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- 1. 2019 Census Content Test: Design and methodology ArchivedSurveys and statistical programs – Documentation: 98-20-00012020020Description:
This fact sheet provides detailed insight into the design and methodology of the content test component of the 2019 Census Test. This test evaluated changes to the wording and flow of some questions, as well as the potential addition of new questions, to help determine the content of the 2021 Census of Population.
Release date: 2020-07-20 - Articles and reports: 12-001-X201300111824Description:
In most surveys all sample units receive the same treatment and the same design features apply to all selected people and households. In this paper, it is explained how survey designs may be tailored to optimize quality given constraints on costs. Such designs are called adaptive survey designs. The basic ingredients of such designs are introduced, discussed and illustrated with various examples.
Release date: 2013-06-28 - 3. Adaptive network and spatial sampling ArchivedArticles and reports: 12-001-X201100211607Description:
This paper describes recent developments in adaptive sampling strategies and introduces new variations on those strategies. Recent developments described included targeted random walk designs and adaptive web sampling. These designs are particularly suited for sampling in networks; for example, for finding a sample of people from a hidden human population by following social links from sample individuals to find additional members of the hidden population to add to the sample. Each of these designs can also be translated into spatial settings to produce flexible new spatial adaptive strategies for sampling unevenly distributed populations. Variations on these sampling strategies include versions in which the network or spatial links have unequal weights and are followed with unequal probabilities.
Release date: 2011-12-21 - Articles and reports: 12-001-X201100211609Description:
This paper presents a review and assessment of the use of balanced sampling by means of the cube method. After defining the notion of balanced sample and balanced sampling, a short history of the concept of balancing is presented. The theory of the cube method is briefly presented. Emphasis is placed on the practical problems posed by balanced sampling: the interest of the method with respect to other sampling methods and calibration, the field of application, the accuracy of balancing, the choice of auxiliary variables and ways to implement the method.
Release date: 2011-12-21 - 5. Innovations in survey sampling design: Discussion of three contributions presented at the U.S. Census Bureau ArchivedArticles and reports: 12-001-X201100211610Description:
In this paper, a discussion of the three papers from the US Census Bureau special compilation is presented.
Release date: 2011-12-21 - Articles and reports: 12-001-X201100111447Description:
This paper introduces a R-package for the stratification of a survey population using a univariate stratification variable X and for the calculation of stratum sample sizes. Non iterative methods such as the cumulative root frequency method and the geometric stratum boundaries are implemented. Optimal designs, with stratum boundaries that minimize either the CV of the simple expansion estimator for a fixed sample size n or the n value for a fixed CV can be constructed. Two iterative algorithms are available to find the optimal stratum boundaries. The design can feature a user defined certainty stratum where all the units are sampled. Take-all and take-none strata can be included in the stratified design as they might lead to smaller sample sizes. The sample size calculations are based on the anticipated moments of the survey variable Y, given the stratification variable X. The package handles conditional distributions of Y given X that are either a heteroscedastic linear model, or a log-linear model. Stratum specific non-response can be accounted for in the design construction and in the sample size calculations.
Release date: 2011-06-29 - Articles and reports: 12-001-X201100111448Description:
In two-phase sampling for stratification, the second-phase sample is selected by a stratified sample based on the information observed in the first-phase sample. We develop a replication-based bias adjusted variance estimator that extends the method of Kim, Navarro and Fuller (2006). The proposed method is also applicable when the first-phase sampling rate is not negligible and when second-phase sample selection is unequal probability Poisson sampling within each stratum. The proposed method can be extended to variance estimation for two-phase regression estimators. Results from a limited simulation study are presented.
Release date: 2011-06-29 - 8. Cost efficiency of repeated cluster surveys ArchivedArticles and reports: 12-001-X201100111449Description:
We analyze the statistical and economic efficiency of different designs of cluster surveys collected in two consecutive time periods, or waves. In an independent design, two cluster samples in two waves are taken independently from one another. In a cluster-panel design, the same clusters are used in both waves, but samples within clusters are taken independently in two time periods. In an observation-panel design, both clusters and observations are retained from one wave of data collection to another. By assuming a simple population structure, we derive design variances and costs of the surveys conducted according to these designs. We first consider a situation in which the interest lies in estimation of the change in the population mean between two time periods, and derive the optimal sample allocations for the three designs of interest. We then propose the utility maximization framework borrowed from microeconomics to illustrate a possible approach to the choice of the design that strives to optimize several variances simultaneously. Incorporating the contemporaneous means and their variances tends to shift the preferences from observation-panel towards simpler panel-cluster and independent designs if the panel mode of data collection is too expensive. We present numeric illustrations demonstrating how a survey designer may want to choose the efficient design given the population parameters and data collection cost.
