A large variety of item response theory (IRT) modeling approaches aim at measuring and correcting for response styles in rating data. Here, we integrate response style models of the divide-by-total model family into one superordinate framework that parameterizes response styles as person-specific shifts in threshold parameters. This superordinate framework allows us to structure and compare existing approaches to modeling response styles and therewith makes model-implied restrictions explicit. With a simulation study, we show how the new framework allows us to assess consequences of violations of model assumptions and to compare response style estimates across different model parameterizations. The integrative framework of divide-by-total modeling approaches facilitates the correction for and examination of response styles. In addition to providing a superordinate framework for psychometric research, it gives guidance to applied researchers for model selection and specification in psychological assessment. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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在项目总数理论模型（第1部分）中，对响应样式进行建模的不同方法：模型集成。

各种各样的项目响应理论（IRT）建模方法旨在测量和校正评级数据中的响应样式。在这里，我们将按比例划分的模型家族的响应样式模型集成到一个上级框架中，该框架将响应样式参数化为阈值参数中因人而异的转变。这个上级框架使我们能够构建和比较现有的建模响应样式的方法，从而使隐含模型的约束变得明确。通过仿真研究，我们展示了新框架如何使我们能够评估违反模型假设的后果，并比较不同模型参数化之间的响应样式估计。按比例划分建模方法的集成框架有助于更正和检查响应样式。除了为心理测量研究提供上级框架之外，它还为应用研究人员在心理评估中的模型选择和规范提供指导。（PsycInfo数据库记录（c）2020 APA，保留所有权利）。