Understanding and using the implicit association test: I. An improved scoring algorithm
Understanding and using the implicit association test: I. An improved scoring algorithm
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DOI：
10.1037/0022-3514.85.2.197
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发表时间：
2003-08-01
影响因子：
7.6
通讯作者：
Banaji, MR
中科院分区：
文献类型：
--
作者：
Greenwald, AG;Nosek, BA;Banaji, MR
在报告隐式协会测试（IAT）的结果中，研究人员最常使用IAT首次出版物中描述的评分公约（A. G. Greenwald，D。E. McGhee，＆J。L. K. Schwartz，1998年）。 Internet上可用的演示IAT生产了大量数据集，这些数据集已在当前文章中用于评估替代评分程序。候选新算法根据其（a）与平行自我报告措施的相关性，（b）对与响应速度相关的工件的抗性，（c）内部一致性，（d）对IAT测量的已知影响的敏感性， （e）对已知程序影响的抵抗。表现最佳的度量结合了IAT实践试验中的数据，使用了由每个受访者的延迟可变性校准的度量，并包括错误的延迟惩罚。这种新算法强烈胜过较早的（常规）程序。
In reporting Implicit Association Test (IAT) results, researchers have most often used scoring conventions described in the first publication of the IAT (A. G. Greenwald, D. E. McGhee, & J. L. K. Schwartz, 1998). Demonstration IATs available on the Internet have produced large data sets that were used in the current article to evaluate alternative scoring procedures. Candidate new algorithms were examined in terms of their (a) correlations with parallel self-report measures, (b) resistance to an artifact associated with speed of responding, (c) internal consistency, (d) sensitivity to known influences on IAT measures, and (e) resistance to known procedural influences. The best-performing measure incorporates data from the IAT's practice trials, uses a metric that is calibrated by each respondent's latency variability, and includes a latency penalty for errors. This new algorithm strongly outperforms the earlier (conventional) procedure.