The Cronbach's alpha is the most widely used method for estimating internal consistency reliability. This procedure has proved very resistant to the passage of time, even if its limitations are well documented and although there are better options as omega coefficient or the different versions of glb, with obvious advantages especially for applied research in which the ítems differ in quality or have skewed distributions. In this paper, using Monte Carlo simulation, the performance of these reliability coefficients under a one-dimensional model is evaluated in terms of skewness and no tau-equivalence. The results show that omega coefficient is always better choice than alpha and in the presence of skew items is preferable to use omega and glb coefficients even in small samples.

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In the context of multidimensional structures, with the presence of a common factor and multiple specific or group factors, estimates of reliability require specific estimators. The use of classical procedures such as the alpha coefficient or omega total that ignore structural complexity are not appropriate, since they can lead to strongly biased estimates. Through a simulation study, the bias of six estimators of reliability in multidimensional measures was evaluated and compared. The study is complemented by an empirical illustration that exemplifies the procedure. Results showed that the estimators with the lowest bias in the estimation of the total reliability parameter are omega total, the two versions of greatest lower bound (GLB) and the alpha coefficient, which in turn are also those that produce the highest overestimation of the reliability of the general factor. Nevertheless, the most appropriate estimators, in that they produce less biased estimates of the reliability parameter of the general factor, are omega limit and omega hierarchical.

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