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A simple variance estimator of change for rotating repeated surveys: an application to the EU-SILC household surveys

Berger, Yves G. and Priam, Rodolphe (2016) A simple variance estimator of change for rotating repeated surveys: an application to the EU-SILC household surveys Journal of the Royal Statistical Society: Series A (Statistics in Society), 179, (1), pp. 251-272. (doi:10.1111/rssa.12116).

Record type: Article

Abstract

A common problem is to compare two cross-sectional estimates for the same study variable taken on two different waves or occasions, and to judge whether the change observed is statistically significant. This involves the estimation of the sampling variance of the estimator of change. The estimation of this variance would be relatively straightforward if cross-sectional estimates were based on the same sample. Unfortunately, samples are not completely overlapping, because of rotations used in repeated surveys. We propose a simple approach based on a multivariate (general) linear regression model. The variance estimator proposed is not a model-based estimator. We show that the estimator proposed is design consistent when the sampling fractions are negligible. It can accommodate stratified and two-stage sampling designs. The main advantage of the approach proposed is its simplicity and flexibility. It can be applied to a wide class of sampling designs and can be implemented with standard statistical regression techniques. Because of its flexibility, the approach proposed is well suited for the estimation of variance for the European Union Statistics on Income and Living Conditions surveys. It allows us to use a common approach for variance estimation for the different types of design. The approach proposed is a useful tool, because it involves only modelling skills and requires limited knowledge of survey sampling theory.

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More information

Accepted/In Press date: 9 January 2015
e-pub ahead of print date: 18 May 2015
Published date: January 2016
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Keywords: design-based approach, linearisation, multivariate regression, stratification, two-stage sampling, unequal inclusion probabilities
Organisations: Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 347142
URI: http://eprints.soton.ac.uk/id/eprint/347142
ISSN: 0964-1998
PURE UUID: 5908f26d-b6dd-4678-a74e-fbb86ecd93d1

Catalogue record

Date deposited: 18 Jan 2013 11:36
Last modified: 08 Sep 2017 16:33

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Contributors

Author: Yves G. Berger
Author: Rodolphe Priam

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