Most of the estimators can be defined in terms of the random effects model
2.
If the random effects assumption holds, the random effects model is more consistent.
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The random effects model assumes in addition that
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A common model used to synthesize heterogeneous research is the random effects model of meta-analysis.
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The Durbin Wu Hausman test is often used to discriminate between the fixed and the random effects model.
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When there is heterogeneity that cannot readily be explained, one analytical approach is to incorporate it into a random effects model.
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The random effects assumption ( made in a random effects model ) is that the individual specific effects are uncorrelated with the independent variables.
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In a random effects model, \ epsilon _ { it } is assumed to vary stochastically over i or t requiring special treatment of the error variance matrix.
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This is in contrast to random effects models and mixed models in which either all or some of the explanatory variables are treated as if they arise from random causes.
10.
Between study variance \ sigma ^ 2 _ { \ eta } is estimated using common estimation procedures for random effects models ( restricted maximum likelihood ( REML ) estimators ).
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