Thanks Coefficient Alpha, We'll Take It From Here
Thanks Coefficient Alpha, We'll Take It From Here
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DOI：
10.1037/met0000144
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发表时间：
2018-09-01
影响因子：
7
通讯作者：
McNeish, Daniel
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文献类型：
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作者：
McNeish, Daniel
心理学的实证研究通常将Cronbach's alpha作为内部一致性可靠性的衡量标准，尽管许多方法论研究表明，Cronbach's alpha充满了源于不切实际假设的问题。在许多情况下，违反这些假设会导致对可靠性的估计过小，使测量结果看起来不如实际可靠。尽管对Cronbach alpha的方法论批评在实证研究中被越来越多地引用，但在本教程中，我们将讨论这一趋势如何不一定会改善文献中使用的方法论。也就是说，许多研究继续使用Cronbach's alpha，而不考虑其假设，或者仅仅引用方法学文章，建议不要使用它来合理化不利的Cronbach's alpha估计。本教程首先提供证据，证明反对Cronbach alpha的建议并没有明显改变实证研究报告可靠性的方式。然后，我们在概念上总结了Cronbach's alpha的缺点，而不依赖于数学或基于模拟的论点，以便这些论点能够被广泛的受众所接受。我们继续讨论几种替代措施，这些措施的假设不那么严格，与Cronbach’s alpha相比，它们提供了合理的更高的可靠性估计。最后，我们用实证例子来说明替代可靠性度量的优点，包括ω total， Revelle's ω total，最大下界和系数h。还提供了详细的软件附录，以帮助研究人员实施替代方法。摘要量表在心理学研究中常用来直接测量动机或抑郁等不可观察的构念。这些量表由多个项目组成，每个项目旨在提供有关兴趣结构的各个方面的信息。每当在心理学研究中使用量表时，报告其可靠性是很重要的。自20世纪50年代以来，获取可靠性的主要方法一直是Cronbach's alpha，这种方法的地位可能是其在任何领域中被引用最多的科学文章之一的最佳例证。尽管克朗巴赫alpha的基本假设广受欢迎，但它最近在统计文献中受到质疑，因为这些假设在65年前很常见，但在更现代的构建尺度的统计方法中基本上已经消失了。尽管这些统计文章中的观点有可能显著改变心理学研究的进行和报告方式，但统计文献中的建议尚未渗透到心理学文献中。在本文中，我们的目标是证明为什么Cronbach’s alpha不再是报告可靠性的最佳方法。为了将本文与统计文献中的文章区分开来，我们在处理Cronbach's alpha问题时很少关注数学或计算细节，因此Cronbach's alpha的缺陷是用文字和例子来说明的，而不是用证明和模拟来说明，这样这些想法就可以影响更大的研究群体——也就是那些经常报告Cronbach's alpha的研究人员。
Empirical studies in psychology commonly report Cronbach's alpha as a measure of internal consistency reliability despite the fact that many methodological studies have shown that Cronbach's alpha is riddled with problems stemming from unrealistic assumptions. In many circumstances, violating these assumptions yields estimates of reliability that are too small, making measures look less reliable than they actually are. Although methodological critiques of Cronbach's alpha are being cited with increasing frequency in empirical studies, in this tutorial we discuss how the trend is not necessarily improving methodology used in the literature. That is, many studies continue to use Cronbach's alpha without regard for its assumptions or merely cite methodological articles advising against its use to rationalize unfavorable Cronbach's alpha estimates. This tutorial first provides evidence that recommendations against Cronbach's alpha have not appreciably changed how empirical studies report reliability. Then, we summarize the drawbacks of Cronbach's alpha conceptually without relying on mathematical or simulation-based arguments so that these arguments are accessible to a broad audience. We continue by discussing several alternative measures that make less rigid assumptions which provide justifiably higher estimates of reliability compared to Cronbach's alpha. We conclude with empirical examples to illustrate advantages of alternative measures of reliability including omega total, Revelle's omega total, the greatest lower bound, and Coefficient H. A detailed software appendix is also provided to help researchers implement alternative methods.Translational AbstractScales are commonly used in psychological research to measure directly unobservable constructs like motivation or depression. These scales are comprised of multiple items, each aiming to provide information about various aspects of the construct of interest. Whenever a scale is used in a psychological study, it is important to report on its reliability. Since the 1950s, the primary method for capturing reliability has been Cronbach's alpha, a method whose status is perhaps best exemplified by its place as one of the most cited scientific articles of all-time, in any field. Despite its overwhelming popularity, the underlying assumptions of Cronbach's alpha have been questioned recently in the statistical literature because these assumptions were commonplace 65 years ago but have largely disappeared from more modern statistical methods for constructing scales. Though the ideas in these statistical articles have the potential to significantly alter how psychological research is conducted and reported, recommendations from the statistical literature have yet to permeate the psychological literature. In this article, the goal is to demonstrate why Cronbach's alpha is no longer the optimal method for reporting on reliability. To differentiate this article from articles appearing in the statistical literature, we approach issues with Cronbach's alpha with very little focus on mathematical or computational detail so that the deficiencies of Cronbach's alpha are illustrated in words and examples rather than proofs and simulations so that these ideas can impact a larger group of researchers-namely, the researchers who most often report Cronbach's alpha.