পদ্ধতির তুলনা করুন

	মাল্টি-গ্রুপ জেনারালাইজেবিলিটি থিওরি	বহু-গোষ্ঠী নির্ভরযোগ্যতা বিশ্লেষণ
ক্ষেত্র	মনোমিতি	মনোমিতি
পরিবার	Latent structure	Latent structure
উদ্ভবের বছর	1963–2001	1990s–2000s
প্রবর্তক	Lee J. Cronbach and colleagues (Cronbach, Gleser, Nanda, Rajaratnam), extended to multi-group contexts by Brennan and others	Classical test theory traditions; synthesized in modern practice by Vandenberg & Lance (2000) and Sijtsma (2009)
ধরন	Variance component / reliability generalization	Reliability estimation and comparison
মৌলিক উৎস	Brennan, R. L. (2001). Generalizability Theory. Springer. ISBN: 978-0387952826	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	MG G-theory, multi-group G-theory, generalizability theory across groups, cross-group G-study	reliability comparison across groups, group-specific reliability estimation, multi-sample reliability analysis, cross-group internal consistency
সম্পর্কিত	6	4
সারসংক্ষেপ	Multi-group generalizability theory (MG G-theory) extends classical generalizability theory to estimate and compare variance components — attributable to persons, items, raters, occasions, and their interactions — simultaneously across two or more defined groups. It reveals whether a measurement procedure is equally reliable and generalizable for every group studied, supporting fair and equitable score interpretation.	Multi-group reliability analysis estimates internal consistency or stability coefficients separately within each group and then formally compares them to determine whether a scale functions with equal precision across populations. It is a foundational step in cross-group measurement research, typically carried out alongside or prior to measurement invariance testing.