Many approaches in the item response theory (IRT) literature have incorporated response styles to control for potential biases. However, the specific assumptions about response styles are often not made explicit. Having integrated different IRT modeling variants into a superordinate framework, we highlighted assumptions and restrictions of the models (Henninger & Meiser, 2020). In this article, we show that based on the superordinate framework, we can estimate the different models as multidimensional extensions of the nominal response models in standard software environments. Furthermore, we illustrate the differences in estimated parameters, restrictions, and model fit of the IRT variants in a German Big Five standardization sample and show that psychometric models can be used to debias trait estimates. Based on this analysis, we suggest 2 novel modeling extensions that combine fixed and estimated scoring weights for response style dimensions, or explain discrimination parameters through item attributes. In summary, we highlight possibilities to estimate, apply, and extend psychometric modeling approaches for response styles in order to test hypotheses on response styles through model comparisons. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

---

在按总项目响应理论模型（第2部分）中对响应样式进行建模的不同方法：应用程序和新颖的扩展。

项目响应理论（IRT）文献中的许多方法都采用了响应样式来控制潜在的偏差。但是，关于响应样式的特定假设通常不明确。将不同的IRT建模变体集成到上级框架后，我们重点介绍了模型的假设和限制（Henninger＆Meiser，2020年）。在本文中，我们证明了基于上级框架，我们可以将不同的模型估计为标准软件环境中名义响应模型的多维扩展。此外，我们在德国大五国标准化样本中说明了IRT变体的估计参数，限制和模型拟合的差异，并表明心理测量模型可用于对性状估计进行反偏。根据此分析，我们建议2种新颖的建模扩展，将固定和估计的计分权重结合起来用于响应样式维度，或通过项目属性解释判别参数。总而言之，我们重点介绍了估计，应用和扩展针对响应样式的心理建模方法的可能性，以便通过模型比较来检验有关响应​​样式的假设。（PsycInfo数据库记录（c）2020 APA，保留所有权利）。