Implications of the Implicit Association Test D-Transformation for Psychological Assessment
Psychometricians strive to eliminate random error from their psychological inventories. When random error affecting tests is diminished, tests more accurately characterize people on the psychological dimension of interest. We document an unusual property of the scoring algorithm for a measure used t...
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| Vydáno v: | Assessment (Odessa, Fla.) Ročník 22; číslo 4; s. 429 - 440 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Los Angeles, CA
SAGE Publications
01.08.2015
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| ISSN: | 1073-1911, 1552-3489, 1552-3489 |
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| Abstract | Psychometricians strive to eliminate random error from their psychological inventories. When random error affecting tests is diminished, tests more accurately characterize people on the psychological dimension of interest. We document an unusual property of the scoring algorithm for a measure used to assess a wide range of psychological states. The “D-score” algorithm for coding the Implicit Association Test (IAT) requires the presence of random noise in order to obtain variability. Without consequential degrees of random noise, all individuals receive extreme scores. We present results from an algebraic proof, a computer simulation, and an online survey of implicit racial attitudes to show how trial error can bias IAT assessments. We argue as a result that the D-score algorithm should not be used for formal assessment purposes, and we offer an alternative to this approach based on multiple regression. Our critique focuses primarily on the IAT designed to measure unconscious racial attitudes, but it applies to any IAT developed to provide psychological assessments within clinical, organizational, and developmental branches of psychology—and in any other field where the IAT might be used. |
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| AbstractList | Psychometricians strive to eliminate random error from their psychological inventories. When random error affecting tests is diminished, tests more accurately characterize people on the psychological dimension of interest. We document an unusual property of the scoring algorithm for a measure used to assess a wide range of psychological states. The "D-score" algorithm for coding the Implicit Association Test (IAT) requires the presence of random noise in order to obtain variability. Without consequential degrees of random noise, all individuals receive extreme scores. We present results from an algebraic proof, a computer simulation, and an online survey of implicit racial attitudes to show how trial error can bias IAT assessments. We argue as a result that the D-score algorithm should not be used for formal assessment purposes, and we offer an alternative to this approach based on multiple regression. Our critique focuses primarily on the IAT designed to measure unconscious racial attitudes, but it applies to any IAT developed to provide psychological assessments within clinical, organizational, and developmental branches of psychology-and in any other field where the IAT might be used. Psychometricians strive to eliminate random error from their psychological inventories. When random error affecting tests is diminished, tests more accurately characterize people on the psychological dimension of interest. We document an unusual property of the scoring algorithm for a measure used to assess a wide range of psychological states. The "D-score" algorithm for coding the Implicit Association Test (IAT) requires the presence of random noise in order to obtain variability. Without consequential degrees of random noise, all individuals receive extreme scores. We present results from an algebraic proof, a computer simulation, and an online survey of implicit racial attitudes to show how trial error can bias IAT assessments. We argue as a result that the D-score algorithm should not be used for formal assessment purposes, and we offer an alternative to this approach based on multiple regression. Our critique focuses primarily on the IAT designed to measure unconscious racial attitudes, but it applies to any IAT developed to provide psychological assessments within clinical, organizational, and developmental branches of psychology-and in any other field where the IAT might be used.Psychometricians strive to eliminate random error from their psychological inventories. When random error affecting tests is diminished, tests more accurately characterize people on the psychological dimension of interest. We document an unusual property of the scoring algorithm for a measure used to assess a wide range of psychological states. The "D-score" algorithm for coding the Implicit Association Test (IAT) requires the presence of random noise in order to obtain variability. Without consequential degrees of random noise, all individuals receive extreme scores. We present results from an algebraic proof, a computer simulation, and an online survey of implicit racial attitudes to show how trial error can bias IAT assessments. We argue as a result that the D-score algorithm should not be used for formal assessment purposes, and we offer an alternative to this approach based on multiple regression. Our critique focuses primarily on the IAT designed to measure unconscious racial attitudes, but it applies to any IAT developed to provide psychological assessments within clinical, organizational, and developmental branches of psychology-and in any other field where the IAT might be used. |
| Author | Burrows, Christopher N. Jaccard, James Blanton, Hart |
| Author_xml | – sequence: 1 givenname: Hart surname: Blanton fullname: Blanton, Hart email: hart.blanton@uconn.edu – sequence: 2 givenname: James surname: Jaccard fullname: Jaccard, James – sequence: 3 givenname: Christopher N. surname: Burrows fullname: Burrows, Christopher N. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25296761$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1037/0033-2909.94.1.18 10.1016/j.jesp.2009.12.003 10.1016/j.jbtep.2012.05.003 10.1016/j.jesp.2005.07.003 10.1348/096317906X118397 10.1016/j.appdev.2007.02.001 10.1037/0022-3514.85.2.197 10.1146/annurev.soc.33.040406.131632 10.1007/s00406-010-0174-2 10.1037/0003-066X.61.1.42 10.1037/a0032734 10.1037/a0021729 10.1037/a0015575 10.1111/j.1754-9434.2008.00081.x 10.1037/0022-3514.74.6.1464 10.1037/0003-066X.61.1.27 10.1037/1089-2699.6.1.101 10.1177/1745691610393980 10.1037/0096-3445.133.2.139 10.1521/soco.20.6.483.22977 10.1037/0003-066X.61.1.62 10.1080/10463280701489053 10.1037/0003-066X.51.10.1065 10.1027/1618-3169.51.3.165 10.1026//0949-3946.48.2.107 |
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| Keywords | psychometrics assessment general processing speed Implicit Association Test |
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