ABSTRACT

This paper explores relationships amongst cross-lagged models allowing trajectories to be freely estimated, some accounting for time-varying differences amongst individuals (Autoregressive Latent Trajectory (ALT), General Cross-lagged Model (GCLM), and Latent Growth Curve Model with Structured Residuals and Unspecified Growth Trajectory (LGCM-SR-UGT)) and some not (Cross-lagged Panel Model (CLPM), Random Intercept Cross-lagged Panel Model (RI-CLPM), and Mean Stationary GCLM). An applied example using LSAY data demonstrates these models. Simulations examine (1) fit indices assessing “good” fit and Bayes Factor for model selection; (2) consequences of ignoring variability in trajectories on cross-lagged estimates. Findings were (1) RMSEA discerned “good” fit and Bayes Factor tended to select models closely related to true model over less related models; (2) various patterns of bias in path estimates and standard errors are found, in particular, causal dominance in conjunction with time-variant between-person variance and covariance were notably influential on bias in path estimates.

摘要

本文探讨了允许自由估计轨迹的交叉滞后模型之间的关系，其中一些解释了个体之间随时间变化的差异（自回归潜在轨迹 (ALT)、一般交叉滞后模型 (GCLM) 和具有结构化残差的潜在增长曲线模型）和未指定的增长轨迹 (LGCM-SR-UGT)），有些不是（交叉滞后面板模型 (CLPM)、随机截距交叉滞后面板模型 (RI-CLPM) 和平均平稳 GCLM）。使用 LSAY 数据的应用示例演示了这些模型。模拟检查 (1) 评估“良好”拟合的拟合指数和用于模型选择的贝叶斯因子；(2) 忽略交叉滞后估计的轨迹可变性的后果。结果是 (1) RMSEA 辨别出“良好”的拟合，贝叶斯因子倾向于选择与真实模型密切相关的模型而不是不太相关的模型；(2) 发现了路径估计和标准误差中的各种偏差模式，特别是因果优势与时变人与人之间的方差和协方差相结合对路径估计中的偏差有显着影响。