・ Multilevel models properly account for the hierarchical data structure causing data dependencies.

・ Multilevel modeling methodology overcomes standard error bias due to clustering that generating inflated Type-I error rates and inaccurate confidence intervals.

・ Multilevel models permit analyzing variables at different levels taking into account cross-level interactions.

・ Multilevel analysis is a more flexible method requiring fewer assumptions than other statistical methods such as, repeated measures of A NOVA.