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  • 31. Agenda 2030 Sustainable Development Goals Report
    Stats in brief: 11-637-X
    Description: This product presents data on the Sustainable Development Goals. They present an overview of the 17 Goals through infographics by leveraging data currently available to report on Canada’s progress towards the 2030 Agenda for Sustainable Development.
    Release date: 2024-01-25
  • 32. The continued impacts of the COVID-19 pandemic: Variations in the economic integration of new immigrants
    Stats in brief: 11-001-X202402237898
    Description: Release published in The Daily – Statistics Canada’s official release bulletin
    Release date: 2024-01-22
  • 33. Longitudinal Immigration Database (IMDB) Technical Report, 2022
    Articles and reports: 11-633-X2024001
    Description: The Longitudinal Immigration Database (IMDB) is a comprehensive source of data that plays a key role in the understanding of the economic behaviour of immigrants. It is the only annual Canadian dataset that allows users to study the characteristics of immigrants to Canada at the time of admission and their economic outcomes and regional (inter-provincial) mobility over a time span of more than 35 years.
    Release date: 2024-01-22
  • 34. 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
  • 35. Analytical Studies: Methods and References
    Journals and periodicals: 11-633-X
    Description: Papers in this series provide background discussions of the methods used to develop data for economic, health, and social analytical studies at Statistics Canada. They are intended to provide readers with information on the statistical methods, standards and definitions used to develop databases for research purposes. All papers in this series have undergone peer and institutional review to ensure that they conform to Statistics Canada's mandate and adhere to generally accepted standards of good professional practice.
    Release date: 2024-01-22
  • 36. Model-based stratification of payment populations in Medicare integrity investigations
    Articles and reports: 12-001-X202300200001
    Description: When a Medicare healthcare provider is suspected of billing abuse, a population of payments X made to that provider over a fixed timeframe is isolated. A certified medical reviewer, in a time-consuming process, can determine the overpayment Y = X - (amount justified by the evidence) associated with each payment. Typically, there are too many payments in the population to examine each with care, so a probability sample is selected. The sample overpayments are then used to calculate a 90% lower confidence bound for the total population overpayment. This bound is the amount demanded for recovery from the provider. Unfortunately, classical methods for calculating this bound sometimes fail to provide the 90% confidence level, especially when using a stratified sample.

