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问得更少,学得更多:通过模拟问题答案信息获取来适应生态瞬间评估调查长度

Jixin Li¹, Aditya Ponnada¹, Wei-Lin Wang²

¹Northeastern University, USA.

Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies

|December 12, 2024

概括

本研究引入了一种用于生态瞬间评估 (EMA) 调查的智能方法. 它通过预测和跳过非信息性的问题来减少调查长度,提高数据质量和用户合规性.

科学领域:

心理学科学心理学科学
计算机科学计算机科学
医疗信息学医疗信息学

背景情况:

生态瞬间评估 (EMA) 通过移动设备在自然环境中收集实时,自我报告的数据.
经过长时间的EMA调查,参与者的高负担可能会损害数据质量和合规性.
目前EMA的方法往往难以平衡数据丰富性和参与者的参与.

研究的目的:

开发和评估一种新的方法,以减少EMA调查的长度,同时尽量减少信息丢失.
为了提高参与者遵守和数据质量在纵向EMA研究.
为了实现更全面的数据收集或减少EMA参与者的负担.

主要方法:

提出了一个问题答案预测模型,以估计每个EMA问题的信息获取.
优先考虑信息性的问题,并跳过非信息性的问题,采用顺序的,逐个问题的方法.
在四个真实世界EMA数据集上使用模拟问题遗漏来评估方法.

主要成果:

与随机遗漏 (50%跳过) 相比,提出的方法减少了15%-52%的归算错误.
在有五个答案选项的调查中,平均调查长度减少了34%-56%.
对于跳过的问题,实时预测准确度达到了72%-95%.

结论:

智能跳过问题方法显著提高了EMA的效率和数据完整性.
这种方法为减少纵向研究中的参与者负担提供了一个可行的解决方案.
通过平衡全面性和用户体验,促进了EMA可持续的数据收集.

关键词:

生态瞬间评价生态瞬间评价经验采样采样经验采样机器学习是机器学习.问题信息性信息性调查长度调查长度调查长度用户负担用户负担.

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