Improving Implicit Attitude Measurement

改进内隐态度测量

• 批准号: 0615478
• 负责人: Brian Payne
• 金额: $ 15.5万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0615478&HistoricalAwards=false
• 关键词: Improving; Implicit; Attitude; Measurement

One of the most important recent insights in the study of attitudes and preferences is that they can be measured implicitly, without asking a person to introspect and self-report. Implicit measurements allow researchers to study attitudes that respondents may be unwilling or unable to report explicitly. They therefore are valuable for studying socially sensitive topics, where respondents may be less than candid. Yet despite this progress, current methods have several psychometric limitations to overcome. Reliability and predictive validity for implicit methods are often lower than accepted for explicit measurement methods. And explicit and implicit measurements of attitudes toward the same topic often differ in multiple ways at the same time, allowing for confounds and alternative explanations. This project develops a novel method for implicitly measuring attitudes, the Affect Misattribution Procedure. It measures attitudes by using the tendency to unintentionally misattribute affective responses from one source to another. Three sets of studies, focusing on racial attitudes and alcohol abuse, compare the method to currently available methods. The first set of studies tests the ability of this method to predict racial discrimination and alcohol abuse behaviors, in comparison to two commonly used implicit methods. The second set tests for resistance to social desirability. The third set uses the method to equate implicit and explicit measures on all dimensions except the crucial difference of intent to express one's attitude. Unlike other implicit methods, the metric for this method is an evaluation rather than response time. Therefore, self-reports and implicit responses can be directly compared on the same scales. By eliminating extraneous differences between implicit and explicit methods, the relationship between implicit and explicit measures can be tested more directly than previously has been possible. The project has potentially important broader impacts, increasing the fidelity of measurement in important domains such as racial discrimination and substance abuse. The project will help advance research infrastructure for social and behavioral science by making publicly available a new validated method for implicit attitude measurement. More accurate measurement will facilitate theory testing and future efforts to identify causes of, and remedies for, destructive behavior.

最近在态度和偏好研究中最重要的见解之一是，它们可以被隐含地衡量，而不需要一个人进行反思和自我报告。隐性测量允许研究人员研究受访者可能不愿或无法明确报告的态度。因此，它们对于研究社会敏感话题很有价值，在这些话题中，受访者可能不那么坦率。然而，尽管取得了这些进展，目前的方法仍有几个心理测量学方面的局限性需要克服。隐式方法的可靠性和预测效度往往低于显式测量方法的接受程度。而且，对同一话题态度的显性和隐性测量往往同时在多个方面不同，允许混淆和替代解释。这个项目开发了一种内隐测量态度的新方法--情感错误归因程序。它通过使用无意中将情感反应从一个来源错误归因于另一个来源的倾向来衡量态度。关注种族态度和酗酒的三组研究将这种方法与目前可用的方法进行了比较。第一组研究测试了这种方法预测种族歧视和酗酒行为的能力，并与两种常用的隐含方法进行了比较。第二组测试的是对社会合意性的抵触。第三组使用的方法是在所有维度上将内隐和外显的测量等同起来，除了表达态度的意图的关键差异。与其他隐式方法不同，此方法的指标是评估，而不是响应时间。因此，自我报告和内隐反应可以在相同的尺度上直接进行比较。通过消除隐式和显式方法之间的无关差异，可以比以前更直接地测试隐式和显式方法之间的关系。该项目可能会产生更广泛的重要影响，提高种族歧视和药物滥用等重要领域测量的保真度。该项目将通过公开提供一种新的经过验证的内隐态度测量方法，帮助推进社会和行为科学的研究基础设施。更准确的测量将有助于理论测试和未来确定破坏性行为的原因和补救措施的努力。