An explanatory mixture IRT model for careless and insufficient effort responding in self‐report measures. (22nd June 2022)

• Record Type: Journal Article
• Authors: Ulitzsch, Esther; Yildirim‐Erbasli, Seyma Nur; Gorgun, Guher; Bulut, Okan
• Is Part Of: British journal of mathematical & statistical psychology. Volume 75:Part 3(2022)
• Page Start: 668
• Page End: 698
• Publication Date: 2022-06-22
• Subjects: careless responses -- data screening -- explanatory IRT -- mixture modelling -- item characteristics; Psychometrics -- Periodicals; Psychology -- Mathematical models -- Periodicals; Psychology -- Statistical methods -- Periodicals; 150.727
• DOI: 10.1111/bmsp.12272
• Languages: English
• ISSNs: 0007-1102

Abstract

Abstract : 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.