Department of Psychology, Florida International University, 11200 SW 8 Street, DM 381B, Miami, FL, 33199, USA.
Behav Res Methods. 2021 Oct;53(5):2127-2157. doi: 10.3758/s13428-020-01532-y. Epub 2021 Mar 29.
Researchers frequently wish to make incremental validity claims, suggesting that a construct of interest significantly predicts a given outcome when controlling for other overlapping constructs and potential confounders. Once the significance of such an effect has been established, it is good practice to also assess and report its magnitude. In OLS regression, this is easily accomplished by calculating the change in R-squared, ΔR, between one's full model and a reduced model that omits all but the target predictor(s) of interest. Because observed variable regression methods ignore measurement error, however, their estimates are prone to bias and inflated type I error rates. As a result, researchers are increasingly encouraged to switch from observed variable modeling conducted in the regression framework to latent variable modeling conducted in the structural equation modeling (SEM) framework. Standard SEM software packages provide overall R measures for each outcome, yet calculation of ΔR is not intuitive in models with latent variables. Omitting all indicators of a latent factor in a reduced model will alter the overidentifying constraints imposed on the model, affecting parameter estimation and fit. Furthermore, omitting variables in a reduced model may affect estimation under missing data, particularly when conditioning on those variables is essential to meeting the MAR assumption. In this article, I describe four approaches to calculating ΔR in SEMs with latent variables and missing data, compare their performance via simulation, describe a set of extensions to the methods, and provide a set of R functions for calculating ΔR in SEM.
研究人员经常希望提出增量有效性主张，即当控制其他重叠结构和潜在混杂因素时，一个感兴趣的结构显著预测给定的结果。一旦确定了这种效果的显著性，评估和报告其大小也是很好的做法。在 OLS 回归中，这可以通过计算全模型和仅包含目标预测变量的简化模型之间的 R 平方变化 ΔR 来轻松完成。然而，由于观察变量回归方法忽略了测量误差，因此它们的估计容易产生偏差和过高的Ⅰ型错误率。因此，研究人员越来越鼓励从回归框架中的观察变量建模转向结构方程建模（SEM）框架中的潜在变量建模。标准 SEM 软件包为每个结果提供了整体 R 度量，但在具有潜在变量的模型中，ΔR 的计算并不直观。在简化模型中省略潜在因素的所有指标将改变模型施加的过度识别约束，从而影响参数估计和拟合。此外，在简化模型中省略变量可能会影响缺失数据下的估计，特别是当对这些变量进行条件处理对于满足 MAR 假设至关重要时。在本文中，我描述了在具有潜在变量和缺失数据的 SEM 中计算 ΔR 的四种方法，通过模拟比较它们的性能，描述了这些方法的一系列扩展，并提供了一组用于在 SEM 中计算 ΔR 的 R 函数。
