পদ্ধতির তুলনা করুন

	কাঠামোগত সমীকরণ মডেলিং (SEM)	মধ্যস্থতা বিশ্লেষণ	মাল্টিলেভেল মডেলিং
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান	গবেষণা পরিসংখ্যান
পরিবার	Latent structure	Hypothesis test	Process / pipeline
উদ্ভবের বছর	1970	1986	1992
প্রবর্তক	Karl Jöreskog (LISREL framework, 1970s)	Baron & Kenny	Anthony Bryk and Stephen Raudenbush
ধরন	Latent variable / causal modeling	Indirect effects / path test	Method
মৌলিক উৎস	Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning. ISBN: 978-1473756540	Baron, R. M. & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research. Journal of Personality and Social Psychology, 51(6), 1173–1182.	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
অপর নাম	Yapısal Eşitlik Modellemesi (SEM), structural equation modelling, covariance structure analysis, latent variable modeling	indirect effects analysis, path-based mediation, PROCESS macro mediation, Aracılık Analizi (Mediation / PROCESS)	HLM, mixed-effects models, random effects models, MLM
সম্পর্কিত	5	5	3
সারসংক্ষেপ	Structural equation modeling is a multivariate statistical framework that simultaneously estimates a measurement model — relating observed indicators to latent constructs — and a structural model specifying directional or reciprocal relationships among those constructs. Rooted in the LISREL tradition developed by Karl Jöreskog in the 1970s, SEM is the standard tool for testing complex theoretical models in the social, behavioural, and management sciences.	Mediation analysis is a statistical procedure that tests whether the effect of an independent variable X on an outcome Y operates wholly or partly through a third variable M, called the mediator. Formalised by Baron and Kenny in 1986, it decomposes the total effect of X on Y into a direct path (c′) and an indirect path (a × b), quantifying how much of the relationship is carried by the mediating mechanism.	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.