Random effects model

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In statistics, a random effect(s) model, also called a variance components model is a kind of hierarchical linear model. It assumes that the data describe a hierarchy of different populations whose differences are constrained by the hierarchy. The fixed effects model is a special case.

Contents 1 Simple example 2 Variance components 3 References 4 See also

[edit] Simple example

Suppose m elementary large schools are chosen randomly from among millions in a large country. Then n pupils are chosen randomly from among those at each such school. Their scores on a standard aptitude test are ascertained. Let Y_ij be the score of the jth pupil at the ith school. Then

where μ is the average of all scores in the whole population, U_i is the deviation of the average of all scores at the ith school from the average in the whole population, and W_ij is the deviation of the jth pupil's score from the average score at the ith school.

[edit] Variance components

The variance of Y_ij is the sum of the variances τ² and σ² of U_i and W_ij respectively.

Let

be the average, not of all scores at the ith school, but of those at the ith school that are included in the random sample. Let

be the "grand average".

Let

be respectively the sum of squares due to differences within groups and the sum of squares due to difference between groups. Then it can be shown that

and

These "expected mean squares" can be used as the basis for estimation of the "variance components" σ² and τ².

[edit] References

• Random effect model at Bandolier (Oxford EBM website)
• Fixed and random effects models
• Distinguishing Between Random and Fixed: Variables, Effects, and Coefficients
• How to Conduct a Meta-Analysis: Fixed and Random Effect Models

[edit] See also

• Bühlmann model
• Meta analysis
• Hierarchical linear modeling