Bianconcini Silvia, Bollen Kenneth A
Department of Statistical Sciences, University of Bologna.
Department of Psychology and Neuroscience and Department of Sociology, University, of North Carolina, Chapel Hill.
Struct Equ Modeling. 2018;25(5):791-808. doi: 10.1080/10705511.2018.1426467. Epub 2018 Jan 30.
In recent years, longitudinal data have become increasingly relevant in many applications, heightening interest in selecting the best longitudinal model to analyze them. Too often traditional practice rather than substantive theory guide the specific model selected. This opens the possibility that alternative models might better correspond to the data. In this paper, we present a general longitudinal model that we call the Latent Variable Autoregressive Latent Trajectory (LV-ALT) model that includes most other longitudinal models with continuous outcomes as special cases. It is capable of specializing to most models dictated by theory or prior research while having the capacity to compare them to alternative ones. If there is little guidance on the best model, the LV-ALT provides a way to determine the appropriate empirical match to the data. We present the model, discuss its identification and estimation, and illustrate how the LV-ALT reveals new things about a widely used empirical example.
近年来，纵向数据在许多应用中变得越来越重要，这使得人们对选择最佳纵向模型来分析这些数据的兴趣日益浓厚。通常情况下，是传统做法而非实质性理论指导具体模型的选择。这就有可能存在其他模型可能更符合数据的情况。在本文中，我们提出了一种通用的纵向模型，我们称之为潜在变量自回归潜在轨迹（LV-ALT）模型，大多数其他具有连续结果的纵向模型都作为特殊情况包含在该模型中。它能够专门化为理论或先前研究所规定的大多数模型，同时有能力将它们与其他替代模型进行比较。如果对于最佳模型几乎没有指导意见，LV-ALT模型提供了一种确定与数据相匹配的合适实证模型的方法。我们展示了该模型，讨论了其识别和估计方法，并举例说明了LV-ALT模型如何揭示一个广泛使用的实证例子中的新情况。
