二十年前,内隐联想测验(IAT)的推出引发了热烈的反应。对于像IAT这样的隐式度量,研究人员希望最终能够弥合自我报告的态度和行为之间的差距。二十年的研究和一些荟萃分析之后,然而,我们必须得出结论,IAT及其衍生品都没有满足这些期望。他们对行为标准的预测价值很弱,并且在自我报告措施之上的增量有效性可以忽略不计。在我们的审查中,我们概述了对这些不令人满意的发现的解释,并描述了有希望的前进方向。多年来,提出了IAT预测效度弱的几个原因。他们指出了四个潜在的问题特征:首先,IAT绝不是对关联中个体差异的纯粹度量,而是受到诸如重新编码之类的外来影响。因此,IAT评分的预测效度不应与关联的预测效度相混淆.第二,IAT,我们通常旨在衡量评价(“喜欢”)而不是动机(“想要”)。然而,行为可能比前者更经常地由后者决定。第三,IAT专注于测量关联而不是命题信念,因此利用了一个可能太不特定的结构来解释行为。最后,关于预测有效性的研究通常以预测因子和标准之间的不匹配为特征(例如,虽然行为是高度特定于上下文的,IAT通常既不考虑情况也不考虑域)。最近的研究,然而,还揭示了解决这些问题的进展,即(1)IAT中重新编码控制的程序和分析进展,(2)评估隐性需求的测量程序,(3)评估内隐信念的测量程序,和(4)增加隐含度量和行为标准之间拟合的方法(例如,通过合并上下文信息)。像IAT这样的隐性措施具有巨大的潜力。为了让他们发挥这种潜力,然而,我们必须完善对这些措施的理解,我们应该纳入最近的概念和方法进步。本审查提供了如何这样做的具体建议。
Two decades ago, the introduction of the Implicit Association Test (IAT) sparked enthusiastic reactions. With implicit measures like the IAT, researchers hoped to finally be able to bridge the gap between self-reported attitudes on one hand and behavior on the other. Twenty years of research and several meta-analyses later, however, we have to conclude that neither the IAT nor its derivatives have fulfilled these expectations. Their predictive value for behavioral criteria is weak and their incremental validity over and above self-report measures is negligible. In our review, we present an overview of explanations for these unsatisfactory findings and delineate promising ways forward. Over the years, several reasons for the IAT\'s weak predictive validity have been proposed. They point to four potentially problematic features: First, the IAT is by no means a pure measure of individual differences in associations but suffers from extraneous influences like recoding. Hence, the predictive validity of IAT-scores should not be confused with the predictive validity of associations. Second, with the IAT, we usually aim to measure evaluation (\"liking\") instead of motivation (\"wanting\"). Yet, behavior might be determined much more often by the latter than the former. Third, the IAT focuses on measuring associations instead of propositional beliefs and thus taps into a construct that might be too unspecific to account for behavior. Finally, studies on predictive validity are often characterized by a mismatch between predictor and criterion (e.g., while behavior is highly context-specific, the IAT usually takes into account neither the situation nor the domain). Recent research, however, also revealed advances addressing each of these problems, namely (1) procedural and analytical advances to control for recoding in the IAT, (2) measurement procedures to assess implicit wanting, (3) measurement procedures to assess implicit beliefs, and (4) approaches to increase the fit between implicit measures and behavioral criteria (e.g., by incorporating contextual information). Implicit measures like the IAT hold an enormous potential. In order to allow them to fulfill this potential, however, we have to refine our understanding of these measures, and we should incorporate recent conceptual and methodological advancements. This review provides specific recommendations on how to do so.