    In this paper, 166 redacted samples from Medicare integrity investigations are displayed and described, along with 156 associated payment populations. The 7,588 examined (Y, X) sample pairs show (1) Medicare audits have high error rates: more than 76% of these payments were considered to have been paid in error; and (2) the patterns in these samples support an “All-or-Nothing” mixture model for (Y, X) previously defined in the literature. Model-based Monte Carlo testing procedures for Medicare sampling plans are discussed, as well as stratification methods based on anticipated model moments. In terms of viability (achieving the 90% confidence level) a new stratification method defined here is competitive with the best of the many existing methods tested and seems less sensitive to choice of operating parameters. In terms of overpayment recovery (equivalent to precision) the new method is also comparable to the best of the many existing methods tested. Unfortunately, no stratification algorithm tested was ever viable for more than about half of the 104 test populations.
    Release date: 2024-01-03
  • 37. A method for estimating the effect of classification errors on statistics for two domains
    Articles and reports: 12-001-X202300200002
    Description: Being able to quantify the accuracy (bias, variance) of published output is crucial in official statistics. Output in official statistics is nearly always divided into subpopulations according to some classification variable, such as mean income by categories of educational level. Such output is also referred to as domain statistics. In the current paper, we limit ourselves to binary classification variables. In practice, misclassifications occur and these contribute to the bias and variance of domain statistics. Existing analytical and numerical methods to estimate this effect have two disadvantages. The first disadvantage is that they require that the misclassification probabilities are known beforehand and the second is that the bias and variance estimates are biased themselves. In the current paper we present a new method, a Gaussian mixture model estimated by an Expectation-Maximisation (EM) algorithm combined with a bootstrap, referred to as the EM bootstrap method. This new method does not require that the misclassification probabilities are known beforehand, although it is more efficient when a small audit sample is used that yields a starting value for the misclassification probabilities in the EM algorithm. We compared the performance of the new method with currently available numerical methods: the bootstrap method and the SIMEX method. Previous research has shown that for non-linear parameters the bootstrap outperforms the analytical expressions. For nearly all conditions tested, the bias and variance estimates that are obtained by the EM bootstrap method are closer to their true values than those obtained by the bootstrap and SIMEX methods. We end this paper by discussing the results and possible future extensions of the method.
    Release date: 2024-01-03
  • 38. Small area prediction of general small area parameters for unit-level count data
    Articles and reports: 12-001-X202300200003
    Description: We investigate small area prediction of general parameters based on two models for unit-level counts. We construct predictors of parameters, such as quartiles, that may be nonlinear functions of the model response variable. We first develop a procedure to construct empirical best predictors and mean square error estimators of general parameters under a unit-level gamma-Poisson model. We then use a sampling importance resampling algorithm to develop predictors for a generalized linear mixed model (GLMM) with a Poisson response distribution. We compare the two models through simulation and an analysis of data from the Iowa Seat-Belt Use Survey.
    Release date: 2024-01-03
  • 39. Bayesian small area models under inequality constraints with benchmarking and double shrinkage
    Articles and reports: 12-001-X202300200004
    Description: We present a novel methodology to benchmark county-level estimates of crop area totals to a preset state total subject to inequality constraints and random variances in the Fay-Herriot model. For planted area of the National Agricultural Statistics Service (NASS), an agency of the United States Department of Agriculture (USDA), it is necessary to incorporate the constraint that the estimated totals, derived from survey and other auxiliary data, are no smaller than administrative planted area totals prerecorded by other USDA agencies except NASS. These administrative totals are treated as fixed and known, and this additional coherence requirement adds to the complexity of benchmarking the county-level estimates. A fully Bayesian analysis of the Fay-Herriot model offers an appealing way to incorporate the inequality and benchmarking constraints, and to quantify the resulting uncertainties, but sampling from the posterior densities involves difficult integration, and reasonable approximations must be made. First, we describe a single-shrinkage model, shrinking the means while the variances are assumed known. Second, we extend this model to accommodate double shrinkage, borrowing strength across means and variances. This extended model has two sources of extra variation, but because we are shrinking both means and variances, it is expected that this second model should perform better in terms of goodness of fit (reliability) and possibly precision. The computations are challenging for both models, which are applied to simulated data sets with properties resembling the Illinois corn crop.
    Release date: 2024-01-03
  • 40. Dealing with undercoverage for non-probability survey samples
    Articles and reports: 12-001-X202300200005
    Description: Population undercoverage is one of the main hurdles faced by statistical analysis with non-probability survey samples. We discuss two typical scenarios of undercoverage, namely, stochastic undercoverage and deterministic undercoverage. We argue that existing estimation methods under the positivity assumption on the propensity scores (i.e., the participation probabilities) can be directly applied to handle the scenario of stochastic undercoverage. We explore strategies for mitigating biases in estimating the mean of the target population under deterministic undercoverage. In particular, we examine a split population approach based on a convex hull formulation, and construct estimators with reduced biases. A doubly robust estimator can be constructed if a followup subsample of the reference probability survey with measurements on the study variable becomes feasible. Performances of six competing estimators are investigated through a simulation study and issues which require further investigation are briefly discussed.
    Release date: 2024-01-03
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Analysis (1,874)

Analysis (1,874) (30 to 40 of 1,874 results)

