利用情节的相对持续时间对序列长度进行归一化处理:在德国博士生轨迹中的应用

IF 1.2 4区 社会学 Q4 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Longitudinal and Life Course Studies Pub Date : 2024-08-08 DOI:10.1332/17579597y2024d000000026
Gesche Brandt, Susanne de Vogel
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引用次数: 0

摘要

为了解决博士生轨迹序列长度差异显著的问题,我们提出了利用情节的相对持续时间进行序列归一化的方法。我们采用了来自德国博士学位获得者面板研究的插曲数据,其中博士轨迹以单月为单位,不同长度的博士轨迹可长达数年。利用归一化序列而非绝对序列,我们能够更好地识别典型轨迹。聚类解决方案的图形展示更准确地描述了基本过程。此外,它还提供了定义无固定长度参考序列的可能性。因此,归一化序列而不是距离证明是一种易于实施的方法,当识别模式是一个优先事项时,可以比较不同长度的序列。
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Normalising sequence lengths using the relative duration of episodes: an application to doctoral trajectories in Germany
To address significant variation of sequence lengths of doctoral trajectories, we propose sequence normalisation using the relative duration of episodes. We employ episode data from a panel study of doctorate holders in Germany where doctoral trajectories are measured in single months and differ in length up to several years. Utilising normalised sequences instead of absolute sequences, we are better able to identify typical trajectories. The graphical presentation of the cluster solutions more accurately depicts the underlying processes. Furthermore, it offers the possibility to define reference sequences without a fixed length. Normalising sequences instead of distances thus proves an easily implementable method to compare sequences of different lengths when the identification of patterns is a priority.
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来源期刊
CiteScore
2.50
自引率
11.10%
发文量
43
期刊最新文献
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