Salīdzināt metodes

	Robustas mērījumu invarianču pārbaudes	Apstiprinošā faktoru analīze (AFA)	Testēšana uz mērījumu ekvivalenci
Nozare	Psihometrija	Psihometrija	Psihometrija
Saime	Latent structure	Latent structure	Latent structure
Izcelsmes gads	1994	1969	2000
Autors	Albert Satorra & Peter M. Bentler	Karl Gustav Jöreskog	Vandenberg & Lance
Tips	Measurement invariance test with robust corrections	Hypothesis-testing latent variable model	Multi-group confirmatory factor analysis procedure
Pirmavots	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.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.	Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature. Organizational Research Methods, 3(1), 4–70.
Citi nosaukumi	robust MI testing, robust measurement equivalence, non-normal measurement invariance, robust multi-group CFA invariance	CFA, confirmatory FA, measurement model, restricted factor analysis	Factorial Invariance, Measurement Equivalence, Configural-Metric-Scalar Testing, Ölçüm Değişmezliği
Saistītās	3	4	3
Kopsavilkums	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.	Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.	Measurement invariance testing is a sequence of nested confirmatory factor analysis (CFA) models that examines whether a psychological scale measures the same latent construct in the same way across distinct groups or time points. Systematized and popularized by Vandenberg and Lance (2000), the procedure tests a hierarchy of constraints — from identical factor patterns to identical item intercepts — so that researchers can justify meaningful group comparisons on latent means.