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永远不要错过声:使用移动传感来调查经验采样研究中的 (非) 合规性
Thomas Reiter1, Ramona Schoedel2
1Department of Psychology, Ludwig-Maximilians-Universität München, Leopoldstraße 13, 80802, Munich, Germany. T.Reiter@psy.lmu.de.
Behavior research methods
|November 6, 2023
概括
经验采样方法中的合规偏差可能会影响研究的有效性. 过去的参与者行为和上下文预测了反应行为,突出了需要仔细的研究设计,以确保数据准确性.
科学领域:
- 心理学方法 心理学方法
- 数据科学数据科学数据科学
- 行为研究 行为研究
背景情况:
- 经验采样方法 (ESM) 越来越多地在各个学科中使用.
- 在ESM中,合规偏见和缺失的数据模式可能会威胁到研究的有效性.
- 了解参与者合规性的预测因素对于可靠的研究结果至关重要.
研究的目的:
- 调查经验抽样研究中的合规偏见.
- 在ESM框架内确定参与者响应行为的关键预测因素.
- 评估机器学习模型在检测合规偏差方面的有效性.
主要方法:
- 利用了592名参与者的大量数据集和超过25,000个观察结果.
- 收集的多方法数据:传统调查,ESM和移动传感.
- 使用并比较线性 (逻辑,弹性净回归) 和非线性 (随机森林) 机器学习模型.
主要成果:
- 发现了遵守偏差的证据,成功预测了响应行为.
- 过去与研究相关的行为,特别是之前的ESM响应率,是最强的预测因素.
- 物理背景变量 (例如,位置:家庭与工作) 也大大预测了合规性.
结论:
- 合规偏见是经验抽样研究中的一个可证明的问题.
- 参与者的历史和上下文因素是理解和潜在地减轻偏见的关键.
- 调查结果为ESM研究的设计和缺乏数据的方法研究提供了信息.
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