Editing and imputation

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  • Articles and reports: 11-522-X200600110442
    Description:

    The District of Columbia Healthy Outcomes of Pregnancy Education (DC-HOPE) project is a randomized trial funded by the National Institute of Child Health and Human Development to test the effectiveness of an integrated education and counseling intervention (INT) versus usual care (UC) to reduce four risk behaviors among pregnant women. Participants were interviewed at baseline and three additional time points. Multiple imputation (MI) was used to estimate data for missing interviews. MI was done twice: once with all data imputed simultaneously, and once with data for women in the INT and UC groups imputed separately. Analyses of both imputed data sets and the pre-imputation data are compared.

    Release date: 2008-03-17

  • Articles and reports: 12-001-X20050018087
    Description:

    In Official Statistics, data editing process plays an important role in terms of timeliness, data accuracy, and survey costs. Techniques introduced to identify and eliminate errors from data are essentially required to consider all of these aspects simultaneously. Among others, a frequent and pervasive systematic error appearing in surveys collecting numerical data, is the unity measure error. It highly affects timeliness, data accuracy and costs of the editing and imputation phase. In this paper we propose a probabilistic formalisation of the problem based on finite mixture models. This setting allows us to deal with the problem in a multivariate context, and provides also a number of useful diagnostics for prioritising cases to be more deeply investigated through a clerical review. Prioritising units is important in order to increase data accuracy while avoiding waste of time due to the follow up of non-really critical units.

    Release date: 2005-07-21

  • Articles and reports: 12-001-X198600114440
    Description:

    Statistics Canada has undertaken a project to develop a generalized edit and imputation system, the intent of which is to meet the processing requirements of most of its surveys. The various approaches to imputation for item non-response, which have been proposed, will be discussed. Important issues related to the implementation of these proposals into a generalized setting will also be addressed.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X197800254830
    Description:

    The problems of dealing with non-response at various stages of survey planning are discussed with implications for the mean square error, practicality and possible advantages and disadvantages. Conceptual issues of editing and imputation are also considered with regard to complexity and levels of imputation. The methods of imputation include weighting, duplication, and substitution of historical records. The paper includes some methodology on the bias and variance.

    Release date: 1978-12-15
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  • Articles and reports: 11-522-X200600110442
    Description:

    The District of Columbia Healthy Outcomes of Pregnancy Education (DC-HOPE) project is a randomized trial funded by the National Institute of Child Health and Human Development to test the effectiveness of an integrated education and counseling intervention (INT) versus usual care (UC) to reduce four risk behaviors among pregnant women. Participants were interviewed at baseline and three additional time points. Multiple imputation (MI) was used to estimate data for missing interviews. MI was done twice: once with all data imputed simultaneously, and once with data for women in the INT and UC groups imputed separately. Analyses of both imputed data sets and the pre-imputation data are compared.

    Release date: 2008-03-17

  • Articles and reports: 12-001-X20050018087
    Description:

    In Official Statistics, data editing process plays an important role in terms of timeliness, data accuracy, and survey costs. Techniques introduced to identify and eliminate errors from data are essentially required to consider all of these aspects simultaneously. Among others, a frequent and pervasive systematic error appearing in surveys collecting numerical data, is the unity measure error. It highly affects timeliness, data accuracy and costs of the editing and imputation phase. In this paper we propose a probabilistic formalisation of the problem based on finite mixture models. This setting allows us to deal with the problem in a multivariate context, and provides also a number of useful diagnostics for prioritising cases to be more deeply investigated through a clerical review. Prioritising units is important in order to increase data accuracy while avoiding waste of time due to the follow up of non-really critical units.

    Release date: 2005-07-21

  • Articles and reports: 12-001-X198600114440
    Description:

    Statistics Canada has undertaken a project to develop a generalized edit and imputation system, the intent of which is to meet the processing requirements of most of its surveys. The various approaches to imputation for item non-response, which have been proposed, will be discussed. Important issues related to the implementation of these proposals into a generalized setting will also be addressed.

    Release date: 1986-06-16

  • Articles and reports: 12-001-X197800254830
    Description:

    The problems of dealing with non-response at various stages of survey planning are discussed with implications for the mean square error, practicality and possible advantages and disadvantages. Conceptual issues of editing and imputation are also considered with regard to complexity and levels of imputation. The methods of imputation include weighting, duplication, and substitution of historical records. The paper includes some methodology on the bias and variance.

    Release date: 1978-12-15
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