Accurate Standard Errors in Multilevel Modeling with Heteroscedasticity: A Computationally More Efficient Jackknife Technique

In random-effects models, hierarchical linear or multilevel it is typically assumed that the variances within higher-level units are homoscedastic, meaning they equal across these units. However, this assumption often violated in research. Depending on degree of violation, can lead to biased standard errors parameters and thus incorrect inferences. article, we describe a resampling technique for obtaining errors—Zitzmann’s jackknife. We conducted Monte Carlo simulation study compare with commonly used delete-1 jackknife, robust error Mplus, modified version Findings revealed techniques clearly outperformed rather small samples high levels heteroscedasticity. Moreover, Zitzmann’s jackknife tended perform somewhat better than two versions was much faster.