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  • Surveys and statistical programs – Documentation: 62F0026M2010004
    Description:

    This report describes the quality indicators produced for the 2007 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.

    Release date: 2010-12-13

  • Surveys and statistical programs – Documentation: 62F0026M2010005
    Description:

    This report describes the quality indicators produced for the 2008 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.

    Release date: 2010-12-13

  • Articles and reports: 12-001-X201000111246
    Description:

    Many surveys employ weight adjustment procedures to reduce nonresponse bias. These adjustments make use of available auxiliary data. This paper addresses the issue of jackknife variance estimation for estimators that have been adjusted for nonresponse. Using the reverse approach for variance estimation proposed by Fay (1991) and Shao and Steel (1999), we study the effect of not re-calculating the nonresponse weight adjustment within each jackknife replicate. We show that the resulting 'shortcut' jackknife variance estimator tends to overestimate the true variance of point estimators in the case of several weight adjustment procedures used in practice. These theoretical results are confirmed through a simulation study where we compare the shortcut jackknife variance estimator with the full jackknife variance estimator obtained by re-calculating the nonresponse weight adjustment within each jackknife replicate.

    Release date: 2010-06-29

  • Articles and reports: 12-001-X201000111252
    Description:

    Nonresponse bias has been a long-standing issue in survey research (Brehm 1993; Dillman, Eltinge, Groves and Little 2002), with numerous studies seeking to identify factors that affect both item and unit response. To contribute to the broader goal of minimizing survey nonresponse, this study considers several factors that can impact survey nonresponse, using a 2007 Animal Welfare Survey Conducted in Ohio, USA. In particular, the paper examines the extent to which topic salience and incentives affect survey participation and item nonresponse, drawing on the leverage-saliency theory (Groves, Singer and Corning 2000). We find that participation in a survey is affected by its subject context (as this exerts either positive or negative leverage on sampled units) and prepaid incentives, which is consistent with the leverage-saliency theory. Our expectations are also confirmed by the finding that item nonresponse, our proxy for response quality, does vary by proximity to agriculture and the environment (residential location, knowledge about how food is grown, and views about the importance of animal welfare). However, the data suggests that item nonresponse does not vary according to whether or not a respondent received incentives.

    Release date: 2010-06-29

  • Articles and reports: 11-522-X200800010976
    Description:

    Many survey organizations use the response rate as an indicator for the quality of survey data. As a consequence, a variety of measures are implemented to reduce non-response or to maintain response at an acceptable level. However, the response rate is not necessarily a good indicator of non-response bias. A higher response rate does not imply smaller non-response bias. What matters is how the composition of the response differs from the composition of the sample as a whole. This paper describes the concept of R-indicators to assess potential differences between the sample and the response. Such indicators may facilitate analysis of survey response over time, between various fieldwork strategies or data collection modes. Some practical examples are given.

    Release date: 2009-12-03

  • Articles and reports: 11-522-X200800010984
    Description:

    The Enterprise Portfolio Manager (EPM) Program at Statistics Canada demonstrated the value of employing a "holistic" approach to managing the relationships we have with our largest and most complex business respondents.

    Understanding that different types of respondents should receive different levels of intervention and having learnt the value of employing an "enterprise-centric" approach to managing relationships with important, complex data providers, STC has embraced a response management strategy that divides its business population into four tiers based on size, complexity and importance to survey estimates. Thus segmented, different response management approaches have been developed appropriate to the relative contribution of the segment. This allows STC to target resources to the areas where it stands to achieve the greatest return on investment. Tier I and Tier II have been defined as critical to survey estimates.

    Tier I represent the largest, most complex businesses in Canada and is managed through the Enterprise Portfolio Management Program.

    Tier II represents businesses that are smaller or less complex than Tier I but still significant in developing accurate measures of the activities of individual industries.

    Tier III includes more medium-sized businesses, those that form the bulk of survey samples.

    Tier IV represents the smallest businesses which are excluded from collection; for these STC relies entirely on tax information.

