Department of Public Health, Weill Medical College of Cornell University, Hospital for Special Surgery, New York, NY 10021, USA.
Stat Med. 2010 Mar 15;29(6):659-70. doi: 10.1002/sim.3853.
Cronbach coefficient alpha (CCA) is a classic measure of item internal consistency of an instrument and is used in a wide range of behavioral, biomedical, psychosocial, and health-care-related research. Methods are available for making inference about one CCA or multiple CCAs from correlated outcomes. However, none of the existing approaches effectively address missing data. As longitudinal study designs become increasingly popular and complex in modern-day clinical studies, missing data have become a serious issue, and the lack of methods to systematically address this problem has hampered the progress of research in the aforementioned fields. In this paper, we develop a novel approach to tackle the complexities involved in addressing missing data (at the instrument level due to subject dropout) within a longitudinal data setting. The approach is illustrated with both clinical and simulated data.
克朗巴赫系数阿尔法（CCA）是一种经典的仪器项目内部一致性度量标准，广泛应用于行为、生物医学、心理社会和医疗保健相关的研究中。存在一些方法可用于从相关结果中推断一个 CCA 或多个 CCA。然而，现有的方法都无法有效地处理缺失数据。随着现代临床研究中纵向研究设计变得越来越流行和复杂，缺失数据已成为一个严重的问题，缺乏系统处理此问题的方法阻碍了上述领域的研究进展。在本文中，我们开发了一种新方法来解决在纵向数据环境中处理缺失数据（由于受试者脱落而导致仪器层面缺失）所涉及的复杂性。该方法通过临床和模拟数据进行了说明。
