Power of latent growth modeling for detecting group differences in linear growth trajectory parameters
Power of latent growth modeling for detecting group differences in linear growth trajectory parameters
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DOI：
10.1207/s15328007sem1003_3
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发表时间：
2003-01-01
影响因子：
6
通讯作者：
Fan, XT
中科院分区：
文献类型：
--
作者：
Fan, XT
本模拟研究的重点是在结构方程模型（SEM）的框架内检测线性增长轨迹参数的组间差异的能力，并将潜在增长模型（LGM）方法与更传统的重复测量方差分析（ANOVA）方法进行了比较。几种模式的线性增长轨迹组的差异被认为是。SEM生长建模始终显示出比重复测量ANOVA更高的检测线性生长斜率组差异的统计功效。对于生长轨迹中的小组差异，大样本量（例如，N > 500）将需要足够的统计功效。对于中等或较大的组差异，中等或较小的样本量足以获得足够的功效。最后对今后的研究方向进行了展望。
This simulation study focused on the power for detecting group differences in linear growth trajectory parameters within the framework of structural equation modeling (SEM) and compared the latent growth modeling (LGM) approach to the more traditional repeated-measures analysis of variance (ANOVA) approach. Several patterns of group differences in linear growth trajectories were considered. SEM growth modeling consistently showed higher statistical power for detecting group differences in the linear growth slope than repeated-measures ANOVA. For small group differences in the growth trajectories, large sample size (e.g., N > 500) would be required for adequate statistical power. For medium or large group differences, moderate or small sample size would be sufficient for adequate power. Some future research directions are discussed.