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测量状态和特征焦虑:多维物件响应理论的应用
Leonardo Carlucci1, Marco Innamorati2, Melissa Ree3
1Learning Science Hub, University of Foggia, 71121 Foggia, Italy.
Behavioral sciences (Basel, Switzerland)
|August 25, 2023
概括
认知和体质焦虑的州特征清单 (STICSA) 准确地测量了焦虑的尺寸. 虽然双因素和多维模型都适合,但多维方法在评估状态和特征焦虑时提供了更高的精度.
科学领域:
- 心理测量 心理测量 心理测量
- 焦虑研究 焦虑研究
- 心理评估 心理评估
背景情况:
- 认知和体质焦虑的状态-特征清单 (STICSA) 是衡量状态和特征焦虑的关键工具.
- 以前的研究支持其在古典测试理论中的有效性和可靠性,但指出了关于维度的未解决问题.
- 对双因素与双因素模型的相互矛盾的发现促使进一步调查.
研究的目的:
- 使用物件响应理论 (IRT) 调查STICSA的维度.
- 为了比较一个双因素模型与STICSA的多维模型的持久性.
- 评估不同IRT模型在评估焦虑时的准确性和精度.
主要方法:
- 使用了多维物品响应理论 (mIRT) 分析.
- 在STICSA数据中应用了双因素和双相关维度模型.
- 分析了3338名意大利参与者的数据.
主要成果:
- 双因素和多维模型都显示出与STICSA数据的良好匹配.
- 多维模型在估计潜在状态和特征焦虑方面表现出卓越的准确性和精度.
- 项目参数在双因素模型和多维模型之间被发现是相当的.
结论:
- STICSA是一种精确的仪器,用于测量认知和体质焦虑的维度.
- 多维IRT模型为理解STICSA数据提供了一个强大的框架.
- 临床医生和研究人员应谨慎使用总分数,以防止精神诊断评估偏差.
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