IF 1.6 Q3 HEALTH CARE SCIENCES & SERVICES
International Journal of Population Data Science Pub Date : 2025-03-25 eCollection Date: 2023-01-01 DOI:10.23889/ijpds.v8i6.2953
Shivani Sickotra
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引用次数: 0

摘要

简介序列分析是研究从学校到工作的纵向轨迹的有力方法。尽管其应用日益广泛,但关于如何准备合适数据集的指导却很有限。本资料详细介绍了如何创建一个专门用于序列分析的数据集,该数据集记录了英格兰 2010/11 年离校学生群体中 556,182 人的年度教育和就业活动状态:该数据集是利用教育部的纵向教育成果(LEO)数据构建的。数据处理是为序列分析量身定制的,包括减少活动状态的数量以及应用层次结构整合教育和就业数据:由此产生的数据集跨越了从 2011/12 年第一个非义务教育状态到 2018/19 年的活动,追踪了从 16/17 岁到 23/24 岁的轨迹。数据集的设计能够根据离校者最初的联合行政区居住地对其进行子集,以帮助对从学校到工作的轨迹进行区域分析。除了纵向地理位置和就业收入数据外,还建立了可与纵向活动历史相联系的个人社会人口特征。此外,还讨论了所开发数据的局限性:本资料为需要为序列分析准备输入数据集经验的研究人员和从业人员提供了重要指导,解决了当前可用资源不足的问题。通过提供分步指导和共享代码,它使用户能够重新创建或调整数据集,以满足其特定的研究需求。它还能按地区进行子集分析,进一步支持对从学校到工作的轨迹进行本地化比较研究,使其成为推进现有研究的重要工具。LEO 数据可通过国家统计局安全研究服务处申请获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data resource profile: a guide for constructing school-to-work sequence analysis trajectories using the longitudinal education outcomes (LEO) data.

Introduction: Sequence analysis is a powerful methodology for examining longitudinal school-to-work trajectories. Despite its growing use, there is limited guidance on preparing suitable datasets. This resource details the creation of a dataset specifically designed for sequence analysis, capturing yearly education and employment activity states for 556,182 individuals from England's 2010/11 school-leaver cohort.

Methods: The dataset was constructed using the Department for Education's Longitudinal Education Outcomes (LEO) data. SQL was used to extract relevant variables, and data linkage and preprocessing was performed using R. Data processing was tailored to sequence analysis, including reducing the number of activity states and applying a hierarchy to integrate education and employment data.

Results: The resulting dataset spans activities from the first non-compulsory state in 2011/12 until 2018/19, tracking trajectories from ages 16/17 to 23/24. The dataset was designed with the ability to subset school-leavers by their initial Combined Authority residence to aid in regional analysis of school-to-work trajectories. Individual-level socio-demographic characteristics that can be linked to the longitudinal activity histories were also built, alongside longitudinal geographic locations and employment earnings data. Additionally, the limitations of the developed data are discussed.

Conclusion: This resource provides crucial guidance for researchers and practitioners who may require experience preparing input datasets for sequence analysis, addressing the current gap in available resources. By offering step-by-step instructions and shared code, it empowers users to recreate or adapt the dataset for their specific research needs. Its ability to subset by region further supports localised and comparative studies of school-to-work trajectories, making it a valuable tool for advancing existing research. The LEO data can be accessed by application through the Office for National Statistics Secure Research Service.

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CiteScore
2.50
自引率
0.00%
发文量
386
审稿时长
20 weeks
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