什么时候项目细微差别在组织中对预测有用? 比较项目级,规模级和整体机器学习模型的有效性
Chen Tang1, Louis Hickman2, Q Chelsea Song3
1Kogod School of Business, American University.
The Journal of applied psychology
|December 15, 2025
概括
项目级预测模型比规模级模型提供更高的准确性,特别是当满足特定条件时,如高内部一致性和大样本大小. 这些模型捕获了在聚合得分中丢失的细微数据.
科学领域:
- 组织心理学 组织心理学
- 预测建模预测建模
- 心理测量 心理测量 心理测量
背景情况:
- 传统的预测模型使用尺度得分,可能会失去有价值的项目特定差异.
- 项目级预测模型显示,在组织环境中提高准确性是有前途的.
- 优于项目级与规模级模型的条件仍然不清楚.
研究的目的:
- 调查项目级或规模级模型何时更适合于组织实践中的预测.
- 确定项目细微差别影响预测有效性的条件.
- 为了比较项目和规模级预测模型的与标准相关的有效性.
主要方法:
- 检查了现实世界的组织数据集,以确定项目细微差别.
- 进行了蒙特卡洛模拟,对细微差别分布,效果大小,内部一致性和样本大小进行了变化.
- 分析了项目级和规模级模型的与标准相关的有效性.
主要成果:
- 当很少有项目有细微差别,细微差别效果大小很大,内部一致性很高,训练样本大小很大时,建议使用项目级模型.
- 有利于项目级模型的条件在组织数据中很常见.
- 当模型选择模糊时,合并模型是可行的替代方案.
结论:
- 在特定条件下,项目级模型可以在组织预测中优于传统规模级模型.
- 了解和利用项目细微差别对于准确的预测建模至关重要.
- 该研究提供了实施各种预测模型的实际建议和R代码.
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