使用调查项目的属性来预测响应时间可能有利于调查研究
Stefan Schneider1,2,3, Haomiao Jin4, Bart Orriens4
1Center for Self-Report Science & Center for Economic and Social Research, University of Southern California, Los Angeles, CA 90089-3332, USA.
概括
研究人员开发了一种机器学习模型来预测调查响应时间,为认知努力提供了新的见解. 该算法为常见的调查项目建立参考值,帮助未来的研究.
科学领域:
- 认知心理学 认知心理学
- 心理测量 心理测量 心理测量
- 机器学习应用程序 机器学习应用程序
背景情况:
- 调查响应时间越来越多地被用作认知努力的指标.
- 了解影响响应时间的因素对于调查设计和数据解释至关重要.
研究的目的:
- 使用机器学习开发调查项目响应时间的预测模型.
- 确定影响调查完成期间认知努力的关键项目属性.
主要方法:
- 利用机器学习算法来分析响应时间.
- 从大量的普通人口样本中收集了41个调查项目的不同属性的数据.
- 开发了一个基于这些属性的预测模型.
主要成果:
- 成功创建了一个能够预测调查响应时间的机器学习模型.
- 确定了特定的项目特征 (例如,长度,格式,语言特征),这些特征显著影响响应持续时间.
- 该模型展示了推导预期响应时间的参考值的潜力.
结论:
- 机器学习提供了一个强大的方法,通过调查响应时间来建模认知努力.
- 开发的算法为设定基准和改进调查方法提供了有价值的工具.
- 这项研究有助于更深入地了解参与调查的认知过程.
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