Measurement invariance—the notion that the measurement properties of a scale are equal across groups, contexts, or time—is an important assumption underlying much of psychology research. The traditional approach for evaluating measurement invariance is to fit a series of nested measurement models using multiple-group confirmatory factor analyses. However, traditional approaches are strict, vary across the field in implementation, and present multiplicity challenges, even in the simplest case of two groups under study. The alignment method was recently proposed as an alternative approach. This method is more automated, requires fewer decisions from researchers, and accommodates two or more groups. However, it has different assumptions, estimation techniques, and limitations from traditional approaches. To address the lack of accessible resources that explain the methodological differences and complexities between the two approaches, we introduce and illustrate both, comparing them side by side. First, we overview the concepts, assumptions, advantages, and limitations of each approach. Based on this overview, we propose a list of four key considerations to help researchers decide which approach to choose and how to document their analytical decisions in a preregistration or analysis plan. We then demonstrate our key considerations on an illustrative research question using an open dataset and provide an example of a completed preregistration. Our illustrative example is accompanied by an annotated analysis report that shows readers, step-by-step, how to conduct measurement invariance tests using R and Mplus. Finally, we provide recommendations for how to decide between and use each approach and next steps for methodological research.

中文翻译：

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使用验证性因子分析和对齐优化进行测量不变性测试：透明分析规划和报告的教程。

测量不变性——量表的测量属性在群体、环境或时间之间是相等的概念——是许多心理学研究的一个重要假设。评估测量不变性的传统方法是使用多组验证性因素分析来拟合一系列嵌套测量模型。然而，传统方法很严格，在实施过程中因领域而异，并且提出了多重挑战，即使是在研究的两个群体的最简单情况下也是如此。最近提出了对齐方法作为替代方法。这种方法更加自动化，需要研究人员做出更少的决定，并且可以容纳两个或更多组。然而，它具有与传统方法不同的假设、估计技术和局限性。为了解决缺乏可解释这两种方法之间的方法差异和复杂性的资源的问题，我们对这两种方法进行了介绍和说明，并将它们并排比较。首先，我们概述每种方法的概念、假设、优点和局限性。基于此概述，我们提出了四个关键考虑因素的列表，以帮助研究人员决定选择哪种方法以及如何在预注册或分析计划中记录他们的分析决策。然后，我们使用开放数据集展示我们对说明性研究问题的关键考虑因素，并提供已完成预注册的示例。我们的说明性示例附有带注释的分析报告，该报告逐步向读者展示如何使用 R 和 Mplus 进行测量不变性测试。最后，我们为如何在每种方法之间做出决定和使用以及方法学研究的后续步骤提供了建议。