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Survey or statistical program
- Census of Population (2)
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All (53)
All (53) (20 to 30 of 53 results)
- Articles and reports: 11-536-X200900110806Description:
Recent work using a pseudo empirical likelihood (EL) method for finite population inferences with complex survey data focused primarily on a single survey sample, non-stratified or stratified, with considerable effort devoted to computational procedures. In this talk we present a pseudo empirical likelihood approach to inference from multiple surveys and multiple-frame surveys, two commonly encountered problems in survey practice. We show that inferences about the common parameter of interest and the effective use of various types of auxiliary information can be conveniently carried out through the constrained maximization of joint pseudo EL function. We obtain asymptotic results which are used for constructing the pseudo EL ratio confidence intervals, either using a chi-square approximation or a bootstrap calibration. All related computational problems can be handled using existing algorithms on stratified sampling after suitable re-formulation.
Release date: 2009-08-11 - Articles and reports: 11-536-X200900110808Description:
Let auxiliary information be available for use in designing of a survey sample. Let the sample selection procedure consist of selecting a probability sample, rejecting the sample if the sample mean of an auxiliary variable is not within a specified distance of the population mean, continuing until a sample is accepted. It is proven that the large sample properties of the regression estimator for the rejective sample are the same as those of the regression estimator for the original selection procedure. Likewise the usual estimator of variance for the regression estimator is appropriate for the rejective sample. In a Monte Carlo experiment, the large sample properties hold for relatively small samples. Also the Monte Carlo results are in agreement with the theoretical orders of approximation. The efficiency effect of the described rejective sampling is o(n-1) relative to regression estimation without rejection, but the effect can be important for particular samples.
Release date: 2009-08-11 - 23. Endogenous post-stratification in surveys ArchivedArticles and reports: 11-536-X200900110810Description:
Post-stratification is frequently used to improve the precision of survey estimators when categorical auxiliary information is available from sources outside the survey. In natural resource surveys, such information is often obtained from remote sensing data, classified into categories and displayed as pixel-based maps. These maps may be constructed based on classification models fitted to the sample data. Post-stratification of the sample data based on categories derived from the sample data ("endogenous post-stratification") violates several standard post-stratification assumptions, and has been generally considered invalid as a design-based estimation method. In this presentation, properties of the endogenous post-stratification estimator are derived for the case of a sample-fitted generalized linear model. Design consistency of the endogenous post-stratification estimator is established under mild conditions. Under a superpopulation model, consistency and asymptotic normality of the endogenous post-stratification estimator are established. Simulation experiments demonstrate that the practical effect of first fitting a model to the survey data before post-stratifying is small, even for relatively small sample sizes.
Release date: 2009-08-11 - Articles and reports: 12-001-X200800210757Description:
Sample weights can be calibrated to reflect the known population totals of a set of auxiliary variables. Predictors of finite population totals calculated using these weights have low bias if these variables are related to the variable of interest, but can have high variance if too many auxiliary variables are used. This article develops an "adaptive calibration" approach, where the auxiliary variables to be used in weighting are selected using sample data. Adaptively calibrated estimators are shown to have lower mean squared error and better coverage properties than non-adaptive estimators in many cases.
Release date: 2008-12-23 - Articles and reports: 12-001-X200800210759Description:
The analysis of stratified multistage sample data requires the use of design information such as stratum and primary sampling unit (PSU) identifiers, or associated replicate weights, in variance estimation. In some public release data files, such design information is masked as an effort to avoid their disclosure risk and yet to allow the user to obtain valid variance estimation. For example, in area surveys with a limited number of PSUs, the original PSUs are split or/and recombined to construct pseudo-PSUs with swapped second or subsequent stage sampling units. Such PSU masking methods, however, obviously distort the clustering structure of the sample design, yielding biased variance estimates possibly with certain systematic patterns between two variance estimates from the unmasked and masked PSU identifiers. Some of the previous work observed patterns in the ratio of the masked and unmasked variance estimates when plotted against the unmasked design effect. This paper investigates the effect of PSU masking on variance estimates under cluster sampling regarding various aspects including the clustering structure and the degree of masking. Also, we seek a PSU masking strategy through swapping of subsequent stage sampling units that helps reduce the resulting biases of the variance estimates. For illustration, we used data from the National Health Interview Survey (NHIS) with some artificial modification. The proposed strategy performs very well in reducing the biases of variance estimates. Both theory and empirical results indicate that the effect of PSU masking on variance estimates is modest with minimal swapping of subsequent stage sampling units. The proposed masking strategy has been applied to the 2003-2004 National Health and Nutrition Examination Survey (NHANES) data release.
