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Description: The newsletter offers information aimed at three main groups, businesses (small to medium), communities and ethno-cultural groups/communities. Articles and outreach materials will assist their understanding of national and local data from the many relevant sources found on the Statistics Canada website.

Description: The Guide to the Job Vacancy and Wage Survey contains a dictionary of concepts and definitions, and covers topics such as survey methodology, data collection, processing, and data quality.

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This paper introduces and explains modifications made to the Labour Force Survey estimates.

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The Public-Use Microdata File (PUMF) for the Survey of Household Spending (SHS) provides information on household expenditures as well as selected information on dwelling characteristics and household equipment. The production of this file includes many safeguards to prevent the identification of any one person or household.

PUMFs were produced on an annual basis for SHS 1997 to 2009, before a redesigned survey was introduced with the 2010 reference year. The SHS 2017 PUMF is the first SHS PUMF based on data collected after the 2010 survey redesign.

Due to changes to data collection, processing and estimation methods introduced with the 2010 redesign, users are advised not to compare data from SHS 1997 to 2009 with data from any subsequent years, unless otherwise noted.

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This American Sign Language video provides an introduction to the Canadian Survey on Disability. Specifically, it includes a brief description of the benefits of participating in the survey, what participating in the survey involves, how respondents were selected to participate, and information on privacy and confidentiality.

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This infographic explains the steps involved in collecting data for all Statistics Canada household and business surveys. The responses are compiled, analyzed and used to make important decisions and are kept strictly confidential.

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The Guide to the Job Vacancy and Wage Survey contains a dictionary of concepts and definitions, and includes topics such as survey methodology, data collection, processing, and data quality.

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Data gathering involves first determining what data you need, then where to find it, how to get it and how to keep it safe. This module introduces you to things you should consider when gathering data.

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The Guide to the Labour Force Survey contains a dictionary of concepts and definitions and covers topics such as survey methodology, data collection, data processing and data quality. It also contains information on products and services, sub-provincial geography descriptions as well as the survey questionnaire.

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This document brings together guidelines and checklists on many issues that need to be considered in the pursuit of quality objectives in the execution of statistical activities. Its focus is on how to assure quality through effective and appropriate design or redesign of a statistical project or program from inception through to data evaluation, dissemination and documentation. These guidelines draw on the collective knowledge and experience of many Statistics Canada employees. It is expected that Quality Guidelines will be useful to staff engaged in the planning and design of surveys and other statistical projects, as well as to those who evaluate and analyze the outputs of these projects.

Description:

The Public-Use Microdata File (PUMF) for the Survey of Household Spending (SHS) provides information on household expenditures as well as selected information on dwelling characteristics and household equipment. The production of this file includes many safeguards to prevent the identification of any one person or household.

PUMFs were produced on an annual basis for SHS 1997 to 2009, before a redesigned survey was introduced with the 2010 reference year. The SHS 2017 PUMF is the first SHS PUMF based on data collected after the 2010 survey redesign.

Due to changes to data collection, processing and estimation methods introduced with the 2010 redesign, users are advised not to compare data from SHS 1997 to 2009 with data from any subsequent years, unless otherwise noted.

Description: The Canadian Tobacco, Alcohol and Drugs Survey (CTADS) is a biennial general population survey of tobacco, alcohol and drug use among Canadians aged 15 years and older, with the primary focus on 15- to 24-year-olds. The CTADS is a telephone survey conducted by Statistics Canada on behalf of Health Canada.

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Statistics Canada was approached by Stentor Resource Centre Incorporated to conduct a survey to monitor the telephone penetration rates across Canada. The survey determines if the respondents have a telephone line in their residence. If they do not have a telephone line, information is collected as to the reasons why. Information is also collected on the income characteristics of the selected households.

The management of the survey was transferred from Stentor to Bell Canada in the Fall of 1998.

The Labour Force Survey (LFS) supplementary capacity is used to conduct this biannual survey. A sample of approximately 44,000 respondents is used for this survey (five out of six rotation groups). The survey data are collected using Computer Assisted Interviewing (CAI). The first data collection procedure took place during November's LFS week in 1996.

This microdata file is prepared biannually and contains the variables from the survey, plus geographical variables from the LFS (province, census metropolitan area, urban/rural breakdown). No other variables from the LFS are added to the file.