  • 31. Analytical Studies: Methods and References
    Journals and periodicals: 11-633-X
    Description: Papers in this series provide background discussions of the methods used to develop data for economic, health, and social analytical studies at Statistics Canada. They are intended to provide readers with information on the statistical methods, standards and definitions used to develop databases for research purposes. All papers in this series have undergone peer and institutional review to ensure that they conform to Statistics Canada's mandate and adhere to generally accepted standards of good professional practice.
    Release date: 2024-01-22
  • 32. Model-based stratification of payment populations in Medicare integrity investigations
    Articles and reports: 12-001-X202300200001
    Description: When a Medicare healthcare provider is suspected of billing abuse, a population of payments X made to that provider over a fixed timeframe is isolated. A certified medical reviewer, in a time-consuming process, can determine the overpayment Y = X - (amount justified by the evidence) associated with each payment. Typically, there are too many payments in the population to examine each with care, so a probability sample is selected. The sample overpayments are then used to calculate a 90% lower confidence bound for the total population overpayment. This bound is the amount demanded for recovery from the provider. Unfortunately, classical methods for calculating this bound sometimes fail to provide the 90% confidence level, especially when using a stratified sample.

    In this paper, 166 redacted samples from Medicare integrity investigations are displayed and described, along with 156 associated payment populations. The 7,588 examined (Y, X) sample pairs show (1) Medicare audits have high error rates: more than 76% of these payments were considered to have been paid in error; and (2) the patterns in these samples support an “All-or-Nothing” mixture model for (Y, X) previously defined in the literature. Model-based Monte Carlo testing procedures for Medicare sampling plans are discussed, as well as stratification methods based on anticipated model moments. In terms of viability (achieving the 90% confidence level) a new stratification method defined here is competitive with the best of the many existing methods tested and seems less sensitive to choice of operating parameters. In terms of overpayment recovery (equivalent to precision) the new method is also comparable to the best of the many existing methods tested. Unfortunately, no stratification algorithm tested was ever viable for more than about half of the 104 test populations.
    Release date: 2024-01-03
  • 33. A method for estimating the effect of classification errors on statistics for two domains
    Articles and reports: 12-001-X202300200002
    Description: Being able to quantify the accuracy (bias, variance) of published output is crucial in official statistics. Output in official statistics is nearly always divided into subpopulations according to some classification variable, such as mean income by categories of educational level. Such output is also referred to as domain statistics. In the current paper, we limit ourselves to binary classification variables. In practice, misclassifications occur and these contribute to the bias and variance of domain statistics. Existing analytical and numerical methods to estimate this effect have two disadvantages. The first disadvantage is that they require that the misclassification probabilities are known beforehand and the second is that the bias and variance estimates are biased themselves. In the current paper we present a new method, a Gaussian mixture model estimated by an Expectation-Maximisation (EM) algorithm combined with a bootstrap, referred to as the EM bootstrap method. This new method does not require that the misclassification probabilities are known beforehand, although it is more efficient when a small audit sample is used that yields a starting value for the misclassification probabilities in the EM algorithm. We compared the performance of the new method with currently available numerical methods: the bootstrap method and the SIMEX method. Previous research has shown that for non-linear parameters the bootstrap outperforms the analytical expressions. For nearly all conditions tested, the bias and variance estimates that are obtained by the EM bootstrap method are closer to their true values than those obtained by the bootstrap and SIMEX methods. We end this paper by discussing the results and possible future extensions of the method.
    Release date: 2024-01-03
  • 34. Small area prediction of general small area parameters for unit-level count data
    Articles and reports: 12-001-X202300200003
    Description: We investigate small area prediction of general parameters based on two models for unit-level counts. We construct predictors of parameters, such as quartiles, that may be nonlinear functions of the model response variable. We first develop a procedure to construct empirical best predictors and mean square error estimators of general parameters under a unit-level gamma-Poisson model. We then use a sampling importance resampling algorithm to develop predictors for a generalized linear mixed model (GLMM) with a Poisson response distribution. We compare the two models through simulation and an analysis of data from the Iowa Seat-Belt Use Survey.
    Release date: 2024-01-03
  • 35. Bayesian small area models under inequality constraints with benchmarking and double shrinkage
    Articles and reports: 12-001-X202300200004
    Description: We present a novel methodology to benchmark county-level estimates of crop area totals to a preset state total subject to inequality constraints and random variances in the Fay-Herriot model. For planted area of the National Agricultural Statistics Service (NASS), an agency of the United States Department of Agriculture (USDA), it is necessary to incorporate the constraint that the estimated totals, derived from survey and other auxiliary data, are no smaller than administrative planted area totals prerecorded by other USDA agencies except NASS. These administrative totals are treated as fixed and known, and this additional coherence requirement adds to the complexity of benchmarking the county-level estimates. A fully Bayesian analysis of the Fay-Herriot model offers an appealing way to incorporate the inequality and benchmarking constraints, and to quantify the resulting uncertainties, but sampling from the posterior densities involves difficult integration, and reasonable approximations must be made. First, we describe a single-shrinkage model, shrinking the means while the variances are assumed known. Second, we extend this model to accommodate double shrinkage, borrowing strength across means and variances. This extended model has two sources of extra variation, but because we are shrinking both means and variances, it is expected that this second model should perform better in terms of goodness of fit (reliability) and possibly precision. The computations are challenging for both models, which are applied to simulated data sets with properties resembling the Illinois corn crop.
