This Monte Carlo simulation study aimed to investigate confirmatory factor analysis (CFA) estimation methods under different conditions, such as sample size, distribution of indicators, test length, average factor loading, and factor structure. Binary data were generated to compare the performance of maximum likelihood (ML), mean and variance adjusted unweighted least squares (ULSMV), mean and variance adjusted weighted least squares (WLSMV), and Bayesian estimators. As a result of the study, it was revealed that increased average factor loading and sample size had a positive effect on the performance of the estimation methods. According to the research findings, it can be said that the methods are sufficient to estimate average factor loading and interfactor correlations, regardless of the estimation methods, in most of the conditions where the average factor loading is 0.7. In small sample sizes particularly, the interfactor correlation was underestimated for skewed indicator conditions. According to the findings of the study, although there is not the most accurate method in all conditions, it can be recommended to use ULSMV method because it performs adequately in more conditions.

中文翻译：

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验证性因子分析估计方法对二元数据的比较

这项蒙特卡洛模拟研究旨在研究在不同条件下的验证性因子分析（CFA）估计方法，例如样本大小，指标分布，测试长度，平均因子负荷和因子结构。生成二进制数据以比较最大似然（ML），均值和方差调整的加权最小二乘（ULSMV），均值和方差调整的加权最小二乘（WLSMV）和贝叶斯估计量的性能。研究结果表明，增加的平均因子负荷和样本量对估计方法的性能有积极影响。根据研究发现，可以说这些方法足以估算平均因子负荷和因子间相关性，而与估算方法无关，在大多数情况下，平均因素负荷为0.7。特别是在小样本量中，偏斜指标条件的因素间相关性被低估了。根据研究结果，尽管并非在所有条件下都使用最准确的方法，但建议使用ULSMV方法，因为它在更多条件下都能发挥足够的性能。