The quantiles of extreme differences matrix for evaluating discriminant validity.

Q3 Mathematics
Epidemiologic Methods Pub Date : 2025-08-25 eCollection Date: 2025-01-01 DOI:10.1515/em-2025-0006
Tyler J VanderWeele, R Noah Padgett
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引用次数: 0

Abstract

When data on multiple indicators of underlying psychosocial constructs are collected, they are often intended as closely related assessments of a relatively unified phenomenon, or alternatively as capturing distinct facets of the phenomenon. Establishing distinctions among construct phenomena, assessments, or indicators is sometimes described as establishing discriminant validity. In the philosophical literature, often extreme instances or limit cases, actual or hypothetical, are used to identify settings in which one phenomenon is present and the other is not, to establish distinctions. We put forward an empirical analogue of this philosophical principle applied to distinctions amongst survey item responses. The quantiles of extreme differences matrix characterizes, for each pair of indicators, how large differences are between indicators at relatively extreme quantiles of the distribution of those differences. We discuss potential uses and properties of this matrix and related matrices for identifying relevant distinctions among indicators or facets of underlying construct phenomena.

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Abstract Image

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极差矩阵的分位数评价判别效度。
当收集关于潜在社会心理结构的多个指标的数据时,它们通常是为了对一个相对统一的现象进行密切相关的评估,或者作为捕捉该现象的不同方面。在构念现象、评估或指标之间建立区别有时被描述为建立区别效度。在哲学文献中,通常使用极端的实例或极限案例,实际的或假设的,来识别一种现象存在而另一种现象不存在的环境,以建立区别。我们提出了这一哲学原理的经验模拟,应用于调查项目反应之间的区别。极端差异矩阵的分位数表示,对于每一对指标,在这些差异分布的相对极端分位数上,指标之间的差异有多大。我们讨论了这个矩阵和相关矩阵的潜在用途和性质,以识别潜在构造现象的指标或方面之间的相关区别。
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来源期刊
Epidemiologic Methods
Epidemiologic Methods Mathematics-Applied Mathematics
CiteScore
2.10
自引率
0.00%
发文量
7
期刊介绍: Epidemiologic Methods (EM) seeks contributions comparable to those of the leading epidemiologic journals, but also invites papers that may be more technical or of greater length than what has traditionally been allowed by journals in epidemiology. Applications and examples with real data to illustrate methodology are strongly encouraged but not required. Topics. genetic epidemiology, infectious disease, pharmaco-epidemiology, ecologic studies, environmental exposures, screening, surveillance, social networks, comparative effectiveness, statistical modeling, causal inference, measurement error, study design, meta-analysis
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