{"title":"捕捉心理过程动态特征变化中的个体差异的分层自回归模型的发展","authors":"Yanling Li, Lindy Williams, Chelsea Muth, Saeideh Heshmati, Sy-Miin Chow, Zita Oravecz","doi":"10.1080/10705511.2024.2402328","DOIUrl":null,"url":null,"abstract":"Several methodological innovations have been advanced in the past decades that combine growth curve models (GCMs) with models of autoregressive (AR) processes. However, most of these approaches do ...","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"124 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Growth of Hierarchical Autoregression Model for Capturing Individual Differences in Changes of Dynamic Characteristics of Psychological Processes\",\"authors\":\"Yanling Li, Lindy Williams, Chelsea Muth, Saeideh Heshmati, Sy-Miin Chow, Zita Oravecz\",\"doi\":\"10.1080/10705511.2024.2402328\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Several methodological innovations have been advanced in the past decades that combine growth curve models (GCMs) with models of autoregressive (AR) processes. However, most of these approaches do ...\",\"PeriodicalId\":21964,\"journal\":{\"name\":\"Structural Equation Modeling: A Multidisciplinary Journal\",\"volume\":\"124 1\",\"pages\":\"\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-10-03\",\"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/10.1080/10705511.2024.2402328\",\"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/10.1080/10705511.2024.2402328","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}
A Growth of Hierarchical Autoregression Model for Capturing Individual Differences in Changes of Dynamic Characteristics of Psychological Processes
Several methodological innovations have been advanced in the past decades that combine growth curve models (GCMs) with models of autoregressive (AR) processes. However, most of these approaches do ...
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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