Multilevel Modeling

Since the 1st edition of this monograph was published in 2004, there have been numerous developments in the statistical and computational methods used in multilevel and longitudinal modeling. Mixed-effects modeling has been solidified as a primary meansfor accurately and efficiently estimating a wide-variety of multilevel and longitudinal models. More complex models that include cross-level interactions, cross-classified random effects, alternative covariances structures, and the like appear much more frequently in the health and social sciences research literature. Sophisticated mixedeffects modeling procedures are now incorporated in most comprehensive statistical software packages (including R, Stata, and SAS), and thus there is less need for specialized multilevel software--