IRTree models decompose observed rating responses into sequences of theory‐based decision nodes, and they provide a flexible framework for analysing trait‐related judgements and response styles. However, most previous applications of IRTree models have been limited to binary decision nodes that reflect qualitatively distinct and unidimensional judgement processes. The present research extends the family of IRTree models for the analysis of response styles to ordinal judgement processes for polytomous decisions and to multidimensional parametrizations of decision nodes. The integration of ordinal judgement processes overcomes the limitation to binary nodes, and it allows researchers to test whether decisions reflect qualitatively distinct response processes or gradual steps on a joint latent continuum. The extension to multidimensional node models enables researchers to specify multiple judgement processes that simultaneously affect the decision between competing response options. Empirical applications highlight the roles of extreme and midpoint response style in rating judgements and show that judgement processes are moderated by different response formats. Model applications with multidimensional decision nodes reveal that decisions among rating categories are jointly informed by trait‐related processes and response styles.

中文翻译：

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IRTree具有顺序和多维决策节点的模型，用于响应样式和基于特征的评级响应。

IRTree模型将观察到的评级响应分解为基于理论的决策节点序列，并且它们为分析与特征相关的判断和响应样式提供了灵活的框架。但是，IRTree模型的大多数以前的应用程序仅限于反映定性上不同的一维判断过程的二元决策节点。本研究扩展了IRTree模型家族，用于对响应样式进行分析，以用于多变量决策的顺序判断过程以及决策节点的多维参数化。顺序判断过程的集成克服了对二元节点的限制，它使研究人员可以测试决策是否在联合潜在连续体上反映出质量上截然不同的响应过程或渐进步骤。多维节点模型的扩展使研究人员可以指定多个判断过程，这些过程同时影响竞争响应选项之间的决策。实证应用强调了极端和中点响应风格在评级判断中的作用，并表明判断过程受不同响应格式的调节。具有多维决策节点的模型应用程序显示，评分类别之间的决策是由特质相关过程和响应样式共同提供的。