The authors study alternative estimators of the impacts of higher level variables in multilevel models. This is important since many of the important variables in social science research arehigher level factors having impacts on many lower level outcomes such as school achievement and contraceptive use. While the large sample properties of alternative estimators for these models are well known, there is little evidence about the relative performance of these estimators in the sample sizes typical in social science research. The authors attempt to fill this gap by presenting evidence about point estimation and standard error estimation for both two-and three-level models. A majorconclusion of the article is that readily available commercial software can be used to obtain both reliable point estimates and coefficient standard errors in models with two or more levels as long as appropriate corrections are made for possible error correlations at the highest level.
Key Words: multilevel models • hierarchical models • multilevel error structure • Monte Carlo simulations