Deng Lifang, Chan Wai
Beihang University, Beijing, China.
The Chinese University of Hong Kong, Hong Kong, China.
Educ Psychol Meas. 2017 Apr;77(2):185-203. doi: 10.1177/0013164416658325. Epub 2016 Jul 18.
Reliable measurements are key to social science research. Multiple measures of reliability of the total score have been developed, including coefficient alpha, coefficient omega, the greatest lower bound reliability, and others. Among these, the coefficient alpha has been most widely used, and it is reported in nearly every study involving the measure of a construct through multiple items in social and behavioral research. However, it is known that coefficient alpha underestimates the true reliability unless the items are tau-equivalent, and coefficient omega is deemed as a practical alternative to coefficient alpha in estimating measurement reliability of the total score. However, many researchers noticed that the difference between alpha and omega is minor in applications. Since the observed differences in alpha and omega can be due to sampling errors, the purpose of the present study, therefore, is to propose a method to evaluate the difference of coefficient alpha ([Formula: see text]) and omega ([Formula: see text]) statistically. In particular, the current article develops a procedure to estimate the of ([Formula: see text]) and consequently the confidence interval (CI) for ([Formula: see text]). This procedure allows us to test whether the observed difference ([Formula: see text]) is due to sample error or [Formula: see text] is significantly greater than [Formula: see text]. The developed procedure is then applied to multiple real data sets from well-known scales to empirically verify the values of ([Formula: see text]) in practice. Results showed that in most of the comparisons the differences are significantly above zero but cases also exist where the CIs contain zero. An R program for calculating [Formula: see text], [Formula: see text], and the of ([Formula: see text]) is also included in the present study so that the developed procedure is easily accessible to applied researchers.
可靠的测量是社会科学研究的关键。已经开发了多种总分可靠性的测量方法，包括α系数、ω系数、最大下界可靠性等。其中，α系数应用最为广泛，几乎在社会和行为研究中每项涉及通过多个项目测量一个构念的研究中都会报告。然而，众所周知，除非项目是τ等价的，否则α系数会低估真实的可靠性，并且ω系数在估计总分的测量可靠性方面被视为α系数的一种实用替代方法。然而，许多研究人员注意到，在应用中α系数和ω系数之间的差异很小。由于观察到的α系数和ω系数之间的差异可能是由于抽样误差，因此本研究的目的是提出一种统计方法来评估α系数（[公式：见原文]）和ω系数（[公式：见原文]）之间的差异。具体而言，本文开发了一种程序来估计（[公式：见原文]）的[具体内容未给出]，从而得到（[公式：见原文]）的置信区间（CI）。这个程序使我们能够检验观察到的差异（[公式：见原文]）是由于抽样误差还是（[公式：见原文]）显著大于（[公式：见原文]）。然后将所开发的程序应用于来自知名量表的多个真实数据集，以实证验证实践中（[公式：见原文]）的值。结果表明，在大多数比较中，差异显著大于零，但也存在置信区间包含零的情况。本研究还包括一个用于计算（[公式：见原文]）、（[公式：见原文]）和（[公式：见原文]）的[具体内容未给出]的R程序，以便应用研究人员能够轻松使用所开发的程序。
