ABSTRACT
Multilevel models have become a mainstream and flexible method by which to account for clustered data. These models have sample sizes at different levels of the hierarchical data structure where the sample size at the highest level is generally the most relevant for assessing whether the analysis may be at risk for small sample estimation bias. Unfortunately, the highest-level sample size is also the most difficult and costly to increase. This chapter reviews the approaches and remedies for small sample sizes in multilevel regression and multilevel structural equation models, from both frequentist and Bayesian perspectives.
