通过选择性项目重权来减轻心理问卷措施的负担
1Department of Neuroimaging, King's College London, London, UK.
Royal Society open science
|April 17, 2025
概括
研究人员开发了一种新的方法来缩短漫长的问卷,改善参与者的体验和数据质量. 这种用拉索估计器 (FIRE-L) 技术减少因子分数项目的技术可以准确预测较少项目的分数.
科学领域:
- 心理学科学 心理学科学
- 心理测量 心理测量 心理测量
- 量化心理学 量化心理学
背景情况:
- 调查问卷措施在心理学研究中是必不可少的,但往往是漫长而繁的.
- 长时间的调查问卷导致参与者体验不佳,数据质量下降,成本增加.
- 现有的缩短措施的方法可能无法准确地保存得分信息.
研究的目的:
- 引入一种新的,简单的方法来创建现有的心理测量措施的缩短版本.
- 为了确保减少项目集准确地确定参与者的总分数,分级分数或因子分数.
- 提供实用工具和指导来实施这个项目减少技术.
主要方法:
- 使用拉索调节回归来进行项目选择和权重.
- 开发了使用拉索估计器 (FIRE-L) 方法减少因子得分项的方法.
- 将该方法应用于示例数据集,以证明其性能.
主要成果:
- 通过FIRE-L方法,成功地识别出了有限的一组项目.
- 使用缩短的措施,可以准确地预测真实得分 (总和,分级,因子).
- 该方法提供了一个可行的解决方案,用于创建高效可靠的简短问卷表格.
结论:
- FIRE-L方法提供了一种有效的方法来缩短心理测量问卷,而不会牺牲得分的准确性.
- 这种方法可以增强参与者的体验,提高数据质量,降低研究成本.
- 该研究为研究人员提供了实际的实施指南和代码.
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