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All (24,405)

All (24,405) (30 to 40 of 24,405 results)

Data (12,036)

Data (12,036) (12,030 to 12,040 of 12,036 results)

  • 12,031. Bilingualism and earnings Archived
    Table: 75-001-X19890022277
    Description:

    This study compares the earnings of bilingual and unilingual workers in three urban centres: Montreal, Toronto and Ottawa-Hull. Differences in the earnings of bilingual and unilingual workers are considered in the light of several demographic and job-related traits.

    Release date: 1989-06-30
Analysis (10,002)

Analysis (10,002) (50 to 60 of 10,002 results)

  • 51. Author’s response to comments on “Exchangeability assumption in propensity-score based adjustment methods for population mean estimation using non-probability samples”
    Articles and reports: 12-001-X202400100005
    Description: In this rejoinder, I address the comments from the discussants, Dr. Takumi Saegusa, Dr. Jae-Kwang Kim and Ms. Yonghyun Kwon. Dr. Saegusa’s comments about the differences between the conditional exchangeability (CE) assumption for causal inferences versus the CE assumption for finite population inferences using nonprobability samples, and the distinction between design-based versus model-based approaches for finite population inference using nonprobability samples, are elaborated and clarified in the context of my paper. Subsequently, I respond to Dr. Kim and Ms. Kwon’s comprehensive framework for categorizing existing approaches for estimating propensity scores (PS) into conditional and unconditional approaches. I expand their simulation studies to vary the sampling weights, allow for misspecified PS models, and include an additional estimator, i.e., scaled adjusted logistic propensity estimator (Wang, Valliant and Li (2021), denoted by sWBS). In my simulations, it is observed that the sWBS estimator consistently outperforms or is comparable to the other estimators under the misspecified PS model. The sWBS, as well as WBS or ABS described in my paper, do not assume that the overlapped units in both the nonprobability and probability reference samples are negligible, nor do they require the identification of overlap units as needed by the estimators proposed by Dr. Kim and Ms. Kwon.
    Release date: 2024-06-25
  • 52. Comments by Takumi Saegusa on “Exchangeability assumption in propensity-score based adjustment methods for population mean estimation using non-probability samples”: Causal inference, non-probability sample, and finite population
    Articles and reports: 12-001-X202400100006
    Description: In some of non-probability sample literature, the conditional exchangeability assumption is considered to be necessary for valid statistical inference. This assumption is rooted in causal inference though its potential outcome framework differs greatly from that of non-probability samples. We describe similarities and differences of two frameworks and discuss issues to consider when adopting the conditional exchangeability assumption in non-probability sample setups. We also discuss the role of finite population inference in different approaches of propensity scores and outcome regression modeling to non-probability samples.
    Release date: 2024-06-25
  • 53. Comments by Jae Kwang Kim and Yonghyun Kwon on “Exchangeability assumption in propensity-score based adjustment methods for population mean estimation using non-probability samples”
    Articles and reports: 12-001-X202400100007
    Description: Pseudo weight construction for data integration can be understood in the two-phase sampling framework. Using the two-phase sampling framework, we discuss two approaches to the estimation of propensity scores and develop a new way to construct the propensity score function for data integration using the conditional maximum likelihood method. Results from a limited simulation study are also presented.
    Release date: 2024-06-25
  • 54. Exchangeability assumption in propensity-score based adjustment methods for population mean estimation using non-probability samples
    Articles and reports: 12-001-X202400100008
    Description: Nonprobability samples emerge rapidly to address time-sensitive priority topics in different areas. These data are timely but subject to selection bias. To reduce selection bias, there has been wide literature in survey research investigating the use of propensity-score (PS) adjustment methods to improve the population representativeness of nonprobability samples, using probability-based survey samples as external references. Conditional exchangeability (CE) assumption is one of the key assumptions required by PS-based adjustment methods. In this paper, I first explore the validity of the CE assumption conditional on various balancing score estimates that are used in existing PS-based adjustment methods. An adaptive balancing score is proposed for unbiased estimation of population means. The population mean estimators under the three CE assumptions are evaluated via Monte Carlo simulation studies and illustrated using the NIH SARS-CoV-2 seroprevalence study to estimate the proportion of U.S. adults with COVID-19 antibodies from April 01-August 04, 2020.
