Comparar métodos

	Evaluación Robusta de la Validez de Contenido	Análisis Robusto de Ítems
Campo	Psicometría	Psicometría
Familia	Latent structure	Latent structure
Año de origen	1975 (base); 2000s–2010s (robust extensions)	1980s–2000s
Autor original	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
Tipo	Validity evidence / expert judgement procedure with outlier-resistant aggregation	Diagnostic / item-level evaluation
Fuente seminal	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
Relacionados	6	5
Resumen	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.