Analysis

Description:

This paper examines the effects of fertility, mortality and migration on the age profile of the Canadian population, particularly the effect of fluctuating fertility patterns which have occurred since the second World War. The author analyses the impact on social services and the economy as the shifting requirements of the “Baby Boom” cohorts move through their life cycle.

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A major packaged goods manufacturer details his firm’s assemblage and application of market understanding information, impact information, market tracking, share/volume forecasting and documentation procedure.

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Increasing costs without a concomitant increase in research budgets are putting severe strains on research quality. Improvements in technology, however, both in the physical domain and in the conceptual domain are sufficient to maintain research productivity at least at its prior level.

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Due to the absence of hard data and the lack of standardization with respect to nonresponse terminology and reporting procedures, U.S. commercial survey researchers have been unable to obtain an accurate assessment of the nature and extent of the nonresponse problem. However, the results of two recent studies conducted by the author among leading U.S. based market and public opinion research firms revealed that nonresponse is one of the major problems now confronting the commercial survey research industry. This paper discusses the results of the two studies and their implications.

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The article provides a general overview of the concepts of incomplete data and non-response. It is recognized that non-response is an important indicator of data quality, as it affects the estimators by introducing bias and increasing variance due to a reduction in the effective sample size. The relationship between bias and the non-response rate is less obvious, since it depends on the extent of non-response and on the difference in the various characteristics between respondents and non-respondents.

The most effective way of dealing with the effects of non-response is to minimize its extent. However, any attempt to control the extent of non-response must be based on a good understanding of its origins. The causes and extent of non-response are fundamentally related to (i) the type of survey, (ii) the data capture methods, and (iii) the sample design. However, given a sample design, the extent of non-response will be influenced by factors such as the type of region and the type of non-response.

There are several ways to handle incomplete data. Each one, in the end, assigns a value to the missing or incorrect data, unless it is decided to publish “raw” data. The procedure for assigning values is called imputation and such an imputed value presumably describes the characteristic of the non-respondent.

The article provides a brief philosophical explanation about validation and imputation and their applications in the methodology of the various imputation procedures. These include weighting, replication, hot deck imputation using previous data and substitution by a zero value. The using of imputation compared with the methods used in the Canadian Labour Force Survey (LFS) is also discussed. A decision table is provided indicating the various steps to follow for a particular case of a partially completed LFS questionnaire.

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This presentation focuses on the present and future social needs of the public, and tracking these needs by surveys. It is divided into two parts. First, the writer gives some history of the tracking systems. Then, he speaks about the future and his forecasts for the new tracking systems.

Description: This paper makes a proposal to create a new type of information bank, the “Synthetic Data Bank”. This type of bank would involve linking information from two data banks to create a third. The result would be that much greater use could be made of existing data banks in conjunction with new data collection activities. This would mean a significant reduction in the amount of data to be collected which, in effect, could potentially reduce both data collection costs and response burden. The paper suggests a number of considerations in developing statistical techniques to facilitate the creation of such an information linkage concept. Some of these techniques are to be found in modern literature’ others may well have to be developed.

Description: My brief as a speaker was to comment on points raised in the opening session, within the general theme of serving the needs of research users in the 1980’s. This scheme did not allow a prepared paper, and my impromptu comments tended to be discussive. Below is a summary of my main points, leaving out anecdotes and examples used in the actual talk.

Description: This paper deals with the desirability of designing surveys in such a way that results can be compared to previous existing data. The writer explains why there are practical difficulties in assessing the significance of data collected in a one-time survey where these data stand alone and are not readily comparable to other existing data, i.e., where control group data or other benchmarks do not exist.

