A further consideration when using a mixed-effects regression model or hierarchical linear model is that the reduction of variance between Vfull and Vnull must be assured to be from only the fixed effects, and not from the random effects. It is possible that, after removing regressors, the random effects account for more than their previous share of the now-increased total residual variance. To accurately assess the reduction in variance due to the fixed effects of the variable of interest, it is therefore necessary to hold constant the variance accounted for by random effects when running the A-only or null models. For example, in a mixed-effects regression model that involves fixed effects of variables A and B, and random intercepts across subjects, A-only and null models must have the same random subject effects as the full AB model.
