Testing main effects and interactions in latent curve analysis
Testing main effects and interactions in latent curve analysis
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DOI：
10.1037/1082-989x.9.2.220
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发表时间：
2004-06-01
影响因子：
7
通讯作者：
Willoughby, MT
中科院分区：
文献类型：
--
作者：
Curran, PJ;Bauer, DJ;Willoughby, MT
潜曲线分析（LCA）的一个关键优势是能够将变化率的个体差异建模为一个或多个解释变量的函数。时间的测量起着关键作用，因为在对重复测量进行预测时，解释变量与时间存在乘法交互作用。然而，在LCA中通常没有利用这种交互作用，因为时间的测量是通过因子载荷矩阵相当巧妙地纳入的。作者的目的是从分析和实证两个方面证明，用于探究多元回归中交互作用的经典技术可以推广到LCA。文中给出了一个实例，并建议在实际估计条件LCA时使用这些技术。
A key strength of latent curve analysis (LCA) is the ability to model individual variability in rates of change as a function of 1 or more explanatory variables. The measurement of time plays a critical role because the explanatory variables multiplicatively interact with time in the prediction of the repeated measures. However, this interaction is not typically capitalized on in LCA because the measure of time is rather subtly incorporated via the factor loading matrix. The authors' goal is to demonstrate both analytically and empirically that classic techniques for probing interactions in multiple regression earl be generalized to LCA. A worked example is presented, and the use of these techniques is recommended whenever estimating conditional LCAs in practice.