ABSTRACT

Multicollinearity between predictors is a common concern in SEM applications. As in linear regression models, high correlations between predictors can lead to unstable parameter estimates (i.e., large standard errors) and reduced statistical power. Regularized estimation methods, which have recently become available for SEMs, may provide more stable estimates in the presence of multicollinearity at the cost of a certain amount of bias in the estimated parameters. In a simulation study, we compared the performance of nonregularized SEM with Ridge and Elastic net regularized SEMs in the presence of strong multicollinearity. The results provide evidence that Ridge and Elastic net regularized SEMs provide more stable estimates and greater statistical power than nonregularized SEM. However, the biases from regularized estimation can result in increased Type I error rates. This phenomenon was more pronounced in Ridge than in Elastic net regularized SEMs. We discuss when the benefits can outweigh this cost.

摘要

预测变量之间的多重共线性是 SEM 应用中常见的问题。与线性回归模型一样，预测变量之间的高度相关性会导致参数估计不稳定（即大的标准误差）和降低的统计功效。最近可用于 SEM 的正则化估计方法可以在存在多重共线性的情况下提供更稳定的估计，但代价是估计参数存在一定量的偏差。在模拟研究中，我们在存在强多重共线性的情况下比较了非正则化 SEM 与 Ridge 和弹性网络正则化 SEM 的性能。结果证明 Ridge 和 Elastic net 正则化 SEM 比非正则化 SEM 提供更稳定的估计和更大的统计功效。然而，正则化估计的偏差会导致 I 类错误率增加。这种现象在 Ridge 中比在弹性网络正则化 SEM 中更为明显。我们讨论何时收益可以超过成本。