Abstract

This study develops a method of measuring the i mportance of latent predictors and testing their importance ordering. A popular measure for relative importance, called dominance analysis, is extended to structural equation models such that the contribution to the variation of the outcome variable is attributed to each latent predictor. This measure is computed through the average R-squared change by adding a predictor into possible subset models, which can be derived from the model-implied correlation matrix of the latent variables. Besides presenting the dominance analysis measure for latent predictors, we calculate its confidence interval using bootstrap sampling and infer its statistical significance. Importance orderings of the latent predictors are formulated by order-constrained hypotheses, which can be evaluated using Bayes factors. Simulation studies demonstrate the performance of the proposed method. A real data example illustrates how to assess relative importance in latent regression models.

摘要

本研究开发了一种测量潜在预测变量重要性并测试其重要性排序的方法。一种流行的相对重要性度量，称为优势分析，被扩展到结构方程模型，使得对结果变量变化的贡献归因于每个潜在预测变量。该度量是通过平均R计算的-通过将预测变量添加到可能的子集模型中来平方变化，这可以从潜在变量的模型隐含相关矩阵中推导出来。除了提出潜在预测变量的优势分析度量外，我们还使用自举抽样计算其置信区间并推断其统计显着性。潜在预测变量的重要性排序由顺序约束假设制定，可以使用贝叶斯因子进行评估。仿真研究证明了所提出方法的性能。一个真实的数据示例说明了如何评估潜在回归模型中的相对重要性。