Variance estimation of imputed estimators of change for repeated rotating surveys
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 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.
deterministic imputation, longitudinal surveys, missing data, nonresponse, overlapping samples, unequal inclusion probabilities
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Emilio L., Escobar
0371e6aa-057f-4562-be43-9d81d01af651
2016
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Emilio L., Escobar
0371e6aa-057f-4562-be43-9d81d01af651
Berger, Yves G. and Emilio L., Escobar
(2016)
Variance estimation of imputed estimators of change for repeated rotating surveys.
International Statistical Review.
(doi:10.1111/insr.12197).
Abstract
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.
Text
Berger_Escobar_2016_Pre_v1.pdf
- Accepted Manuscript
More information
e-pub ahead of print date: 15 September 2016
Published date: 2016
Keywords:
deterministic imputation, longitudinal surveys, missing data, nonresponse, overlapping samples, unequal inclusion probabilities
Organisations:
Statistical Sciences Research Institute
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Local EPrints ID: 376675
URI: http://eprints.soton.ac.uk/id/eprint/376675
ISSN: 0306-7734
PURE UUID: dcde073f-5e3e-47f1-9d2a-36d7d46f7c20
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Date deposited: 05 May 2015 14:15
Last modified: 15 Mar 2024 03:01
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Author:
Escobar Emilio L.
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