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This product provides an overview of trends in the surveying and mapping services industry. It provides users with information required for making corporate decisions, monitoring programs and reviewing policies. The tables focus on financial and operating data.

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This topic presents data on education, labour, place of work, commuting to work and language of work in Canada. The topic presents data on the paid work of the Canadian workforce, including detailed industry and occupation data, class of worker, and work activity during the reference year. Data on workers' place of work and journey to work are also included.

This topic also presents data on four main education concepts: completed education credentials, major field of study, location of study and attendance at school.

Together, these data provide information on education and the work activities of Canadians.

Analytical products

The analytical document provides analysis on the key findings in the data, and is complimented with the short articles found in NHS in Brief and the data in NHS Focus on Geography Series.

Data products

The NHS Profile is one data product that provides a statistical overview of user selected geographic areas based on several detailed variables and/or groups of variables. Other data products include data tables which represent a series of cross tabulations ranging in complexity and are available for various levels of geography.

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This product, offered on CD-ROM, contains the public microdata for the Adult Literacy and Life Skills Survey.

The Adult Literacy and Life Skills Survey (ALL) is a large-scale co-operative effort undertaken in 2003, 2006 and 2008 by governments, national statistics agencies, research institutions and multi-lateral agencies. The ALL study builds on the International Adult Literacy Survey (IALS),the world's first internationally comparative survey of adult skills undertaken in three rounds of data collection between 1994 and 1998.

The foundation skills measured in the ALL survey include prose literacy, document literacy, numeracy and problem solving. Additional skills assessed indirectly include familiarity with and use of information and communication technologies.

The development and management of the study were co-ordinated by Statistics Canada and the Educational Testing Service (ETS) in collaboration with the National Center for Education Statistics (NCES) of the United States Department of Education, the Organisation for Economic Cooperation and Development (OECD), the Regional Office for Latin America and the Caribbean (OREALC) and the Institute for Statistics (UIS) of the United Nations Educational, Scientific and Cultural Organisation (UNESCO).

This dataset is composed of seven countries or regions that collected data in 2003. They include Bermuda, Canada, Italy, Norway, Switzerland, the United States and the Mexican State of Nuevo Leon. Additionally data is included for three other countries that collected data in 2006 and 2008 which are Hungary, Netherlands, and New Zealand.

Description: This report details the methodology and results of the Farm Environmental Management Survey (FEMS).

The FEMS is conducted to gather information about farming practices on Canadian crop and livestock operations. The survey focuses on information related to manure storage and spreading, pesticide application, crop and nutrient management, grazing and the implementation of environmental farm plans.

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This table presents a cross-tabulation of data using selected characteristics from the National Household Survey.

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This table presents a cross-tabulation of data using selected characteristics from the National Household Survey.

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This table presents a cross-tabulation of data using selected characteristics from the National Household Survey.

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At national statistical institutes experiments embedded in ongoing sample surveys are frequently conducted, for example to test the effect of modifications in the survey process on the main parameter estimates of the survey, to quantify the effect of alternative survey implementations on these estimates, or to obtain insight into the various sources of non-sampling errors. A design-based analysis procedure for factorial completely randomized designs and factorial randomized block designs embedded in probability samples is proposed in this paper. Design-based Wald statistics are developed to test whether estimated population parameters, like means, totals and ratios of two population totals, that are observed under the different treatment combinations of the experiment are significantly different. The methods are illustrated with a real life application of an experiment embedded in the Dutch Labor Force Survey.

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Regression models are routinely used in the analysis of survey data, where one common issue of interest is to identify influential factors that are associated with certain behavioral, social, or economic indices within a target population. When data are collected through complex surveys, the properties of classical variable selection approaches developed in i.i.d. non-survey settings need to be re-examined. In this paper, we derive a pseudo-likelihood-based BIC criterion for variable selection in the analysis of survey data and suggest a sample-based penalized likelihood approach for its implementation. The sampling weights are appropriately assigned to correct the biased selection result caused by the distortion between the sample and the target population. Under a joint randomization framework, we establish the consistency of the proposed selection procedure. The finite-sample performance of the approach is assessed through analysis and computer simulations based on data from the hypertension component of the 2009 Survey on Living with Chronic Diseases in Canada.

