{"title":"具有缺失数据的贝叶斯分段增长模型的模型拟合性能和选择指标","authors":"Ihnwhi Heo, Fan Jia, Sarah Depaoli","doi":"10.1080/10705511.2023.2264514","DOIUrl":null,"url":null,"abstract":"The Bayesian piecewise growth model (PGM) is a useful class of models for analyzing nonlinear change processes that consist of distinct growth phases. In applications of Bayesian PGMs, it is import...","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"26 2","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Performance of Model Fit and Selection Indices for Bayesian Piecewise Growth Modeling with Missing Data\",\"authors\":\"Ihnwhi Heo, Fan Jia, Sarah Depaoli\",\"doi\":\"10.1080/10705511.2023.2264514\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Bayesian piecewise growth model (PGM) is a useful class of models for analyzing nonlinear change processes that consist of distinct growth phases. In applications of Bayesian PGMs, it is import...\",\"PeriodicalId\":21964,\"journal\":{\"name\":\"Structural Equation Modeling: A Multidisciplinary Journal\",\"volume\":\"26 2\",\"pages\":\"\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2023-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Structural Equation Modeling: A Multidisciplinary Journal\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1080/10705511.2023.2264514\",\"RegionNum\":2,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structural Equation Modeling: A Multidisciplinary Journal","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1080/10705511.2023.2264514","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Performance of Model Fit and Selection Indices for Bayesian Piecewise Growth Modeling with Missing Data
The Bayesian piecewise growth model (PGM) is a useful class of models for analyzing nonlinear change processes that consist of distinct growth phases. In applications of Bayesian PGMs, it is import...
期刊介绍：
Structural Equation Modeling: A Multidisciplinary Journal publishes refereed scholarly work from all academic disciplines interested in structural equation modeling. These disciplines include, but are not limited to, psychology, medicine, sociology, education, political science, economics, management, and business/marketing. Theoretical articles address new developments; applied articles deal with innovative structural equation modeling applications; the Teacher’s Corner provides instructional modules on aspects of structural equation modeling; book and software reviews examine new modeling information and techniques; and advertising alerts readers to new products. Comments on technical or substantive issues addressed in articles or reviews published in the journal are encouraged; comments are reviewed, and authors of the original works are invited to respond.
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