    Release date: 2024-01-03
  • 36. Dealing with undercoverage for non-probability survey samples
    Articles and reports: 12-001-X202300200005
    Description: Population undercoverage is one of the main hurdles faced by statistical analysis with non-probability survey samples. We discuss two typical scenarios of undercoverage, namely, stochastic undercoverage and deterministic undercoverage. We argue that existing estimation methods under the positivity assumption on the propensity scores (i.e., the participation probabilities) can be directly applied to handle the scenario of stochastic undercoverage. We explore strategies for mitigating biases in estimating the mean of the target population under deterministic undercoverage. In particular, we examine a split population approach based on a convex hull formulation, and construct estimators with reduced biases. A doubly robust estimator can be constructed if a followup subsample of the reference probability survey with measurements on the study variable becomes feasible. Performances of six competing estimators are investigated through a simulation study and issues which require further investigation are briefly discussed.
    Release date: 2024-01-03
  • 37. Sample designs and estimators for multimode surveys with face-to-face data collection
    Articles and reports: 12-001-X202300200006
    Description: Survey researchers are increasingly turning to multimode data collection to deal with declines in survey response rates and increasing costs. An efficient approach offers the less costly modes (e.g., web) followed with a more expensive mode for a subsample of the units (e.g., households) within each primary sampling unit (PSU). We present two alternatives to this traditional design. One alternative subsamples PSUs rather than units to constrain costs. The second is a hybrid design that includes a clustered (two-stage) sample and an independent, unclustered sample. Using a simulation, we demonstrate the hybrid design has considerable advantages.
    Release date: 2024-01-03
  • 38. Design-based conformal prediction
    Articles and reports: 12-001-X202300200007
    Description: Conformal prediction is an assumption-lean approach to generating distribution-free prediction intervals or sets, for nearly arbitrary predictive models, with guaranteed finite-sample coverage. Conformal methods are an active research topic in statistics and machine learning, but only recently have they been extended to non-exchangeable data. In this paper, we invite survey methodologists to begin using and contributing to conformal methods. We introduce how conformal prediction can be applied to data from several common complex sample survey designs, under a framework of design-based inference for a finite population, and we point out gaps where survey methodologists could fruitfully apply their expertise. Our simulations empirically bear out the theoretical guarantees of finite-sample coverage, and our real-data example demonstrates how conformal prediction can be applied to complex sample survey data in practice.
    Release date: 2024-01-03
  • 39. Constructing all determinantal sampling designs
    Articles and reports: 12-001-X202300200008
    Description: In this article, we use a slightly simplified version of the method by Fickus, Mixon and Poteet (2013) to define a flexible parameterization of the kernels of determinantal sampling designs with fixed first-order inclusion probabilities. For specific values of the multidimensional parameter, we get back to a matrix from the family PII from Loonis and Mary (2019). We speculate that, among the determinantal designs with fixed inclusion probabilities, the minimum variance of the Horvitz and Thompson estimator (1952) of a variable of interest is expressed relative to PII. We provide experimental R programs that facilitate the appropriation of various concepts presented in the article, some of which are described as non-trivial by Fickus et al. (2013). A longer version of this article, including proofs and a more detailed presentation of the determinantal designs, is also available.
    Release date: 2024-01-03
  • 40. QR prediction for statistical data integration
    Articles and reports: 12-001-X202300200009
    Description: In this paper, we investigate how a big non-probability database can be used to improve estimates of finite population totals from a small probability sample through data integration techniques. In the situation where the study variable is observed in both data sources, Kim and Tam (2021) proposed two design-consistent estimators that can be justified through dual frame survey theory. First, we provide conditions ensuring that these estimators are more efficient than the Horvitz-Thompson estimator when the probability sample is selected using either Poisson sampling or simple random sampling without replacement. Then, we study the class of QR predictors, introduced by Särndal and Wright (1984), to handle the less common case where the non-probability database contains no study variable but auxiliary variables. We also require that the non-probability database is large and can be linked to the probability sample. We provide conditions ensuring that the QR predictor is asymptotically design-unbiased. We derive its asymptotic design variance and provide a consistent design-based variance estimator. We compare the design properties of different predictors, in the class of QR predictors, through a simulation study. This class includes a model-based predictor, a model-assisted estimator and a cosmetic estimator. In our simulation setups, the cosmetic estimator performed slightly better than the model-assisted estimator. These findings are confirmed by an application to La Poste data, which also illustrates that the properties of the cosmetic estimator are preserved irrespective of the observed non-probability sample.
    Release date: 2024-01-03
Reference (363)

