পদ্ধতির তুলনা করুন

	দৃঢ় পরিমাপের অপরিবর্তনীয়তা পরীক্ষা (Robust Measurement Invariance Testing)	কনফার্মেটরি ফ্যাক্টর অ্যানালাইসিস (CFA)
ক্ষেত্র	মনোমিতি	মনোমিতি
পরিবার	Latent structure	Latent structure
উদ্ভবের বছর	1994	1969
প্রবর্তক	Albert Satorra & Peter M. Bentler	Karl Gustav Jöreskog
ধরন	Measurement invariance test with robust corrections	Hypothesis-testing latent variable model
মৌলিক উৎস	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.
অপর নাম	robust MI testing, robust measurement equivalence, non-normal measurement invariance, robust multi-group CFA invariance	CFA, confirmatory FA, measurement model, restricted factor analysis
সম্পর্কিত	3	4
সারসংক্ষেপ	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.