分解真实分数变异的评级对不同思维任务的反应来评估创造力:一个多特征多方法分析
1Department for Methods for Intensive Data in Psychology, University of Konstanz, 78464 Konstanz, Germany.
Journal of Intelligence
|October 25, 2024
概括
这项研究引入了一种分析分歧思维 (DT) 任务的新模型,揭示了创造性特征之间的高相关性,但在不同DT措施中一致性较低. 这表明在测量工具之间存在的变化比在创造力方面更大.
科学领域:
- 心理学 心理学 心理学
- 心理测量 心理测量 心理测量
- 认知科学 认知科学
背景情况:
- 不同思维 (DT) 任务对于评估创造力至关重要.
- 以前的分析往往忽略了由多个评分器和DT任务产生的复杂数据结构.
- 确认因素分析 (CFA) 方法在处理交叉分类数据方面存在局限性.
研究的目的:
- 适应和应用相关的特征相关的方法减一 (CTC(M-1)) 多特征多方法模型用于交叉分类数据,以分析DT任务响应.
- 为了明确考虑DT任务的基于评级者的评分所固有的交叉分类数据结构.
- 将真分差分解为特定于目标,特定于任务,特定于评分者和特定于交互的组件.
主要方法:
- 对交叉分类数据的CTC的修改和应用.
- 真正分数变异的分解成多个组件 (目标,任务,评分者,相互作用).
- 使用贝叶斯统计学估计测量无误差参数,并与最大概率方法进行比较.
主要成果:
- 经过调整的CTC(M-1) 模型成功地解释了DT任务响应中的交叉分类结构.
- 观察到高特征相关性,表明不同创造力方面之间存在密切的关系.
- 不同DT测量的低一致性表明使用的测量仪器之间存在显著的异质性.
结论:
- 该研究为分析复杂的DT任务数据提供了一个强大的统计框架,为创造力测量提供了细微的见解.
- 这些发现强调了在创造力研究中考虑测量仪器变量的重要性.
- 该模型提供了有价值的参数,用于理解特征的一致性,方法特异性和评分器效应在创造力评估中.
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