基于机器学习开发一个简短的Buss-Warren攻击性问卷
Xiuyu Jiang1, Yitian Yang1, Junyi Li1
1College of Psychology, Sichuan Normal University, Chengdu 610066, China.
Behavioral sciences (Basel, Switzerland)
|October 27, 2023
概括
使用机器学习开发了一个新的,更短的Buss-Warren侵略问卷 (BWAQ-ML). 这个由四个项目组成的工具有效地识别了青少年的攻击性,减少了测量时间和不足的努力反应.
科学领域:
- 心理学 心理学 心理学
- 机器学习 机器学习
- 青少年健康 青少年健康
背景情况:
- 高度的青少年侵略性与犯罪和自杀等负面结果相关.
- 巴斯-沃伦侵略问卷 (BWAQ) 是一个长篇的34项工具.
- 较长的问卷可能会导致不充分的努力响应 (IER) 和增加成本.
研究的目的:
- 使用机器学习 (ML) 开发一个更短,更有效的攻击性问卷.
- 为了减少不足的努力响应 (IER) 和实施成本.
- 为了保持测量的准确性和有效性,在一个缩小的格式.
主要方法:
- 开发了一个初始的简短问卷,使用渐进回归和ANOVA F-test.
- 使用机器学习算法创建最佳简短形式问卷 (BWAQ-ML).
- 使用独立样本验证了BWAQ-ML的可靠性和性能.
主要成果:
- BWAQ-ML仅包括四个项目,大大减少了长度.
- 实现了高性能指标:AUC (0.85),精度 (0.85),回忆 (0.89),精度 (0.83),以及F1得分 (0.86).
- 证明了良好的可靠性 (克朗巴赫α=0.84) 和构造有效性.
结论:
- BWAQ-ML是衡量青少年侵略性的有效和高效工具.
- 它的简洁性提高了大规模评估的效率,并将IER降到最低.
- 促进了对侵略性青少年的早期识别和干预.
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