A tutorial for understanding SEM using R: Where do all the numbers come from?

IF 1.8 3区 心理学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Yves Rosseel, Marc Vidal
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引用次数: 0

Abstract

Structural equation modeling (SEM) is often seen as a complex and difficult method, especially for those who want to understand how the numbers in SEM software output are actually computed. Although many open-source SEM tools are now available-especially in the R programming environment-looking into their source code to understand the underlying calculations can still be overwhelming. This tutorial aims to provide a clear and accessible introduction to the basic computations behind standard SEM analyses. Using two well-known example datasets, we show how to manually reproduce key results such as parameter estimates, standard errors, and fit measures using simple R scripts. The focus is on clarity and understanding rather than speed or efficiency. We hope that by following this tutorial, readers will gain a better grasp of how SEM works "under the hood," and be able to apply similar ideas in their own research.

用R理解SEM的教程:所有的数字是从哪里来的?
结构方程建模(SEM)通常被认为是一种复杂而困难的方法,特别是对于那些想要了解SEM软件输出中的数字是如何实际计算的人来说。尽管现在有很多开源的SEM工具可用——特别是在R编程环境中——但是要了解它们的源代码来理解底层的计算仍然是非常困难的。本教程旨在为标准SEM分析背后的基本计算提供一个清晰易懂的介绍。使用两个众所周知的示例数据集,我们将展示如何使用简单的R脚本手动重现关键结果,如参数估计、标准误差和拟合度量。重点是清晰和理解,而不是速度或效率。我们希望通过本教程,读者能够更好地掌握SEM的工作原理,并能够在自己的研究中应用类似的思想。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.00
自引率
3.80%
发文量
34
审稿时长
>12 weeks
期刊介绍: The British Journal of Mathematical and Statistical Psychology publishes articles relating to areas of psychology which have a greater mathematical or statistical aspect of their argument than is usually acceptable to other journals including: • mathematical psychology • statistics • psychometrics • decision making • psychophysics • classification • relevant areas of mathematics, computing and computer software These include articles that address substantitive psychological issues or that develop and extend techniques useful to psychologists. New models for psychological processes, new approaches to existing data, critiques of existing models and improved algorithms for estimating the parameters of a model are examples of articles which may be favoured.
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