The Effects of Ignoring a Level in Multilevel Analysis

Abstract

Ignoring a level can have a substantial impact on the conclusions of a multilevel analysis. For intercept-only models and for balanced data, we derive these effects analytically. For more complex random intercept models or for unbalanced data, a simulation study is performed. Most important effects concern estimates and corresponding standard errors of the variance parameters at adjacent levels and of the coefficients of the predictors at the ignored and bordering levels. Therefore, we conclude that if the researcher is interested in a specific level, she/he should account for both the upper and lower level. Conclusions are illustrated using empirical data from educational research.