临床仪器的物品响应建模与过器问题:解开症状的存在和严重程度
1Boston College, Chestnut Hill, MA, USA.
Applied psychological measurement
|August 21, 2024
概括
多变量障碍分级响应模型 (MH-GRM) 比传统模型更好地测量所有严重程度的精神病理,特别是临床仪器中常见的过量零数据.
科学领域:
- 心理测量 心理测量 心理测量
- 心理病理学的测量方法
- 统计建模 统计建模
背景情况:
- 具有过/跟踪格式的临床仪器通常会产生多余的零数据.
- 一维分级响应模型 (GRMs) 与零膨胀数据扎,低估了较低严重程度的个体差异.
研究的目的:
- 评估多变量障碍分级响应模型 (MH-GRM) 对零膨胀问卷数据的有效性.
- 在捕捉心理病理学中的个人差异方面,将MH-GRM与单维GRM进行比较.
主要方法:
- 利用含有多余零的模拟和经验数据.
- 应用了单维GRM和拟议的MH-GRM.
- MH-GRM包含两个潜在变量:易感性和严重性.
主要成果:
- 与单维GRM相比,MH-GRM表现出在更广泛的精神病理谱中捕捉个人差异的卓越能力.
- 一维GRMs,当应用到过问题数据时,在很大程度上无法测量较低严重程度的差异.
结论:
- MH-GRM是分析临床仪器零膨胀数据的更合适和更全面的模型.
- 计算过多的零值对于准确评估整个严重程度范围内的精神病理学个体差异至关重要.
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