In general, random effects is efficient, and should be used (over fixed effects) if the assumptions underlying it are believed to be satisfied.

This can be tested by running random effects, then fixed effects, and doing a Hausman specification test.

The type of statistical tests that are employed in multilevel models depend on whether one is examining fixed effects or variance components.

When examining fixed effects, the tests are compared with the standard error of the fixed effect, which results in a Z-test.

The two projects were fitted as fixed effects.

A gene never has a fixed effect, so how is it possible to speak of a gene for long legs?

If we assume fixed effects, we impose time independent effects for each entity that are possibly correlated with the regressors.

To see this, establish that the fixed effects estimator is:

Assumptions about the error term determine whether we speak of fixed effects or random effects.

In this hierarchical generalized linear model, the fixed effect is described by , which is the same for all observations.