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Estimating Components of Mean Squared Error to Evaluate the Benefits of Mixing Data Collection Modes

Caroline Roberts
  • Institute of Social Sciences, University of Lausanne, Bâtiment Géopolis, Quartier Mouline, CH-1015 Lausanne, Switzerland.
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/ Caroline Vandenplas
Published Online: 2017-06-12 | DOI: https://doi.org/10.1515/jos-2017-0016


Mixed mode data collection designs are increasingly being adopted with the hope that they may reduce selection errors in single mode survey designs. Yet possible reductions in selection errors achieved by mixing modes may be offset by a potential increase in total survey error due to extra measurement error being introduced by the additional mode(s). Few studies have investigated this empirically, however. In the present study, we compute the Mean Squared Error (MSE) for a range of estimates using data from a mode comparison experiment. We compare two mixed mode designs (a sequential web plus mail survey, and a combined concurrent and sequential CATI plus mail survey) with a single mode mail survey. The availability of auxiliary data on the sampling frame allows us to estimate several components of MSE (sampling variance, non-coverage, nonresponse and measurement bias) for a number of sociodemographic and target variables. Overall, MSEs are lowest for the single mode survey, and highest for the CATI plus mail design, though this pattern is not consistent across all estimates. Mixing modes generally reduces total bias, but the relative contribution to total survey error from different sources varies by design and by variable type.

This article offers supplementary material which is provided at the end of the article.

Keywords: Nonresponse error; measurement error; coverage error; sampling variance

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About the article

Received: 2016-02-01

Revised: 2017-03-01

Accepted: 2017-04-01

Published Online: 2017-06-12

Published in Print: 2017-06-01

Citation Information: Journal of Official Statistics, Volume 33, Issue 2, Pages 303–334, ISSN (Online) 2001-7367, DOI: https://doi.org/10.1515/jos-2017-0016.

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© 2017 Caroline Roberts 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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