Variance estimation of imputed estimators of change for repeated rotating surveys
A common problem in survey sampling is to compare two cross-sectional estimates for the same study variable taken on 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 esti...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
2016.
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| Subjects: | |
| Online Access: | Get fulltext |
| Summary: | A common problem in survey sampling is to compare two cross-sectional estimates for the same study variable taken on 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 due to rotation in repeated surveys. We propose using a multivariate linear regression approach and show how it can be used to accommodate the effect of imputation. The regression approach gives 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. |
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