Abstract
ABSTRACT Unequal group sizes (imbalance) and small sample sizes are common in multilevel confirmatory factor analyses (ML-CFA). This simulation study examined the influence of imbalance combined with small sample sizes on both levels on estimation performance in ML-CFA. Imbalance did not influence estimation performance given the minimum sample size requirements. Greater sample sizes on one level compensated for smaller sample sizes on the respective other level. Additionally, the degree of intraclass correlation () interacted with sample sizes. Based on the results of the simulation study, recommendations for practical applications are delineated. For instance, at least 100 Level-2 units with an average cluster size of four or 150 Level-2 units with an average cluster size of two are recommended given an of .30 or above.
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