基于特征的聚类与心理时间序列的温和介绍和应用
Jannis Kreienkamp1, Maximilian Agostini1, Rei Monden2
1Department of Psychology, University of Groningen, Groningen, Netherlands.
Multivariate behavioral research
|December 11, 2024
概括
本研究引入了基于特征的时间序列聚类用于心理学研究,使得使用动态措施分析复杂的参与者发展. 该方法解决了传统集群的局限性,为理解发展轨迹中的个体差异提供了灵活的方法.
科学领域:
- 心理学 心理学 心理学
- 数据科学数据科学数据科学
- 计算社会科学 计算社会科学
背景情况:
- 心理学研究越来越多地涉及复杂的时间序列数据,以了解参与者的发展.
- 由于模型假设,现有的集群方法往往无法纳入有意义的动态指标 (例如,不稳定性,惯性).
- 需要针对心理时间序列数据量身定制的灵活和透明的集群方法.
研究的目的:
- 为心理学研究提出和说明基于特征的时间序列聚类.
- 根据有意义的动态发展措施,使参与者能够聚集在一起.
- 解决生态瞬间评估 (EMA) 数据中的挑战,包括多变量概念化,缺失和非静止性.
主要方法:
- 建议基于特征的时间序列聚类,使用常见的聚类算法对提取的动态措施进行聚类.
- 关键步骤包括输入选择,特征提取,特征减少,集群和集群评估.
- 这种方法是使用真实世界EMA数据来证明的.
主要成果:
- 基于特征的聚类成功地基于动态发展措施聚类参与者.
- 该方法有效地处理心理时间序列数据中常见的复杂性,例如缺失的值和变化的趋势.
- 为实施提供了实际指导和代码.
结论:
- 基于特征的时间序列聚类为分析心理发育数据提供了一种灵活,透明和有效的方法.
- 这种方法克服了传统方法的局限性,允许对个体差异有更丰富的见解.
- 提出的方法和附带的资源有助于对复杂的心理时间序列进行高级分析.
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