Sammenlign metoder

	Robust Rasch-model	Robust konfirmatorisk faktoranalyse
Fagområde	Psykometri	Statistik
Familie	Latent structure	Latent structure
Oprindelsesår	1982	1984–1994
Ophavsperson	Mislevy & Bock (robust ability estimation); broader robust IRT formalized through 1980s–2000s	Satorra & Bentler (robust SE/chi-square corrections); Browne (ADF estimator)
Type	Robust item calibration model	Confirmatory latent variable model with robust estimation
Oprindelig kilde	Strobl, C., Wickelmaier, F., & Zeileis, A. (2011). Accounting for individual differences in Bradley-Terry models by means of recursive partitioning. Journal of Educational and Behavioral Statistics, 36(2), 135–153.	Satorra, A. & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage.
Aliasser	robust IRT Rasch, robust dichotomous Rasch, outlier-resistant Rasch model, robust item calibration	Robust CFA, CFA with robust standard errors, Satorra-Bentler CFA, non-normal CFA
Relaterede	5	6
Resumé	The robust Rasch model applies the standard one-parameter logistic Rasch framework with estimation procedures designed to limit the influence of outlying item responses, aberrant respondents, or mild model violations, producing stable item and person parameter estimates that are less sensitive to data contamination than ordinary maximum likelihood or conditional maximum likelihood Rasch estimation.	Robust confirmatory factor analysis fits a pre-specified factor structure to observed data while correcting standard errors and goodness-of-fit statistics for violations of multivariate normality. It is the preferred variant of CFA whenever Likert-type, skewed, or kurtotic indicators make the classical normal-theory estimator unreliable.