Is Coefficient Alpha Robust to Non-Normal Data?

Frontiers in Psychology

Yanyan Sheng¹ 

关键词: coefficient alpha;    true score distribution;    error score distribution;    non-normality;    skew;    kurtosis;    Monte Carlo;    power method polynomials;   

DOI  :  10.3389/fpsyg.2012.00034

学科分类：心理学（综合）

来源: Frontiers

【 摘 要 】

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 non-normal error score distributions, or limited conditions. In view of this and the availability of advanced methods for generating univariate non-normal data, Monte Carlo simulations were conducted to show that non-normal 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 non-normal data.

【 授权许可】

CC BY