পদ্ধতির তুলনা করুন

	মাল্টি-গ্রুপ জেনারালাইজেবিলিটি থিওরি	Multi-group measurement invariance
ক্ষেত্র	মনোমিতি	মনোমিতি
পরিবার	Latent structure	Latent structure
উদ্ভবের বছর	1963–2001	1971–1993
প্রবর্তক	Lee J. Cronbach and colleagues (Cronbach, Gleser, Nanda, Rajaratnam), extended to multi-group contexts by Brennan and others	Jöreskog, K. G. (1971); Meredith, W. (1993)
ধরন	Variance component / reliability generalization	Model comparison / hypothesis testing
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	measurement invariance, factorial invariance, cross-group invariance, MI testing
সম্পর্কিত	6	6
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 measurement invariance testing examines whether a latent construct is measured in the same way across two or more distinct groups — such as cultures, genders, or age cohorts. It is a prerequisite for meaningful group comparisons of latent means or relationships, ensuring that observed score differences reflect true differences rather than measurement artifacts.