Johnson Sara K
Eliot-Pearson Department of Child Study and Human Development, Tufts University, Medford, MA, 02145, USA.
New Dir Child Adolesc Dev. 2021 Jan;2021(175):111-139. doi: 10.1002/cad.20398. Epub 2021 Feb 26.
Developmental scientists are often interested in subgroups of people who share commonalities in aspects of development; these subgroups often cannot be captured directly but instead must be inferred from other information. Mixture models can be used in these situations. Two specific types of mixture models, latent profile transition analyses and growth mixture models, are highly relevant to developmental science because they can identify subgroups of people who are similar in their patterns of change. This guide highlights foundational aspects of these two types of models and is intended for readers who have not previously conducted either an LPTA or a GMM, or perhaps no mixture model analyses at all. It includes four primary sections. The first focuses on understanding mixture models conceptually and applying that knowledge to identifying appropriate research questions. The second section addresses data requirements, including planning for data collection or evaluating the suitability of previously collected data, and data preparation. The third section focuses on conducting analyses, with step-by-step instructions and syntax, and the final section discusses presenting the results. I illustrate these concepts and procedures with an example data set and research questions derived from the Five Cs model of positive youth development.
发展科学家通常对在发展方面有共同特征的人群亚组感兴趣；这些亚组往往无法直接确定，而是必须从其他信息中推断出来。在这些情况下可以使用混合模型。两种特定类型的混合模型，即潜在剖面转换分析和生长混合模型，与发展科学高度相关，因为它们可以识别在变化模式上相似的人群亚组。本指南重点介绍这两种模型的基础方面，面向那些以前没有进行过潜在剖面转换分析或生长混合模型分析，甚至可能根本没有进行过混合模型分析的读者。它包括四个主要部分。第一部分侧重于从概念上理解混合模型，并将这些知识应用于确定合适的研究问题。第二部分涉及数据要求，包括规划数据收集或评估先前收集数据的适用性以及数据准备。第三部分重点介绍进行分析，提供逐步说明和语法，最后一部分讨论结果呈现。我用一个示例数据集以及源自积极青少年发展的五C模型的研究问题来说明这些概念和程序。
