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 ($ICC$) 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 $ICC$ of .30 or above.

摘要

在多级验证性因素分析 (ML-CFA) 中，群体规模不等（不平衡）和小样本量很常见。该模拟研究检查了不平衡与小样本量对两个级别的 ML-CFA 估计性能的影响。考虑到最小样本量要求，不平衡不会影响估计性能。一个级别上较大的样本量补偿了相应另一级别上较小的样本量。此外，类内相关程度（$\mathrm{一世}CC$) 与样本大小相互作用。根据模拟研究的结果，提出了实际应用的建议。例如，建议至少有 100 个平均集群规模为 4 的二级单位或 150 个平均集群规模为 2 的二级单位。$\mathrm{一世}CC$ 0.30 或以上。