    The presentation will outline:It works! Results and metrics from the programs that have operationalized the Holistic Response Management strategy.Developing a less subjective, methodological approach to segment the business survey population for HRM. The project team's work to capture the complexity factors intrinsically used by experienced staff to rank respondents. What our so called "problem" respondents have told us about the issues underlying non-response.

    Release date: 2009-12-03

  • Articles and reports: 11-522-X200800010999
    Description:

    The choice of number of call attempts in a telephone survey is an important decision. A large number of call attempts makes the data collection costly and time-consuming; and a small number of attempts decreases the response set from which conclusions are drawn and increases the variance. The decision can also have an effect on the nonresponse bias. In this paper we study the effects of number of call attempts on the nonresponse rate and the nonresponse bias in two surveys conducted by Statistics Sweden: The Labour Force Survey (LFS) and Household Finances (HF).

    By use of paradata we calculate the response rate as a function of the number of call attempts. To estimate the nonresponse bias we use estimates of some register variables, where observations are available for both respondents and nonrespondents. We also calculate estimates of some real survey parameters as functions of varying number of call attempts. The results indicate that it is possible to reduce the current number of call attempts without getting an increased nonresponse bias.

    Release date: 2009-12-03

  • Articles and reports: 11-536-X200900110804
    Description:

    This paper deals with calibration estimation for surveys with nonresponse. Efficient weighting adjustment for unit nonresponse requires powerful auxiliary information. The weights in the calibration estimator are computed on information about a specified auxiliary vector. Even with the "best possible" auxiliary vector, some bias remains in the estimator. An indicator of the remaining bias is presented and analyzed.

    The many potential auxiliary variables allow the statistician to compose a wide variety of possible auxiliary vectors. The need arises to compare these vectors to assess their effectiveness for bias reduction. To this end we examine an indicator useful for ranking alternative auxiliary vectors in regard to their ability to reduce the bias. The indicator is computed on the auxiliary vector values for the sampled units, responding and nonresponding. An advantage is its independence of the study variables, of which there are many in a large survey.

    The properties of the indicator are examined in empirical studies. A synthetic population is constructed and potential auxiliary vectors are ranked with the aid of the indicator. Another empirical illustration illustrates how the indicator is used for selecting auxiliary variables in a large survey at Statistics Sweden.

    Release date: 2009-08-11

  • Articles and reports: 11-536-X200900110805
    Description:

    The estimation of a finite population distribution function is considered in the presence of nonresponse. An imputation approach is discussed which may also be interpreted as a form of weighted estimation. It is assumed that there are complete measurements on at least one auxiliary variable which is strongly related to the variable of interest. The paper is motivated by an application to the estimation of the distribution of hourly pay using data from the Labour Force Survey in the United Kingdom. In this case the main auxiliary variable is a proxy measure of the variable of interest. Techniques discussed include predictive mean matching, nearest neighbour imputation, fractional imputation and propensity score matching. Some theoretical and numerical properties of alternative procedures will be discussed.

    Release date: 2009-08-11

  • Articles and reports: 11-536-X200900110813
    Description:

    The National Agricultural Statistics Service (NASS) has increasingly been using a delete-a-group (DAG) jackknife to estimate variances. In surveys where this technique is used, each sampled element is given 16 weights: the element's actual sampling weight after incorporating all nonresponse and calibration adjustments and 15 jackknife replicate weights. NASS recommends constructing confidence intervals for univariate statistics assuming its DAG jackknife has 14 degrees of freedom. This paper discusses methods of modifying the DAG jackknife to reduce the potential finite-sample bias. It also describes a method of measuring the effective degrees of freedom in situations where the NASS recommendation of 14 may be too generous.

    Release date: 2009-08-11
Data (7)

Data (7) ((7 results))

  • Public use microdata: 66M0001X
    Description: Records relate to the activities of Canadians travelling outside the country and visitors to Canada: Canadian residents; travellers; non-residents; expenditures; length of stay; type of transportation; purpose of trip; accommodation used; places visited; expenditure by categories.

