Estimating stochastic survey response errors using the multitrait-multierror model

Alexandru Cernat, Daniel Oberski

Research output: Contribution to journalArticlepeer-review


Surveys are well-known to contain response errors of different types, including acquiescence, social desirability, common method variance, and random error – simultaneously. Nevertheless, a single error source at a time is all that most methods developed to estimate and correct for such errors consider in practice. Consequently, estimation of response errors is inefficient, their relative importance is unknown, and the optimal question format may not be discoverable. To remedy this situation, we demonstrate how multiple types of errors can be estimated concurrently with the recently introduced “multitrait-multierror” (MTME) approach. MTME combines the theory of design of experiments (DoE) with latent variable modeling to estimate response error variances of different error types simultaneously. This allows researchers to evaluate which errors are most impactful, and aids in the discovery of optimal question formats. We apply this approach using representative data from the UK to six survey items measuring attitudes towards immigrants that are commonly used across public opinion studies.
Original languageEnglish
JournalJournal of the Royal Statistical Society Series A
Publication statusAccepted/In press - 2021


  • measurement error
  • latent variable modeling
  • experimental designs
  • survey data


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