Although there are a number of approaches to constructing a measure of multidimensional social exclusion in later life, theoretical and methodological challenges exist around the aggregation and weighting of constituent indicators. This is in addition to a reliance on secondary data sources that were not designed to collect information on social exclusion. In this paper, we address these challenges by comparing a range of existing and novel approaches to constructing a composite measure and assess their performance in explaining social exclusion in later life. We focus on three widely used approaches (sum-of-scores with an applied threshold; principal component analysis; normalisation with linear aggregation), and three novel supervised machine-learning based approaches (least absolute shrinkage and selection operator; classification and regression tree; random forest). Using an older age social exclusion conceptual framework, these approaches are applied empirically with data from Wave 1 of The Irish Longitudinal Study on Ageing (TILDA). The performances of the approaches are assessed using variables that are causally related to social exclusion.

尽管在以后的生活中有许多方法可以构建多维社会排斥的度量标准，但围绕构成指标的汇总和加权仍存在着理论和方法上的挑战。这是对依赖于并非旨在收集有关社会排斥信息的辅助数据源的补充。在本文中，我们通过比较一系列现有的和新颖的方法来构建一种综合措施来应对这些挑战，并评估它们在解释以后的社会排斥中的表现。我们专注于三种广泛使用的方法（具有应用阈值的总和；主成分分析；线性聚合归一化），以及三种基于监督的新型机器学习方法（最小绝对收缩和选择算子；分类和回归树；随机森林）。使用更老的社会排斥概念框架，这些方法在经验上与来自爱尔兰纵向老龄化研究（TILDA）第一波的数据结合使用。使用与社会排斥有因果关系的变量评估方法的效果。