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

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.

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

Journal of Official Statistics

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

Mixed mode data collection designs are increasingly being adopted with the hope that they may reduce selection errors in single mode survey designs. Yet...

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

Published Online: Jun 12, 2017

Page range: 303 - 334

Received: Feb 01, 2016

Accepted: Apr 01, 2017

DOI: https://doi.org/10.1515/jos-2017-0016

Keywords
Nonresponse error, measurement error, coverage error, sampling variance

© 2017 Caroline Roberts et al., published by De Gruyter Open

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

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

Published Online: Jun 12, 2017

Page range: 303 - 334

Received: Feb 01, 2016

Accepted: Apr 01, 2017

DOI: https://doi.org/10.1515/jos-2017-0016

KeywordsNonresponse error, measurement error, coverage error, sampling variance

© 2017 Caroline Roberts et al., published by De Gruyter Open

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

Keywords
Nonresponse error, measurement error, coverage error, sampling variance