Optimizing Implicit Attitude Measurement

优化隐式态度测量

• 批准号: 0924252
• 负责人: Brian Payne
• 金额: $ 28万
• 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: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0924252&HistoricalAwards=false
• 关键词: Optimizing; Implicit; Attitude; Measurement

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Some of the traits social scientists most want to measure are the ones that research volunteers are least likely to report. Socially sensitive topics such as racial prejudice have historically presented challenges for social science researchers because respondents are reluctant to report about them on questionnaires. Implicit tests offer a way around that obstacle by measuring attitudes without relying on self-reports. Instead, implicit tests assess attitudes indirectly via their impact on task performance. Implicit attitude tests have thus become a critical tool for studying race attitudes in modern societies, where blatant expressions of prejudice are taboo. The goal of this project is to develop an optimally reliable and valid implicit test of racial attitudes. The Affect Misattribution Procedure (AMP) is an implicit measure that works on the principle that people have difficulty distinguishing their feelings toward two items presented in rapid succession. Using this principle, the test measures favorable or unfavorable attitudes toward various racial groups without asking respondents to express their attitudes toward those groups. Initial research has shown the AMP to be a valid test with several advantages over other available tests. This project aims to build on past progress by using item response theory, a state-of-the-art psychometric approach, to optimize the quality of the test. The study will use a nationally representative sample to calibrate a pool of items with known psychometric properties. The items can then be used to measure racial attitudes on a standardized scale. The project will produce a short test with optimal measurement properties that is freely available to researchers in the form of user-friendly software. Such a test will contribute to research infrastructure and enhance scientific understanding across many fields where racial attitudes are important, including psychology, political science, economics, sociology, and law.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。社会科学家最想衡量的一些特质是研究志愿者最不可能报告的。种族偏见等社会敏感话题历来对社会科学研究人员构成挑战，因为受访者不愿意在问卷中报告这些话题。内隐测试提供了一种绕过这一障碍的方法，它可以在不依赖自我报告的情况下测量态度。相反，内隐测试通过对任务表现的影响间接评估态度。因此，内隐态度测试已成为研究现代社会种族态度的重要工具，在现代社会，公然表达偏见是禁忌。本项目的目标是开发一个最佳的可靠和有效的种族态度的内隐测试。情感错误归因程序（AMP）是一种内隐测量方法，其原理是人们很难区分自己对两个快速连续呈现的项目的感受。利用这一原则，测试测量对各种种族群体的有利或不利的态度，而不要求受访者表达他们对这些群体的态度。初步研究表明，AMP是一种有效的测试，与其他可用的测试相比具有几个优势。该项目旨在通过使用项目反应理论（一种最先进的心理测量方法）来优化测试质量。这项研究将使用一个具有全国代表性的样本来校准一个具有已知心理测量特性的项目池。然后，这些项目可以用来衡量标准化规模上的种族态度。该项目将产生一个具有最佳测量特性的短期测试，以用户友好的软件形式免费提供给研究人员。这样的测试将有助于研究基础设施，并加强对种族态度重要的许多领域的科学理解，包括心理学，政治学，经济学，社会学和法律。