Alternative structural models for multivariate longitudinal data analysis
Alternative structural models for multivariate longitudinal data analysis
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DOI：
10.1207/s15328007sem1004_1
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发表时间：
2003-01-01
影响因子：
6
通讯作者：
McArdle, JJ
中科院分区：
文献类型：
--
作者：
Ferrer, E;McArdle, JJ
结构方程模型作为替代模型检查纵向数据。这些模型包括（a）交叉滞后回归模型，（B）基于潜在增长曲线的因素模型，以及（c）基于潜在差异分数的动态模型。说明性的数据是在他们的高中第一学期的学生的动机和感知能力。这3种模式产生了不同的结果，这种差异进行了讨论的概念化的变化基础的每一个模型。最后一个模型被认为是对这些数据最合理的，因为它捕捉到了所研究的结构之间的动态相互关系，同时确定了变量的潜在增长。
Structural equation models are presented as alternative models for examining longitudinal data. The models include (a) a cross-lagged regression model, (b) a factor model based on latent growth curves, and (c) a dynamic model based on latent difference scores. The illustrative data are on motivation and perceived competence of students during their first semester in high school. The 3 models yielded different results and such differences were discussed in terms of the conceptualization of change underlying each model. The last model was defended as the most reasonable for these data because it captured the dynamic interrelations between the examined constructs and, at the same time, identified potential growth in the variables.