Release date: 2011-06-29 - Articles and reports: 12-001-X201000111249Description:
For many designs, there is a nonzero probability of selecting a sample that provides poor estimates for known quantities. Stratified random sampling reduces the set of such possible samples by fixing the sample size within each stratum. However, undesirable samples are still possible with stratification. Rejective sampling removes poor performing samples by only retaining a sample if specified functions of sample estimates are within a tolerance of known values. The resulting samples are often said to be balanced on the function of the variables used in the rejection procedure. We provide modifications to the rejection procedure of Fuller (2009a) that allow more flexibility on the rejection rules. Through simulation, we compare estimation properties of a rejective sampling procedure to those of cube sampling.
Release date: 2010-06-29 - Articles and reports: 75F0002M2010002Description:
This report compares the aggregate income estimates as published by four different statistical programs. The System of National Accounts provides a portrait of economic activity at the macro economic level. The three other programs considered generate data from a micro-economic perspective: two are survey based (Census of Population and Survey of Labour and Income Dynamics) and the third derives all its results from administrative data (Annual Estimates for Census Families and Individuals). A review of the conceptual differences across the sources is followed by a discussion of coverage issues and processing discrepancies that might influence estimates. Aggregate income estimates with adjustments where possible to account for known conceptual differences are compared. Even allowing for statistical variability, some reconciliation issues remain. These are sometimes are explained by the use of different methodologies or data gathering instruments but they sometimes also remain unexplained.
Release date: 2010-04-06
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- Articles and reports: 12-001-X201300111824Description:
In most surveys all sample units receive the same treatment and the same design features apply to all selected people and households. In this paper, it is explained how survey designs may be tailored to optimize quality given constraints on costs. Such designs are called adaptive survey designs. The basic ingredients of such designs are introduced, discussed and illustrated with various examples.
Release date: 2013-06-28 - 2. Adaptive network and spatial sampling ArchivedArticles and reports: 12-001-X201100211607Description:
This paper describes recent developments in adaptive sampling strategies and introduces new variations on those strategies. Recent developments described included targeted random walk designs and adaptive web sampling. These designs are particularly suited for sampling in networks; for example, for finding a sample of people from a hidden human population by following social links from sample individuals to find additional members of the hidden population to add to the sample. Each of these designs can also be translated into spatial settings to produce flexible new spatial adaptive strategies for sampling unevenly distributed populations. Variations on these sampling strategies include versions in which the network or spatial links have unequal weights and are followed with unequal probabilities.
Release date: 2011-12-21 - Articles and reports: 12-001-X201100211609Description:
This paper presents a review and assessment of the use of balanced sampling by means of the cube method. After defining the notion of balanced sample and balanced sampling, a short history of the concept of balancing is presented. The theory of the cube method is briefly presented. Emphasis is placed on the practical problems posed by balanced sampling: the interest of the method with respect to other sampling methods and calibration, the field of application, the accuracy of balancing, the choice of auxiliary variables and ways to implement the method.
Release date: 2011-12-21 - 4. Innovations in survey sampling design: Discussion of three contributions presented at the U.S. Census Bureau ArchivedArticles and reports: 12-001-X201100211610Description:
In this paper, a discussion of the three papers from the US Census Bureau special compilation is presented.
Release date: 2011-12-21 - Articles and reports: 12-001-X201100111447Description:
This paper introduces a R-package for the stratification of a survey population using a univariate stratification variable X and for the calculation of stratum sample sizes. Non iterative methods such as the cumulative root frequency method and the geometric stratum boundaries are implemented. Optimal designs, with stratum boundaries that minimize either the CV of the simple expansion estimator for a fixed sample size n or the n value for a fixed CV can be constructed. Two iterative algorithms are available to find the optimal stratum boundaries. The design can feature a user defined certainty stratum where all the units are sampled. Take-all and take-none strata can be included in the stratified design as they might lead to smaller sample sizes. The sample size calculations are based on the anticipated moments of the survey variable Y, given the stratification variable X. The package handles conditional distributions of Y given X that are either a heteroscedastic linear model, or a log-linear model. Stratum specific non-response can be accounted for in the design construction and in the sample size calculations.
Release date: 2011-06-29 - Articles and reports: 12-001-X201100111448Description:
In two-phase sampling for stratification, the second-phase sample is selected by a stratified sample based on the information observed in the first-phase sample. We develop a replication-based bias adjusted variance estimator that extends the method of Kim, Navarro and Fuller (2006). The proposed method is also applicable when the first-phase sampling rate is not negligible and when second-phase sample selection is unequal probability Poisson sampling within each stratum. The proposed method can be extended to variance estimation for two-phase regression estimators. Results from a limited simulation study are presented.