    Release date: 2024-06-25
  • 55. Authors’ response to comments on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
    Articles and reports: 12-001-X202400100009
    Description: Our comments respond to discussion from Sen, Brick, and Elliott. We weigh the potential upside and downside of Sen’s suggestion of using machine learning to identify bogus respondents through interactions and improbable combinations of variables. We join Brick in reflecting on bogus respondents’ impact on the state of commercial nonprobability surveys. Finally, we consider Elliott’s discussion of solutions to the challenge raised in our study.
    Release date: 2024-06-25
  • 56. Comments by Aditi Sen on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
    Articles and reports: 12-001-X202400100010
    Description: This discussion summarizes the interesting new findings around measurement errors in opt-in surveys by Kennedy, Mercer and Lau (KML). While KML enlighten readers about “bogus responding” and possible patterns in them, this discussion suggests combining these new-found results with other avenues of research in nonprobability sampling, such as improvement of representativeness.
    Release date: 2024-06-25
  • 57. Comments by Michael R. Elliott on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
    Articles and reports: 12-001-X202400100011
    Description: Kennedy, Mercer, and Lau explore misreporting by respondents in non-probability samples and discover a new feature, namely that of deliberate misreporting of demographic characteristics. This finding suggests that the “arms race” between researchers and those determined to disrupt the practice of social science is not over and researchers need to account for such respondents if using high-quality probability surveys to help reduce error in non-probability samples.
    Release date: 2024-06-25
  • 58. Comments by J. Michael Brick on “Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith”
    Articles and reports: 12-001-X202400100012
    Description: Nonprobability samples are quick and low-cost and have become popular for some types of survey research. Kennedy, Mercer and Lau examine data quality issues associated with opt-in nonprobability samples frequently used in the United States. They show that the estimates from these samples have serious problems that go beyond representativeness. A total survey error perspective is important for evaluating all types of surveys.
    Release date: 2024-06-25
  • 59. Exploring the assumption that commercial online nonprobability survey respondents are answering in good faith
    Articles and reports: 12-001-X202400100013
    Description: Statistical approaches developed for nonprobability samples generally focus on nonrandom selection as the primary reason survey respondents might differ systematically from the target population. Well-established theory states that in these instances, by conditioning on the necessary auxiliary variables, selection can be rendered ignorable and survey estimates will be free of bias. But this logic rests on the assumption that measurement error is nonexistent or small. In this study we test this assumption in two ways. First, we use a large benchmarking study to identify subgroups for which errors in commercial, online nonprobability samples are especially large in ways that are unlikely due to selection effects. Then we present a follow-up study examining one cause of the large errors: bogus responding (i.e., survey answers that are fraudulent, mischievous or otherwise insincere). We find that bogus responding, particularly among respondents identifying as young or Hispanic, is a significant and widespread problem in commercial, online nonprobability samples, at least in the United States. This research highlights the need for statisticians working with commercial nonprobability samples to address bogus responding and issues of representativeness – not just the latter.
    Release date: 2024-06-25
  • 60. Preface to the special issue for papers presented at the 29th Morris Hansen Lecture on the use of nonprobability samples
    Articles and reports: 12-001-X202400100014
    Description: This paper is an introduction to the special issue on the use of nonprobability samples featuring three papers that were presented at the 29th Morris Hansen Lecture by Courtney Kennedy, Yan Li and Jean-François Beaumont.
    Release date: 2024-06-25
Reference (1,892)

Reference (1,892) (80 to 90 of 1,892 results)

  • 81. Pathways indicators for registered apprentices in Canada, 2021
    Surveys and statistical programs – Documentation: 37-20-00012022006
    Description:

    This 2021 technical reference guide is intended for users of the Education and Labour Market Longitudinal Platform (ELMLP). The data products associated with this release are derived from integrating the longitudinal Registered Apprenticeship Information System (RAIS) data with other administrative data. Statistics Canada has derived a series of indicators on the pathways of newly registered journeypersons by cohort size and selected trades, for Canada, all provinces and for grouped territories.

