Journal Article

The Effect of Small Sample Size on Two-Level Model Estimates: A Review and Illustration

McNeish, Daniel M., Stapleton, Laura M.

2016

Overview

Multilevel models are an increasingly popular method to analyze data that originate from a clustered or hierarchical structure. To effectively utilize multilevel models, one must have an adequately large number of clusters; otherwise, some model parameters will be estimated with bias. The goals for this paper are to (1) raise awareness of the problems associated with a small number of clusters, (2) review previous studies on multilevel models with a small number of clusters, (3) to provide an illustrative simulation to demonstrate how a simple model becomes adversely affected by small numbers of clusters, (4) to provide researchers with remedies if they encounter clustered data with a small number of clusters, and (5) to outline methodological topics that have yet to be addressed in the literature.

• Publisher: Springer,Springer US,Springer Nature B.V
• Subject: Child and School Psychology / Cluster Grouping / Data analysis / Education / Educational aspects / Educational Psychology / Effect Size / Estimation bias / Estimation methods / Hierarchical Linear Modeling / Learning and Instruction / Least Squares Statistics / Literature Reviews / Maximum likelihood estimation / Maximum Likelihood Statistics / Multilevel models / Multivariate Analysis / Population estimates / Research methods / Research Problems / Review Article / REVIEW ARTICLES / Sample Size / Sampling distributions / Simulation / Simulations / Standard error / Statistical Analysis / Statistical variance / Studies