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区分极端和中点响应风格模型作为单个维度与两个独立维度的对立极点之间的模型:模拟研究

Martijn Schoenmakers¹, Maria Bolsinova¹, Jesper Tijmstra¹

¹Department of Methodology and Statistics, Tilburg University, Tilburg, Netherlands.

Applied psychological measurement

|September 16, 2025

概括

本研究研究了如何在问卷中建模极端和中点响应风格. 结果表明,像AIC和BIC这样的信息标准可以区分具有足够数据的响应风格模型.

科学领域:

心理测量心理测量心理测量
量化心理学量化心理学
调查方法调查方法

背景情况:

极端和中点响应风格可以降低利克特类问卷数据的有效性.
现有的文献提供了对这些响应风格的不同的概念化:单维 (相反的极点) 与多维 (单独的维度).
模型的选择影响复杂性,参数估计,以及在物件响应理论 (IRT) 模型中检测和纠正响应风格的能力.

研究的目的:

实证地确定极点和中点响应是否可以作为单独的维度与单一维度的相反极点区分.
为了比较响应风格的不同建模方法的性能.
评估信息标准 (AIC,BIC) 在响应风格分析的模型选择中的有用性.

主要方法:

利用多维名义响应模型来表示响应风格的各种概念化.
使用阿卡伊克信息标准 (AIC) 和贝叶斯信息标准 (BIC) 来进行模型比较.
进行模拟研究并分析经验数据集以评估模型性能.

主要成果:

当样本大小,测试长度和响应风格强度充足时,AIC和BIC在区分竞争模型方面表现良好.
在识别没有响应风格的模型方面,BIC表现出了卓越的表现.
当存在多个响应样式维度时,AIC的表现优于BIC.

结论:

信息标准 (AIC,BIC) 是在IRT中选择适当的响应风格模型的有效工具,只要提供足够的数据.
选择AIC和BIC可能取决于数据的特定特征,特别是多个响应样式维度的存在.
结果为研究人员提供了关于建模和纠正响应风格的实用指南,以提高问卷有效性.

关键词:

极端的响应风格极端的响应风格项目响应理论是物品响应理论.中点响应风格的中点响应风格.多维名义响应模型多维名义响应模型响应方式响应风格模拟研究是模拟研究.

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