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- 1. Analysis of categorical data from surveys with complex designs: Some Canadian experiences ArchivedArticles and reports: 12-001-X198400214354Description:
Goodness of fit tests, tests for independence in a two-way contingency table, log-linear models and logistic regression models are investigated in the context of samples which are obtained from complex survey designs. Suggested approximations to the null distributions are reviewed and some examples from the Canada Health Survey and Canadian Labour Force Survey are given. Software implementation for using these methods is briefly discussed.
Release date: 1984-12-14 - 2. On analytical statistics from complex samples ArchivedArticles and reports: 12-001-X198400114346Description:
This presentation describes the important and urgent task of providing useful expressions for analytical statistics for complex sample designs. The following topics are discussed: effects of complex designs, sampling error for analytical statistics, subclasses involved in analytical statistics, comparisons of paired means, computation of analytical statistics and categorical data analysis.
Release date: 1984-06-15 - Articles and reports: 12-001-X198400114347Description:
Univariate statistical models, linear regression models and generalized linear models are briefly reviewed. Examples of a two-way analysis of variance, a three-way analysis of variance and logistic regression for a three way layout are given.
Release date: 1984-06-15 - 4. Examining expenditures on energy ArchivedArticles and reports: 12-001-X198400114349Description:
Using data from the Family Expenditures Surveys over time, consumer expenditures on in-home and transportation energy from 1969 to 1982 are being studied. This article briefly summarizes some of the procedures being used to explore the data, summarize it and develop insights into shifts in consumption for policy implications purposes. With such a complex data set and such a complex, multi-faceted subject for analysis some effort must be made to reduce information flows and at the same time increase the information content of each factor of both input and output in the analyses.
Release date: 1984-06-15 - Articles and reports: 12-001-X198400114350Description:
Standard chisquared (X^2) or likelihood ratio (G^2) tests for logistic regression analysis, involving a binary response variable, are adjusted to take account of the survey design. The adjustments are based on certain generalized design effects. The adjusted statistics are utilized to analyse some data from the October 1980 Canadian Labour Force Survey (LFS). The Wald statistic, which also takes the survey design into account, is also examined for goodness-of-fit of the model and for testing hypotheses on the parameters of the assumed model. Logistic regression diagnostics to detect any outlying cell proportions in the table and influential points in the factor space are applied to the LFS data, after making necessary adjustments to account for the survey design.
Release date: 1984-06-15 - Articles and reports: 12-001-X198400114351Description:
Most sample surveys conducted by organizations such as Statistics Canada or the U.S. Bureau of the Census employ complex designs. The design-based approach to statistical inference, typically the institutional standard of inference for simple population statistics such as means and totals, may be extended to parameters of analytic models as well. Most of this paper focuses on application of design-based inferences to such models, but rationales are offered for use of model-based alternatives in some instances, by way of explanation for the author’s observation that both modes of inference are used in practice at his own institution.
Within the design-based approach to inference, the paper briefly describes experience with linear regression analysis. Recently, variance computations for a number of surveys of the Census Bureau have been implemented through “replicate weighting”; the principal application has been for variances of simple statistics, but this technique also facilitates variance computation for virtually any complex analytic model. Finally, approaches and experience with log-linear models are reported.
Release date: 1984-06-15 - Articles and reports: 12-001-X198400114352Description:
The paper shows different estimation methods for complex survey designs. Among others, estimation of mean, ratio and regression coefficient is presented. The standard errors are estimated by different methods: the ordinary least squares procedure, the stratified weighted sample procedure, the stratified unit weight procedure, etc. Theory of large samples and conditions to apply it are also presented.
Release date: 1984-06-15
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Analysis (7)
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- 1. Analysis of categorical data from surveys with complex designs: Some Canadian experiences ArchivedArticles and reports: 12-001-X198400214354Description:
Goodness of fit tests, tests for independence in a two-way contingency table, log-linear models and logistic regression models are investigated in the context of samples which are obtained from complex survey designs. Suggested approximations to the null distributions are reviewed and some examples from the Canada Health Survey and Canadian Labour Force Survey are given. Software implementation for using these methods is briefly discussed.
Release date: 1984-12-14 - 2. On analytical statistics from complex samples ArchivedArticles and reports: 12-001-X198400114346Description:
This presentation describes the important and urgent task of providing useful expressions for analytical statistics for complex sample designs. The following topics are discussed: effects of complex designs, sampling error for analytical statistics, subclasses involved in analytical statistics, comparisons of paired means, computation of analytical statistics and categorical data analysis.
Release date: 1984-06-15 - Articles and reports: 12-001-X198400114347Description:
Univariate statistical models, linear regression models and generalized linear models are briefly reviewed. Examples of a two-way analysis of variance, a three-way analysis of variance and logistic regression for a three way layout are given.
Release date: 1984-06-15 - 4. Examining expenditures on energy ArchivedArticles and reports: 12-001-X198400114349Description:
Using data from the Family Expenditures Surveys over time, consumer expenditures on in-home and transportation energy from 1969 to 1982 are being studied. This article briefly summarizes some of the procedures being used to explore the data, summarize it and develop insights into shifts in consumption for policy implications purposes. With such a complex data set and such a complex, multi-faceted subject for analysis some effort must be made to reduce information flows and at the same time increase the information content of each factor of both input and output in the analyses.
Release date: 1984-06-15 - Articles and reports: 12-001-X198400114350Description:
Standard chisquared (X^2) or likelihood ratio (G^2) tests for logistic regression analysis, involving a binary response variable, are adjusted to take account of the survey design. The adjustments are based on certain generalized design effects. The adjusted statistics are utilized to analyse some data from the October 1980 Canadian Labour Force Survey (LFS). The Wald statistic, which also takes the survey design into account, is also examined for goodness-of-fit of the model and for testing hypotheses on the parameters of the assumed model. Logistic regression diagnostics to detect any outlying cell proportions in the table and influential points in the factor space are applied to the LFS data, after making necessary adjustments to account for the survey design.
Release date: 1984-06-15 - Articles and reports: 12-001-X198400114351Description:
Most sample surveys conducted by organizations such as Statistics Canada or the U.S. Bureau of the Census employ complex designs. The design-based approach to statistical inference, typically the institutional standard of inference for simple population statistics such as means and totals, may be extended to parameters of analytic models as well. Most of this paper focuses on application of design-based inferences to such models, but rationales are offered for use of model-based alternatives in some instances, by way of explanation for the author’s observation that both modes of inference are used in practice at his own institution.
Within the design-based approach to inference, the paper briefly describes experience with linear regression analysis. Recently, variance computations for a number of surveys of the Census Bureau have been implemented through “replicate weighting”; the principal application has been for variances of simple statistics, but this technique also facilitates variance computation for virtually any complex analytic model. Finally, approaches and experience with log-linear models are reported.
Release date: 1984-06-15 - Articles and reports: 12-001-X198400114352Description:
The paper shows different estimation methods for complex survey designs. Among others, estimation of mean, ratio and regression coefficient is presented. The standard errors are estimated by different methods: the ordinary least squares procedure, the stratified weighted sample procedure, the stratified unit weight procedure, etc. Theory of large samples and conditions to apply it are also presented.
Release date: 1984-06-15
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