Thanks coefficient alpha, we'll take it from here.

By Daniel M. McNeish. Psychological methods, 2017

2,054 citations recorded by the source corpus.

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. (PsycINFO Database Record

Subfields: Experimental and Cognitive Psychology, General Decision Sciences, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5530f030b158f94c46b828ac58f7740706b83c0d

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Citations in the corpus

Use Omega Rather than Cronbach’s Alpha for Estimating Reliability. But… (2020, methodology): “…it is true that α tends to underestimate reliability when the essential tau-equivalence assumption is violated, research shows that in some circumstances, such as when the random measurement errors are correlated across items, α can overestimate reliability (see McNeish, 2018; Raykov, 1997a, 2001).”
An empirical analysis of alleged misunderstandings of coefficient alpha (2018, neutral): “Recently, McNeish (2017) wrote a paper with the provocative title ‘Thanks coefficient alpha: We’ll take it from here’, in which he argued that alpha is ‘riddled with problems’ (p. 1).”
Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods (2020, neutral): “Moreover, we generally question the strong preference of a model-based assessment of reliability from a factor analysis (e.g., McNeish, 2018; Sijtsma, 2009; Yang and Green, 2011) compared to designbased reliability measures (i.e., internal consistency, see Meyer, 2010, for assumptions of Cronbach’s…”
A review of the methodological misconceptions and guidelines related to the application of structural equation modeling: A Malaysian scenario (2017, background): “These new findings, meticulous procedures © 2017 Journal of Applied Structural Equation Modeling ii and well thought-out recommendations can be found in the articles published in some of the top journals, such as Organization Research Methods, Psychological Methods, Journal of Management Studies and”
A Closed-Form Alternative for Estimating ω Reliability under Unidimensionality (2020, background): “Far and away the most common such index is the so-called Cronbach’s coefficient α (Cronbach, 1951; Guttman, 1945), used in homogeneous or unidimensional settings, that is, where items are believed to assess a single underlying construct (McNeish, 2018; Sijtsma, 2009).”
A journey around alpha and omega to estimate internal consistency reliability (2017, methodology): “Thus, before thanking alpha for its services and using SEM derived estimators henceforth (McNeish, 2017) or completely avoiding SEM models due to their difficulties (Davenport et al.”
Alpha, Omega, and H Internal Consistency Reliability Estimates: Reviewing These Options and When to Use Them (2021, methodology): “Despite the popularity of coefficient alpha, its use can be problematic, as coefficient alpha requires that the data meet a set of specific assumptions, which are oftentimes overlooked (Dunn et al., 2014; McNeish, 2018).”
The use of rating and Likert scales in Natural Language Generation human evaluation tasks: A review and some recommendations (2019, methodology): “(8)Although the use of Cronbach’s α was recently criticized in McNeish (2018).”
Enhancing Rigor in Quantitative Entrepreneurship Research (2019, methodology): “…that many widely used “rules of thumb” and cutoff criteria have been dismissed in the methods literature as “methodological myths” (Lance & Vandenberg, 2009), such as the false notion that a measure is internally consistent when Cronbach’s α exceeds .70 (Guide & Ketokivi, 2015; McNeish, 2018).”
One Size Doesn’t Fit All: Using Factor Analysis to Gather Validity Evidence When Using Surveys in Your Research (2019, background): “The use and misuse of coefficient alpha as an estimate of reliability has been extensively discussed by researchers (e.g., Green and Yang, 2009; Sijtsma, 2009; Raykov and Marcoulides, 2017; McNeish, 2018).”