<Table> <Tr> <Td> </Td> <Td> This article needs attention from an expert in statistics . Please add a reason or a talk parameter to this template to explain the issue with the . WikiProject Statistics may be able to help recruit an expert . (January 2011) </Td> </Tr> </Table> <Tr> <Td> </Td> <Td> This article needs attention from an expert in statistics . Please add a reason or a talk parameter to this template to explain the issue with the . WikiProject Statistics may be able to help recruit an expert . (January 2011) </Td> </Tr> <P> 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). </P> <P> Such models assist in controlling for unobserved heterogeneity when this heterogeneity is constant over time and uncorrelated with independent variables . This constant can be removed from the data through differencing, for example by taking a first difference which will remove any time invariant components of the model . </P>

When do you use a random effects model
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