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理解并运用简版内隐联想测验:推荐的计分程序。

Understanding and using the brief Implicit Association Test: recommended scoring procedures.

作者信息

Nosek Brian A, Bar-Anan Yoav, Sriram N, Axt Jordan, Greenwald Anthony G

机构信息

University of Virginia, Charlottesville, VA, United States of America; Center for Open Science, Charlottesville, VA, United States of America.

Ben-Gurion University of the Negev, Beer-Sheva, Israel.

出版信息

PLoS One. 2014 Dec 8;9(12):e110938. doi: 10.1371/journal.pone.0110938. eCollection 2014.

Abstract

A brief version of the Implicit Association Test (BIAT) has been introduced. The present research identified analytical best practices for overall psychometric performance of the BIAT. In 7 studies and multiple replications, we investigated analytic practices with several evaluation criteria: sensitivity to detecting known effects and group differences, internal consistency, relations with implicit measures of the same topic, relations with explicit measures of the same topic and other criterion variables, and resistance to an extraneous influence of average response time. The data transformation algorithms D outperformed other approaches. This replicates and extends the strong prior performance of D compared to conventional analytic techniques. We conclude with recommended analytic practices for standard use of the BIAT.

摘要

一种内隐联想测验简版(BIAT)已被引入。本研究确定了BIAT整体心理测量性能的分析最佳实践方法。在7项研究及多次重复研究中,我们采用了几种评估标准来研究分析方法:检测已知效应和群体差异的敏感性、内部一致性、与同一主题的内隐测量指标的关系、与同一主题的外显测量指标及其他标准变量的关系,以及对平均反应时外部影响的抗性。数据转换算法D优于其他方法。这重复并扩展了与传统分析技术相比D先前的出色表现。我们最后给出了BIAT标准使用的推荐分析方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/38f8/4259300/dfe645fbf502/pone.0110938.g002.jpg

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