    International travel data are collected in two flows: Canadian returning from abroad; visitors from the USA and from other countries to Canada.

    Release date: 2019-12-24

  • Public use microdata: 82M0020X
    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.
    Release date: 2018-11-01

  • Public use microdata: 89M0017X
    Description:

    The public use microdata file from the 2010 Canada Survey of Giving, Volunteering and Participating is now available. This file contains information collected from nearly 15,000 respondents aged 15 and over residing in private households in the provinces.The public use microdata file provides provincial-level information about the ways in which Canadians donate money and in-kind gifts to charitable and nonprofit organizations; volunteer their time to these organizations; provide help directly to others. Socio-demographic, income and labour force data are also included on the file.

    Release date: 2012-05-04

  • Public use microdata: 71M0016X
    Description:

    The Public Service Employee Survey was designed to solicit the views of Public Service employees on their work environment and overall job satisfaction. Employees expressed their opinions on their work units, their communications with their supervisors, skills and career aspirations, client services and labour management relations. General information such as age, gender, years of service and province of work were collected and questions were asked on specific themes such as staffing fairness, official languages, health and safety, harassment and discrimination and retention issues. The results were aggregated at the department and Public Service-wide levels. The survey ensures a measurement capacity between the 1999, 2002 and 2005 questionnaires.

    In 2008, the 2005 questionnaire was used as the basis for the survey. New questions were added to construct an employee engagement model that will be used to evaluate each organization. As well, the scale of the response category was increased from 4 to 5 to include a neutral category.

    Release date: 2012-03-19

  • Public use microdata: 56M0002G
    Description:

    This guide is for the Household Internet Use Survey microdata file. The Household Internet Use Survey is being conducted by Statistics Canada on behalf of Industry Canada. The information from this survey will assist the Science and Technology Redesign Project at Statistics Canada to fulfil a three-year contractual agreement between them and the Telecommunications and Policy Branch of Industry Canada. The Household Internet Use Survey is a voluntary survey. It will provide information on the use of computers for communication purposes, and households' access and use of the Internet from home.

    The objective of this survey is to measure the demand for telecommunications services by Canadian households. To assess the demand, we measure the frequency and intensity of use of what is commonly referred to as "the information highway" among other things. This was done by asking questions relating to the accessibility of the Internet to Canadian households both at home, the workplace and a number of other locations. The information collected will be used to update and expand upon previous studies done by Statistics Canada on the topic of the Information Highway.

    Release date: 2004-09-28

  • Public use microdata: 81M0013X
    Description:

    The Adult Education and Training Survey (AETS) is Canada's most comprehensive source of data on individual participation in formal adult education and training. It is the only Canadian survey to collect detailed information about the skill development efforts of the entire adult Canadian population. The AETS provides information about the main subject of training activities, their provider, duration and the sources and types of support for training. Furthermore, the AETS allows for the examination of the socio-economic and demographic profiles of both training participants and non-participants. This survey also identifies barriers faced by individuals who wish to take some form of training but cannot. The AETS was administered three times during the 1990s, in 1992, 1994 and 1998, as a supplement to the Labour Force Survey (LFS).

    The content of the AETS was revised to take into account recommendations coming from consultation exercises. As a result, more than half of the 2003 survey is made up of new questions and the target population has been modified.

    The main objectives are:1) To measure the incidence and intensity of adults' participation in job-related formal training.2) To profile employer support to job-related formal training.3) To analyze the aspects of job-related training activities such as: training provider, expenses, financial support, motivations, outcomes and difficulties experienced while training.4) To identify the barriers preventing individuals from participating in the job-related formal training they want or need to take.5) To identify reasons explaining adults' lack of participation and of interest in job-related formal training.6) To relate adults' current participation patterns to their past involvement in and plans about future participation in job-related training.7) To measure the incidence and frequency of adults' participation in job-related informal training.8) To examine the interactions between participation in formal and informal job-related training.