Description: The government survey sponsor should plan carefully what he expects to get from the supplier, specifying who is to do what, when, including details of what the sponsor will do. If there are many eligible suppliers, only a small number should be invited to submit proposals, increasing as the value of the contract increases. Procedures for screening suppliers and selecting the successful one should be organized before proposals are received. These should include visits to review suppliers, facilities and organization, as a good relationship between a sponsor and a supplier depends largely on good faith and willing cooperation. Sponsor-supplier relationships are more formal, and more time-consuming in the selection process, than in the private sector.

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: This short video explains how it can be very effective for all levels of governments and organizations that serve communities to use disaggregated data to make evidence-informed public policy decisions. By using disaggregated data, policymakers are able to design more appropriate and effective policies that meet the needs of each diverse and unique Canadian.

Description: This short video explains how the use of disaggregated data can help policymakers to develop more targeted and effective policies by identifying the unique needs and challenges faced by different demographic groups.

Description: Release published in The Daily – Statistics Canada’s official release bulletin

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.

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description: This course is intended for Government of Canada employees who would like to learn about evaluating the quality of data for a particular use. Whether you are a new employee interested in learning the basics, or an experienced subject matter expert looking to refresh your skills, this course is here to help.

Description: Release published in The Daily – Statistics Canada’s official release bulletin

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Gathering, exploring, analyzing and interpreting data are essential steps in producing information that benefits society, the economy and the environment. In this video, we will discuss the importance of considering data ethics throughout the process of producing statistical information.

As a pre-requisite to this video, make sure to watch the video titled “Data Ethics: An introduction” also available in Statistics Canada’s data literacy training catalogue.

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In this video, you will learn the answers to the following questions: What are the different types of error? What are the types of error that lead to statistical bias? Where during the data journey statistical bias can occur?

Description: This article provides information about population totals in the Labour Force Survey (LFS), including details on who is included in the survey target population, and a description of the methodology used to produce monthly population totals in the LFS. The note also provides guidance on how to interpret population statistics in the LFS, and discusses the extent to which the LFS can be used to examine disaggregated labour market indicators for new immigrants and non-permanent residents.

Description: This document briefly describes the small area estimation methodology developed to produce monthly estimates of employment and unemployment rate for census metropolitan areas, census agglomerations, and self-contained labour areas using data from the Labour Force Survey, Employment Insurance statistics and population projections.

Description: 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.

Description: In this paper, we look for presence of heterogeneity in conducting impact evaluations of the Skills Development intervention delivered under the Labour Market Development Agreements. We use linked longitudinal administrative data covering a sample of Skills Development participants from 2010 to 2017. We apply a causal machine-learning estimator as in Lechner (2019) to estimate the individualized program impacts at the finest aggregation level. These granular impacts reveal the distribution of net impacts facilitating further investigation as to what works for whom. The findings suggest statistically significant improvements in labour market outcomes for participants overall and for subgroups of policy interest.

Description: Inspired by the two excellent discussions of our paper, we offer some new insights and developments into the problem of estimating participation probabilities for non-probability samples. First, we propose an improvement of the method of Chen, Li and Wu (2020), based on best linear unbiased estimation theory, that more efficiently leverages the available probability and non-probability sample data. We also develop a sample likelihood approach, similar in spirit to the method of Elliott (2009), that properly accounts for the overlap between both samples when it can be identified in at least one of the samples. We use best linear unbiased prediction theory to handle the scenario where the overlap is unknown. Interestingly, our two proposed approaches coincide in the case of unknown overlap. Then, we show that many existing methods can be obtained as a special case of a general unbiased estimating function. Finally, we conclude with some comments on nonparametric estimation of participation probabilities.

Description: We provide comparisons among three parametric methods for the estimation of participation probabilities and some brief comments on homogeneous groups and post-stratification.

