Variance Estimation of Imputed Estimators of Change for Repeated Rotating Surveys. (15th September 2016)

• Record Type: Journal Article
• Authors: Berger, Yves G.; Escobar, Emilio L.
• Is Part Of: International statistical review. Volume 85:Number 3(2017)
• Page Start: 421
• Page End: 438
• Publication Date: 2016-09-15
• Subjects: Longitudinal surveys -- missing data -- non‐response -- overlapping samples -- rotation -- unequal inclusion probabilities; Statistics -- Periodicals; Statistics -- Bibliography -- Periodicals; Statistics -- Bibliography; Statistics -- Periodicals; Statistique; Statistique -- Périodiques; Statistique -- Bibliographie -- Périodiques; Statistique -- Étude et enseignement -- Périodiques; Statistique -- Étude et enseignement -- Bibliographie -- Périodiques; Ressource Internet (Descripteur de forme); Périodique électronique (Descripteur de forme); 519.2
• DOI: 10.1111/insr.12197
• Languages: English
• ISSNs: 0306-7734

Abstract

Summary: A common problem in survey sampling is to compare two cross‐sectional estimates for the same study variable taken from two different waves or occasions. These cross‐sectional estimates often include imputed values to compensate for item non‐response. The estimation of the sampling variance of the estimator of change is useful to judge whether the observed change is statistically significant. Estimating the variance of a change is not straightforward because of the rotation in repeated surveys and imputation. We propose using a multivariate linear regression approach and show how it can be used to accommodate the effect of rotation and imputation. The regression approach gives a design‐consistent estimation of the variance of change when the sampling fraction is small. We illustrate the proposed approach using random hot‐deck imputation, although the proposed estimator can be implemented with other imputation techniques.