Comparer des méthodes

	Évaluation robuste de la validité de contenu	Analyse d'items robuste
Domaine	Psychométrie	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	1975 (base); 2000s–2010s (robust extensions)	1980s–2000s
Auteur d'origine	Grounded in Lawshe (1975) CVR framework; robust extensions draw on Huber, Wilcox, and robust statistics tradition	Robust methods tradition (Huber, Hampel, Tukey); applied to item analysis by Wilcox and colleagues
Type	Validity evidence / expert judgement procedure with outlier-resistant aggregation	Diagnostic / item-level evaluation
Source fondatrice	Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575.	Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838
Alias	robust CVR, outlier-resistant content validity, robust content validity index, robust expert-panel validation	robust item statistics, outlier-resistant item analysis, robust classical item analysis
Apparentées	6	5
Résumé	Robust content validity assessment applies outlier-resistant statistical methods to the aggregation of expert panel ratings in content validation studies. By detecting and down-weighting idiosyncratic or extreme rater judgements, it yields Content Validity Ratio (CVR) and Content Validity Index (CVI) estimates that reflect the consensus of the panel more accurately than standard averaging when one or a few raters deviate sharply from the group.	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.