In exploratory factor analysis (EFA), cross-loadings frequently occur in empirical research, but its effects on determining the number of factors to retain are seldom known. In this paper, we analyzed whether and how cross-loadings affected the performance of the parallel analysis (PA), the empirical Kaiser criterion (EKC), the likelihood ratio test (LRT), the comparative fit index (CFI), the Tucker-Lewis index (TLI), and the root mean square error of approximation (RMSEA) in determining the number of factors to retain. A large-scale simulation study was also conducted. A few conclusions can be drawn: (1) overall, PA provides the most accurate performance, especially when data are non-normally distributed; (2) cross-loadings noticeably affect the performance of PA, CFI, and TLI with different patterns, and they virtually have no effect on EKC, LRT, and RMSEA; (3) no method is immune to the sizable detrimental effect of normality assumption violation. Several recommendations were provided.

在探索性因素分析（EFA）中，在经验研究中经常发生交叉负荷，但是很少知道它对确定保留因素数量的影响。在本文中，我们分析了交叉加载是否以及如何影响并行分析（PA），经验Kaiser准则（EKC），似然比检验（LRT），比较拟合指数（CFI），Tucker- Lewis指数（TLI）和确定保留因子数量的近似均方根误差（RMSEA）。还进行了大规模的模拟研究。可以得出以下结论：（1）总体而言，PA提供最准确的性能，尤其是在数据非正态分布时；（2）交叉加载会以不同的模式显着影响PA，CFI和TLI的性能，并且实际上对EKC，LRT没有影响，和RMSEA；（3）没有任何方法能够避免违反正常性假设的巨大不利影响。提供了一些建议。