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

Ma Yan*, Gonzalez Della Valle Alejandro, Hui Zhang, X. M. Tu

*Corresponding author for this work

Research output: Contribution to journalArticle

7 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)659-670
Number of pages12
JournalStatistics in Medicine
Volume29
Issue number6
DOIs
StatePublished - Mar 15 2010

Keywords

  • Cronbach coefficient alpha
  • Inverse probability weighting
  • Missing data
  • Monotone missing data pattern
  • U-statistics

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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