Compara mètodes

	Prova d'invariança de mesura robusta	Modelització d'equacions estructurals (SEM)
Camp	Psicometria	Estadística
Família	Latent structure	Latent structure
Any d'origen	1994	1970
Autor original	Albert Satorra & Peter M. Bentler	Karl Jöreskog (LISREL framework, 1970s)
Tipus	Measurement invariance test with robust corrections	Latent variable / causal modeling
Font seminal	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.	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540
Àlies	robust MI testing, robust measurement equivalence, non-normal measurement invariance, robust multi-group CFA invariance	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling
Relacionats	3	5
Resum	Robust measurement invariance testing evaluates whether a psychometric instrument measures the same latent construct in the same way across groups when observed data violate multivariate normality. It adapts standard multi-group CFA sequences by replacing ordinary chi-square statistics with robust alternatives such as the Satorra-Bentler scaled statistic, yielding trustworthy conclusions about factor loadings, intercepts, and residual variances even with skewed or ordinal data.	Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences.