Specifying piecewise latent trajectory models for longitudinal data
Specifying piecewise latent trajectory models for longitudinal data
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DOI：
10.1080/10705510802154349
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发表时间：
2008-07-01
影响因子：
6
通讯作者：
Flora, David B.
中科院分区：
文献类型：
--
作者：
Flora, David B.
纵向数据的分段潜在轨迹模型在各种情况下都是有用的，例如当需要U简单模型来描述非线性变化时，或者当谎言分析的目的是评估关于在较长的整体时间范围内的模型内的特定时间段内发生的变化的假设时，例如在治疗01开始后发生的变化，一些其他事件。然而，规范的各种形式的分段模型尚未完全阐明的结构方程模型（SEM）框架。本文将分段模型描述为线性增长基本SEM模型的直接扩展，这使得它们相对容易指定和解释。在详细介绍了2个线性斜坡（或块）的模型之后，本文讨论了包括附加线性斜坡（即，3件模型）或二次因子（即，混合线性二次模型）。
Piecewise latent trajectory models for longitudinal data are useful in a wide variety of situations, such as when U Simple model is needed to describe nonlinear change, or when the purpose of (lie analysis is to evaluate hypotheses about change occurring during a particular period of time within a model for a longer overall time frame, such as change that occurs following Onset Of it treatment 01, some other event. However, the specification of various forms of piecewise models has not been fully explicated for the structural equation modeling (SEM) framework. This article describes piecewise models as a straightforward extension of the basic SEM model for linear growth, which makes them relatively easy both to specify and to interpret. After presenting models for 2 linear slopes (or pieces) in detail, the article discusses extensions that include additional linear slopes (i.e., a 3-piece model) or a quadratic factor (i.e., a hybrid linear-quadratic model).