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• 1. Automatic editing with hard and soft edits Archived

  Articles and reports: 12-001-X201300111825

  Description:

  A considerable limitation of current methods for automatic data editing is that they treat all edits as hard constraints. That is to say, an edit failure is always attributed to an error in the data. In manual editing, however, subject-matter specialists also make extensive use of soft edits, i.e., constraints that identify (combinations of) values that are suspicious but not necessarily incorrect. The inability of automatic editing methods to handle soft edits partly explains why in practice many differences are found between manually edited and automatically edited data. The object of this article is to present a new formulation of the error localisation problem which can distinguish between hard and soft edits. Moreover, it is shown how this problem may be solved by an extension of the error localisation algorithm of De Waal and Quere (2003).

  Release date: 2013-06-28

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• 2. Variance estimation under composite imputation: The methodology behind SEVANI Archived

  Articles and reports: 12-001-X201100211605

  Description:

  Composite imputation is often used in business surveys. The term "composite" means that more than a single imputation method is used to impute missing values for a variable of interest. The literature on variance estimation in the presence of composite imputation is rather limited. To deal with this problem, we consider an extension of the methodology developed by Särndal (1992). Our extension is quite general and easy to implement provided that linear imputation methods are used to fill in the missing values. This class of imputation methods contains linear regression imputation, donor imputation and auxiliary value imputation, sometimes called cold-deck or substitution imputation. It thus covers the most common methods used by national statistical agencies for the imputation of missing values. Our methodology has been implemented in the System for the Estimation of Variance due to Nonresponse and Imputation (SEVANI) developed at Statistics Canada. Its performance is evaluated in a simulation study.

  Release date: 2011-12-21

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• 3. Improvements in 2005 to the Labour Force Survey (LFS) Archived

  Surveys and statistical programs – Documentation: 71F0031X2005002

  Description:

  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

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• 1. Automatic editing with hard and soft edits Archived

  Articles and reports: 12-001-X201300111825

  Description:

  A considerable limitation of current methods for automatic data editing is that they treat all edits as hard constraints. That is to say, an edit failure is always attributed to an error in the data. In manual editing, however, subject-matter specialists also make extensive use of soft edits, i.e., constraints that identify (combinations of) values that are suspicious but not necessarily incorrect. The inability of automatic editing methods to handle soft edits partly explains why in practice many differences are found between manually edited and automatically edited data. The object of this article is to present a new formulation of the error localisation problem which can distinguish between hard and soft edits. Moreover, it is shown how this problem may be solved by an extension of the error localisation algorithm of De Waal and Quere (2003).

  Release date: 2013-06-28

  ---

• 2. Variance estimation under composite imputation: The methodology behind SEVANI Archived

  Articles and reports: 12-001-X201100211605

  Description:

  Composite imputation is often used in business surveys. The term "composite" means that more than a single imputation method is used to impute missing values for a variable of interest. The literature on variance estimation in the presence of composite imputation is rather limited. To deal with this problem, we consider an extension of the methodology developed by Särndal (1992). Our extension is quite general and easy to implement provided that linear imputation methods are used to fill in the missing values. This class of imputation methods contains linear regression imputation, donor imputation and auxiliary value imputation, sometimes called cold-deck or substitution imputation. It thus covers the most common methods used by national statistical agencies for the imputation of missing values. Our methodology has been implemented in the System for the Estimation of Variance due to Nonresponse and Imputation (SEVANI) developed at Statistics Canada. Its performance is evaluated in a simulation study.

  Release date: 2011-12-21

Reference (1) ((1 result))

• 1. Improvements in 2005 to the Labour Force Survey (LFS) Archived

  Surveys and statistical programs – Documentation: 71F0031X2005002

  Description:

  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