<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Statistics Netherlands and Maastricht University

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Nowadays sample survey data collection strategies combine web, telephone, face-to-face, or other modes of interviewing in a sequential fashion. Measurement...

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

Published Online: Jun 12, 2017

Page range: 513 - 531

Received: Jan 01, 2016

Accepted: Mar 01, 2017

DOI: https://doi.org/10.1515/jos-2017-0024

Keywords
Generalized regression, mode effects, selection bias, response mode calibration, counterfactuals

© 2017 Bart Buelens et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Published Online: Jun 12, 2017

Page range: 513 - 531

Received: Jan 01, 2016

Accepted: Mar 01, 2017

DOI: https://doi.org/10.1515/jos-2017-0024

KeywordsGeneralized regression, mode effects, selection bias, response mode calibration, counterfactuals

© 2017 Bart Buelens et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Generalized regression, mode effects, selection bias, response mode calibration, counterfactuals