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Understanding and Using the Implicit Association Test: I. An Improved Scoring Algorithm

Understanding and Using the Implicit Association Test: I. An Improved Scoring Algorithm 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Personality and Social Psychology American Psychological Association

Understanding and Using the Implicit Association Test: I. An Improved Scoring Algorithm

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References (36)

Publisher
American Psychological Association
Copyright
Copyright © 2003 American Psychological Association
ISSN
0022-3514
eISSN
1939-1315
DOI
10.1037/0022-3514.85.2.197
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Journal of Personality and Social PsychologyAmerican Psychological Association

Published: Aug 1, 2003

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