方法对比

	稳健内容效度评估	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	1975 (base); 2000s–2010s (robust extensions)	1980s–2000s
提出者	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
类型	Validity evidence / expert judgement procedure with outlier-resistant aggregation	Diagnostic / item-level evaluation
开创性文献	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
别名	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
相关	6	5
摘要	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.