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  • 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

  • Articles and reports: 12-001-X201100111444
    Description:

    Data linkage is the act of bringing together records that are believed to belong to the same unit (e.g., person or business) from two or more files. It is a very common way to enhance dimensions such as time and breadth or depth of detail. Data linkage is often not an error-free process and can lead to linking a pair of records that do not belong to the same unit. There is an explosion of record linkage applications, yet there has been little work on assuring the quality of analyses using such linked files. Naively treating such a linked file as if it were linked without errors will, in general, lead to biased estimates. This paper develops a maximum likelihood estimator for contingency tables and logistic regression with incorrectly linked records. The estimation technique is simple and is implemented using the well-known EM algorithm. A well known method of linking records in the present context is probabilistic data linking. The paper demonstrates the effectiveness of the proposed estimators in an empirical study which uses probabilistic data linkage.

    Release date: 2011-06-29

  • Articles and reports: 12-001-X201100111445
    Description:

    In this paper we study small area estimation using area level models. We first consider the Fay-Herriot model (Fay and Herriot 1979) for the case of smoothed known sampling variances and the You-Chapman model (You and Chapman 2006) for the case of sampling variance modeling. Then we consider hierarchical Bayes (HB) spatial models that extend the Fay-Herriot and You-Chapman models by capturing both the geographically unstructured heterogeneity and spatial correlation effects among areas for local smoothing. The proposed models are implemented using the Gibbs sampling method for fully Bayesian inference. We apply the proposed models to the analysis of health survey data and make comparisons among the HB model-based estimates and direct design-based estimates. Our results have shown that the HB model-based estimates perform much better than the direct estimates. In addition, the proposed area level spatial models achieve smaller CVs than the Fay-Herriot and You-Chapman models, particularly for the areas with three or more neighbouring areas. Bayesian model comparison and model fit analysis are also presented.

    Release date: 2011-06-29

  • Articles and reports: 12-001-X201100111446
    Description:

    Small area estimation based on linear mixed models can be inefficient when the underlying relationships are non-linear. In this paper we introduce SAE techniques for variables that can be modelled linearly following a non-linear transformation. In particular, we extend the model-based direct estimator of Chandra and Chambers (2005, 2009) to data that are consistent with a linear mixed model in the logarithmic scale, using model calibration to define appropriate weights for use in this estimator. Our results show that the resulting transformation-based estimator is both efficient and robust with respect to the distribution of the random effects in the model. An application to business survey data demonstrates the satisfactory performance of the method.

    Release date: 2011-06-29

  • Articles and reports: 12-001-X201100111447
    Description:

    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-X201100111448
    Description:

    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

  • Articles and reports: 12-001-X201100111449
    Description:

    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-X201100111450
    Description:

    This paper examines the efficiency of the Horvitz-Thompson estimator from a systematic probability proportional to size (PPS) sample drawn from a randomly ordered list. In particular, the efficiency is compared with that of an ordinary ratio estimator. The theoretical results are confirmed empirically with of a simulation study using Dutch data from the Producer Price Index.

    Release date: 2011-06-29

  • Articles and reports: 12-001-X201100111451
    Description:

    In the calibration method proposed by Deville and Särndal (1992), the calibration equations take only exact estimates of auxiliary variable totals into account. This article examines other parameters besides totals for calibration. Parameters that are considered complex include the ratio, median or variance of auxiliary variables.

    Release date: 2011-06-29

  • Articles and reports: 75F0002M2011002
    Description:

    In order to provide a holographic or complete picture of low income, Statistics Canada uses three complementary low income lines: the Low Income Cut-offs (LICOs), the Low Income Measures (LIMs) and the Market Basket Measure (MBM). While the first two lines were developed by Statistics Canada, the MBM is based on concepts developed by Human Resources and Skill Development Canada. Though these measures differ from one another, they give a generally consistent picture of low income status over time. None of these measures is the best. Each contributes its own perspective and its own strengths to the study of low income, so that cumulatively, the three provide a better understanding of the phenomenon of low income as a whole. These measures are not measures of poverty, but strictly measures of low income.

    Release date: 2011-06-15
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Analysis (197)

Analysis (197) (10 to 20 of 197 results)

  • Articles and reports: 62F0014M2019005
    Description:

    This document describes the updated methodology for Investment Banking Services Price Index (IBSPI).

    Release date: 2019-07-08

  • Articles and reports: 13-605-X201900100006
    Description:

    This paper examines the parameters needed to calculate the mass of cannabis metabolites in wastewater, and the additional parameters needed to estimate the total quantity of cannabis consumed.

