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Evaluating Mode Effects in Mixed-Mode Survey Data Using Covariate Adjustment Models

Author

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  • Vannieuwenhuyze Jorre T.A.

    (Institute for Social & Economic Research, University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ, United Kingdom)

  • Loosveldt Geert

    (Centre for Sociological Research, KU Leuven, Parkstraat 45, Leuven 3000, Belgium)

  • Molenberghs Geert

    (I-BioStat, KU Leuven, Leuven, and Universiteit Hasselt, Diepenbeek, Belgium)

Abstract

The confounding of selection and measurement effects between different modes is a disadvantage of mixed-mode surveys. Solutions to this problem have been suggested in several studies. Most use adjusting covariates to control selection effects. Unfortunately, these covariates must meet strong assumptions, which are generally ignored. This article discusses these assumptions in greater detail and also provides an alternative model for solving the problem. This alternative uses adjusting covariates, explaining measurement effects instead of selection effects. The application of both models is illustrated by using data from a survey on opinions about surveys, which yields mode effects in line with expectations for the latter model, and mode effects contrary to expectations for the former model. However, the validity of these results depends entirely on the (ad hoc) covariates chosen. Research into better covariates might thus be a topic for future studies.

Suggested Citation

  • Vannieuwenhuyze Jorre T.A. & Loosveldt Geert & Molenberghs Geert, 2014. "Evaluating Mode Effects in Mixed-Mode Survey Data Using Covariate Adjustment Models," Journal of Official Statistics, Sciendo, vol. 30(1), pages 1-21, March.
    • Handle: RePEc:vrs:offsta:v:30:y:2014:i:1:p:1-21:n:1
    DOI: 10.2478/jos-2014-0001
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    References listed on IDEAS

    as
    1. Jorre T. A. Vannieuwenhuyze & Geert Loosveldt & Geert Molenberghs, 2012. "A Method to Evaluate Mode Effects on the Mean and Variance of a Continuous Variable in Mixed-Mode Surveys," International Statistical Review, International Statistical Institute, vol. 80(2), pages 306-322, August.
    2. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
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    Cited by:

    1. Thomas Klausch & Barry Schouten & Joop J. Hox, 2017. "Evaluating Bias of Sequential Mixed-mode Designs Against Benchmark Surveys," Sociological Methods & Research, , vol. 46(3), pages 456-489, August.

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