Individual response style behaviors, unrelated to the latent trait of interest, may influence responses to ordinal survey items. Response style can introduce bias in the total score with respect to the trait of interest, threatening valid interpretation of scores. Despite claims of response style stability across scales, there has been little research into stability across multiple scales from the beneficial perspective of item response trees. This study examines an extension of the IRTree methodology to include mixed item formats, providing an empirical example of responses to three scales measuring perceptions of social media, climate change, and medical marijuana use. Results show extreme and midpoint response styles were not stable across scales within a single administration and 5-point Likert-type items elicited higher levels of extreme response style than the 4-point items. Latent trait of interest estimation varied, particularly at the lower end of the score distribution, across response style models, demonstrating as appropriate response style model is important for adequate trait estimation using Bayesian Markov chain Monte Carlo estimation.

与感兴趣的潜在特征无关的个人反应风格行为可能会影响对有序调查项目的反应。反应风格可以在总分中引入与感兴趣特征相关的偏差，威胁对分数的有效解释。尽管声称跨尺度的响应风格稳定性，但从项目响应树的有益角度对跨多个尺度的稳定性的研究很少。本研究检查了 IRTree 方法的扩展，以包括混合项目格式，提供了对三个量表的响应的经验示例，这些量表测量社交媒体、气候变化和医用大麻的使用。结果显示极端和中点反应风格在单一管理范围内不稳定，5 点李克特类型项目比 4 点项目引起更高水平的极端反应风格。兴趣估计的潜在特征各不相同，尤其是在分数分布的低端，跨反应风格模型，证明适当的反应风格模型对于使用贝叶斯马尔可夫链蒙特卡罗估计进行充分的特征估计很重要。