{"title":"Combined Logistic and Confined Exponential Growth Models: Estimation Using SEM Software","authors":"Phillip K. Wood","doi":"10.1080/10705511.2023.2220918","DOIUrl":null,"url":null,"abstract":"<p><b>Abstract</b></p><p>The logistic and confined exponential curves are frequently used in studies of growth and learning. These models, which are nonlinear in their parameters, can be estimated using structural equation modeling software. This paper proposes a single combined model, a weighted combination of both models. Mplus, Proc Calis, and lavaan code for the model are provided. Monte Carlo simulations varying the number of measurement occasions (5, 10, and 15), internal consistency (α = 0.5, 0.7, and 0.8), and sample size (<i>N</i> = 1,000, 500, and 300) were examined to understand whether the model can be successfully fit with SEM software. Convergence failures were appreciable when model parameters were equal to special cases of logistic or confined exponential curves. At least ten measurement occasions and a moderate degree of reliability (α > 0.7) were required to identify the model as superior to its stand-alone alternatives.</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"24 9","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2023-07-14","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/10.1080/10705511.2023.2220918","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}
引用次数: 0
Abstract
The logistic and confined exponential curves are frequently used in studies of growth and learning. These models, which are nonlinear in their parameters, can be estimated using structural equation modeling software. This paper proposes a single combined model, a weighted combination of both models. Mplus, Proc Calis, and lavaan code for the model are provided. Monte Carlo simulations varying the number of measurement occasions (5, 10, and 15), internal consistency (α = 0.5, 0.7, and 0.8), and sample size (N = 1,000, 500, and 300) were examined to understand whether the model can be successfully fit with SEM software. Convergence failures were appreciable when model parameters were equal to special cases of logistic or confined exponential curves. At least ten measurement occasions and a moderate degree of reliability (α > 0.7) were required to identify the model as superior to its stand-alone alternatives.
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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