Analysis
Results
All (4)
All (4) ((4 results))
- Articles and reports: 12-001-X202500200007Description: Although probability samples have been regarded as the gold standard to collect information for population-based study, non-probability samples have been used frequently in practice due to low cost, convenience, and the lack of the sampling frame for the survey. Naïve estimates based on non-probability samples without any adjustments may be misleading due to selection bias. Recently, a valid data integration approach that includes mass imputation, propensity score weighting, and calibration has been used to improve the representativeness of non-probability samples. The effectiveness of the mass imputation approach depends on the underlying model assumptions. In this paper, we propose using deep learning for the mass imputation in the combining of probability and non-probability samples and compare it with several modern machine learning-based mass imputation approaches, including generalized additive modeling, regression tree, random forest, and XG-boosting. In the simulation study, deep learning-based approaches have been shown to be more robust and effective than other mass imputation approaches against the failure of underlying model assumptions under non-linearity scenarios.Release date: 2025-12-23
- Articles and reports: 36-28-0001202501100001Description: Citizenship acquisition marks a pivotal milestone in immigrant integration, influencing social cohesion and political participation. While aggregate naturalization rates provide macro-level insights, disparities by source country reveal diverse integration pathways. Through comparative analysis of Australia and Canada—nations with comparable immigration scales and broadly similar immigration approaches, yet notable differences in naturalization frameworks—this study investigates how source-country characteristics affect naturalization patterns.Release date: 2025-11-26
- Articles and reports: 36-28-0001202500700001Description: Postsecondary education is a key element in developing a skilled workforce. International students are often seen as a potential source of labour supply beyond their temporary employment while studying. This study examines the alignment between the fields of study and occupations of immigrants with a postsecondary education who held study permits before becoming permanent residents from 2011 to 2021. It compares them with other immigrants who became permanent residents during the same period and Canadian-born postsecondary graduates.Release date: 2025-07-23
- Articles and reports: 36-28-0001202500600001Description: The United States would be a useful comparison country for Canada in studying immigrant naturalization, as both are major immigrant-receiving nations with close geographic and economic ties. However, differences in available data complicate comparisons of immigrant citizenship rates. This article examines key data sources for studying immigrant citizenship in both countries and highlights the challenges in comparing citizenship rates and trends.Release date: 2025-06-25
Articles and reports (4)
Articles and reports (4) ((4 results))
- Articles and reports: 12-001-X202500200007Description: Although probability samples have been regarded as the gold standard to collect information for population-based study, non-probability samples have been used frequently in practice due to low cost, convenience, and the lack of the sampling frame for the survey. Naïve estimates based on non-probability samples without any adjustments may be misleading due to selection bias. Recently, a valid data integration approach that includes mass imputation, propensity score weighting, and calibration has been used to improve the representativeness of non-probability samples. The effectiveness of the mass imputation approach depends on the underlying model assumptions. In this paper, we propose using deep learning for the mass imputation in the combining of probability and non-probability samples and compare it with several modern machine learning-based mass imputation approaches, including generalized additive modeling, regression tree, random forest, and XG-boosting. In the simulation study, deep learning-based approaches have been shown to be more robust and effective than other mass imputation approaches against the failure of underlying model assumptions under non-linearity scenarios.Release date: 2025-12-23
- Articles and reports: 36-28-0001202501100001Description: Citizenship acquisition marks a pivotal milestone in immigrant integration, influencing social cohesion and political participation. While aggregate naturalization rates provide macro-level insights, disparities by source country reveal diverse integration pathways. Through comparative analysis of Australia and Canada—nations with comparable immigration scales and broadly similar immigration approaches, yet notable differences in naturalization frameworks—this study investigates how source-country characteristics affect naturalization patterns.Release date: 2025-11-26
- Articles and reports: 36-28-0001202500700001Description: Postsecondary education is a key element in developing a skilled workforce. International students are often seen as a potential source of labour supply beyond their temporary employment while studying. This study examines the alignment between the fields of study and occupations of immigrants with a postsecondary education who held study permits before becoming permanent residents from 2011 to 2021. It compares them with other immigrants who became permanent residents during the same period and Canadian-born postsecondary graduates.Release date: 2025-07-23
- Articles and reports: 36-28-0001202500600001Description: The United States would be a useful comparison country for Canada in studying immigrant naturalization, as both are major immigrant-receiving nations with close geographic and economic ties. However, differences in available data complicate comparisons of immigrant citizenship rates. This article examines key data sources for studying immigrant citizenship in both countries and highlights the challenges in comparing citizenship rates and trends.Release date: 2025-06-25