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Web surveys are generally connected with low response rates. Common suggestions in textbooks on Web survey research highlight the importance of the welcome screen in encouraging respondents to take part. The importance of this screen has been empirically proven in research, showing that most respondents breakoff at the welcome screen. However, there has been little research on the effect of the design of this screen on the level of the breakoff rate. In a study conducted at the University of Konstanz, three experimental treatments were added to a survey of the first-year student population (2,629 students) to assess the impact of different design features of this screen on the breakoff rates. The methodological experiments included varying the background color of the welcome screen, varying the promised task duration on this first screen, and varying the length of the information provided on the welcome screen explaining the privacy rights of the respondents. The analyses show that the longer stated length and the more attention given to explaining privacy rights on the welcome screen, the fewer respondents started and completed the survey. However, the use of a different background color does not result in the expected significant difference.

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Measures of subjective well-being are increasingly prominent in international policy discussions about how best to measure "societal progress" and the well-being of national populations. This has implications for national statistical offices, as calls have been made for them to include measures of subjective well-being in their household surveys (Organization for Economic Cooperation and Development 2013). Statistics Canada has included measures of subjective well-being - particularly life satisfaction - in its surveys for twenty-five years, although the wording of these questions and the response categories have evolved over time. Statistics Canada's General Social Survey (GSS) and Canadian Community Health Survey (CCHS) offer a valuable opportunity to examine the stability of life satisfaction responses and their correlates from year to year using a consistent analytical framework.

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It is routine practice for survey organizations to provide replication weights as part of survey data files. These replication weights are meant to produce valid and efficient variance estimates for a variety of estimators in a simple and systematic manner. Most existing methods for constructing replication weights, however, are only valid for specific sampling designs and typically require a very large number of replicates. In this paper we first show how to produce replication weights based on the method outlined in Fay (1984) such that the resulting replication variance estimator is algebraically equivalent to the fully efficient linearization variance estimator for any given sampling design. We then propose a novel weight-calibration method to simultaneously achieve efficiency and sparsity in the sense that a small number of sets of replication weights can produce valid and efficient replication variance estimators for key population parameters. Our proposed method can be used in conjunction with existing resampling techniques for large-scale complex surveys. Validity of the proposed methods and extensions to some balanced sampling designs are also discussed. Simulation results showed that our proposed variance estimators perform very well in tracking coverage probabilities of confidence intervals. Our proposed strategies will likely have impact on how public-use survey data files are produced and how these data sets are analyzed.

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The National Household Survey in Brief (NHS in Brief) series documents complement the National Household Survey analytical documents by focusing on specific topics of interest.

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Survey quality is a multi-faceted concept that originates from two different development paths. One path is the total survey error paradigm that rests on four pillars providing principles that guide survey design, survey implementation, survey evaluation, and survey data analysis. We should design surveys so that the mean squared error of an estimate is minimized given budget and other constraints. It is important to take all known error sources into account, to monitor major error sources during implementation, to periodically evaluate major error sources and combinations of these sources after the survey is completed, and to study the effects of errors on the survey analysis. In this context survey quality can be measured by the mean squared error and controlled by observations made during implementation and improved by evaluation studies. The paradigm has both strengths and weaknesses. One strength is that research can be defined by error sources and one weakness is that most total survey error assessments are incomplete in the sense that it is not possible to include the effects of all the error sources. The second path is influenced by ideas from the quality management sciences. These sciences concern business excellence in providing products and services with a focus on customers and competition from other providers. These ideas have had a great influence on many statistical organizations. One effect is the acceptance among data providers that product quality cannot be achieved without a sufficient underlying process quality and process quality cannot be achieved without a good organizational quality. These levels can be controlled and evaluated by service level agreements, customer surveys, paradata analysis using statistical process control, and organizational assessment using business excellence models or other sets of criteria. All levels can be improved by conducting improvement projects chosen by means of priority functions. The ultimate goal of improvement projects is that the processes involved should gradually approach a state where they are error-free. Of course, this might be an unattainable goal, albeit one to strive for. It is not realistic to hope for continuous measurements of the total survey error using the mean squared error. Instead one can hope that continuous quality improvement using management science ideas and statistical methods can minimize biases and other survey process problems so that the variance becomes an approximation of the mean squared error. If that can be achieved we have made the two development paths approximately coincide.