Release date: 2011-06-29 - 7. Cost efficiency of repeated cluster surveys ArchivedArticles and reports: 12-001-X201100111449Description:
We analyze the statistical and economic efficiency of different designs of cluster surveys collected in two consecutive time periods, or waves. In an independent design, two cluster samples in two waves are taken independently from one another. In a cluster-panel design, the same clusters are used in both waves, but samples within clusters are taken independently in two time periods. In an observation-panel design, both clusters and observations are retained from one wave of data collection to another. By assuming a simple population structure, we derive design variances and costs of the surveys conducted according to these designs. We first consider a situation in which the interest lies in estimation of the change in the population mean between two time periods, and derive the optimal sample allocations for the three designs of interest. We then propose the utility maximization framework borrowed from microeconomics to illustrate a possible approach to the choice of the design that strives to optimize several variances simultaneously. Incorporating the contemporaneous means and their variances tends to shift the preferences from observation-panel towards simpler panel-cluster and independent designs if the panel mode of data collection is too expensive. We present numeric illustrations demonstrating how a survey designer may want to choose the efficient design given the population parameters and data collection cost.
Release date: 2011-06-29 - Articles and reports: 12-001-X201000111249Description:
For many designs, there is a nonzero probability of selecting a sample that provides poor estimates for known quantities. Stratified random sampling reduces the set of such possible samples by fixing the sample size within each stratum. However, undesirable samples are still possible with stratification. Rejective sampling removes poor performing samples by only retaining a sample if specified functions of sample estimates are within a tolerance of known values. The resulting samples are often said to be balanced on the function of the variables used in the rejection procedure. We provide modifications to the rejection procedure of Fuller (2009a) that allow more flexibility on the rejection rules. Through simulation, we compare estimation properties of a rejective sampling procedure to those of cube sampling.
Release date: 2010-06-29 - Articles and reports: 75F0002M2010002Description:
This report compares the aggregate income estimates as published by four different statistical programs. The System of National Accounts provides a portrait of economic activity at the macro economic level. The three other programs considered generate data from a micro-economic perspective: two are survey based (Census of Population and Survey of Labour and Income Dynamics) and the third derives all its results from administrative data (Annual Estimates for Census Families and Individuals). A review of the conceptual differences across the sources is followed by a discussion of coverage issues and processing discrepancies that might influence estimates. Aggregate income estimates with adjustments where possible to account for known conceptual differences are compared. Even allowing for statistical variability, some reconciliation issues remain. These are sometimes are explained by the use of different methodologies or data gathering instruments but they sometimes also remain unexplained.
Release date: 2010-04-06 - Articles and reports: 11-522-X200800010988Description:
Online data collection emerged in 1995 as an alternative approach for conducting certain types of consumer research studies and has grown in 2008. This growth has been primarily in studies where non-probability sampling methods are used. While online sampling has gained acceptance for some research applications, serious questions remain concerning online samples' suitability for research requiring precise volumetric measurement of the behavior of the U.S. population, particularly their travel behavior. This paper reviews literature and compares results from studies using probability samples and online samples to understand whether results differ from the two sampling approaches. The paper also demonstrates that online samples underestimate critical types of travel even after demographic and geographic weighting.
Release date: 2009-12-03
Reference (5)
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- 1. 2019 Census Content Test: Design and methodology ArchivedSurveys and statistical programs – Documentation: 98-20-00012020020Description:
This fact sheet provides detailed insight into the design and methodology of the content test component of the 2019 Census Test. This test evaluated changes to the wording and flow of some questions, as well as the potential addition of new questions, to help determine the content of the 2021 Census of Population.
Release date: 2020-07-20 - Surveys and statistical programs – Documentation: 71F0031X2005002Description:
This paper introduces and explains modifications made to the Labour Force Survey estimates in January 2005. Some of these modifications include the adjustment of all LFS estimates to reflect population counts based on the 2001 Census, updates to industry and occupation classification systems and sample redesign changes.
Release date: 2005-01-26 - 3. Sampling and Weighting (Reference Products: Technical Reports: 1996 Census of Population) ArchivedSurveys and statistical programs – Documentation: 92-371-XDescription:
This report deals with sampling and weighting, a process whereby certain characteristics are collected and processed for a random sample of dwellings and persons identified in the complete census enumeration. Data for the whole population are then obtained by scaling up the results for the sample to the full population level. The use of sampling may lead to substantial reductions in costs and respondent burden, or alternatively, can allow the scope of a census to be broadened at the same cost.
Release date: 1999-12-07 - Notices and consultations: 13F0026M1999002Description:
This document summarizes the comments and feedback received on an earlier document: Towards a new Canadian asset and debt survey - A content discussion paper. The new asset and debt survey (now called the Survey of Financial Security) is to update the wealth information on Canadian families and unattached individuals. Since the last data collection was conducted in 1984, it was essential to include a consultative process in the development of the survey in order to obtain feedback on issues of concern and to define the conceptual framework for the survey.
Comments on the content discussion paper are summarized by major theme and sections indicate how the suggestions are being incorporated into the survey or why they could not be incorporated. This paper also mentions the main objectives of the survey and provides an overview of the survey content, revised according to the feedback from the discussion paper.
Release date: 1999-03-23 - Surveys and statistical programs – Documentation: 75F0002M1994001Description:
This paper describes the Survey of Labour and Income Dynamics (SLID) following rules, which govern who is traced and who is interviewed. It also outlines the conceptual basis for these procedures.
Release date: 1995-12-30
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