Description: Beaumont, Bosa, Brennan, Charlebois and Chu (2024) propose innovative model selection approaches for estimation of participation probabilities for non-probability sample units. We focus our discussion on the choice of a likelihood and parameterization of the model, which are key for the effectiveness of the techniques developed in the paper. We consider alternative likelihood and pseudo-likelihood based methods for estimation of participation probabilities and present simulations implementing and comparing the AIC based variable selection. We demonstrate that, under important practical scenarios, the approach based on a likelihood formulated over the observed pooled non-probability and probability samples performed better than the pseudo-likelihood based alternatives. The contrast in sensitivity of the AIC criteria is especially large for small probability sample sizes and low overlap in covariates domains.

Description: Non-probability samples are being increasingly explored in National Statistical Offices as an alternative to probability samples. However, it is well known that the use of a non-probability sample alone may produce estimates with significant bias due to the unknown nature of the underlying selection mechanism. Bias reduction can be achieved by integrating data from the non-probability sample with data from a probability sample provided that both samples contain auxiliary variables in common. We focus on inverse probability weighting methods, which involve modelling the probability of participation in the non-probability sample. First, we consider the logistic model along with pseudo maximum likelihood estimation. We propose a variable selection procedure based on a modified Akaike Information Criterion (AIC) that properly accounts for the data structure and the probability sampling design. We also propose a simple rank-based method of forming homogeneous post-strata. Then, we extend the Classification and Regression Trees (CART) algorithm to this data integration scenario, while again properly accounting for the probability sampling design. A bootstrap variance estimator is proposed that reflects two sources of variability: the probability sampling design and the participation model. Our methods are illustrated using Statistics Canada’s crowdsourcing and survey data.

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.

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.

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.

Description: Since 1984, an annual international symposium on methodological issues has been sponsored by Statistics Canada. Proceedings have been available since 1987.

Description: The journal publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves.

Description: This series provides detailed documentation on income developments, including survey design issues, data quality evaluation and exploratory research.

Description: This report summarizes the annual achievements of the Methodology Research and Development Program (MRDP) sponsored by the Modern Statistical Methods and Data Science Branch at Statistics Canada. This program covers research and development activities in statistical methods with potentially broad application in the agency’s statistical programs; these activities would otherwise be less likely to be carried out during the provision of regular methodology services to those programs. The MRDP also includes activities that provide support in the application of past successful developments in order to promote the use of the results of research and development work. Selected prospective research activities are also presented.

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The Geography working paper series is intended to stimulate discussion on a variety of topics covering conceptual, methodological or technical work to support the development and dissemination of the division's data, products and services. Readers of the series are encouraged to contact the Geography Division with comments and suggestions.

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Historical works allow readers to peer into the past, not only to satisfy our curiosity about “the way things were,” but also to see how far we’ve come, and to learn from the past. For Statistics Canada, such works are also opportunities to commemorate the agency’s contributions to Canada and its people, and serve as a reminder that an institution such as this continues to evolve each and every day.

On the occasion of Statistics Canada’s 100th anniversary in 2018, Standing on the shoulders of giants: History of Statistics Canada: 1970 to 2008, builds on the work of two significant publications on the history of the agency, picking up the story in 1970 and carrying it through the next 36 years, until 2008. To that end, when enough time has passed to allow for sufficient objectivity, it will again be time to document the agency’s next chapter as it continues to tell Canada’s story in numbers.

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The Record Linkage Project Process Model (RLPPM) was developed by Statistics Canada to identify the processes and activities involved in record linkage. The RLPPM applies to linkage projects conducted at the individual and enterprise level using diverse data sources to create new data sources to meet analytical and operational needs.

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This document briefly describes Demosim, the microsimulation population projection model, how it works as well as its methods and data sources. It is a methodological complement to the analytical products produced using Demosim.

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This publication is a catalogue of strategies and mechanisms that a statistical organization should consider adopting, according to its particular context. This compendium is based on lessons learned and best practices of leadership and management of statistical agencies within the scope of Statistics Canada’s International Statistical Fellowship Program (ISFP). It contains four broad sections including, characteristics of an effective national statistical system; core management practices; improving, modernizing and finding efficiencies; and, strategies to better inform and engage key stakeholders.