Mixed-effect models

Mixed-effect modeling is recommended for data with repeated measures, as often encountered in designed experiments as well as in corpus-based studies. The mixed-effect model provides a flexible instrument for studying data sets with both fixed-effect factors and random-effect factors, as well as numerical covariates, that allows conclusions to generalize to the pop-ulations sampled by the random-effect factors. Mixed-effect models can straightforwardly incorporate two or more random-effect factors. By providing shrinkage estimates for the effects associated with the units sampled with a given random-effect factor, the mixed model provides enhanced prediction accuracy. Mixed-effect models also make available enhanced instruments for modeling interactions of random-effect and fixed-effect predictors. As mixed-effects models do not depend on prior aggregation, they also offer the researcher the possi-bility to bring longitudinal effects into the statistical model