. The concept of cointegration is applied to address the question to which extent the alternative series represent the same phenomena as the series observed with the repeated survey. The methodology is applied to the Dutch Consumer Confidence Survey and a sentiment index derived from social media.
Release date: 2017-12-21 - Surveys and statistical programs – Documentation: 11-522-X201700014749Description:
As part of the Tourism Statistics Program redesign, Statistics Canada is developing the National Travel Survey (NTS) to collect travel information from Canadian travellers. This new survey will replace the Travel Survey of Residents of Canada and the Canadian resident component of the International Travel Survey. The NTS will take advantage of Statistics Canada’s common sampling frames and common processing tools while maximizing the use of administrative data. This paper discusses the potential uses of administrative data such as Passport Canada files, Canada Border Service Agency files and Canada Revenue Agency files, to increase the efficiency of the NTS sample design.
Release date: 2016-03-24 - 9. The 2006 Canadian Birth-Census Cohort ArchivedArticles and reports: 82-003-X201600114306Description:
This article is an overview of the creation, content, and quality of the 2006 Canadian Birth-Census Cohort Database.
Release date: 2016-01-20 - Articles and reports: 82-003-X201500214140Description:
This study examines the feasibility and limitations of applying a non-categorical approach (focused on service utilization rather than on specific diagnoses) to administrative data in order to identify children with health problems.
Release date: 2015-02-18
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Analysis (21)
Analysis (21) (0 to 10 of 21 results)
- Articles and reports: 12-001-X202200100006Description:
In the last two decades, survey response rates have been steadily falling. In that context, it has become increasingly important for statistical agencies to develop and use methods that reduce the adverse effects of non-response on the accuracy of survey estimates. Follow-up of non-respondents may be an effective, albeit time and resource-intensive, remedy for non-response bias. We conducted a simulation study using real business survey data to shed some light on several questions about non-response follow-up. For instance, assuming a fixed non-response follow-up budget, what is the best way to select non-responding units to be followed up? How much effort should be dedicated to repeatedly following up non-respondents until a response is received? Should they all be followed up or a sample of them? If a sample is followed up, how should it be selected? We compared Monte Carlo relative biases and relative root mean square errors under different follow-up sampling designs, sample sizes and non-response scenarios. We also determined an expression for the minimum follow-up sample size required to expend the budget, on average, and showed that it maximizes the expected response rate. A main conclusion of our simulation experiment is that this sample size also appears to approximately minimize the bias and mean square error of the estimates.
Release date: 2022-06-21 - Articles and reports: 46-28-0001202200100001Description:
When a survey publishes statistics with a quality indicator, it is usually derived from measures based on sampling theory. The production of quality indicators is a significant challenge when statistics are produced using alternative sources for which no sampling is done. This paper describes a new method used to create a quality indicator that combines indicators obtained at different stages of data processing. An example of the application of the method in the Canadian Housing Statistics Program is provided in the Appendix.
Release date: 2022-01-06 - Articles and reports: 11-522-X202100100005Description: The Permanent Census of Population and Housing is the new census strategy adopted in Italy in 2018: it is based on statistical registers combined with data collected through surveys specifically designed to improve registers quality and assure Census outputs. The register at the core of the Permanent Census is the Population Base Register (PBR), whose main administrative sources are the Local Population Registers. The population counts are determined correcting the PBR data with coefficients based on the coverage errors estimated with surveys data, but the need for additional administrative sources clearly emerged while processing the data collected with the first round of Permanent Census. The suspension of surveys due to global-pandemic emergency, together with a serious reduction in census budget for next years, makes more urgent a change in estimation process so to use administrative data as the main source. A thematic register has been set up to exploit all the additional administrative sources: knowledge discovery from this database is essential to extract relevant patterns and to build new dimensions called signs of life, useful for population estimation. The availability of the collected data of the two first waves of Census offers a unique and valuable set for statistical learning: association between surveys results and ‘signs of life’ could be used to build classification model to predict coverage errors in PBR. This paper present the results of the process to produce ‘signs of life’ that proved to be significant in population estimation.
Key Words: Administrative data; Population Census; Statistical Registers; Knowledge discovery from databases.