Release date: 2008-12-23 - Articles and reports: 12-001-X200800210760Description:
The design of a stratified simple random sample without replacement from a finite population deals with two main issues: the definition of a rule to partition the population into strata, and the allocation of sampling units in the selected strata. This article examines a tree-based strategy which plans to approach jointly these issues when the survey is multipurpose and multivariate information, quantitative or qualitative, is available. Strata are formed through a hierarchical divisive algorithm that selects finer and finer partitions by minimizing, at each step, the sample allocation required to achieve the precision levels set for each surveyed variable. In this way, large numbers of constraints can be satisfied without drastically increasing the sample size, and also without discarding variables selected for stratification or diminishing the number of their class intervals. Furthermore, the algorithm tends not to define empty or almost empty strata, thus avoiding the need for strata collapsing aggregations. The procedure was applied to redesign the Italian Farm Structure Survey. The results indicate that the gain in efficiency held using our strategy is nontrivial. For a given sample size, this procedure achieves the required precision by exploiting a number of strata which is usually a very small fraction of the number of strata available when combining all possible classes from any of the covariates.
Release date: 2008-12-23 - 27. A balanced sampling approach for multi-way stratification designs for small area estimation ArchivedArticles and reports: 12-001-X200800210763Description:
The present work illustrates a sampling strategy useful for obtaining planned sample size for domains belonging to different partitions of the population and in order to guarantee the sampling errors of domain estimates be lower than given thresholds. The sampling strategy that covers the multivariate multi-domain case is useful when the overall sample size is bounded and consequently the standard solution of using a stratified sample with the strata given by cross-classification of variables defining the different partitions is not feasible since the number of strata is larger than the overall sample size. The proposed sampling strategy is based on the use of balanced sampling selection technique and on a GREG-type estimation. The main advantages of the solution is the computational feasibility which allows one to easily implement an overall small area strategy considering jointly the sampling design and the estimator and improving the efficiency of the direct domain estimators. An empirical simulation on real population data and different domain estimators shows the empirical properties of the examined sample strategy.
Release date: 2008-12-23 - 28. Bayesian estimation in small areas when the sampling design strata differ from the study domains ArchivedArticles and reports: 12-001-X200700210495Description:
The purpose of this work is to obtain reliable estimates in study domains when there are potentially very small sample sizes and the sampling design stratum differs from the study domain. The population sizes are unknown as well for both the study domain and the sampling design stratum. In calculating parameter estimates in the study domains, a random sample size is often necessary. We propose a new family of generalized linear mixed models with correlated random effects when there is more than one unknown parameter. The proposed model will estimate both the population size and the parameter of interest. General formulae for full conditional distributions required for Markov chain Monte Carlo (MCMC) simulations are given for this framework. Equations for Bayesian estimation and prediction at the study domains are also given. We apply the 1998 Missouri Turkey Hunting Survey, which stratified samples based on the hunter's place of residence and we require estimates at the domain level, defined as the county in which the turkey hunter actually hunted.
Release date: 2008-01-03 - Articles and reports: 12-001-X200700210498Description:
In this paper we describe a methodology for combining a convenience sample with a probability sample in order to produce an estimator with a smaller mean squared error (MSE) than estimators based on only the probability sample. We then explore the properties of the resulting composite estimator, a linear combination of the convenience and probability sample estimators with weights that are a function of bias. We discuss the estimator's properties in the context of web-based convenience sampling. Our analysis demonstrates that the use of a convenience sample to supplement a probability sample for improvements in the MSE of estimation may be practical only under limited circumstances. First, the remaining bias of the estimator based on the convenience sample must be quite small, equivalent to no more than 0.1 of the outcome's population standard deviation. For a dichotomous outcome, this implies a bias of no more than five percentage points at 50 percent prevalence and no more than three percentage points at 10 percent prevalence. Second, the probability sample should contain at least 1,000-10,000 observations for adequate estimation of the bias of the convenience sample estimator. Third, it must be inexpensive and feasible to collect at least thousands (and probably tens of thousands) of web-based convenience observations. The conclusions about the limited usefulness of convenience samples with estimator bias of more than 0.1 standard deviations also apply to direct use of estimators based on that sample.
