Longitudinal analysis has been widely employed to examine between-individual differences in within-individual change. One challenge of such analyses lies in that the rate-of-change is only available indirectly when change patterns are nonlinear with respect to time. Latent change score models (LCSMs), which can be employed to investigate the change in growth rate at the individual level, have been developed to address this challenge. We extend an existing LCSM with the Jenss-Bayley growth curve (Grimm et al., 2016, Chapter 18) and propose a novel expression of change scores that allows for (1) unequally-spaced study waves and (2) individual measurement occasions around each wave. We also extend the existing model to estimate the individual ratio of growth acceleration (that largely determines the trajectory shape and is viewed as the most important parameter in the Jenss-Bayley model). We present the proposed model by simulation studies and a real-world data analysis. Our simulation studies demonstrate that the proposed model generally estimates the parameters of interest unbiasedly, precisely, and exhibits appropriate confidence interval coverage. More importantly, the proposed model with the novel expression of change scores performed better than the existing model shown by simulation studies. An empirical example using longitudinal reading scores shows that the model can estimate the individual ratio of growth acceleration and generate individual growth rate in practice. We also provide the corresponding code of the proposed model.
翻译:为了研究个人内部变化中的个人差异,广泛采用了纵向分析来研究个人之间在个人内部变化方面的差异。这种分析的一个挑战在于,变化率只有在变化模式非线性时才能间接得到。可以用来调查个人增长率变化的早期变化得分模型(LCSMs)已经开发出来,用于调查个人增长率的变化,以应对这一挑战。我们通过延斯-巴伊利增长曲线(Grimm等人,2016年,第18章)扩展了现有的LCSM,并提出了新的变化得分表示,允许(1) 空间间研究波和(2) 每波周围的个别计量时间不均。我们还扩展了现有模型来估计个人增长加速率(主要决定轨迹形状,并被视为Jens-Bayley模型中最重要的参数) 。我们通过模拟研究和真实世界数据分析来介绍拟议的模型。我们的模拟研究表明,拟议的模型一般地评估了利息的参数,准确无误,并展示了适当的信任间隔范围。更重要的是,拟议的模型与新显示的变化得分数的模型相比,我们还可以用模拟模型来展示个人增长速度的模型的模型。我们通过模拟模型来展示了个人增长指数。