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Variance estimation of imputed estimators of change over time from repeated surveys

Variance estimation of imputed estimators of change over time from repeated surveys
Variance estimation of imputed estimators of change over time from repeated surveys
Measuring change over time is a central problem for many users of social, eco-nomic and demographic data and is of interest in many areas of economics and social sciences. Smith et al. (2003 JRSS-D) recognised that assessing change is one of the most important challenges in survey statistics. The primary interest of many users is often in changes or trends from one time period to another. A common problem 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. Covariances play an important role in estimating the variance of a change. We propose to use a multivariate linear regression approach to estimate covariances. The proposed estimator is not a model-based estimator, as this estimator is valid even if the model does not fit the data. We show how this approach can be used to accommodate the effect of imputation.
Berger, Y.G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Escobar, E.L.
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Berger, Y.G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Escobar, E.L.
b2431dc1-bbfb-4f94-bbae-3877cd1de7d9

Berger, Y.G. and Escobar, E.L. (2012) Variance estimation of imputed estimators of change over time from repeated surveys. Journées de Méthodologie Statistiques 2012, France. 24 - 26 Jan 2012. 8 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Measuring change over time is a central problem for many users of social, eco-nomic and demographic data and is of interest in many areas of economics and social sciences. Smith et al. (2003 JRSS-D) recognised that assessing change is one of the most important challenges in survey statistics. The primary interest of many users is often in changes or trends from one time period to another. A common problem 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. Covariances play an important role in estimating the variance of a change. We propose to use a multivariate linear regression approach to estimate covariances. The proposed estimator is not a model-based estimator, as this estimator is valid even if the model does not fit the data. We show how this approach can be used to accommodate the effect of imputation.

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More information

Published date: 26 January 2012
Venue - Dates: Journées de Méthodologie Statistiques 2012, France, 2012-01-24 - 2012-01-26
Organisations: Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 350490
URI: https://eprints.soton.ac.uk/id/eprint/350490
PURE UUID: 54f8238b-346d-406f-9d7d-546f2c0da5c0
ORCID for Y.G. Berger: ORCID iD orcid.org/0000-0002-9128-5384

Catalogue record

Date deposited: 08 Apr 2013 10:03
Last modified: 18 Oct 2019 00:37

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