Release date: 2008-01-03 - Articles and reports: 12-001-X20070019849Description:
In sample surveys where units have unequal probabilities of inclusion in the sample, associations between the probability of inclusion and the statistic of interest can induce bias. Weights equal to the inverse of the probability of inclusion are often used to counteract this bias. Highly disproportional sample designs have large weights, which can introduce undesirable variability in statistics such as the population mean estimator or population regression estimator. Weight trimming reduces large weights to a fixed cutpoint value and adjusts weights below this value to maintain the untrimmed weight sum, reducing variability at the cost of introducing some bias. Most standard approaches are ad-hoc in that they do not use the data to optimize bias-variance tradeoffs. Approaches described in the literature that are data-driven are a little more efficient than fully-weighted estimators. This paper develops Bayesian methods for weight trimming of linear and generalized linear regression estimators in unequal probability-of-inclusion designs. An application to estimate injury risk of children rear-seated in compact extended-cab pickup trucks using the Partners for Child Passenger Safety surveillance survey is considered.
Release date: 2007-06-28
Data (1)
Data (1) ((1 result))
- 1. Ontario Adult Literacy Survey ArchivedPublic use microdata: 89M0018XDescription:
This is a CD-ROM product from the Ontario Adult Literacy Survey (OALS), conducted in the spring of 1998 with the goal of providing information on: the ability of Ontario immigrants to use either English or French in their daily activities; and on their self-perceived literacy skills, training needs and barriers to training.
In order to cover the majority of Ontario immigrants, the Census Metropolitan Areas (CMAs) of Toronto, Hamilton, Ottawa, Kitchener, London and St. Catharines were included in the sample. With these 6 CMAs, about 83% of Ontario immigrants were included in the sample frame. This sample of 7,107 dwellings covered the population of Ontario immigrants in general as well as specifically targetting immigrants with a mother tongue of Italian, Chinese, Portuguese, Polish, and Spanish and immigrants born in the Caribbean Islands with a mother tongue of English.
Each interview was approximately 1.5 hours in duration and consisted of a half-hour questionnaire, asking demographic and literacy-related questions as well as a one-hour literacy test. This literacy test was derived from that used in the 1994 International Adult Literacy Survey (IALS) and covered the domains of document and quantitative literacy. An overall response rate to the survey of 76% was achieved, resulting in 4,648 respondents.
Release date: 1999-10-29
Analysis (48)
Analysis (48) (0 to 10 of 48 results)
- Articles and reports: 11-522-X202100100008Description:
Non-probability samples are being increasingly explored by National Statistical Offices as a complement to probability samples. We consider the scenario where the variable of interest and auxiliary variables are observed in both a probability and non-probability sample. Our objective is to use data from the non-probability sample to improve the efficiency of survey-weighted estimates obtained from the probability sample. Recently, Sakshaug, Wisniowski, Ruiz and Blom (2019) and Wisniowski, Sakshaug, Ruiz and Blom (2020) proposed a Bayesian approach to integrating data from both samples for the estimation of model parameters. In their approach, non-probability sample data are used to determine the prior distribution of model parameters, and the posterior distribution is obtained under the assumption that the probability sampling design is ignorable (or not informative). We extend this Bayesian approach to the prediction of finite population parameters under non-ignorable (or informative) sampling by conditioning on appropriate survey-weighted statistics. We illustrate the properties of our predictor through a simulation study.
Key Words: Bayesian prediction; Gibbs sampling; Non-ignorable sampling; Statistical data integration.
Release date: 2021-10-29 - Articles and reports: 12-001-X201500214230Description:
This paper develops allocation methods for stratified sample surveys where composite small area estimators are a priority, and areas are used as strata. Longford (2006) proposed an objective criterion for this situation, based on a weighted combination of the mean squared errors of small area means and a grand mean. Here, we redefine this approach within a model-assisted framework, allowing regressor variables and a more natural interpretation of results using an intra-class correlation parameter. We also consider several uses of power allocation, and allow the placing of other constraints such as maximum relative root mean squared errors for stratum estimators. We find that a simple power allocation can perform very nearly as well as the optimal design even when the objective is to minimize Longford’s (2006) criterion.
Release date: 2015-12-17 - Articles and reports: 12-001-X201500214248Description:
Unit level population models are often used in model-based small area estimation of totals and means, but the models may not hold for the sample if the sampling design is informative for the model. As a result, standard methods, assuming that the model holds for the sample, can lead to biased estimators. We study alternative methods that use a suitable function of the unit selection probability as an additional auxiliary variable in the sample model. We report the results of a simulation study on the bias and mean squared error (MSE) of the proposed estimators of small area means and on the relative bias of the associated MSE estimators, using informative sampling schemes to generate the samples. Alternative methods, based on modeling the conditional expectation of the design weight as a function of the model covariates and the response, are also included in the simulation study.