    The population covered by the AETS consists of Canadians 25 years of age and older. This is a change from the population previously targeted by the AETS, which consisted of Canadians aged 17 years of age and older. A primary consideration for this change was the practical difficulties in applying the definition of adult education to individuals in the 17 to 24 years of age group. By definition, adult education excludes students who are still involved in their first or initial stage of schooling. As previous AETS did not precisely identify students still in their initial stage of schooling, analyses using these data had to rely on an ad hoc definition of adult learners. According to this definition, individuals aged 17 to 24 who were not in one of the following situations were excluded from the analysis: full-time students subsidized by an employer and full-time students over 19 enrolled in elementary or secondary programs.

    Release date: 2004-05-27

  • Public use microdata: 82M0009X
    Description:

    The National Population Health Survey (NPHS) used the Labour Force Survey sampling frame to draw the initial sample of approximately 20,000 households starting in 1994 and for the sample top-up this third cycle. The survey is conducted every two years. The sample collection is distributed over four quarterly periods followed by a follow-up period and the whole process takes a year. In each household, some limited health information is collected from all household members and one person in each household is randomly selected for a more in-depth interview.

    The survey is designed to collect information on the health of the Canadian population and related socio-demographic information. The first cycle of data collection began in 1994, and continues every second year thereafter. The survey is designed to produce both cross-sectional and longitudinal estimates. The questionnaires includes content related to health status, use of health services, determinants of health, a health index, chronic conditions and activity restrictions. The use of health services is probed through visits to health care providers, both traditional and non-traditional, and the use of drugs and other mediciations. Health determinants include smoking, alcohol use and physical activity. A special focus content for this cycle includes family medical history with questions about certain chronic conditions among immediate family members and when they were acquired. As well, a section on self care has also been included this cycle. The socio-demographic information includes age, sex, education, ethnicity, household income and labour force status.

    Release date: 2000-12-19
Analysis (103)

Analysis (103) (0 to 10 of 103 results)

  • Journals and periodicals: 62F0026M
    Description: This series provides detailed documentation on the issues, concepts, methodology, data quality and other relevant research related to household expenditures from the Survey of Household Spending, the Homeowner Repair and Renovation Survey and the Food Expenditure Survey.
    Release date: 2023-10-18

  • Articles and reports: 89-648-X2022001
    Description:

    This report explores the size and nature of the attrition challenges faced by the Longitudinal and International Study of Adults (LISA) survey, as well as the use of a non-response weight adjustment and calibration strategy to mitigate the effects of attrition on the LISA estimates. The study focuses on data from waves 1 (2012) to 4 (2018) and uses practical examples based on selected demographic variables, to illustrate how attrition be assessed and treated.

    Release date: 2022-11-14

  • Articles and reports: 12-001-X201800254957
    Description:

    When a linear imputation method is used to correct non-response based on certain assumptions, total variance can be assigned to non-responding units. Linear imputation is not as limited as it seems, given that the most common methods – ratio, donor, mean and auxiliary value imputation – are all linear imputation methods. We will discuss the inference framework and the unit-level decomposition of variance due to non-response. Simulation results will also be presented. This decomposition can be used to prioritize non-response follow-up or manual corrections, or simply to guide data analysis.

    Release date: 2018-12-20

  • Articles and reports: 12-001-X201600114546
    Description:

    Adjusting the base weights using weighting classes is a standard approach for dealing with unit nonresponse. A common approach is to create nonresponse adjustments that are weighted by the inverse of the assumed response propensity of respondents within weighting classes under a quasi-randomization approach. Little and Vartivarian (2003) questioned the value of weighting the adjustment factor. In practice the models assumed are misspecified, so it is critical to understand the impact of weighting might have in this case. This paper describes the effects on nonresponse adjusted estimates of means and totals for population and domains computed using the weighted and unweighted inverse of the response propensities in stratified simple random sample designs. The performance of these estimators under different conditions such as different sample allocation, response mechanism, and population structure is evaluated. The findings show that for the scenarios considered the weighted adjustment has substantial advantages for estimating totals and using an unweighted adjustment may lead to serious biases except in very limited cases. Furthermore, unlike the unweighted estimates, the weighted estimates are not sensitive to how the sample is allocated.

