Software Engineering (SE) researchers are increasingly paying attention to organizational and human factors. Rather than focusing only on variables that can be directly measured, such as lines of code, SE research studies now also consider unobservable variables, such as organizational culture and trust. To measure such latent variables, SE scholars have adopted Partial Least Squares Structural Equation Modeling (PLS-SEM), which is one member of the larger SEM family of statistical analysis techniques. As the SE field is facing the introduction of new methods such as PLS-SEM, a key issue is that not much is known about how to evaluate such studies. To help SE researchers learn about PLS-SEM, we draw on the latest methodological literature on PLS-SEM to synthesize an introduction. Further, we conducted a survey of PLS-SEM studies in the SE literature and evaluated those based on recommended guidelines.

中文翻译：

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用于软件工程研究的 PLS-SEM

软件工程 (SE) 研究人员越来越关注组织和人为因素。SE 研究现在不仅关注可以直接测量的变量，例如代码行，还考虑不可观察的变量，例如组织文化和信任。为了测量这些潜在变量，SE 学者采用了偏最小二乘结构方程建模 (PLS-SEM)，它是更大的 SEM 统计分析技术家族中的一员。由于 SE 领域正面临 PLS-SEM 等新方法的引入，一个关键问题是对如何评估此类研究知之甚少。为了帮助 SE 研究人员了解 PLS-SEM，我们借鉴了有关 PLS-SEM 的最新方法学文献来综合介绍。进一步，