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Medientyp:
E-Artikel
Titel:
Estimating Stochastic Survey Response Errors using the Multitrait-Multierror Model
Beteiligte:
Cernat, Alexandru;
Oberski, Daniel L.
Erschienen:
Oxford University Press (OUP), 2022
Erschienen in:
Journal of the Royal Statistical Society Series A: Statistics in Society, 185 (2022) 1, Seite 134-155
Sprache:
Englisch
DOI:
10.1111/rssa.12733
ISSN:
0964-1998;
1467-985X
Entstehung:
Anmerkungen:
Beschreibung:
Abstract 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 with latent variable modelling 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 United Kingdom to six survey items measuring attitudes towards immigrants that are commonly used across public opinion studies.