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- Title
Fully efficient estimation of coefficients of correlation in the presence of imputed survey data.
- Authors
Chauvet, Guillaume; Haziza, David
- Abstract
Marginal imputation, that consists of imputing items separately, generally leads to biased estimators of bivariate parameters such as finite population coefficients of correlation. To overcome this problem, two main approaches have been considered in the literature: the first consists of using customary imputation methods such as random hot-deck imputation and adjusting for the bias at the estimation stage. This approach was studied in Skinner & Rao . In this paper, we extend the results of Skinner & Rao to the case of arbitrary sampling designs and three variants of random hot-deck imputation. The second approach consists of using an imputation method, which preserves the relationship between variables. Shao & Wang proposed a joint random regression imputation procedure that succeeds in preserving the relationships between two study variables. One drawback of the Shao-Wang procedure is that it suffers from an additional variability (called the imputation variance) due to the random selection of residuals, resulting in potentially inefficient estimators. Following Chauvet, Deville, & Haziza , we propose a fully efficient version of the Shao-Wang procedure that preserves the relationship between two study variables, while virtually eliminating the imputation variance. Results of a simulation study support our findings. An application using data from the Workplace and Employees Survey is also presented. The Canadian Journal of Statistics 40: 124-149; 2012 © 2011 Statistical Society of Canada
- Subjects
REGRESSION analysis; STATISTICS; EMPLOYEE attitude surveys; STATISTICAL bias; STATISTICAL sampling
- Publication
Canadian Journal of Statistics, 2012, Vol 40, Issue 1, p124
- ISSN
0319-5724
- Publication type
Article
- DOI
10.1002/cjs.10133