ABSTRACT

This study focuses on three popular methods to model interactions between two constructs containing measurement error in predicting an observed binary outcome: logistic regression using (1) observed scores, (2) factor scores, and (3) Structural Equation Modeling (SEM). It is still unclear how they compare with respect to bias and precision in the estimated interaction when item scores underlying the interaction constructs are skewed and ordinal. In this article, we investigated this issue using both a Monte Carlo simulation and an empirical illustration of the effect of Type D personality on cardiac events. Our results indicated that the logistic regression using SEM performed best in terms of bias and confidence interval coverage, especially at sample sizes of 500 or larger. Although for most methods bias increased when item scores were skewed and ordinal, SEM produced relatively unbiased interaction effect estimates when items were modeled as ordered categorical.

摘要

本研究侧重于对包含测量误差的两个构造之间的相互作用进行建模的三种流行方法，用于预测观察到的二元结果：使用 (1) 观察分数、(2) 因子分数和 (3) 结构方程建模 (SEM) 的逻辑回归。当交互结构的项目得分偏斜和有序时，它们如何比较估计交互中的偏差和精度，目前尚不清楚。在本文中，我们使用蒙特卡罗模拟和 D 型人格对心脏事件影响的经验说明来研究这个问题。我们的结果表明，使用 SEM 的逻辑回归在偏差和置信区间覆盖率方面表现最佳，尤其是在 500 或更大的样本量时。