Pushing the Limits: The Performance of Maximum Likelihood and Bayesian Estimation With Small and Unbalanced Samples in a Latent Growth Model

IF 2 3区 心理学 Q2 PSYCHOLOGY, MATHEMATICAL
Mariëlle Zondervan-Zwijnenburg, S. Depaoli, M. Peeters, R. van de Schoot
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引用次数: 10

Abstract

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.
突破极限:潜在增长模型中小样本和不平衡样本的最大似然和贝叶斯估计的性能
纵向发展研究通常侧重于不同(亚)个体群体的变化或成长模式。特别是在某些研究背景下,发展调查可能涉及一个或多个性质较小的(子)群体,因此难以通过统计分析正确捕捉。本研究通过模拟研究探讨了多组潜在增长模型中子样本大小的下限。我们特别关注当(子)样本量小时,最大似然(ML)和贝叶斯估计方法如何不同。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预计有限时,先验信息的添加可能是检测组之间差异的关键。在这种情况下,对较小群体的先验信息的获取尤其有影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.70
自引率
6.50%
发文量
16
审稿时长
36 weeks
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