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- Articles and reports: 12-001-X202600100001Description: Wayne A. Fuller is a leading figure in statistics whose career at Iowa State University (ISU) began in 1959; he is now Distinguished Professor Emeritus in Statistics and Economics. This article briefly recounts his early life and training in agricultural economics at ISU and highlights influential contributions spanning time series analysis, measurement error models, and survey sampling. It documents his impact through seminal textbooks, methodological advances such as the Dickey-Fuller test and regression estimation, sustained work on major operational surveys (e.g., the National Resources Inventory), and mentorship of many graduate students. The article includes an interview conducted on May 20th, 2025, at Professor Fuller’s home.Release date: 2026-06-29
- Surveys and statistical programs – Documentation: 19-20-00012026002Description: This reference document provides answers on selected topics related to the use, interpretation, and calculation of trend-cycle estimates for seasonally adjusted data. It is designed to complement more technical discussions of seasonal adjustment and trend-cycle estimation found in Statistics Canada publications and reference manuals.Release date: 2026-06-08
- Journals and periodicals: 11-633-XDescription: 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: 2026-05-27
- Surveys and statistical programs – Documentation: 19-20-00012026001Description: This reference document provides nontechnical answers on selected topics related to the use and interpretation of seasonally adjusted data. It is designed to complement more technical discussions of seasonal adjustment found in Statistics Canada publications and reference manuals.Release date: 2026-05-11
- Articles and reports: 75-006-X202400100007Description: This study uses data from multiple waves of the Canadian Social Survey (CSS) to examine trends in three key Quality of Life indicators, namely life satisfaction, experiences of financial hardship, and future outlook. Monitoring these well-being indicators following periods of considerable social and economic change is particularly important. Beginning in the summer of 2021, the CSS, a new quarterly survey, captured the latter part of the COVID-19 pandemic as well as the rising cost of living in Canada, allowing for an understanding of how Canadians are coping with these challenges.Release date: 2024-09-13
- Stats in brief: 11-001-X202425738424Description: Release published in The Daily – Statistics Canada’s official release bulletinRelease date: 2024-09-13
- Articles and reports: 12-001-X202300100010Description: Precise and unbiased estimates of response propensities (RPs) play a decisive role in the monitoring, analysis, and adaptation of data collection. In a fixed survey climate, those parameters are stable and their estimates ultimately converge when sufficient historic data is collected. In survey practice, however, response rates gradually vary in time. Understanding time-dependent variation in predicting response rates is key when adapting survey design. This paper illuminates time-dependent variation in response rates through multi-level time-series models. Reliable predictions can be generated by learning from historic time series and updating with new data in a Bayesian framework. As an illustrative case study, we focus on Web response rates in the Dutch Health Survey from 2014 to 2019.Release date: 2023-06-30
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- Articles and reports: 12-001-X202600100001Description: Wayne A. Fuller is a leading figure in statistics whose career at Iowa State University (ISU) began in 1959; he is now Distinguished Professor Emeritus in Statistics and Economics. This article briefly recounts his early life and training in agricultural economics at ISU and highlights influential contributions spanning time series analysis, measurement error models, and survey sampling. It documents his impact through seminal textbooks, methodological advances such as the Dickey-Fuller test and regression estimation, sustained work on major operational surveys (e.g., the National Resources Inventory), and mentorship of many graduate students. The article includes an interview conducted on May 20th, 2025, at Professor Fuller’s home.Release date: 2026-06-29
- Journals and periodicals: 11-633-XDescription: 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: 2026-05-27
- Articles and reports: 75-006-X202400100007Description: This study uses data from multiple waves of the Canadian Social Survey (CSS) to examine trends in three key Quality of Life indicators, namely life satisfaction, experiences of financial hardship, and future outlook. Monitoring these well-being indicators following periods of considerable social and economic change is particularly important. Beginning in the summer of 2021, the CSS, a new quarterly survey, captured the latter part of the COVID-19 pandemic as well as the rising cost of living in Canada, allowing for an understanding of how Canadians are coping with these challenges.Release date: 2024-09-13
- Stats in brief: 11-001-X202425738424Description: Release published in The Daily – Statistics Canada’s official release bulletinRelease date: 2024-09-13
- Articles and reports: 12-001-X202300100010Description: Precise and unbiased estimates of response propensities (RPs) play a decisive role in the monitoring, analysis, and adaptation of data collection. In a fixed survey climate, those parameters are stable and their estimates ultimately converge when sufficient historic data is collected. In survey practice, however, response rates gradually vary in time. Understanding time-dependent variation in predicting response rates is key when adapting survey design. This paper illuminates time-dependent variation in response rates through multi-level time-series models. Reliable predictions can be generated by learning from historic time series and updating with new data in a Bayesian framework. As an illustrative case study, we focus on Web response rates in the Dutch Health Survey from 2014 to 2019.Release date: 2023-06-30
Reference (2)
Reference (2) ((2 results))
- Surveys and statistical programs – Documentation: 19-20-00012026002Description: This reference document provides answers on selected topics related to the use, interpretation, and calculation of trend-cycle estimates for seasonally adjusted data. It is designed to complement more technical discussions of seasonal adjustment and trend-cycle estimation found in Statistics Canada publications and reference manuals.Release date: 2026-06-08
- Surveys and statistical programs – Documentation: 19-20-00012026001Description: This reference document provides nontechnical answers on selected topics related to the use and interpretation of seasonally adjusted data. It is designed to complement more technical discussions of seasonal adjustment found in Statistics Canada publications and reference manuals.Release date: 2026-05-11