Fit criteria for heterogenous growth curve models and related nonlinear models

异质增长曲线模型和相关非线性模型的拟合标准

• 批准号: 315340858
• 负责人: Professor Dr. Andreas Klein
• 金额: --
• 依托单位: Abteilung für Psychologische Methodenlehre, Evaluation und Forschungsmethodik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2018-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/315340858?language=en
• 关键词: Fit; criteria; heterogenous; growth; curve

Latent growth curve modeling (LGM) is a technique used to identify the developmental trajectories of individuals over a period of time. An extension of a conventional LGM is the heterogenous growth curve model (HGM), which permits a modeling of the growth trajectory that flexibly depends on the individual initial status. In particular, the HGMs are able to represent the directedness of individual growth trajectories over time. Whereas the goodness of fit of a conventional LGM is usually evaluated by a chi-square test, descriptive fit indices, or concordance coefficients, there exist no adequate model fit criteria for heterogenous LGMs. Because of this, we aim at developing a goodness of fit test for HGM. The main objectives are (1) new development and testing of an overall model fit for a HGM based on a quasi-maximum likelihood approach, (2) modification of these tests to devise descriptive fit measures for HGMs and other types of nonlinear models, (3) evaluation of the tests and model fit criteria using Monte Carlo studies as well as empirical data, and (4) the implementation of the new measures in appropriate software.

潜在生长曲线模型（LGM）是一种用于确定个体在一段时间内的发展轨迹的技术。一个传统的LGM的扩展是异质生长曲线模型（HGM），它允许的增长轨迹，灵活地依赖于个人的初始状态的建模。特别是，HGM能够代表个人增长轨迹随时间的方向性。而一个传统的LGM的拟合优度通常是通过卡方检验，描述性拟合指数，或一致性系数进行评估，有没有足够的模型拟合异质LGM的标准。正因为如此，我们的目标是开发一个适合HGM检验的优度。主要目标是：（1）基于准最大似然方法，对HGM的整体模型拟合进行新的开发和测试，（2）修改这些测试，以设计HGM和其他类型非线性模型的描述性拟合措施，（3）使用Monte Carlo研究以及经验数据评估测试和模型拟合标准，及（4）在适当软件中实施新措施。

项目成果

A Quasi-Likelihood Approach to Assess Model Fit in Quadratic and Interaction SEM

• DOI: 10.1080/00273171.2019.1689349
• 发表时间: 2019-12
• 期刊: Multivariate Behavioral Research
• 影响因子: 3.8
• 作者: Rebecca D. Büchner;Andreas G. Klein

A Fit Index to Assess Model Fit and Detect Omitted Terms in Nonlinear SEM

• DOI: 10.1080/10705511.2016.1268923
• 发表时间: 2017-05-01
• 期刊: STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL
• 影响因子: 6
• 作者: Gerhard, Carla;Buechner, Rebecca D.;Schermelleh-Engel, Karin
• 通讯作者: Schermelleh-Engel, Karin