The Importance of Identifying Levels in Multilevel Analysis: An Illustration of the Effects of Ignoring the Top or Intermediate Levels in School Effectiveness Research

Abstract

In this article, the effect of ignoring one or more levels of variation in hierarchical linear regression analysis is explored. A model with four hierarchical levels is used as a reference model. A distinction is made between ignoring top and intermediate levels. The effects of ignoring levels on the fixed and on the random parameters of different random intercept models are explored by means of a real data set. The results show that ignoring an important level causes an effect on specific fixed coefficients, variance components and their corresponding standard error. Therefore, ignoring an important level can lead to different research conclusions.