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Robust Cronbach's Alpha

Robust Cronbach's alpha adapts the classical internal consistency coefficient to data that violate the assumption of multivariate normality or contain influential outliers. By replacing the conventional sample covariance matrix with a robust counterpart, it yields a reliability estimate that is resistant to distortion by non-normal response distributions, contaminated observations, or small violations of model assumptions common in applied psychometric work.

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Sources

  1. Yuan, K.-H., & Bentler, P. M. (2002). On robustness of the normal-theory based asymptotic distributions of three reliability coefficient estimates. Psychometrika, 67(2), 251–268. DOI: 10.1007/BF02294845
  2. Zhang, Z., & Yuan, K.-H. (2016). Robust coefficients alpha and omega and confidence intervals with outlying observations and missing data: Methods and software. Educational and Psychological Measurement, 76(3), 387–411. DOI: 10.1177/0013164415594658

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Referenced by

ScholarGateRobust Cronbach's Alpha (Robust Cronbach's Alpha Reliability Coefficient). Retrieved 2026-06-04 from https://scholargate.app/en/psychometrics/robust-cronbachs-alpha