There are Many Greater Lower Bounds than Cronbach’s α: A Monte Carlo Simulation Study

IF 0.6 Q3 SOCIAL SCIENCES, INTERDISCIPLINARY

Measurement-Interdisciplinary Research and Perspectives Pub Date : 2023-01-02 DOI:10.1080/15366367.2022.2031484

Josip Novak, B. Rebernjak

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Abstract

ABSTRACT A Monte Carlo simulation study was conducted to examine the performance of α, λ2, λ4, μ2, ωT, GLBMRFA, and GLBAlgebraic coefficients. Population reliability, distribution shape, sample size, test length, and number of response categories were varied simultaneously. The results indicate that α and λ2 perform the worst overall. However, the performance of α is improved if the population reliability is high. λ4 is relatively unbiased but the most imprecise. μ2 and ωT perform relatively well under most conditions. GLBAlgebraic outperforms other coefficients under many conditions. GLBMRFA is useful under few conditions if the population reliability is high. The results corroborate previous suggestions that large samples, longer tests, higher number of response categories, and normally distributed results can make reliability estimates more dependable. Some insights on the interaction of these factors are provided. We discuss the findings compared to previous research. The complete R code used for the simulation is provided in the online supplement.

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有许多比Cronbach α更大的下界:一个蒙特卡罗模拟研究

摘要通过蒙特卡罗仿真研究了α、λ2、λ4、μ2、ωT、GLBMRFA和GLBAlgebraic系数的性能。总体信度、分布形状、样本量、测试长度和反应类别数量同时发生变化。结果表明，α和λ2的综合性能最差。而种群可靠性越高，α的性能越好。λ4相对无偏，但最不精确。μ2和ωT在大多数条件下表现相对较好。在许多情况下，GLBAlgebraic优于其他系数。如果种群可靠性高，GLBMRFA在少数条件下是有用的。结果证实了先前的建议，即大样本，更长的测试，更多的响应类别和正态分布的结果可以使可靠性估计更可靠。对这些因素的相互作用提供了一些见解。我们将这些发现与之前的研究进行比较。用于模拟的完整R代码在在线补充中提供。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Measurement-Interdisciplinary Research and Perspectives SOCIAL SCIENCES, INTERDISCIPLINARY-

CiteScore

1.80

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0.00%

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