Bray Bethany C, Lanza Stephanie T, Collins Linda M
Department of Psychology, Virginia Polytechnic Institute and State University.
Struct Equ Modeling. 2010 Dec 1;17(4):541-569. doi: 10.1080/10705511.2010.510043.
To understand one developmental process, it is often helpful to investigate its relations with other developmental processes. Statistical methods that model development in multiple processes simultaneously over time include latent growth curve models with time-varying covariates, multivariate latent growth curve models, and dual trajectory models. These models are designed for growth represented by continuous, unidimensional trajectories. The purpose of this article is to present a flexible approach to modeling relations in development among two or more discrete, multidimensional latent variables based on the general framework of loglinear modeling with latent variables called associative latent transition analysis (ALTA). Focus is given to the substantive interpretation of different associative latent transition models, and exactly what hypotheses are expressed in each model. An empirical demonstration of ALTA is presented to examine the association between the development of alcohol use and sexual risk behavior during adolescence.
为了理解一个发育过程，研究它与其他发育过程的关系通常会有所帮助。随着时间同时对多个过程中的发育进行建模的统计方法包括具有随时间变化协变量的潜在增长曲线模型、多元潜在增长曲线模型和双轨迹模型。这些模型是为以连续、单维轨迹表示的增长而设计的。本文的目的是基于称为关联潜在转变分析（ALTA）的具有潜在变量的对数线性建模的一般框架，提出一种灵活的方法来对两个或多个离散、多维潜在变量之间的发育关系进行建模。重点在于不同关联潜在转变模型的实质性解释，以及每个模型所表达的具体假设。本文给出了ALTA的一个实证示范，以检验青少年时期饮酒行为发展与性风险行为之间的关联。
