In this commentary, we welcome Schimmack's reanalysis of Bar-Anan and Vianello's multitrait multimethod (MTMM) data set, and we highlight some limitations of both the original and the secondary analyses. We note that when testing the fit of a confirmatory model to a data set, theoretical justifications for the choices of the measures to include in the model and how to construct the model improve the informational value of the results. We show that making different, theory-driven specification choices leads to different results and conclusions than those reported by Schimmack (this issue, p. ♦♦♦). Therefore, Schimmack's reanalyses of our data are insufficient to cast doubt on the Implicit Association Test (IAT) as a measure of automatic judgment. We note other reasons why the validation of the IAT is still incomplete but conclude that, currently, the IAT is the best available candidate for measuring automatic judgment at the person level.

中文翻译：

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隐式关联测试可以衡量自动判断吗？验证继续。

在这篇评论中，我们欢迎Schimmack对Bar-Anan和Vianello的多特征多方法（MTMM）数据集进行的重新分析，并强调了原始分析和辅助分析的一些局限性。我们注意到，当测试验证模型对数据集的拟合度时，选择要包括在模型中的度量以及如何构建模型的理论依据会提高结果的信息价值。我们表明，做出不同的，由理论驱动的规范选择会导致与Schimmack报告的结果和结论不同（此问题，第♦♦♦页）。因此，Schimmack对我们数据的重新分析不足以对作为自动判断手段的内隐联想测验（IAT）产生怀疑。我们注意到IAT验证仍不完整的其他原因，但得出以下结论：