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Stat Med. 2010 Mar 15;29(6):659-70. doi: 10.1002/sim.3853.

A U-statistics-based approach for modeling Cronbach coefficient alpha within a longitudinal data setting.

Author information

1
Department of Public Health, Weill Medical College of Cornell University, Hospital for Special Surgery, New York, NY 10021, USA. yam2007@med.cornell.edu

Abstract

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.

PMID:
20087849
PMCID:
PMC2830369
DOI:
10.1002/sim.3853
[Indexed for MEDLINE]
Free PMC Article
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