使用序列分析选择定性病例:一种混合方法,以深入了解生命过程轨迹
Guillaume Le Roux1, Matthias Studer2, Arnaud Bringé1
1Institut national d'études démographiques - French Institute for Demographic Research (INED), 9 cours des Humanités CS 50004 93322, Aubervilliers Cedex, France.
Advances in life course research
|December 6, 2023
概括
本研究引入了一种新的序列分析方法,用于从定量数据中选择定性病例. 该方法增强了对使用边缘性和收益指标的生命历程轨迹和统计学关联的理解.
科学领域:
- 社会科学 社会科学 社会科学
- 量化心理学 量化心理学
- 社会学 社会学 社会学
背景情况:
- 传统的分析通常将定量和定性数据分开.
- 终身研究受益于对个体轨迹的整体视角.
- 选择具有代表性或说明性的案例进行定性分析可能具有挑战性.
研究的目的:
- 提出基于序列分析的方法来选择基于定量结果的定性案例.
- 制定指标来评估纵向研究中的案例典型性和说明性.
- 为在生命历程研究中应用这些指标提供准则.
主要方法:
- 开发两个互补的指标:边际性和收益.
- 对纵向数据的序列分析的应用.
- 使用TraMineRextras R包进行指示器计算.
- 插图:关于住宅轨迹和社会化的研究.
主要成果:
- 拟议的方法允许根据它们对定量关系的贡献选择不同的定性案例.
- 边际性和收益指标有效地通过典型性和说明性来标记观测.
- 该方法提高了对统计学关联的理解,并在生命历程分析的背景下确定了它们的局限性.
结论:
- 基于序列分析的方法提供了一个强大的方法,用于将定量和定性数据整合到生命历程研究中.
- 边际性和收益指标为案例选择提供了有价值的见解.
- 该方法有可能扩展到更广泛的研究场景.
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