When Cluster-Robust Inferences Fail

Abstract

Although cluster-robust standard errors (CRSEs) are commonly used to account for violations of observations independence found in nested data, an underappreciated issue is that there are several instances when CRSEs can fail to properly maintain the nominally accepted Type I error rate. These situations (e.g., analyzing data with imbalanced cluster sizes) can readily be found in various types of education-related datasets and are important to consider when computing statistical inference tests when using cluster-level predictors. Using a Monte Carlo simulation, we investigated these conditions and tested alternative estimators and degrees of freedom (df) adjustments to assess how well they could ameliorate the issues related to the use of the traditional CRSE (CR1) estimator using both continuous and dichotomous predictors. Findings showed that the bias-reduced linearization estimator (CR2) and the jackknife estimator (CR3) together with df adjustments were generally effective at maintaining Type I error rates for most of the conditions tested. Results also indicated that the CR1 when paired with df based on the effective cluster size was also acceptable. We emphasize the importance of clearly describing the nested data structure as the characteristics of the dataset can influence Type I error rates when using CRSEs.