Random Effects Model
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A Random Effects Model is a hierarchical linear model where the dataset being analysed consists of a hierarchy of different populations whose differences relate to that hierarchy.
- AKA: Variance Components Model.
- Context:
- It can be a component of a Mixed Effects Model.
- …
- Counter-Example(s):
- See: Panel Data, Fixed Effects Estimator, Latent Variables.
References
2017
- (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Random_effects_model Retrieved:2017-2-13.
- In statistics, a random effects model, also called a variance components model, is a kind of hierarchical linear model. It assumes that the data being analysed are drawn from a hierarchy of different populations whose differences relate to that hierarchy. In econometrics, random effects models are used in the analysis of hierarchical or panel data when one assumes no fixed effects (it allows for individual effects). The random effects model is a special case of the fixed effects model. Contrast this to the biostatistics definitions, as biostatisticians use "fixed" and "random" effects to respectively refer to the population-average and subject-specific effects (and where the latter are generally assumed to be unknown, latent variables).