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Collinearities among explanatory variables in linear regression models affect estimates from survey data just as they do in non-survey data. Undesirable effects are unnecessarily inflated standard errors, spuriously low or high t-statistics, and parameter estimates with illogical signs. The available collinearity diagnostics are not generally appropriate for survey data because the variance estimators they incorporate do not properly account for stratification, clustering, and survey weights. In this article, we derive condition indexes and variance decompositions to diagnose collinearity problems in complex survey data. The adapted diagnostics are illustrated with data based on a survey of health characteristics.

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This paper focuses on the application of graph theory to the development and testing of survey research instruments. A graph-theoretic approach offers several advantages over conventional approaches in the structure and features of a specifications system for research instruments, especially for large, computer-assisted instruments. One advantage is to verify the connectedness of all components and a second advantage is the ability to simulate an instrument. This approach also allows for the generation of measures to describe an instrument such as the number of routes and paths. The concept of a 'basis' is discussed in the context of software testing. A basis is the smallest set of paths within an instrument which covers all link-and-node pairings. These paths may be used as an economic and comprehensive set of test cases for instrument testing.

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Survey data are often used to fit linear regression models. The values of covariates used in modeling are not controlled as they might be in an experiment. Thus, collinearity among the covariates is an inevitable problem in the analysis of survey data. Although many books and articles have described the collinearity problem and proposed strategies to understand, assess and handle its presence, the survey literature has not provided appropriate diagnostic tools to evaluate its impact on regression estimation when the survey complexities are considered. We have developed variance inflation factors (VIFs) that measure the amount that variances of parameter estimators are increased due to having non-orthogonal predictors. The VIFs are appropriate for survey-weighted regression estimators and account for complex design features, e.g., weights, clusters, and strata. Illustrations of these methods are given using a probability sample from a household survey of health and nutrition.

Description: The Guide to the Job Vacancy and Wage Survey contains a dictionary of concepts and definitions, and covers topics such as survey methodology, data collection, processing, and data quality.

Description:

This paper introduces and explains modifications made to the Labour Force Survey estimates.

Description:

The Guide to the Job Vacancy and Wage Survey contains a dictionary of concepts and definitions, and includes topics such as survey methodology, data collection, processing, and data quality.

Description:

The Guide to the Labour Force Survey contains a dictionary of concepts and definitions and covers topics such as survey methodology, data collection, data processing and data quality. It also contains information on products and services, sub-provincial geography descriptions as well as the survey questionnaire.

Description:

This document brings together guidelines and checklists on many issues that need to be considered in the pursuit of quality objectives in the execution of statistical activities. Its focus is on how to assure quality through effective and appropriate design or redesign of a statistical project or program from inception through to data evaluation, dissemination and documentation. These guidelines draw on the collective knowledge and experience of many Statistics Canada employees. It is expected that Quality Guidelines will be useful to staff engaged in the planning and design of surveys and other statistical projects, as well as to those who evaluate and analyze the outputs of these projects.

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The Guide to the Job Vacancy and Wage Survey contains a dictionary of concepts and definitions, and covers topics such as survey methodology, data collection, processing, and data quality. The guide covers both components of the survey: the job vacancy component, which is quarterly, and the wage component, which is annual.

Description:

The Guide to the Job Vacancy and Wage Survey contains a dictionary of concepts and definitions, and covers topics such as survey methodology, data collection, processing, and data quality. The guide covers both components of the survey: the job vacancy component, which is quarterly, and the wage component, which is annual.

Description:

The Canadian Labour Force Survey (LFS) is the official source of monthly estimates of total employment and unemployment. Following the 2011 census, the LFS underwent a sample redesign to account for the evolution of the population and labour market characteristics, to adjust to changes in the information needs and to update the geographical information used to carry out the survey. The redesign program following the 2011 census culminated with the introduction of a new sample at the beginning of 2015. This report is a reference on the methodological aspects of the LFS, covering stratification, sampling, collection, processing, weighting, estimation, variance estimation and data quality.

Description:

The Guide to the Job Vacancy and Wage Survey contains a dictionary of concepts and definitions, and covers topics such as survey methodology, data collection, processing, and data quality. The guide covers both components of the survey: the job vacancy component, which is quarterly, and the wage component, which is annual.

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This overview document describes the conceptual underpinnings of the Integrated Business Statistics Program and explains how program components facilitate a more integrated approach to economic surveying at Statistics Canada.