Sammenlign metoder

	Robust Item Analysis	Item Response Theory (IRT)
Fagområde	Psykometri	Psykometri
Familie	Latent structure	Latent structure
Oprindelsesår	1980s–2000s	1952–1968
Ophavsperson	Robust methods tradition (Huber, Hampel, Tukey); applied to item analysis by Wilcox and colleagues	Frederic M. Lord (and Allan Birnbaum for the 2PL/3PL models)
Type	Diagnostic / item-level evaluation	Probabilistic measurement model
Oprindelig kilde	Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838	Lord, F. M. & Novick, M. R. (1968). Statistical Theories of Mental Test Scores. Addison-Wesley.
Aliasser	robust item statistics, outlier-resistant item analysis, robust classical item analysis	IRT, latent trait theory, item characteristic curve theory, modern test theory
Relaterede	5	5
Resumé	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.	Item response theory models the probability that a respondent answers an item correctly (or endorses it) as a function of the respondent's latent trait level and the item's own statistical properties — difficulty, discrimination, and guessing. Unlike classical test theory, IRT places persons and items on the same scale, yielding measurement that is sample-independent for items and test-independent for persons.