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Journal of Official Statistics

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Comparing Two Inferential Approaches to Handling Measurement Error in Mixed-Mode Surveys

Bart Buelens / Jan A. Van den Brakel
Published Online: 2017-06-12 | DOI: https://doi.org/10.1515/jos-2017-0024

Abstract

Nowadays sample survey data collection strategies combine web, telephone, face-to-face, or other modes of interviewing in a sequential fashion. Measurement bias of survey estimates of means and totals are composed of different mode-dependent measurement errors as each data collection mode has its own associated measurement error. This article contains an appraisal of two recently proposed methods of inference in this setting. The first is a calibration adjustment to the survey weights so as to balance the survey response to a prespecified distribution of the respondents over the modes. The second is a prediction method that seeks to correct measurements towards a benchmark mode. The two methods are motivated differently but at the same time coincide in some circumstances and agree in terms of required assumptions. The methods are applied to the Labour Force Survey in the Netherlands and are found to provide almost identical estimates of the number of unemployed. Each method has its own specific merits. Both can be applied easily in practice as they do not require additional data collection beyond the regular sequential mixed-mode survey, an attractive element for national statistical institutes and other survey organisations.

Keywords: Generalized regression; mode effects; selection bias; response mode calibration; counterfactuals

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About the article

Received: 2016-01-01

Revised: 2017-02-01

Accepted: 2017-03-01

Published Online: 2017-06-12

Published in Print: 2017-06-01


Citation Information: Journal of Official Statistics, Volume 33, Issue 2, Pages 513–531, ISSN (Online) 2001-7367, DOI: https://doi.org/10.1515/jos-2017-0024.

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© 2017 Bart Buelens et al., published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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