Using SymPy (Symbolic Python) for Understanding Structural Equation Modeling
Structural Equation Modeling (SEM) continues to grow in popularity with numerous articles, books, courses, and workshops available to help researchers become proficient with SEM quickly. However, few resources are available to help users gain a deep understanding of the analytic steps involved in SE...
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| Published in: | Structural equation modeling Vol. 31; no. 6; pp. 1104 - 1115 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
Routledge
01.11.2024
Psychology Press |
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| ISSN: | 1070-5511, 1532-8007 |
| Online Access: | Get full text |
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| Abstract | Structural Equation Modeling (SEM) continues to grow in popularity with numerous articles, books, courses, and workshops available to help researchers become proficient with SEM quickly. However, few resources are available to help users gain a deep understanding of the analytic steps involved in SEM, with even fewer providing reproducible syntax for those learning the technique. This work builds off of the original work by Ferron and Hess to provide computer syntax, written in python, for the specification, estimation, and numerical optimization steps necessary for SEM. The goal is to provide readers with many of the numerical and analytic details of SEM that may not be regularly taught in workshops and courses. This work extends the original demonstration by Ferron and Hess to incorporate the reticular action model notation for specification as well as the estimation of variable means. All of the code listed is provided in the appendix. |
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| AbstractList | Structural Equation Modeling (SEM) continues to grow in popularity with numerous articles, books, courses, and workshops available to help researchers become proficient with SEM quickly. However, few resources are available to help users gain a deep understanding of the analytic steps involved in SEM, with even fewer providing reproducible syntax for those learning the technique. This work builds off of the original work by Ferron and Hess (2007) to provide computer syntax, written in python, for the specification, estimation, and numerical optimization steps necessary for SEM. The goal is to provide readers with many of the numerical and analytic details of SEM that may not be regularly taught in workshops and courses. This work extends the original demonstration by Ferron and Hess to incorporate the reticular action model notation for specification as well as the estimation of variable means. All of the code listed is provided in the appendix. Structural Equation Modeling (SEM) continues to grow in popularity with numerous articles, books, courses, and workshops available to help researchers become proficient with SEM quickly. However, few resources are available to help users gain a deep understanding of the analytic steps involved in SEM, with even fewer providing reproducible syntax for those learning the technique. This work builds off of the original work by Ferron and Hess to provide computer syntax, written in python, for the specification, estimation, and numerical optimization steps necessary for SEM. The goal is to provide readers with many of the numerical and analytic details of SEM that may not be regularly taught in workshops and courses. This work extends the original demonstration by Ferron and Hess to incorporate the reticular action model notation for specification as well as the estimation of variable means. All of the code listed is provided in the appendix. Structural Equation Modeling (SEM) continues to grow in popularity with numerous articles, books, courses, and workshops available to help researchers become proficient with SEM quickly. However, few resources are available to help users gain a deep understanding of the analytic steps involved in SEM, with even fewer providing reproducible syntax for those learning the technique. This work builds off of the original work by Ferron and Hess (2007) to provide computer syntax, written in python, for the specification, estimation, and numerical optimization steps necessary for SEM. The goal is to provide readers with many of the numerical and analytic details of SEM that may not be regularly taught in workshops and courses. This work extends the original demonstration by Ferron and Hess to incorporate the reticular action model notation for specification as well as the estimation of variable means. All of the code listed is provided in the appendix.Structural Equation Modeling (SEM) continues to grow in popularity with numerous articles, books, courses, and workshops available to help researchers become proficient with SEM quickly. However, few resources are available to help users gain a deep understanding of the analytic steps involved in SEM, with even fewer providing reproducible syntax for those learning the technique. This work builds off of the original work by Ferron and Hess (2007) to provide computer syntax, written in python, for the specification, estimation, and numerical optimization steps necessary for SEM. The goal is to provide readers with many of the numerical and analytic details of SEM that may not be regularly taught in workshops and courses. This work extends the original demonstration by Ferron and Hess to incorporate the reticular action model notation for specification as well as the estimation of variable means. All of the code listed is provided in the appendix. |
| Author | Steele, Joel S. Grimm, Kevin J. |
| Author_xml | – sequence: 1 givenname: Joel S. orcidid: 0009-0008-4127-5664 surname: Steele fullname: Steele, Joel S. organization: University of North Dakota – sequence: 2 givenname: Kevin J. orcidid: 0000-0002-8576-4469 surname: Grimm fullname: Grimm, Kevin J. organization: Arizona State University |
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| References | Gunzler D. (e_1_3_1_10_1) 2013; 25 Preacher K. J. (e_1_3_1_18_1) 2010; 1 Asparouhov T. (e_1_3_1_4_1) 2006 e_1_3_1_9_1 e_1_3_1_14_1 e_1_3_1_8_1 e_1_3_1_13_1 e_1_3_1_12_1 e_1_3_1_11_1 Van Rossum G. (e_1_3_1_20_1) 2009 e_1_3_1_5_1 e_1_3_1_17_1 e_1_3_1_7_1 e_1_3_1_16_1 e_1_3_1_6_1 e_1_3_1_15_1 e_1_3_1_3_1 e_1_3_1_2_1 e_1_3_1_19_1 |
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| Title | Using SymPy (Symbolic Python) for Understanding Structural Equation Modeling |
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