经验采样方法需要的不仅仅是数字
Laura F Bringmann1, Guðrún R Guðmundsdóttir2, Leonie Schorrlepp3,4
1Department of Psychometrics and Statistics, University of Groningen, Groningen, The Netherlands. l.f.bringmann@rug.nl.
Communications psychology
|February 23, 2026
概括
研究人员应该在经验抽样方法 (ESM) 中重新引入开放式问题,以获得更深入的见解. 分析这些文本响应提供了关键的背景和理解,不仅仅是数字数据.
科学领域:
- 心理学研究方法 心理学研究方法
- 数字化表型化是指数字化表型化.
- 定性数据分析是指对数据进行定性分析.
背景情况:
- 经验采样方法 (ESM) 传统上收集关于感受,行为和周围环境的实时数据.
- 目前的ESM研究主要集中在数值数据上,忽视了有价值的定性见解.
- 开放式的响应历来是ESM的一部分,但现在未被充分利用.
研究的目的:
- 倡导在ESM研究中重新收集和分析开放式响应.
- 强调定性数据对于解释ESM定量发现的重要性.
- 突出纯数值数据在捕捉体验丰富性方面的局限性.
主要方法:
- 审查现行实践在经验采样方法.
- 在ESM框架内整合定性文本分析的论点.
- 识别开放式数据对心理学研究的独特贡献.
主要成果:
- 开放式的答案提供了必要的背景,解释了参与者的经历背后的"为什么".
- 定性数据捕捉了诸如事件的时间顺序等细微差别,这些细微差别仅凭数字无法传达.
- 整合开放项目丰富了ESM数据,将其建立在生活和现实世界的经验中.
结论:
- 通过系统地收集和分析开放式文本项目,ESM研究将大大受益.
- 未来的研究和方法指南应优先考虑将定性数据纳入ESM.
- 恢复开放式问题对于通过ESM更全面地了解人类经验至关重要.
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