方法对比

	稳健的 Cronbach's Alpha (Robust Cronbach's Alpha)	稳健项目分析
领域	心理测量学	心理测量学
方法族	Latent structure	Latent structure
起源年份	2002–2016	1980s–2000s
提出者	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
类型	Robust reliability coefficient	Diagnostic / item-level evaluation
开创性文献	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
别名	robust alpha, outlier-resistant Cronbach's alpha, robust internal consistency, robust coefficient alpha	robust item statistics, outlier-resistant item analysis, robust classical item analysis
相关	3	5
摘要	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.