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Optimizing Implicit Attitude Measurement

Optimizing Implicit Attitude Measurement
优化隐式态度测量
批准号:
0924252
负责人:
Brian Payne
金额:
$28.0万
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
项目状态:
已结题
起止时间:
2009-09-15 至 2013-08-31

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中文摘要
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英文摘要
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.
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