    Release date: 2016-06-22

  • Articles and reports: 12-001-X201500114162
    Description:

    The operationalization of the Population and Housing Census in Portugal is managed by a hierarchical structure in which Statistics Portugal is at the top and local government institutions at the bottom. When the Census takes place every ten years, local governments are asked to collaborate with Statistics Portugal in the execution and monitoring of the fieldwork operations at the local level. During the Pilot Test stage of the 2011 Census, local governments were asked for additional collaboration: to answer the Perception of Risk survey, whose aim was to gather information to design a quality assurance instrument that could be used to monitor the Census operations. The response rate of the survey was desired to be 100%, however, by the deadline of data collection nearly a quarter of local governments had not responded to the survey and thus a decision was made to make a follow up mailing. In this paper, we examine whether the same conclusions could have been reached from survey without follow ups as with them and evaluate the influence of follow ups on the conception of the quality assurance instrument. Comparison of responses on a set of perception variables revealed that local governments answering previous or after the follow up did not differ. However the configuration of the quality assurance instrument changed when including follow up responses.

    Release date: 2015-06-29

  • Articles and reports: 12-001-X201500114173
    Description:

    Nonresponse is present in almost all surveys and can severely bias estimates. It is usually distinguished between unit and item nonresponse. By noting that for a particular survey variable, we just have observed and unobserved values, in this work we exploit the connection between unit and item nonresponse. In particular, we assume that the factors that drive unit response are the same as those that drive item response on selected variables of interest. Response probabilities are then estimated using a latent covariate that measures the will to respond to the survey and that can explain a part of the unknown behavior of a unit to participate in the survey. This latent covariate is estimated using latent trait models. This approach is particularly relevant for sensitive items and, therefore, can handle non-ignorable nonresponse. Auxiliary information known for both respondents and nonrespondents can be included either in the latent variable model or in the response probability estimation process. The approach can also be used when auxiliary information is not available, and we focus here on this case. We propose an estimator using a reweighting system based on the previous latent covariate when no other observed auxiliary information is available. Results on its performance are encouraging from simulation studies on both real and simulated data.

    Release date: 2015-06-29

  • Articles and reports: 12-001-X201400214091
    Description:

    Parametric fractional imputation (PFI), proposed by Kim (2011), is a tool for general purpose parameter estimation under missing data. We propose a fractional hot deck imputation (FHDI) which is more robust than PFI or multiple imputation. In the proposed method, the imputed values are chosen from the set of respondents and assigned proper fractional weights. The weights are then adjusted to meet certain calibration conditions, which makes the resulting FHDI estimator efficient. Two simulation studies are presented to compare the proposed method with existing methods.

    Release date: 2014-12-19

  • Articles and reports: 12-001-X201200211753
    Description:

    Nonresponse in longitudinal studies often occurs in a nonmonotone pattern. In the Survey of Industrial Research and Development (SIRD), it is reasonable to assume that the nonresponse mechanism is past-value-dependent in the sense that the response propensity of a study variable at time point t depends on response status and observed or missing values of the same variable at time points prior to t. Since this nonresponse is nonignorable, the parametric likelihood approach is sensitive to the specification of parametric models on both the joint distribution of variables at different time points and the nonresponse mechanism. The nonmonotone nonresponse also limits the application of inverse propensity weighting methods. By discarding all observed data from a subject after its first missing value, one can create a dataset with a monotone ignorable nonresponse and then apply established methods for ignorable nonresponse. However, discarding observed data is not desirable and it may result in inefficient estimators when many observed data are discarded. We propose to impute nonrespondents through regression under imputation models carefully created under the past-value-dependent nonresponse mechanism. This method does not require any parametric model on the joint distribution of the variables across time points or the nonresponse mechanism. Performance of the estimated means based on the proposed imputation method is investigated through some simulation studies and empirical analysis of the SIRD data.

