多变量追踪研究的模型整合与拓展：考察往复式影响与增长趋势

追踪研究当中, 交叉滞后模型可以探究多变量之间往复式影响, 潜增长模型可以探究个体增长趋势。对两类模型进行整合, 例如同时关注往复式影响与个体增长趋势, 同时可以定义测量误差、随机截距等变异成分, 衍生出随机截距交叉滞后模型、特质-状态-误差模型、自回归潜增长模型、结构化残差潜增长模型等。以交叉滞后模型和潜增长模型分别作为基础模型, 从个体间/个体内变异分解的角度对上述各类模型梳理, 整合出此类模型的分析框架, 并拓展建立“因子结构化潜增长模型(factor latent curve model with structured reciprocals)”作为统合框架。通过实证研究(早期儿童的追踪研究-幼儿园版, ECLS-K), 建立21049名儿童的阅读和数学能力的往复式影响与增长趋势。研究发现, 分离了稳定特质的模型拟合最优。研究也对模型建模思路和模型选择提供了建议。

A unification and extension on the multivariate longitudinal models: Examining reciprocal effect and growth trajectory

When conducting the multivariate longitudinal studies, reciprocal relationship and latent trajectory are two of the focusing issues. The reciprocal relationship is often examined by a cross-lagged model that could build autoregressive influence and the multivariate influence between target variables, while the latent trajectory is usually defined by a latent growth model that explores the growth pattern simultaneously with individual difference. These two kinds of models are easily built under the SEM framework, at the same time could be flexibly combined by other research questions, such as the measurement error, the random factor, as well as the combination of the above issues. Such a combination yields a more complex model definition exploring the longitudinal relations, such as factor cross-lagged model, random-intercept cross-lagged model, trait-state-error model, autoregressive trajectory model, latent change score model, etc.