While there have been mixed results regarding the predictive validity of the IAT, some studies have shown that the test is a reliable measure of implicit attitudes. For example, Nosek et al. (2007) found that the IAT was able to predict voting behaviour better than self- report measures, suggesting that the test may be useful in certain contexts. Similarly, Axt et al. (2014) found that the IAT was able to predict discriminatory behaviour in a hiring scenario, suggesting that the test may be useful in identifying implicit biases in the workplace.
However, it is important to note that the IAT is not without its limitations. As mentioned earlier, the test has been criticised for its low test-retest reliability, which can limit its usefulness in longitudinal studies or when measuring changes in attitudes over time (Oswald et al., 2013). Additionally, there is some debate about the extent to which the IAT measures true implicit attitudes, as the test may also reflect explicit attitudes or other factors such as response bias (Hofmann et al., 2005).
Despite these limitations, the IAT remains a widely used tool for assessing implicit attitudes, and it has been used in a variety of fields, including psychology, sociology, and political science (Greenwald et al., 2003). As with any measure, it is important to consider the strengths and weaknesses of the IAT in light of the specific research question and context.
Recommendation:
Based on the available evidence, it is recommended that the implementation of the Implicit Association Test (IAT) should be considered for broader use across the country to promote diversity and inclusion.
Studies have shown that the IAT is a reliable and valid tool for assessing implicit attitudes and biases related to race, gender, and sexual orientation (Greenwald et al., 1998; Nosek et al., 2002). Furthermore, research has demonstrated that implicit biases can have significant negative effects on decision-making, behaviour, and outcomes in a variety of settings, including healthcare, education, and the workplace (FitzGerald & Hurst, 2017; Hall et al., 2015; Greenwald et al., 2009).
Using the IAT in combination with other measures can provide a more comprehensive understanding of the issues being studied. For example, in healthcare settings, the IAT can be used in conjunction with clinical observations and patient feedback to assess the impact of implicit biases on healthcare outcomes (FitzGerald & Hurst, 2017).
In conclusion, based on the available evidence, the implementation of the IAT should be considered for broader use across the country as part of efforts to promote diversity and inclusion. However, its limitations should be recognized, and its results should not be the sole basis for making decisions about individuals or groups. Its use should be in conjunction with other measures of attitudes and behaviours. Additionally, interventions aimed at reducing implicit biases should be designed to incorporate observational learning and modelling behaviours that promote positive attitudes towards stigmatized groups.
- References:
- Axt, J. R., Ebersole, C. R., & Nosek, B. A. (2014). The rules of implicit evaluation by race, religion, and age. Psychological science, 25(9), 1804–1815. https://doi.org/10.1177/0956797614543801
- Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall. https://doi.org/10.1177/105960117700200317
- Blair, I. V., Steiner, J. F., Hanratty, R., Price, D. W., Fairclough, D. L., Daugherty, S. L., Bronsert, M., Magid, D. J., & Havranek, E. P. (2014). An investigation of associations between clinicians’ ethnic or racial bias and hypertension treatment, medication adherence and blood pressure control. Journal of General Internal Medicine, 29(7), 987-95. https://doi.org/10.1007/s11606-014-2795-z
- Blanton, H., Jaccard, J., Strauts, E., Mitchell, G., & Tetlock, P. E. (2015). Toward a meaningful metric of implicit prejudice. The Journal of applied psychology, 100(5), 1468–1481. https://doi.org/10.1037/a0038379
- Cooper, J., Blackman, S., & Keller, K. (2015). The Science of Attitudes. Taylor & Francis.https://essexonline.vitalsource.com/books/9781317509615
- Dasgupta, N., & Asgari, S. (2004). Seeing is believing: Exposure to counterstereotypic women leaders and its effect on the malleability of automatic gender stereotyping.