    Release date: 2019-05-21

  • Articles and reports: 62F0014M2019003
    Description:

    This paper describes the changes in the methodology for estimating the telephone services index.

    Release date: 2019-04-30

  • Articles and reports: 18-001-X2017002
    Description:

    This working paper presents a methodology to measure remoteness at the community level. The method takes into account some of the recent literature on the subject, as well as new computational opportunities provided by the integration of official statistics with data from non-official statistical sources. The approach that was used in the computations accounts for multiple points of access to services; it also establishes a continuum between communities with different transportation infrastructures and connectivity while at the same time retaining the information on the community transportation infrastructures in the database. In addition, a method to implement accessibility measures to selected services is also outlined and a sample of accessibility measures are computed.

    Release date: 2017-05-09

  • Articles and reports: 11-633-X2016002
    Description:

    Immigrants comprise an ever-increasing percentage of the Canadian population—at more than 20%, which is the highest percentage among the G8 countries (Statistics Canada 2013a). This figure is expected to rise to 25% to 28% by 2031, when at least one in four people living in Canada will be foreign-born (Statistics Canada 2010).

    This report summarizes the linkage of the Immigrant Landing File (ILF) for all provinces and territories, excluding Quebec, to hospital data from the Discharge Abstract Database (DAD), a national database containing information about hospital inpatient and day-surgery events. A deterministic exact-matching approach was used to link data from the 1980-to-2006 ILF and from the DAD (2006/2007, 2007/2008 and 2008/2009) with the 2006 Census, which served as a “bridge” file. This was a secondary linkage in that it used linkage keys created in two previous projects (primary linkages) that separately linked the ILF and the DAD to the 2006 Census. The ILF–DAD linked data were validated by means of a representative sample of 2006 Census records containing immigrant information previously linked to the DAD.

    Release date: 2016-08-17

  • Articles and reports: 11F0019M2016381
    Description:

    Changes in health status may affect not just the individuals who experience such changes, but also their family members. For example, if the main earner in a family loses his or her ability to generate income due to a health shock, it invariably affects the financial situation of the spouse and other dependents. In addition, spouses and working-age children may themselves increase or reduce their labour supply to make up for the lost income (“added worker effect”) or care for a sick family member (“caregiver effect”). Since consumption smoothing and self-insurance occur at the household level, the financial effects of health for other family members have important policy implications. To shed light on such effects, this study analyzes how one spouse’s cancer diagnosis affects the employment and earnings of the other spouse and (before-tax) total family income using administrative data from Canada.

    Release date: 2016-07-22

  • Articles and reports: 82-003-X201600714644
    Geography: Census metropolitan area
    Description: Children younger than age 18 enumerated in the 2006 Census who lived in Toronto, Montreal or Vancouver were linked to published air pollution exposure land use regression models to assign exposure at the Dissemination Area level. Associations between both socioeconomic and visible minority status and exposure to ambient nitrogen dioxide among children in these three cities were examined in a series of regression models.
    Release date: 2016-07-20

  • Articles and reports: 82-003-X201600714645
    Description:

    The data are from the Canadian Cancer Registry, with mortality follow-up through record linkage to the Canadian Vital Statistics Death Database. Increases in five-year relative survival ratios between 1992-to-1994 and 2006-to-2008 were calculated by age and sex for all leukemias combined and for each of the main types.

    Release date: 2016-07-20

  • Articles and reports: 11F0019M2016380
    Description:

    Every year, thousands of workers lose their job in many industrialized countries (OECD 2013). Faced with job loss, displaced workers may choose to return to school to help them reintegrate into the labour force. Job losses in a given local labour market may also induce workers who have not yet been laid off to pre-emptively enrol in postsecondary (PS) institutions, as a precautionary measure. Combining microdata and grouped data, this study examines these two dimensions of the relationship between layoffs and PS enrolment over the 2001-to-2011 period.

    Release date: 2016-07-19

  • Articles and reports: 75F0002M2016002
    Description:

    Statistics Canada currently measures low-income using three low income lines: the Low Income Cut-offs (LICOs), the Low Income Measures (LIMs), and the Market Basket Measure (MBM). This publication provides a description of the methods used to arrive at each of these thresholds. It also explains how low-income status and various low-income statistics are determined. Tables presenting thresholds and low-income statistics are available on CANSIM.

    Release date: 2016-07-08
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