Statistical analysis of datasets comprising several levels of hierarchy (e.g., students nested in classes nested in schools or hierarchical forecasting). For questions about mixed models use [mixed-model] tag. For nested random effects, use [nested-data].
Multilevel analysis is a general term referring to statistical methods appropriate for the analysis of data sets comprising several types of unit of analysis. The levels in the multilevel analysis are another name for the different types of unit of analysis. Each level of analysis will correspond to a population, so that multilevel studies will refer to several populations...
-T.A.B. Snijders, Multilevel Analysis, p. 673-677 in M. Lewis-Beck, A.E. Bryman, and T.F. Liao (eds.), The SAGE Encyclopedia of Social Science Research Methods (Volume II). Sage, 2003.
- mixed-model for linear multilevel (mixed) models
- random-effects-model for models with random intercepts
- glmm for generalized linear mixed models (binary, ordinal, count response)
- lme4-nlme for
- gllamm for Stata implementation
- hierarchical-bayesian for Bayesian models comprising several levels of hierarchy of priors and hyperpriors
Please use these tags to make your question more specific and easier to find.