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：自评量表（如李克特量表）被广泛用于主观健康感知的评估。近年来，基于滑块的视觉模拟量表因其能精准、便捷地评估人的感受而日益普及。这类数据可能受应答风格的影响，这是一种独立于问卷指导语的用户依赖型系统性倾向。尽管应答风格（在视觉模拟量表中称为应答概况）在个体间分析中至关重要，但由于视觉模拟量表主要用于个体内监测且受应答概况影响较小，因此对其处理方法的研究一直鲜有关注。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评，这使得传统适用于李克特量表的方法难以直接应用。本研究针对多种类型的重复测量视觉模拟量表数据，提出了一种新颖的应答概况表征方法。该方法通过混合类应答风格分布将应答概况建模为分布参数θ，并采用自助抽样技术处理重复测量中数据不平衡的问题。我们通过模拟伪数据和实证研究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，该方法能准确估计应答概况参数θ，展现出良好的稳健性。此外，将所提方法应用于真实视觉模拟量表数据集时发现：即使在重复视觉模拟量表测量中，个体间也存在应答概况异质性，这与李克特量表的发现一致。该方法使视觉模拟量表数据分析能类似李克特量表数据一样考虑应答概况异质性。