Dyadic or, more generally, polyadic life course sequences can be more associated within dyads or polyads than between randomly assigned dyadic/polyadic member sequences, a phenomenon reflecting the life course principle of linked lives. In this article, I propose a method of U and V measures for quantifying and assessing linked life course trajectories in sequence data. Specifically, I compare the sequence distance between members of an observed dyad/polyad against a set of randomly generated dyads/polyads. TheU measure quantifies how much greater, in terms of a given distance measure, the members in a dyad/polyad resemble one another than do members of randomly generated dyads/polyads, and the V measure quantifies the degree of linked lives in terms of how much observed dyads/polyads outperform randomized dyads/polyads. I present a simulation study, an empirical study analyzing dyadic family formation sequence data from the Longitudinal Study of Generations, and a random seed sensitivity analysis in the online supplement. Through these analyses, I demonstrate the versatility and usefulness of the proposed method for quantifying linked lives analysis with sequence data. The method has broad applicability to sequence data in life course, business and organizational, and social network research.

中文翻译：

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使用序列分析来量化生活课程的关联程度

与随机分配的二元/多元成员序列之间相比，二元或更普遍的多元生命历程序列在二元或多元生命历程序列中的关联度更高，这种现象反映了关联生命的生命历程原则。在本文中，我提出了一种 U 和 V 度量方法，用于量化和评估序列数据中关联的生命历程轨迹。具体来说，我将观察到的 dyad/polyad 成员之间的序列距离与一组随机生成的 dyads/polyad 进行比较。U 度量量化了在给定的距离度量方面，二元组/多元组中的成员彼此之间的相似程度比随机生成的二元组/多元组成员之间的相似程度要大多少，而 V 度量则根据多少来量化链接生命的程度观察到的 dyads/polyads 优于随机 dyads/polyads。我提出了一项模拟研究，一项分析来自世代纵向研究的二元家庭形成序列数据的实证研究，以及在线补充中的随机种子敏感性分析。通过这些分析，我证明了所提出的使用序列数据量化关联寿命分析的方法的多功能性和实用性。该方法对生命历程、商业和组织以及社交网络研究中的数据排序具有广泛的适用性。