检查表
潜在类模型
潜变量
弹道
心理学
德尔菲法
计算机科学
事件(粒子物理)
概率潜在语义分析
计量经济学
估计
认知心理学
数学
机器学习
人工智能
工程类
量子力学
物理
系统工程
天文
作者
Rens van de Schoot,Marit Sijbrandij,Sonja D. Winter,Sarah Depaoli,Jeroen K. Vermunt
标识
DOI:10.1080/10705511.2016.1247646
摘要
Estimating models within the mixture model framework, like latent growth mixture modeling (LGMM) or latent class growth analysis (LCGA), involves making various decisions throughout the estimation process. This has led to a wide variety in how results of latent trajectory analysis are reported. To overcome this issue, using a 4-round Delphi study, we developed Guidelines for Reporting on Latent Trajectory Studies (GRoLTS). The purpose of GRoLTS is to present criteria that should be included when reporting the results of latent trajectory analysis across research fields. We have gone through a systematic process to identify key components that, according to a panel of experts, are necessary when reporting results for trajectory studies. We applied GRoLTS to 38 papers where LGMM or LCGA was used to study trajectories of posttraumatic stress after a traumatic event.
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