Kim Minjung, Hsu Hsien-Yuan, Kwok Oi-Man, Seo Sunmi
Quantitative Research, Evaluation, and Measurement, Department of Educational Studies, The Ohio State University, Columbus, OH, United States.
Children's Learning Institute, University of Texas Health Science Center at Houston, Houston, TX, United States.
Front Psychol. 2018 Mar 27;9:349. doi: 10.3389/fpsyg.2018.00349. eCollection 2018.
This simulation study aims to propose an optimal starting model to search for the accurate growth trajectory in Latent Growth Models (LGM). We examine the performance of four different starting models in terms of the complexity of the mean and within-subject variance-covariance (V-CV) structures when there are time-invariant covariates embedded in the population models. Results showed that the model search starting with the fully saturated model (i.e., the most complex mean and within-subject V-CV model) recovers best for the true growth trajectory in simulations. Specifically, the fully saturated starting model with using ΔBIC and ΔAIC performed best (over 95%) and recommended for researchers. An illustration of the proposed method is given using the empirical secondary dataset. Implications of the findings and limitations are discussed.
本模拟研究旨在提出一种最优起始模型，以在潜在增长模型（LGM）中寻找准确的增长轨迹。当总体模型中嵌入时间不变协变量时，我们从均值和个体内方差协方差（V-CV）结构的复杂性方面检验了四种不同起始模型的性能。结果表明，在模拟中，从完全饱和模型（即最复杂的均值和个体内V-CV模型）开始的模型搜索对真实增长轨迹的恢复效果最佳。具体而言，使用ΔBIC和ΔAIC的完全饱和起始模型表现最佳（超过95%），并推荐给研究人员。使用经验性二次数据集对所提出的方法进行了说明。讨论了研究结果的意义和局限性。
