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.

翻译：自我报告测量工具（如李克特量表）被广泛用于评估主观健康感知。近年来，视觉模拟量表作为一种基于滑动条的测量工具，因其能够精确且便捷地评估个体感受而日益普及。此类数据可能受到响应风格的影响——这是一种独立于问卷指导语、依赖于用户的系统性反应倾向。尽管响应风格在个体间分析中尤为重要，但针对视觉模拟量表中响应风格（本文称为响应特征）的处理方法却鲜有关注，这主要是因为该量表多用于个体内监测且受响应特征影响较小。然而，视觉模拟量表测量通常需要对相同问卷条目进行重复自我报告，这使得传统基于李克特量表的方法难以直接适用。本研究针对各类重复测量的视觉模拟量表数据，开发了一种新颖的响应特征表征方法。该方法通过混合类响应风格分布将响应特征建模为分布参数 ${\theta}$，并采用自助抽样技术处理重复测量数据的不平衡问题。我们通过模拟伪数据和实证研究中的真实数据集评估了所提方法的有效性。参数恢复评估表明，本方法能准确估计响应特征参数 ${\theta}$，证明了其稳健性。此外，将本方法应用于真实视觉模拟量表数据集时，发现了即使在重复测量中仍存在的个体响应特征异质性，这与李克特量表的研究结论相似。本研究提出的方法实现了类似李克特量表数据分析中具有响应特征异质性感知能力的视觉模拟量表数据分析。