Careless and insufficient effort responding (C/IER) on self-report measures results in responses that do not reflect the trait to be measured, thereby posing a major threat to the quality of survey data. Reliable approaches for detecting C/IER aid in increasing the validity of inferences being made from survey data. First, once detected, C/IER can be taken into account in data analysis. Second, approaches for detecting C/IER support a better understanding of its occurrence, which facilitates designing surveys that curb the prevalence of C/IER. Previous approaches for detecting C/IER are limited in that they identify C/IER at the aggregate respondent or scale level, thereby hindering investigations of item characteristics evoking C/IER. We propose an explanatory mixture item response theory model that supports identifying and modelling C/IER at the respondent-by-item level, can detect a wide array of C/IER patterns, and facilitates a deeper understanding of item characteristics associated with its occurrence. As the approach only requires raw response data, it is applicable to data from paper-and-pencil and online surveys. The model shows good parameter recovery and can well handle the simultaneous occurrence of multiple types of C/IER patterns in simulated data. The approach is illustrated on a publicly available Big Five inventory data set, where we found later item positions to be associated with higher C/IER probabilities. We gathered initial supporting validity evidence for the proposed approach by investigating agreement with multiple commonly employed indicators of C/IER.
Original languageEnglish
JournalBritish Journal of Mathematical and Statistical Psychology
Publication statusE-pub ahead of print - 22.06.2022
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    Research areas

  • Methodological research and method development - careless responses, data screening, explanatory IRT, mixture modelling, item characteristics

ID: 1897112