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内隐联想测验能否测量自动判断?验证仍在继续。

Can the Implicit Association Test Measure Automatic Judgment? The Validation Continues.

机构信息

Department of Philosophy, Sociology, Education and Applied Psychology, University of Padova.

School of Psychological Sciences, Tel-Aviv University.

出版信息

Perspect Psychol Sci. 2021 Mar;16(2):415-421. doi: 10.1177/1745691619897960. Epub 2020 Feb 12.

Abstract

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. 396). 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.

摘要

在这篇评论中,我们欢迎 Schimmack 对 Bar-Anan 和 Vianello 的多特质多方法(MTMM)数据集的重新分析,并强调了原始分析和二次分析的一些局限性。我们注意到,当测试验证模型对数据集的拟合度时,对纳入模型的测量选择和如何构建模型的理论依据提高了结果的信息价值。我们表明,做出不同的、有理论依据的规范选择会导致与 Schimmack 报告的结果和结论不同(本期,第 396 页)。因此,Schimmack 对我们数据的重新分析不足以对内隐联想测验(IAT)作为自动判断的测量手段提出质疑。我们注意到其他原因导致 IAT 的验证仍不完整,但得出结论,目前,IAT 是衡量个体自动判断的最佳可用候选者。

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