Reference (363) (30 to 40 of 363 results)

  • 31. Longitudinal Immigration Database (IMDB) Technical Report, 2014 Archived
    Surveys and statistical programs – Documentation: 11-633-X2017007
    Description:

    The Longitudinal Immigration Database (IMDB) is a comprehensive source of data that plays a key role in the understanding of the economic behaviour of immigrants. It is the only annual Canadian dataset that allows users to study the characteristics of immigrants to Canada at the time of admission and their economic outcomes and regional (inter-provincial) mobility over a time span of more than 30 years. The IMDB combines administrative files on immigrant admissions and non-permanent resident permits from Immigration, Refugees and Citizenship Canada (IRCC) with tax files from the Canadian Revenue Agency (CRA). Information is available for immigrant taxfilers admitted since 1980. Tax records for 1982 and subsequent years are available for immigrant taxfilers.

    This report will discuss the IMDB data sources, concepts and variables, record linkage, data processing, dissemination, data evaluation and quality indicators, comparability with other immigration datasets, and the analyses possible with the IMDB.

    Release date: 2017-06-16
  • 32. Statistics Canada's Quality Assurance Framework
    Surveys and statistical programs – Documentation: 12-586-X
    Description:

    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
  • 33. Using family-related variables from the Census of Population and the National Household Survey microdata files Archived
    Surveys and statistical programs – Documentation: 91F0015M2016012
    Description:

    This article provides information on using family-related variables from the microdata files of Canada’s Census of Population. These files exist internally at Statistics Canada, in the Research Data Centres (RDCs), and as public-use microdata files (PUMFs). This article explains certain technical aspects of all three versions, including the creation of multi-level variables for analytical purposes.

