방법 비교

	로버스트 크론바흐 알파	강건한 문항 분석
분야	심리측정학	심리측정학
계열	Latent structure	Latent structure
기원 연도	2002–2016	1980s–2000s
창시자	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
유형	Robust reliability coefficient	Diagnostic / item-level evaluation
원전	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
별칭	robust alpha, outlier-resistant Cronbach's alpha, robust internal consistency, robust coefficient alpha	robust item statistics, outlier-resistant item analysis, robust classical item analysis
관련	3	5
요약	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.