Comparer des méthodes

	Alpha de Cronbach robuste	Analyse d'items robuste
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	2002–2016	1980s–2000s
Auteur d'origine	Derived from Lee J. Cronbach (1951); robust variants formalized by Yuan & Bentler (2002) and Zhang & Yuan (2016)	Robust methods tradition (Huber, Hampel, Tukey); applied to item analysis by Wilcox and colleagues
Type	Robust reliability coefficient	Diagnostic / item-level evaluation
Source fondatrice	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.	Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838
Alias	robust alpha, outlier-resistant Cronbach's alpha, robust internal consistency, robust coefficient alpha	robust item statistics, outlier-resistant item analysis, robust classical item analysis
Apparentées	3	5
Résumé	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.	Robust item analysis applies outlier-resistant statistical methods to the evaluation of individual test or scale items. Instead of classical means and Pearson correlations — both sensitive to extreme scores — it uses trimmed means, Winsorized correlations, or M-estimators to obtain item difficulty and item-total discrimination indices that remain stable when respondent distributions are skewed or contaminated by outliers.