创建潜在因素的规模测量:一种遗传算法方法
Kelsey L Lowman1, Thomas H Costello2,3, Rochelle A Stewart1
1Florida State University, Tallahassee, USA.
Assessment
|September 25, 2025
概括
遗传算法 (GA) 为开发新的心理尺度提供了一种新的方法. 这项研究使用了GA来为三级精神病症框架创建精确的测量,从而改善了临床评估.
科学领域:
- 心理学 心理学 心理学
- 量化心理学 量化心理学
- 心理测量 心理测量 心理测量
背景情况:
- 遗传算法 (GA) 主要用于缩小规模.
- 三级精神病的框架包括大胆,卑和无节制.
- 新规模的开发需要有效的项目选择方法.
研究的目的:
- 应用修改后的GA来开发新的尺度,用于三级精神病的框架.
- 用模型估计的因子得分作为项目选择的目标.
- 通过结构和项目响应建模来改进选择的项目集.
主要方法:
- 模拟的三阶层精神病态结构 (大胆,卑,无抑制) 作为潜在的因素.
- 应用了修改后的GA来从基本精神病评估中选择项目.
- 使用结构和项目响应建模用于缩放精细化.
主要成果:
- 开发的EPA-Triarchic尺度与目标因子得分高度相关.
- 与现有指标相比,新的天平显示了较强的负载.
- GA有效地选择了索引潜在因素的项目.
结论:
- 遗传算法可以有效地用于心理测量的新规模开发.
- 开发的EPA-Triarchic尺度提供了一个强大的心理病构造的衡量标准.
- 这种GA方法在临床评估和心理测量方面具有广泛的潜在应用.
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