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Structural equation modeling: strengths, limitations, and misconceptions.
Annual Review of Clinical Psychology ( IF 18.4 ) Pub Date : 2007-08-25 , DOI: 10.1146/annurev.clinpsy.1.102803.144239
Andrew J Tomarken 1 , Niels G Waller
Affiliation  

Because structural equation modeling (SEM) has become a very popular data-analytic technique, it is important for clinical scientists to have a balanced perception of its strengths and limitations. We review several strengths of SEM, with a particular focus on recent innovations (e.g., latent growth modeling, multilevel SEM models, and approaches for dealing with missing data and with violations of normality assumptions) that underscore how SEM has become a broad data-analytic framework with flexible and unique capabilities. We also consider several limitations of SEM and some misconceptions that it tends to elicit. Major themes emphasized are the problem of omitted variables, the importance of lower-order model components, potential limitations of models judged to be well fitting, the inaccuracy of some commonly used rules of thumb, and the importance of study design. Throughout, we offer recommendations for the conduct of SEM analyses and the reporting of results.

中文翻译:

结构方程建模:优势,局限性和误解。

由于结构方程模型(SEM)已成为一种非常流行的数据分析技术,因此对于临床科学家来说,平衡地了解其优势和局限性很重要。我们回顾了SEM的几种优势,特别关注最近的创新(例如,潜在增长建模,多级SEM模型以及处理缺失数据和违反正态性假设的方法),这些强调了SEM如何成为广泛的数据分析工具具有灵活和独特功能的框架。我们还考虑了SEM的一些局限性以及它容易引起的一些误解。强调的主要主题是变量遗漏的问题,低阶模型组件的重要性,被认为非常合适的模型的潜在局限性,一些常用的经验法则的不准确性,以及研究设计的重要性。在整个过程中,我们为进行SEM分析和结果报告提供建议。
更新日期:2019-11-01
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