    Release date: 2016-12-22
  • 34. 2016 Census Program Content Test: Design and Results
    Notices and consultations: 92-140-X2016001
    Description:

    The 2016 Census Program Content Test was conducted from May 2 to June 30, 2014. The Test was designed to assess the impact of any proposed content changes to the 2016 Census Program and to measure the impact of including a social insurance number (SIN) question on the data quality.

    This quantitative test used a split-panel design involving 55,000 dwellings, divided into 11 panels of 5,000 dwellings each: five panels were dedicated to the Content Test while the remaining six panels were for the SIN Test. Two models of test questionnaires were developed to meet the objectives, namely a model with all the proposed changes EXCEPT the SIN question and a model with all the proposed changes INCLUDING the SIN question. A third model of 'control' questionnaire with the 2011 content was also developed. The population living in a private dwelling in mail-out areas in one of the ten provinces was targeted for the test. Paper and electronic response channels were part of the Test as well.

    This report presents the Test objectives, the design and a summary of the analysis in order to determine potential content for the 2016 Census Program. Results from the data analysis of the Test were not the only elements used to determine the content for 2016. Other elements were also considered, such as response burden, comparison over time and users’ needs.

    Release date: 2016-04-01
  • 35. The Alternative Data Solution – Experience of the Producer Prices Division Archived
    Surveys and statistical programs – Documentation: 11-522-X201700014706
    Description:

    Over the last decade, Statistics Canada’s Producer Prices Division has expanded its service producer price indexes program and continued to improve its goods and construction producer price indexes program. While the majority of price indexes are based on traditional survey methods, efforts were made to increase the use of administrative data and alternative data sources in order to reduce burden on our respondents. This paper focuses mainly on producer price programs, but also provides information on the growing importance of alternative data sources at Statistics Canada. In addition, it presents the operational challenges and risks that statistical offices could face when relying more and more on third-party outputs. Finally, it presents the tools being developed to integrate alternative data while collecting metadata.

    Release date: 2016-03-24
  • 36. Challenges and results in using Audit trail data to monitor Labour Force Survey data quality Archived
    Surveys and statistical programs – Documentation: 11-522-X201700014707
    Description:

    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
  • 37. Statistics Canada’s Household Survey Frames Programme – Strategic Research Enabling a shift to increased use of Admin Data as Input to the Social statistics program Archived
    Surveys and statistical programs – Documentation: 11-522-X201700014708
    Description:

    Statistics Canada’s Household Survey Frames (HSF) Programme provides various universe files that can be used alone or in combination to improve survey design, sampling, collection, and processing in the traditional “need to contact a household model.” Even as surveys are migrating onto these core suite of products, the HSF is starting to plan the changes to infrastructure, organisation, and linkages with other data assets in Statistics Canada that will help enable a shift to increased use of a wide variety of administrative data as input to the social statistics programme. The presentation will provide an overview of the HSF Programme, foundational concepts that will need to be implemented to expand linkage potential, and will identify strategic research being under-taken toward 2021.

    Release date: 2016-03-24
  • 38. The Data Warehouse and analytical tools to facilitate the integration of the Canadian Macroeconomic Accounts Archived
    Surveys and statistical programs – Documentation: 11-522-X201700014710
    Description:

    The Data Warehouse has modernized the way the Canadian System of Macroeconomic Accounts (MEA) are produced and analyzed today. Its continuing evolution facilitates the amounts and types of analytical work that is done within the MEA. It brings in the needed element of harmonization and confrontation as the macroeconomic accounts move toward full integration. The improvements in quality, transparency, and timeliness have strengthened the statistics that are being disseminated.

    Release date: 2016-03-24
  • 39. Comparing Survey Data to Administrative Sources: Immigration, Labour, and Demographic data from the Longitudinal and International Study of Adults Archived
    Surveys and statistical programs – Documentation: 11-522-X201700014716
    Description:

    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
  • 40. Student Pathways and Graduate Outcomes Archived
    Surveys and statistical programs – Documentation: 11-522-X201700014717
    Description:

    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

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