Response Style Characterization for Repeated Measures Using the Visual Analogue Scale
<|reference_start|>Response Style Characterization for Repeated Measures Using the Visual Analogue Scale: Self-report measures (e.g., Likert scales) are widely used to evaluate subjective health perceptions. Recently, the visual analog scale (VAS), a slider-based scale, has become popular owing to its ability to precisely and easily assess how people feel. These data can be influenced by the response style (RS), a user-dependent systematic tendency that occurs regardless of questionnaire instructions. Despite its importance, especially in between-individual analysis, little attention has been paid to handling the RS in the VAS (denoted as response profile (RP)), as it is mainly used for within-individual monitoring and is less affected by RP. However, VAS measurements often require repeated self-reports of the same questionnaire items, making it difficult to apply conventional methods on a Likert scale. In this study, we developed a novel RP characterization method for various types of repeatedly measured VAS data. This approach involves the modeling of RP as distributional parameters ${\theta}$ through a mixture of RS-like distributions, and addressing the issue of unbalanced data through bootstrap sampling for treating repeated measures. We assessed the effectiveness of the proposed method using simulated pseudo-data and an actual dataset from an empirical study. The assessment of parameter recovery showed that our method accurately estimated the RP parameter ${\theta}$, demonstrating its robustness. Moreover, applying our method to an actual VAS dataset revealed the presence of individual RP heterogeneity, even in repeated VAS measurements, similar to the findings of the Likert scale. Our proposed method enables RP heterogeneity-aware VAS data analysis, similar to Likert-scale data analysis.<|reference_end|>
@article{minusa2024response,
    title={Response Style Characterization for Repeated Measures Using the Visual
  Analogue Scale},
    author={Shunsuke Minusa, Tadayuki Matsumura, Kanako Esaki, Yang Shao, Chihiro
  Yoshimura, and Hiroyuki Mizuno},
    journal={arXiv preprint arXiv:2403.10136},
    year={2024},
    doi={10.1109/ACCESS.2024.3445410},
    archivePrefix={arXiv},
    eprint={2403.10136},
    primaryClass={stat.ME cs.AI}
}