Latent Class Analysis for Developmental Research
Latent Class Analysis for Developmental Research
复制标题
发展研究中的潜在类分析
DOI：
10.1111/cdep.12163
复制
发表时间：
2016-03-01
影响因子：
6.4
通讯作者：
Cooper, Brittany R.
中科院分区：
文献类型：
--
作者：
Lanza, Stephanie T.;Cooper, Brittany R.
在本文中，我们考虑了潜在类别分析（LCA）和相关方法的广泛适用性，以推进儿童发展的研究。首先，我们描述了以人为中心的方法（例如生命周期分析）在发展研究中的作用，并回顾了生命周期分析在发展研究和相关研究领域的先前应用。然后我们提出了在发展研究中应用 LCA 时的实际考虑因素，包括模型选择和统计功效。最后，我们介绍了 LCA 中最近的几项方法创新，包括 LCA 中的因果推理、预测 LC 成员资格的远端结果和 LC 调节（其中 LCA 量化了观察和实验研究中影响的多维调节因素），并讨论了它们推进发展科学的潜力。最后，我们对使用 LCA 进行持续的开发研究提出了建议。
In this article, we consider the broad applicability of latent class analysis (LCA) and related approaches to advance research on child development. First, we describe the role of person-centered methods such as LCA in developmental research, and review prior applications of LCA to the study of development and related areas of research. Then we present practical considerations when applying LCA in developmental research, including model selection and statistical power. Finally, we introduce several recent methodological innovations in LCA, including causal inference in LCA, predicting a distal outcome from LC membership, and LC moderation (in which LCA quantifies multidimensional moderators of effects in observational and experimental studies), and we discuss their potential to advance developmental science. We conclude with suggestions for ongoing developmental research using LCA.