Release date: 2021-10-22 - 4. 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: 82-003-X201901200003Description:
This article provides a description of the Canadian Census Health and Environment Cohorts (CanCHECs), a population-based linked datasets of the household population at the time of census collection. The CanCHEC datasets are rich national data resources that can be used to measure and examine health inequalities across socioeconomic and ethnocultural dimensions for different periods and locations. These datasets can also be used to examine the effects of exposure to environmental factors on human health.
Release date: 2019-12-18 - 6. Canadian Cancer Treatment Linkage Project ArchivedArticles and reports: 11-633-X2018016Description:
Record linkage has been identified as a potential mechanism to add treatment information to the Canadian Cancer Registry (CCR). The purpose of the Canadian Cancer Treatment Linkage Project (CCTLP) pilot is to add surgical treatment data to the CCR. The Discharge Abstract Database (DAD) and the National Ambulatory Care Reporting System (NACRS) were linked to the CCR, and surgical treatment data were extracted. The project was funded through the Cancer Data Development Initiative (CDDI) of the Canadian Partnership Against Cancer (CPAC).
The CCTLP was developed as a feasibility study in which patient records from the CCR would be linked to surgical treatment records in the DAD and NACRS databases, maintained by the Canadian Institute for Health Information. The target cohort to whom surgical treatment data would be linked was patients aged 19 or older registered on the CCR (2010 through 2012). The linkage was completed in Statistics Canada’s Social Data Linkage Environment (SDLE).
Release date: 2018-03-27 - 7. Social media as a data source for official statistics; the Dutch Consumer Confidence Index ArchivedArticles and reports: 12-001-X201700254871Description:
In this paper the question is addressed how alternative data sources, such as administrative and social media data, can be used in the production of official statistics. Since most surveys at national statistical institutes are conducted repeatedly over time, a multivariate structural time series modelling approach is proposed to model the series observed by a repeated surveys with related series obtained from such alternative data sources. Generally, this improves the precision of the direct survey estimates by using sample information observed in preceding periods and information from related auxiliary series. This model also makes it possible to utilize the higher frequency of the social media to produce more precise estimates for the sample survey in real time at the moment that statistics for the social media become available but the sample data are not yet available. The concept of cointegration is applied to address the question to which extent the alternative series represent the same phenomena as the series observed with the repeated survey. The methodology is applied to the Dutch Consumer Confidence Survey and a sentiment index derived from social media.
Release date: 2017-12-21 - 8. The 2006 Canadian Birth-Census Cohort ArchivedArticles and reports: 82-003-X201600114306Description:
This article is an overview of the creation, content, and quality of the 2006 Canadian Birth-Census Cohort Database.
Release date: 2016-01-20 - Articles and reports: 82-003-X201500214140Description:
This study examines the feasibility and limitations of applying a non-categorical approach (focused on service utilization rather than on specific diagnoses) to administrative data in order to identify children with health problems.
Release date: 2015-02-18 - 10. Estimating the prevalence of COPD in Canada: Reported diagnosis versus measured airflow obstruction ArchivedArticles and reports: 82-003-X201400311908Geography: CanadaDescription:
This study compares prevalence estimates of chronic obstructive pulmonary disease based on self-reports with those based on lung function measurements from cycle 1 of Statistics Canada's Canadian Health Measures Survey.
Release date: 2014-03-19
Reference (2)
Reference (2) ((2 results))
- Surveys and statistical programs – Documentation: 11-522-X201700014749Description:
As part of the Tourism Statistics Program redesign, Statistics Canada is developing the National Travel Survey (NTS) to collect travel information from Canadian travellers. This new survey will replace the Travel Survey of Residents of Canada and the Canadian resident component of the International Travel Survey. The NTS will take advantage of Statistics Canada’s common sampling frames and common processing tools while maximizing the use of administrative data. This paper discusses the potential uses of administrative data such as Passport Canada files, Canada Border Service Agency files and Canada Revenue Agency files, to increase the efficiency of the NTS sample design.
Release date: 2016-03-24 - Surveys and statistical programs – Documentation: 75F0002M1995015Description:
This research paper represents a reference guide for the contact and demographic modules of the Survey of Labour and Income Dynamics (SLID).
Release date: 1995-12-30
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