Release date: 2015-12-17 - Articles and reports: 12-001-X201300111823Description:
Although weights are widely used in survey sampling their ultimate justification from the design perspective is often problematical. Here we will argue for a stepwise Bayes justification for weights that does not depend explicitly on the sampling design. This approach will make use of the standard kind of information present in auxiliary variables however it will not assume a model relating the auxiliary variables to the characteristic of interest. The resulting weight for a unit in the sample can be given the usual interpretation as the number of units in the population which it represents.
Release date: 2013-06-28 - 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 - Articles and reports: 12-001-X201200111682Description:
Sample allocation issues are studied in the context of estimating sub-population (stratum or domain) means as well as the aggregate population mean under stratified simple random sampling. A non-linear programming method is used to obtain "optimal" sample allocation to strata that minimizes the total sample size subject to specified tolerances on the coefficient of variation of the estimators of strata means and the population mean. The resulting total sample size is then used to determine sample allocations for the methods of Costa, Satorra and Ventura (2004) based on compromise allocation and Longford (2006) based on specified "inferential priorities". In addition, we study sample allocation to strata when reliability requirements for domains, cutting across strata, are also specified. Performance of the three methods is studied using data from Statistics Canada's Monthly Retail Trade Survey (MRTS) of single establishments.
Release date: 2012-06-27 - Articles and reports: 12-001-X201200111683Description:
We consider alternatives to poststratification for doubly classified data in which at least one of the two-way cells is too small to allow the poststratification based upon this double classification. In our study data set, the expected count in the smallest cell is 0.36. One approach is simply to collapse cells. This is likely, however, to destroy the double classification structure. Our alternative approaches allows one to maintain the original double classification of the data. The approaches are based upon the calibration study by Chang and Kott (2008). We choose weight adjustments dependent upon the marginal classifications (but not full cross classification) to minimize an objective function of the differences between the population counts of the two way cells and their sample estimates. In the terminology of Chang and Kott (2008), if the row and column classifications have I and J cells respectively, this results in IJ benchmark variables and I + J - 1 model variables. We study the performance of these estimators by constructing simulation simple random samples from the 2005 Quarterly Census of Employment and Wages which is maintained by the Bureau of Labor Statistics. We use the double classification of state and industry group. In our study, the calibration approaches introduced an asymptotically trivial bias, but reduced the MSE, compared to the unbiased estimator, by as much as 20% for a small sample.
Release date: 2012-06-27 - Articles and reports: 12-001-X201100111443Description:
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 - 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 - 10. 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
Reference (3)
Reference (3) ((3 results))
- Surveys and statistical programs – Documentation: 71-526-XDescription:
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: 92-370-XDescription:
Series description
This series includes five general reference products - the Preview of Products and Services; the Catalogue; the Dictionary; the Handbook and the Technical Reports - as well as geography reference products - GeoSuite and Reference Maps.
Product description
Technical Reports examine the quality of data from the 1996 Census, a large and complex undertaking. While considerable effort was taken to ensure high quality standards throughout each step, the results are subject to a certain degree of error. Each report looks at the collection and processing operations and presents results from data evaluation, as well as notes on historical comparability.
Technical Reports are aimed at moderate and sophisticated users but are written in a manner which could make them useful to all census data users. Most of the technical reports have been cancelled, with the exception of Age, Sex, Marital Status and Common-law Status, Coverage and Sampling and Weighting. These reports will be available as bilingual publications as well as being available in both official languages on the Internet as free products.
This report deals with coverage errors, which occured when persons, households, dwellings or families were missed by the 1996 Census or enumerated in error. Coverage errors are one of the most important types of error since they affect not only the accuracy of the counts of the various census universes but also the accuracy of all of the census data describing the characteristics of these universes. With this information, users can determine the risks involved in basing conclusions or decisions on census data.
Release date: 1999-12-14 - Surveys and statistical programs – Documentation: 75F0002M1993014Description:
This paper presents the results from test 3A of the Survey of Labour and Income Dynamics (SLID), conducted in January 1993, with a view to identify any necessary changes to the questions or to the algorithm used to derive labour force status.
Release date: 1995-12-30
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