Longitudinal developmental research is often focused on patterns of change or growth across different (sub)groups of individuals. Particular to some research contexts, developmental inquiries may involve one or more (sub)groups that are small in nature and therefore difficult to properly capture through statistical analysis. The current study explores the lower-bound limits of subsample sizes in a multiple group latent growth modeling by means of a simulation study. We particularly focus on how the maximum likelihood (ML) and Bayesian estimation approaches differ when (sub)sample sizes are small. The results show that Bayesian estimation resolves computational issues that occur with ML estimation and that the addition of prior information can be the key to detect a difference between groups when sample and effect sizes are expected to be limited. The acquisition of prior information with respect to the smaller group is especially influential in this context.

中文翻译：

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突破极限

纵向发展研究通常侧重于不同（亚）人群的变化或增长方式。特定于某些研究背景，发展性探究可能涉及一个或多个（子）群体，这些群体本质上很小，因此很难通过统计分析正确地捕获。本研究通过模拟研究探索了多组潜在生长模型中子样本大小的下限。当（子）样本大小小时，我们特别关注最大似然（ML）和贝叶斯估计方法的不同。结果表明，贝叶斯估计解决了ML估计中出现的计算问题，并且当样本和效果大小受到限制时，先验信息的添加可能是检测组之间差异的关键。