A NOTE ON THE COMPUTER GENERATION OF MEAN AND COVARIANCE EXPECTATIONS IN LATENT GROWTH CURVE ANALYSIS
A NOTE ON THE COMPUTER GENERATION OF MEAN AND COVARIANCE EXPECTATIONS IN LATENT GROWTH CURVE ANALYSIS
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DOI：
10.1016/s1475-9144(05)04015-4
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发表时间：
2005-01-01
期刊：
影响因子：
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通讯作者：
McArdle, John J.
中科院分区：
文献类型：
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作者：
Grimm, Kevin J.;McArdle, John J.
每个“结构模型”都是由一组协方差和平均预期定义的。这些预期是参数估计、拟合统计和实质性解释的来源。Cortina，Pant和Smith-Darden((本卷))最近的一章。摘自：F.Dansereau和F.J.Yammarino(编辑)，多层次问题研究(第4卷)。英国牛津：Elsevier)展示了对纵向数据的数据协方差矩阵的正式调查如何能够更好地理解线性增长模型中协方差项的估计。Cortina等人提出的调查。(这一卷)是合理的，并可能为使用线性变化增长模型的研究人员提供信息。然而，对于行为研究人员来说，考虑更复杂的模型是很常见的，在这种情况下，将需要各种更复杂的技术来计算期望。在本章中，我们将演示如何使用可用的计算机程序，如Maple，自动为每个结构模型的平均值和协方差创建代数期望。这里给出的例子可以用于任何复杂的潜在增长模型，包括线性和非线性过程，以及任何数量的纵向测量。
Every "structural model" is defined by the set of covariance and mean expectations. These expectations are the source of parameter estimates, fit statistics, and substantive interpretation. The recent chapter by Cortina, Pant, and Smith-Darden ((this volume). In: F. Dansereau & F. J. Yammarino (Eds), Research in multi-level issues (Vol. 4). Oxford, England: Elsevier) shows how a formal investigation of the data covariance matrix of longitudinal data can lead to an improved understanding of the estimates of covariance terms among linear growth models. The investigations presented by Cortina et al. (this volume) are reasonable and potentially informative for researchers using linear change growth models. However, it is quite common for behavioral researchers to consider more complex models, in which case a variety of more complex techniques for the calculation of expectations will be needed. In this chapter we demonstrate how available computer programs, such as Maple, can be used to automatically create algebraic expectations for the means and the covariances of every structural model. The examples presented here can be used for a latent growth model of any complexity, including linear and nonlinear processes, and any number of longitudinal measurements.