{"title":"An Evaluation of Non-Iterative Estimators in the Structural after Measurement (SAM) Approach to Structural Equation Modeling (SEM)","authors":"Sara Dhaene, Yves Rosseel","doi":"10.1080/10705511.2023.2220135","DOIUrl":null,"url":null,"abstract":"<p><b>Abstract</b></p><p>In Structural Equation Modeling (SEM), the measurement part and the structural part are typically estimated simultaneously via an iterative Maximum Likelihood (ML) procedure. In this study, we compare performance of the standard procedure to the Structural After Measurement (SAM) approach, where the structural part is separated from the measurement part. One appealing feature of the latter multi-step procedure is that it extends the scope of possible estimators, as now also non-iterative methods from factor-analytic literature can be used to estimate the measurement models. In our simulations, the SAM approach outperformed vanilla SEM in small to moderate samples (i.e., no convergence issues, no inadmissible solutions, smaller MSE values). Notably, this held regardless of the estimator used for the measurement part, with negligible differences between iterative and non-iterative estimators. This may call into question the added value of advanced iterative algorithms over closed-form expressions (which generally require less computational time and resources).</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"23 18","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/10.1080/10705511.2023.2220135","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
In Structural Equation Modeling (SEM), the measurement part and the structural part are typically estimated simultaneously via an iterative Maximum Likelihood (ML) procedure. In this study, we compare performance of the standard procedure to the Structural After Measurement (SAM) approach, where the structural part is separated from the measurement part. One appealing feature of the latter multi-step procedure is that it extends the scope of possible estimators, as now also non-iterative methods from factor-analytic literature can be used to estimate the measurement models. In our simulations, the SAM approach outperformed vanilla SEM in small to moderate samples (i.e., no convergence issues, no inadmissible solutions, smaller MSE values). Notably, this held regardless of the estimator used for the measurement part, with negligible differences between iterative and non-iterative estimators. This may call into question the added value of advanced iterative algorithms over closed-form expressions (which generally require less computational time and resources).
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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