- Journal of Experimental Social Psychology, 40(5), 642-658. https://psycnet.apa.org/doi/10.1016/j.jesp.2004.02.003
- Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of personality and social psychology, 56(1), 5-18. DOI: 10.1037//0022- 3514.56.1.5
- Devine, P. G., Forscher, P. S., Austin, A. J., & Cox, W. T. (2012). Long-term reduction in implicit race bias: A prejudice habit-breaking intervention. Journal of experimental social psychology, 48(6), 1267–1278. https://doi.org/10.1016/j.jesp.2012.06.003
- FitzGerald, C., Hurst, S. (2017) Implicit bias in healthcare professionals: a systematic review.BMC Med Ethics 18, 19. https://doi.org/10.1186/s12910-017-0179-8
- Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74(6), 1464–1480. https://doi.org/10.1037/0022- 3514.74.6.1464
- Greenwald, A. G., Banaji, M. R., Rudman, L. A., Farnham, S. D., Nosek, B. A., & Mellott, D. S. (2002). A unified theory of implicit attitudes, stereotypes, self-esteem, and self- concept_. Psychological Review, 109_(1), 3-25. DOI: 10.1037//0033-295X.109.1.3
- Greenwald, A. G., Poehlman, T. A., Uhlmann, E. L., & Banaji, M. R. (2009). Understanding and using the implicit association test: III. Meta-analysis of predictive validity. Journal of Personality and Social Psychology, 97(1), 17–41.https://doi.org/10.1037/a0015575
- Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the implicit association test: I. An improved scoring algorithm. Journal of personality and social psychology, 85(2), 197–216. https://doi.org/10.1037/0022-3514.85.2.197
- Hall, W. J., Chapman, M. V., Lee, K. M., Merino, Y. M., Thomas, T. W., Payne, B. K., Eng, E., Day, S. H., Coyne-Beasley, T., & Coa, K. (2015). Implicit racial/ethnic bias among health care professionals and its influence on health care outcomes: A systematic review. American Journal of Public Health, 105(12), 60-76. doi: 10.2105/AJPH.2015.302903
- Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., & Schmitt, M. (2005). A meta- analysis on the correlation between the Implicit Association Test and explicit self- report measures. Personality and Social Psychology Bulletin, 31(10), 1369-1385. doi: 10.1177/0146167205275613
- Kang, Y., Gray, J. R., & Dovidio, J. F. (2014). The nondiscriminating heart: lovingkindness meditation training decreases implicit intergroup bias. Journal of experimental psychology. General, 143(3), 1306–1313. https://doi.org/10.1037/a0034150
- Lu, H. J., Chang, L., & Li, Y. (2017). Does the Implicit Association Test (IAT) really measure implicit attitudes? A comparative test of the IAT and explicit measures. Personality and Social Psychology Bulletin, 43(5), 559-569.
- Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). Harvesting implicit group attitudes and beliefs from a demonstration website_. Group Dynamics: Theory, Research, and Practice, 6_(1), 101–115. https://doi.org/10.1037/1089-2699.6.1.101
- Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2007). The Implicit Association Test at age 7: A methodological and conceptual review. In J. A. Bargh (Ed_.), Social psychology and the unconscious: The automaticity of higher mental processes_ (pp. 265–292). Psychology Press.https://faculty.washington.edu/agg/pdf/Nosek%20&%20al.IATatage7.2007.pdf
- Okonofua, J. A., Walton, G. M., & Eberhardt, J. L. (2016). A vicious cycle: A social– psychological account of extreme racial disparities in school discipline. Perspectives on Psychological Science, 11(3), 381-398. https://psycnet.apa.org/doi/10.1177/1745691616635592
- Olson, M. A., & Fazio, R. H. (2009). Implicit and explicit measures of attitudes: The perspective of the MODE model. In R. E. Petty, R. H. Fazio, & P. Brinol (Eds.), Attitudes: Insights from the new implicit measures (pp. 19-63). New York, NY: Psychology Press. https://shorturl.at/mIJT8
- Oswald, F. L., Mitchell, G., Blanton, H., Jaccard, J., & Tetlock, P. E. (2013). Predicting ethnic and racial discrimination: A meta-analysis of IAT criterion studies. Journal of Personality and Social Psychology, 105(2), 171–192. https://psycnet.apa.org/doi/10.1037/a0032734
- Rudman, L. A., & Phelan, J. E. (2010). The interpersonal power of feminism: Is feminism good for romantic relationships? Sex Roles, 62(3-4), 197-207. DOI:10.1007/s11199- 007-9319-9
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