Tutz Gerhard, Schauberger Gunther, Berger Moritz
Ludwig-Maximilians-Universität München, Germany.
Institut für Medizinische Biometrie, Informatik und Epidemiologie, Universitätsklinikum Bonn, München, Germany.
Appl Psychol Meas. 2018 Sep;42(6):407-427. doi: 10.1177/0146621617748322. Epub 2018 Jan 12.
In the modeling of ordinal responses in psychological measurement and survey-based research, response styles that represent specific answering patterns of respondents are typically ignored. One consequence is that estimates of item parameters can be poor and considerably biased. The focus here is on the modeling of a tendency to extreme or middle categories. An extension of the partial credit model is proposed that explicitly accounts for this specific response style. In contrast to existing approaches, which are based on finite mixtures, explicit person-specific response style parameters are introduced. The resulting model can be estimated within the framework of generalized mixed linear models. It is shown that estimates can be seriously biased if the response style is ignored. In applications, it is demonstrated that a tendency to extreme or middle categories is not uncommon. A software tool is developed that makes the model easy to apply.
在心理测量和基于调查的研究中对有序反应进行建模时，代表受访者特定回答模式的反应风格通常被忽略。一个后果是项目参数的估计可能很差且存在相当大的偏差。这里的重点是对极端或中间类别倾向的建模。提出了部分信用模型的扩展，该扩展明确考虑了这种特定的反应风格。与基于有限混合的现有方法不同，引入了明确的个人特定反应风格参数。由此产生的模型可以在广义混合线性模型的框架内进行估计。结果表明，如果忽略反应风格，估计可能会严重偏差。在应用中，证明了极端或中间类别倾向并不罕见。开发了一个软件工具，使该模型易于应用。
