Sammenlign metoder

	Robust Differential Item Functioning (Robust DIF)	Robust Item Analysis
Fagområde	Psykometri	Psykometri
Familie	Latent structure	Latent structure
Oprindelsesår	1990s–2000s	1980s–2000s
Ophavsperson	Building on DIF work by Cleary & Hilton (1968) and Mantel-Haenszel by Holland & Thayer (1988); robust extensions developed through 1990s–2000s	Robust methods tradition (Huber, Hampel, Tukey); applied to item analysis by Wilcox and colleagues
Type	Item bias / fairness analysis	Diagnostic / item-level evaluation
Oprindelig kilde	Magis, D., Beland, S., Tuerlinckx, F., & De Boeck, P. (2011). A general framework and an R package for the detection of dichotomous differential item functioning. Behavior Research Methods, 43(3), 847–862.	Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838
Aliasser	Robust DIF, outlier-resistant DIF detection, robust item bias analysis, DIF with robust estimation	robust item statistics, outlier-resistant item analysis, robust classical item analysis
Relaterede	6	5
Resumé	Robust differential item functioning analysis detects items that behave differently across demographic groups after matching respondents on the underlying trait, while protecting the procedure against distortion by outliers, model misfit, or contaminated anchor items. It is applied in educational testing, clinical assessment, and survey research to ensure that a scale measures the same construct equally fairly for all groups.	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.