Personality constructs, attitudes and other non‐cognitive variables are often measured using rating or Likert‐type scales, which does not come without problems. Especially in low‐stakes assessments, respondents may produce biased responses due to response styles (RS) that reduce the validity and comparability of the measurement. Detecting and correcting RS is not always straightforward because not all respondents show RS and the ones who do may not do so to the same extent or in the same direction. The present study proposes the combination of a multidimensional IRTree model with a mixture distribution item response theory model and illustrates the application of the approach using data from the Programme for the International Assessment of Adult Competencies (PIAAC). This joint approach allows for the differentiation between different latent classes of respondents who show different RS behaviours and respondents who show RS versus respondents who give (largely) unbiased responses. We illustrate the application of the approach by examining extreme RS and show how the resulting latent classes can be further examined using external variables and process data from computer‐based assessments to develop a better understanding of response behaviour and RS.

中文翻译：

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结合混合分布和多维IRTree模型来测量极端响应样式。

人格建构，态度和其他非认知变量通常使用等级或李克特型量表来衡量，这并非没有问题。特别是在低风险评估中，由于响应方式（RS）降低了测量的有效性和可比性，受访者可能会产生有偏差的响应。检测和校正RS并不总是那么简单，因为并非所有受访者都显示RS，而这样做的人可能未在相同程度或相同方向上这样做。本研究提出了多维IRTree模型与混合物分配项响应理论模型的组合，并使用国际成人能力评估计划（PIAAC）的数据说明了该方法的应用。这种联合方法可以区分显示不同RS行为的不同潜在类别的受访者和显示RS的受访者与做出（大部分）无偏见的受访者。我们通过检查极端RS来说明该方法的应用，并展示如何使用外部变量和基于计算机的评估中的过程数据来进一步检查所得的潜在类别，以更好地理解响应行为和RS。