A beta mixture model for careless respondent detection in visual analogue scale data

Visual Analogue Scales (VASs) are increasingly popular in psychological, social, andmedical research. However, VASs can also be more demanding for respondents, potentiallyleading to quicker disengagement and a higher risk of careless responding. Existing mixturemodeling approaches for careless response detection have so far only been available forLikert-type and unbounded continuous data but have not been tailored to VAS data. Thisstudy introduces and evaluates a model-based approach specifically designed to detect andaccount for careless respondents in VAS data. We integrate existing measurement modelsfor VASs with mixture item response theory models for identifying and modeling carelessresponding. Simulation results show that the proposed model effectively detects carelessresponding and recovers key parameters. We illustrate the model’s potential for identifyingand accounting for careless responding using real data from both VASs and Likert scales.First, we show how the model can be used to compare careless responding across differentscale types, revealing a higher proportion of careless respondents in VAS compared toLikert scale data. Second, we demonstrate that item parameters from the proposed modelexhibit improved psychometric properties compared to those from a model that ignorescareless responding. These findings underscore the model’s potential to enhance dataquality by identifying and addressing careless responding.