Mediation analysis is an important approach for investigating causal pathways. One approach used in mediation analysis is the test of an indirect effect, which seeks to measure how the effect of an independent variable impacts an outcome variable through 1 or more mediators. However, in many situations the proposed tests of indirect effects, including popular confidence interval-based methods, tend to produce poor Type I error rates when mediation does not occur and, more generally, only allow dichotomous decisions of "not significant" or "significant" with regards to the statistical conclusion. To remedy these issues, we propose a new method, a likelihood ratio test (LRT), that uses nonlinear constraints in what we term the model-based constrained optimization (MBCO) procedure. The MBCO procedure (a) offers a more robust Type I error rate than existing methods; (b) provides a p value, which serves as a continuous measure of compatibility of data with the hypothesized null model (not just a dichotomous reject or fail-to-reject decision rule); (c) allows simple and complex hypotheses about mediation (i.e., 1 or more mediators; different mediational pathways); and (d) allows the mediation model to use observed or latent variables. The MBCO procedure is based on a structural equation modeling framework (even if latent variables are not specified) with specialized fitting routines, namely with the use of nonlinear constraints. We advocate using the MBCO procedure to test hypotheses about an indirect effect in addition to reporting a confidence interval to capture uncertainty about the indirect effect because this combination transcends existing methods. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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中介分析中的改进推论：引入了基于模型的约束优化程序。

调解分析是调查因果关系的重要方法。调解分析中使用的一种方法是间接影响的测试，该方法旨在衡量自变量的影响如何通过1个或多个调解员影响结果变量。但是，在许多情况下，建议的间接影响测试（包括基于流行的置信区间的方法）在不进行调解的情况下往往会产生较差的I型错误率，并且更普遍的是，只能做出“不重要”或“重要”的二元决策。关于统计结论。为了解决这些问题，我们提出了一种新方法，似然比检验（LRT），该方法在我们称为基于模型的约束优化（MBCO）过程中使用了非线性约束。MBCO程序（a）比现有方法提供更可靠的I型错误率；（b）提供ap值，作为数据与假设的空模型（不仅是二分式拒绝或失败拒绝决策规则）之间的兼容性的连续度量；（c）提供关于调解的简单和复杂假设（即，一个或多个调解人；不同的调解途径）；（d）允许调解模型使用观察变量或潜在变量。MBCO过程基于具有专用拟合例程的结构方程建模框架（即使未指定潜在变量），即使用非线性约束。除了报告置信区间以捕获有关间接影响的不确定性外，我们提倡使用MBCO程序来测试有关间接影响的假设，因为这种组合超越了现有方法。（PsycINFO数据库记录（c）2020 APA，保留所有权利）。