In statistics, hierarchical linear modeling (HLM), also known as multi-level analysis, is a more advanced form of simple linear regression and multiple linear regression. Multilevel analysis allows variance in outcome variables to be analysed at multiple hierarchical levels, whereas in simple linear and multiple linear regression all effects are modeled to occur at a single level. Thus, HLM is appropriate for use with nested data. A graph of a Normal bell curve showing statistics used in educational assessment and comparing various grading methods. ... Multilevel models are known by several names: hierarchical models, nested models and split-plot designs. ... A linear regression in which there is only one covariate (predictor variable). ... We dont have an article called Multiple linear regression Start this article Search for Multiple linear regression in. ... In computer science and mathematics, a variable is a symbol denoting a quantity or symbolic representation. ...
For example, in educational research, data is often considered as pupils nested within classrooms nested within schools. In organizational psychology research, data from individuals must often be nested within teams or other functional units. For repeated measures data, time can be considered as another level which occurs within participants. This article or section does not cite any references or sources. ...
Multilevel analysis has been extended to include multilevel structural equation modeling, multilevel latent class modeling, and other more general models. Structural equation modeling (SEM) is a statistical technique for building and testing statistical models, which are sometimes called causal models. ... In statistics the latent class model (LCM) relates a set of discrete multivariate variables to a set of latent variables. ...
External links
Books
Hierarchical Linear Models (Second Edition). Thousand Oaks: Sage Publications, 2002. Stephen Raudenbush and Anthony Bryk.
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