A Two-level Moderated Latent Variable Model with Single Level Data
A Two-level Moderated Latent Variable Model with Single Level Data
复制标题
单水平数据的两水平调节潜变量模型
DOI：
10.1080/00273171.2019.1689350
复制
发表时间：
2019-11
影响因子：
3.8
通讯作者：
Liu Fang
中科院分区：
文献类型：
--
作者：
Liu Hongyun;Yuan Ke-Hai;Liu Fang
袁、程和麦克斯韦利用单水平数据建立了一个两水平回归模型，以进行更精确的慢化分析。本文将两水平回归模型扩展为两水平调节潜变量模型，并利用贝叶斯方法估计和检验调节效应。MonteCarlo结果表明：1）新方法比单水平模型下的产品指标法（PI）和潜变量交互作用法（LVI）更准确地估计了交互作用效应，而这两种方法也都是用贝叶斯方法估计的：2）2MLV模型的可信区间覆盖率比其他方法更接近标称95%; 3）调节效应存在性检验在控制I类误差方面比PI和LVI更可靠，尤其是在异方差条件下。此外，基于2MLV模型开发了一种更具可解释性的效应量测量方法，该方法直接回答了调节剂可以在多大程度上解释预测变量和结局变量之间的系数变化的问题。一个真实的数据实例说明了新方法的应用。
Abstract With single-level data, Yuan, Cheng and Maxwell developed a two-level regression model for more accurate moderation analysis. This article extends the two-level regression model to a two-level moderated latent variable (2MLV) model, and uses a Bayesian approach to estimate and test the moderation effects. Monte Carlo results indicate that: 1) the new method yields more accurate estimate of the interaction effect than those via the product-indicator (PI) approach and latent variable interaction (LVI) with single-level model, both are also estimated via Bayesian method; 2) the coverage rates of the credibility interval following the 2MLV model are closer to the nominal 95% than those following the other methods; 3) the test for the existence of the moderation effect is more reliable in controlling Type I errors than both PI and LVI, especially under heteroscedasticity conditions. Moreover, a more interpretable measure of effect size is developed based on the 2MLV model, which directly answers the question as to what extent a moderator can account for the change of the coefficient between the predictor and the outcome variable. A real data example illustrates the application of the new method.
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影响因子：
22.4
作者：
BUSEMEYER, JR;JONES, LE
通讯作者：
JONES, LE
DOI：
10.1080/10705511.2011.582408
发表时间：
2011-01-01
影响因子：
6
作者：
Kelava, Augustin;Werner, Christina S.;West, Stephen G.
通讯作者：
West, Stephen G.
影响因子：
7.6
作者：
BARON, RM;KENNY, DA
通讯作者：
KENNY, DA
影响因子：
3.7
作者：
Wall, MM;Amemiya, Y
通讯作者：
Amemiya, Y
影响因子：
22.4
作者：
JACCARD, J;WAN, CK
通讯作者：
WAN, CK