    Release date: 2022-09-27
  • 82. Agricultural Ecumene Boundary File - Reference Guide
    Geographic files and documentation: 92-639-G
    Description:

    This reference guide provides an overview of the Agricultural Ecumene Boundary File product (see the notes below), including the general methodology used to create the files and other important technical information.

    This product is provided in printable format.

    Release date: 2022-09-27
  • 83. Agricultural Ecumene Boundary File
    Geographic files and documentation: 92-639-X
    Description:

    The Agricultural Ecumene Boundary File delineates areas of significant agricultural activity in Canada. This product is generalized for small-scale mapping and can be used to clip or intersect with any geographic boundary file, administrative or natural, to limit the data display to those areas where agricultural activity is concentrated in Canada. When used in dot density and choropleth thematic maps, the ecumene concept provides a more accurate depiction of the spatial distribution of data within geographic areas.

    Release date: 2022-09-27
  • 84. 2021 Census Data Quality Guidelines
    Surveys and statistical programs – Documentation: 98-26-0006
    Description:

    These guidelines provide information to help people effectively use and interpret the data quality indicators for the 2021 Census.

    Release date: 2022-09-21
  • 85. Housing Characteristics Reference Guide, Census of Population, 2021
    Surveys and statistical programs – Documentation: 98-500-X2021005
    Description: This reference guide provides information to help users effectively use and interpret housing characteristics data from the 2021 Census. This guide contains definitions and explanations of concepts, questions, classifications, data quality and comparability with other sources for this topic.
    Release date: 2022-09-21
  • 86. Indigenous Peoples Reference Guide, Census of Population, 2021
    Surveys and statistical programs – Documentation: 98-500-X2021009
    Description:

    This reference guide provides information to help users effectively use and interpret Indigenous peoples data from the 2021 Census. This guide contains definitions and explanations of concepts, questions, classifications, data quality and comparability with other sources for this topic.

    Release date: 2022-09-21
  • 87. Census Forward Sortation Area Boundary File, Reference Guide
    Geographic files and documentation: 92-179-G
    Description:

    This guide describes the content of the product, as well as providing information on data quality, record layouts and methodology.

    This data includes information copied with permission from Canada Post Corporation.

    Release date: 2022-09-21
  • 88. Census Forward Sortation Area Boundary File
    Geographic files and documentation: 92-179-X
    Description:

    The Forward Sortation Area Boundary File depicts the boundaries of forward sortation areas (FSAs) derived from postal codesOM captured from census questionnaires.

    The Forward Sortation Area Boundary File is available in two types: digital and cartographic. The digital boundary file depicts the full extent of forward sortation areas, including the coastal water area. The cartographic boundary file depicts forward sortation areas with the shoreline of the major land mass of Canada and its coastal islands. The files provide a framework for mapping and spatial analysis.

    ©This data includes information copied with permission from Canada Post Corporation.

    A reference guide is included (92-179-G).

    OM. Postal code is an official mark of Canada Post Corporation.

    Release date: 2022-09-21
  • 89. T1 Family File, Final Estimates, 2020
    Surveys and statistical programs – Documentation: 72-212-X2022001
    Description:

    Data on income of census families, individuals and seniors are derived from income tax returns. The data for the products associated with this release are derived from the T1 file that Statistics Canada receives from Canada Revenue Agency (CRA) thirteen months after the end of the taxation year.

    Release date: 2022-08-09
  • 90. Families, Households and Marital Status Reference Guide, Census of Population, 2021
    Surveys and statistical programs – Documentation: 98-500-X2021002
    Description: This reference guide provides information to help users effectively use and interpret family, household and marital status data from the 2021 Census. This guide contains definitions and explanations of concepts, questions, classifications, data quality and comparability with other sources for this topic.
    Release date: 2022-07-13
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