系数α的数学界限是什么?

IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY Psychological methods Pub Date : 2023-05-25 DOI:10.1037/met0000583
Niels Waller, William Revelle
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引用次数: 0

摘要

系数α虽然在研究文献中无处不在,但经常被批评为测试信度的不良估计。本文考虑α的值域,并证明它没有下界(即α∈(-∞,1])。在概述我们的证明时,我们提出了生成数据集的算法,这些数据集将产生α在其范围内的任何固定值。我们也证明了对于一些数据集——甚至那些具有明显项目相关性的数据集——α是未定义的。虽然α是平行形式之间相关性的假定估计,但它不是相关性,因为α可以假设低于1的任何值(α值低于0是无意义的可靠性估计)。在在线补充材料中,我们提供了R代码来复制我们的经验发现,并生成具有用户定义的α值的数据集。我们希望研究人员将使用这个代码来更好地理解α作为量表可靠性指标的局限性。(PsycInfo数据库记录(c) 2023 APA,版权所有)。
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What are the mathematical bounds for coefficient α?

Coefficient α, although ubiquitous in the research literature, is frequently criticized for being a poor estimate of test reliability. In this note, we consider the range of α and prove that it has no lower bound (i.e., α ∈ ( - ∞, 1]). While outlining our proofs, we present algorithms for generating data sets that will yield any fixed value of α in its range. We also prove that for some data sets-even those with appreciable item correlations-α is undefined. Although α is a putative estimate of the correlation between parallel forms, it is not a correlation as α can assume any value below-1 (and α values below 0 are nonsensical reliability estimates). In the online supplemental materials, we provide R code for replicating our empirical findings and for generating data sets with user-defined α values. We hope that researchers will use this code to better understand the limitations of α as an index of scale reliability. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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来源期刊
Psychological methods
Psychological methods PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
13.10
自引率
7.10%
发文量
159
期刊介绍: Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.
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