在利克尔特尺度中使用中间类别的内部和个人之间的差异建模
Jesper Tijmstra1, Maria Bolsinova1
1Tilburg University, The Netherlands.
Applied psychological measurement
|March 5, 2025
概括
利克特等级可能具有挑战性,难以有效分析. 这项研究引入了一个新模型来区分中性反应和非反应,改善心理属性测量.
科学领域:
- 心理测量 心理测量
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 利克特尺度在心理学研究中被广泛使用.
- 在利克特尺度中,中间的响应选项带来了测量挑战.
- 受访者可以使用中间选项作为中立性或非响应.
研究的目的:
- 提出一种用于分析利克特尺度数据的新型统计模型.
- 为了解决利克特尺度中中间响应类别的模两可.
- 改进使用利克特数据对心理属性的有效测量.
主要方法:
- 开发一个响应混合的IRTree模型.
- 建模中间类别具有信息性的概率.
- 对兴趣属性和不响应倾向的估计.
主要成果:
- 拟议的模型区分信息中性和非信息性非响应.
- 模拟研究证明了模型的性能.
- 对人格心理学数据的应用显示了实际的实用性.
结论:
- 响应组合IRTree模型为利克特尺度数据分析提供了更有效的方法.
- 对响应混合物的计算提高了心理属性测量的准确性.
- 这种方法为使用利克特尺度的研究人员提供了一个强大的工具.
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