Coefficient alpha has been a widely used measure by which internal consistency reliability is assessed. In addition to essential tau-equivalence and uncorrelated errors, normality has been noted as another important assumption for alpha. Earlier work on evaluating this assumption considered either exclusively nonnormal error score distributions, or limited conditions. In view of this and the availability of advanced methods for generating univariate nonnormal data, Monte Carlo simulations were conducted to show that nonnormal distributions for true or error scores do create problems for using alpha to estimate the internal consistency reliability. The sample coefficient alpha is affected by leptokurtic true score distributions, or skewed and/or kurtotic error score distributions. Increased sample sizes, not test lengths, help improve the accuracy, bias or precision of using it with nonnormal data.
Sheng, Yanyan and Sheng, Zhaohui. "Is Coefficient Alpha Robust to Non-normal Data?." (Feb 2012).