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内隐联想测验能否作为自动认知的有效测量手段?——回应施马克(2021)。

Can the Implicit Association Test Serve as a Valid Measure of Automatic Cognition? A Response to Schimmack (2021).

机构信息

Department of Psychology, Cornell University.

Department of Psychology, University of Florida.

出版信息

Perspect Psychol Sci. 2021 Mar;16(2):422-434. doi: 10.1177/1745691620904080. Epub 2020 May 6.

Abstract

Much of human thought, feeling, and behavior unfolds automatically. Indirect measures of cognition capture such processes by observing responding under corresponding conditions (e.g., lack of intention or control). The Implicit Association Test (IAT) is one such measure. The IAT indexes the strength of association between categories such as "planes" and "trains" and attributes such as "fast" and "slow" by comparing response latencies across two sorting tasks (planes-fast/trains-slow vs. trains-fast/planes-slow). Relying on a reanalysis of multitrait-multimethod (MTMM) studies, Schimmack (this issue, p. 396) argues that the IAT and direct measures of cognition, for example, Likert scales, can serve as indicators of the same latent construct, thereby purportedly undermining the validity of the IAT as a measure of individual differences in automatic cognition. Here we note the compatibility of Schimmack's empirical findings with a range of existing theoretical perspectives and the importance of considering evidence beyond MTMM approaches to establishing construct validity. Depending on the nature of the study, different standards of validity may apply to each use of the IAT; however, the evidence presented by Schimmack is easily reconcilable with the potential of the IAT to serve as a valid measure of automatic processes in human cognition, including in individual-difference contexts.

摘要

许多人类的思维、情感和行为都是自动展开的。间接认知测量通过在相应条件下观察反应(例如,缺乏意图或控制)来捕捉这些过程。内隐联想测验(IAT)就是这样一种测量方法。IAT 通过比较两个分类任务(飞机-快/火车-慢与火车-快/飞机-慢)中的反应时来衡量“飞机”和“火车”等类别与“快”和“慢”等属性之间的关联强度。Schimmack(本期,第 396 页)依赖于对多特质-多方法(MTMM)研究的重新分析,认为 IAT 和直接认知测量(例如,李克特量表)可以作为相同潜在结构的指标,从而据称破坏了 IAT 作为自动认知个体差异的衡量标准的有效性。在这里,我们注意到 Schimmack 的经验发现与一系列现有理论观点的兼容性,以及考虑超越 MTMM 方法的证据对于建立构念效度的重要性。根据研究的性质,每个 IAT 的使用可能适用不同的有效性标准;然而,Schimmack 提出的证据很容易与 IAT 作为人类认知中自动过程的有效衡量标准(包括在个体差异背景下)相协调。

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