reference effect measures for quantifying comparing and visualizing variation from random and fixed effects in nonnormal multilevel models with applications to site variation in medical procedure use and outcomes background multilevel models for nonnormal outcomes are widely used in medical and health sciences research while methods for interpreting fixed effects are welldeveloped methods to quantify and interpret random cluster variation and compare it with other sources of variation are less established random cluster variation sometimes referred to as general contextual effects gce may be the main focus of a study therefore easily interpretable methods are needed to quantify gce we propose a reference effect measure rem approach to 1 quantify gce and compare it to individual subject and cluster covariate effects and 2 quantify relative magnitudes of gce and variation from sets of measured factorsmethods to illustrate rem we consider a twolevel mixed logistic model with patients clustered within hospitals and a random intercept for hospitals we compare patients at hospitals at given percentiles of the estimated random effect distribution to patients at a median or reference hospital these estimates are then compared numerically and graphically to individual fixed effects to quantify gce in the context of effects of other measured variables aim 1 we then extend this approach by comparing variation from the random effect distribution to variation from sets of fixed effects to understand their magnitudes relative to overall outcome variation aim 2results using an example of initiation of rhythm control treatment in atrial fibrillation af patients within the veterans affairs va we use rem to demonstrate that random variation across hospitals gce in initiation of treatment 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 are contrasted with a relatively small gce compared with patient factors in 1 year mortality following hospitalization for af patientsconclusions rem provides a means of quantifying random effect variation gce with multilevel data and can be used to explore drivers of outcome variation this method is easily interpretable and can be presented visually rem offers a simple interpretable approach for evaluating questions of growing importance in the study of health care systems