পদ্ধতির তুলনা করুন

	বহু-গোষ্ঠী বৈষম্যমূলক বৈধতা মূল্যায়ন	বহু-গোষ্ঠী নিশ্চিতকারী ফ্যাক্টর বিশ্লেষণ (MG-CFA)
ক্ষেত্র	মনোমিতি	মনোমিতি
পরিবার	Latent structure	Latent structure
উদ্ভবের বছর	1981 (foundational criterion); multi-group extension 1990s–2000s	1971
প্রবর্তক	Fornell & Larcker (for the AVE-based criterion); extended to multi-group settings by the SEM invariance literature	Karl Jöreskog
ধরন	Validity assessment / model comparison	Measurement model / invariance test
মৌলিক উৎস	Fornell, C. & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.	Vandenberg, R. J. & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.
অপর নাম	cross-group discriminant validity, multi-sample discriminant validity, MGDV, discriminant validity across groups	MG-CFA, multi-group CFA, measurement invariance testing, multi-sample CFA
সম্পর্কিত	5	6
সারসংক্ষেপ	Multi-group discriminant validity assessment tests whether constructs measured by a scale are empirically distinct not just in one sample but consistently across two or more groups (e.g., cultures, genders, age cohorts). It extends standard discriminant validity criteria — such as the AVE rule and the HTMT ratio — into a multi-group confirmatory factor analysis framework to verify that conceptual distinctness is replicable across subpopulations.	Multi-group confirmatory factor analysis tests whether a measurement model holds equivalently across two or more groups — such as cultures, genders, or time points. By imposing increasingly stringent equality constraints and comparing model fit, it determines whether comparisons of latent mean scores are justified.