Multilevel models for non-normal outcomes are widely used in medical and health sciences research. A common case is patients in hospitals. Easily interpretable methods to quantify, interpret and visualize random cluster variation (RCV) and compare it with other sources of variation are needed. We propose Reference Effect Measures (REM) to quantify and compare RCV to 1) individual subject and cluster covariate effects, and 2) variation from sets of covariates, e.g. all patient or all hospital covariates. REM is based on percentiles of random effect distributions, transformed to the effect scale. As an example, we used REM to show that for initiation of rhythm control for atrial fibrillation (AF) patients in the Veterans Affairs (VA), RCV across hospitals is substantially greater than that due to most individual patient factors, and explains at least as much variation in treatment initiation as do all patient factors combined. These results contrast with small RCV compared with patient factors for one-year mortality for AF patients. We also apply REM to overdispersed clustered counts and joint longitudinal/survival models. Results are easily visualized in forest or other plots.