The article introduces two new reliability indices, ordinal coefficient alpha and ordinal coefficient theta, designed to address the limitations of traditional coefficient alpha in Likert rating scales. A simulation study was conducted to compare these new indices with coefficient alpha using Likert data. The results indicate that ordinal coefficients alpha and theta consistently provide accurate estimates of theoretical reliability, regardless of the magnitude of the theoretical reliability, the number of scale points, and the skewness of the scale point distributions. In contrast, coefficient alpha is generally biased towards underestimating reliability. The authors recommend using ordinal coefficients alpha and theta as alternatives to coefficient alpha when estimating reliability based on Likert response items. The choice between the two ordinal coefficients depends on whether a factor analysis model (ordinal coefficient alpha) or a principal components analysis model (ordinal coefficient theta) is assumed. The article also discusses the methodology, including the simulation study and real data examples, and provides recommendations for researchers.The article introduces two new reliability indices, ordinal coefficient alpha and ordinal coefficient theta, designed to address the limitations of traditional coefficient alpha in Likert rating scales. A simulation study was conducted to compare these new indices with coefficient alpha using Likert data. The results indicate that ordinal coefficients alpha and theta consistently provide accurate estimates of theoretical reliability, regardless of the magnitude of the theoretical reliability, the number of scale points, and the skewness of the scale point distributions. In contrast, coefficient alpha is generally biased towards underestimating reliability. The authors recommend using ordinal coefficients alpha and theta as alternatives to coefficient alpha when estimating reliability based on Likert response items. The choice between the two ordinal coefficients depends on whether a factor analysis model (ordinal coefficient alpha) or a principal components analysis model (ordinal coefficient theta) is assumed. The article also discusses the methodology, including the simulation study and real data examples, and provides recommendations for researchers.