Zhang Zhiyong, Yuan Ke-Hai
University of Notre Dame, Notre Dame, IN, USA.
Educ Psychol Meas. 2016 Jun;76(3):387-411. doi: 10.1177/0013164415594658. Epub 2015 Jul 24.
Cronbach's coefficient alpha is a widely used reliability measure in social, behavioral, and education sciences. It is reported in nearly every study that involves measuring a construct through multiple items. With non-tau-equivalent items, McDonald's omega has been used as a popular alternative to alpha in the literature. Traditional estimation methods for alpha and omega often implicitly assume that data are complete and normally distributed. This study proposes robust procedures to estimate both alpha and omega as well as corresponding standard errors and confidence intervals from samples that may contain potential outlying observations and missing values. The influence of outlying observations and missing data on the estimates of alpha and omega is investigated through two simulation studies. Results show that the newly developed robust method yields substantially improved alpha and omega estimates as well as better coverage rates of confidence intervals than the conventional nonrobust method. An R package coefficientalpha is developed and demonstrated to obtain robust estimates of alpha and omega.
克朗巴哈系数α是社会、行为和教育科学中广泛使用的一种信度度量。几乎在每一项涉及通过多个项目测量一个构念的研究中都会报告该系数。对于非τ等价项目，麦克唐纳ω系数在文献中已被用作α系数的一种流行替代方法。α系数和ω系数的传统估计方法通常隐含地假设数据是完整的且呈正态分布。本研究提出了稳健程序，用于从可能包含潜在异常观测值和缺失值的样本中估计α系数和ω系数以及相应的标准误差和置信区间。通过两项模拟研究，考察了异常观测值和缺失数据对α系数和ω系数估计值的影响。结果表明，新开发的稳健方法比传统的非稳健方法能显著改进α系数和ω系数估计值，以及提高置信区间的覆盖率。开发了一个R包“coefficientalpha”，并展示了如何用它来获得α系数和ω系数的稳健估计值。