    Release date: 2012-12-19

  • Articles and reports: 75-001-X201200111629
    Geography: Canada
    Description:

    This article investigates the factors associated with voting during the May 2011 federal election. Voting rates are examined across personal, family and labour market characteristics. Multivariate techniques are used to account for many of the characteristics associated with voting. The study is based on several supplemental questions, commissioned by Elections Canada, that were added to the May Labour Force Survey. Voting trends and international comparisons, based on administrative data, are also presented.

    Release date: 2012-02-24

  • Articles and reports: 12-001-X201100111443
    Description:

    Dual frame telephone surveys are becoming common in the U.S. because of the incompleteness of the landline frame as people transition to cell phones. This article examines nonsampling errors in dual frame telephone surveys. Even though nonsampling errors are ignored in much of the dual frame literature, we find that under some conditions substantial biases may arise in dual frame telephone surveys due to these errors. We specifically explore biases due to nonresponse and measurement error in these telephone surveys. To reduce the bias resulting from these errors, we propose dual frame sampling and weighting methods. The compositing factor for combining the estimates from the two frames is shown to play an important role in reducing nonresponse bias.

    Release date: 2011-06-29
Reference (22)

Reference (22) (0 to 10 of 22 results)

  • Surveys and statistical programs – Documentation: 98-26-0006
    Description:

    These guidelines provide information to help people effectively use and interpret the data quality indicators for the 2021 Census.

    Release date: 2022-09-21

  • Surveys and statistical programs – Documentation: 12-539-X
    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.

    Release date: 2019-12-04

  • Surveys and statistical programs – Documentation: 71-526-X
    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.

    Release date: 2017-12-21

  • Surveys and statistical programs – Documentation: 62F0026M2011001
    Description:

    This report describes the quality indicators produced for the 2009 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.

    Release date: 2011-06-16

  • Surveys and statistical programs – Documentation: 62F0026M2010004
    Description:

    This report describes the quality indicators produced for the 2007 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.

    Release date: 2010-12-13

  • Surveys and statistical programs – Documentation: 62F0026M2010005
    Description:

    This report describes the quality indicators produced for the 2008 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.

    Release date: 2010-12-13

  • Surveys and statistical programs – Documentation: 62F0026M2005006
    Description:

    This report describes the quality indicators produced for the 2003 Survey of Household Spending. These quality indicators, such as coefficients of variation, nonresponse rates, slippage rates and imputation rates, help users interpret the survey data.

    Release date: 2005-10-06

  • Surveys and statistical programs – Documentation: 75F0002M2005009
    Description:

    The release of the 2003 data from the Survey of Labour and Income Dynamics (SLID) was accompanied by a historical revision which accomplished three things. First, the survey weights were updated to take into account new population projections based on the 2001 Census of Population, instead of the 1996 Census. Second, a new procedure in the weight adjustments was introduced to take into account an external source of information on the overall distribution of income in the population, namely the T4 file of employer remittances to Canada Revenue Agency. Third, the low income estimates were revised due to new low income cut-offs (LICOs). This paper describes the second of these improvements' the new weighting procedure to reflect the distribution of income in the population with greater accuracy. Part 1 explains in non-technical terms how this new procedure came about and how it works. Part 2 provides some examples of the impacts on the results for previous years.

    Release date: 2005-07-22

  • Surveys and statistical programs – Documentation: 62F0026M2005002
    Description:

    This document will provide an overview of the differences between the old and the new weighting methodologies and the effect of the new weighting system on estimations.

    Release date: 2005-06-30

  • Surveys and statistical programs – Documentation: 92-393-X
    Description:

    This report is a brief guide to users of census income data. It provides a general description of the various 2001 Census phases, from data collection, through processing for non-response, to dissemination. Descriptions of, and summary data on, the changes to income data that occurred during the processing stages are given. Comparative data from national accounts and tax data sources at a highly aggregated level are also presented to put the quality of the 2001 Census income data into perspective. For users wishing to compare census income data over time, changes in income content and universe coverage over the years are explained. Finally, a complete description of all census products containing income data is also supplied.

    Release date: 2004-09-16
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