Meta-analytic structural equation modeling (MASEM) is a statistical technique to fit hypothesized models on the combined data of multiple independent studies. Lv and Maeda (2019) present a simulation study on the performance of three fixed-effects correlation-based MASEM methods with varying levels of data missing completely at random (MCAR). In this commentary, we discuss several coding errors and other issues that we identified, which demonstrate that Lv and Maeda did not evaluate any of the three intended methods. Furthermore, the authors report very surprising results and offer specific recommendations for the application of the three methods; these actions compel us to express our concerns regarding the validity of the conclusions provided by Lv and Maeda.

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吕与前田评语 (2019)

元分析结构方程模型 (MASEM) 是一种统计技术，用于在多个独立研究的组合数据上拟合假设模型。Lv 和 Maeda (2019) 对三种基于固定效应相关性的 MASEM 方法的性能进行了模拟研究，这些方法具有不同级别的数据完全随机缺失 (MCAR)。在这篇评论中，我们讨论了几个编码错误和我们发现的其他问题，这表明 Lv 和 Maeda 没有评估三种预期方法中的任何一种。此外，作者报告了非常令人惊讶的结果，并为三种方法的应用提供了具体建议；这些行动迫使我们对吕和前田提供的结论的有效性表达我们的担忧。