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
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• 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.
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• 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.
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• 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
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Pros and Cons of Using the IAT:

One advantage of using the IAT is that it is less susceptible to social desirability biases, as it measures implicit attitudes that are often outside of an individual’s conscious awareness (Nosek et al., 2002). This is important because people may not always be willing or able to report their true attitudes, especially if they are concerned about how others may perceive them. By measuring implicit attitudes, the IAT can provide a more accurate picture of an individual’s attitudes and beliefs.

Additionally, the IAT can be administered remotely and quickly, making it a relatively low- cost and efficient tool for assessing attitudes. This is particularly useful in large-scale research studies, where researchers may need to assess the attitudes of many participants in a short period of time. The IAT can be administered online, which also allows researchers to reach a more diverse pool of participants than they might be able to through in-person testing (Greenwald et al., 1998).
There are two additional advantages of using the IAT are its ability to identify unconscious biases and its potential for identifying the underlying mechanisms of biases in social-cultural contexts.
Firstly, the IAT is able to identify unconscious biases that individuals may not even be aware of, which can be particularly valuable in identifying and addressing systemic biases in social- cultural contexts. Unconscious biases can have a significant impact on social interactions, decision-making processes, and behaviors, which can perpetuate inequalities in society (Devine, 1989). By identifying these biases, the IAT can serve as a starting point for individuals and organizations to address and correct them (Kang et al., 2014).
Secondly, the IAT has the potential to identify the underlying mechanisms of biases in social- cultural contexts. For example, research has used the IAT to explore the impact of social norms on implicit attitudes towards stigmatized groups (Rudman & Phelan, 2010). This research has found that individuals may hold implicit biases even if they consciously endorse egalitarian beliefs, which suggests that social norms may play a significant role in shaping implicit attitudes. By identifying the underlying mechanisms of biases, the IAT can provide insight into the complex processes that contribute to social inequalities and inform interventions to address them.

However, the IAT has also been criticized for its limited ability to predict behaviour. This is because the test only measures automatic associations, which may not always translate into real-world behaviour (Greenwald et al., 2002). In other words, just because someone has an implicit bias on the IAT does not necessarily mean that they will behave in a biased way in real life.

Furthermore, the IAT has been shown to have low test-retest reliability, indicating that an individual’s scores on the test may vary over time (Oswald et al., 2013). This is an important consideration for researchers who may be using the IAT to track changes in attitudes over time or to evaluate the effectiveness of interventions aimed at reducing bias. Low test-retest reliability suggests that the IAT may not be a reliable tool for measuring changes in attitudes over time.

Another potential disadvantage of the IAT is that it may not always be culturally appropriate or relevant for certain populations. For example, some studies have found that the IAT may not accurately capture implicit attitudes among individuals from non-Western cultures (Lu et. al., 2017). This may be because the test was developed and normed using Western samples, and the underlying assumptions and associations may not be universally applicable.
On the other hand, one potential benefit of the IAT is that it can provide feedback to individuals about their own biases, which can be a useful tool for personal growth and development (Blanton et al., 2015). This can be particularly valuable in contexts such as education and training, where individuals may be motivated to change their attitudes and behaviours.

In summary, the IAT has both advantages and limitations as a tool for assessing implicit attitudes. While it can provide a more accurate picture of an individual’s attitudes and beliefs, it may not always be culturally appropriate or relevant, and may not reliably predict behaviour or changes in attitudes over time. Researchers and practitioners should carefully consider the strengths and weaknesses of the IAT when deciding whether to use it in their research or practice.

Social learning theory and its role in the issue:

One theory of attitude formation and change that is relevant to the discussion on the IAT is the social learning theory. Social learning theory suggests that attitudes can be acquired through direct experience, observation of others’ behaviours, and through the influence of media and cultural norms (Bandura, 1977). This theory posits that attitudes can change through the process of observational learning, where an individual learns from the consequences of others’ behaviours.

In the context of the IAT, social learning theory suggests that implicit attitudes may be influenced by exposure to biased media representations or cultural norms that perpetuate stereotypes and prejudice. For example, an individual who is repeatedly exposed to negative stereotypes of a particular racial or ethnic group in the media may develop implicit biases towards that group, even if they consciously reject those stereotypes (Olson & Fazio, 2009). Moreover, social learning theory also suggests that attitudes can be changed through the process of modelling. This means that individuals can learn new attitudes by observing the behaviours of others and the consequences of those behaviours (Bandura, 1977). Therefore, interventions aimed at reducing implicit biases, such as the IAT, may be more effective if they incorporate modelling behaviours that promote positive attitudes towards stigmatized groups. For example, research has shown that exposure to positive counter-stereotypical exemplars (Dasgupta & Asgari, 2004) as well as diversity training program (Devine et al., 2012) can lead to a reduction in implicit biases towards various social groups. Overall, the social learning theory provides a useful framework for understanding how implicit attitudes are formed and changed, and highlights the importance of environmental factors in shaping attitudes.

Introduction

Attitudes are a fundamental aspect of human behaviour, and they can shape our thoughts, feelings, and actions towards different stimuli. It is a well-established fact that attitudes can be explicit or implicit. Explicit attitudes are consciously held and often expressed through self-report measures, while implicit attitudes are unconscious and automatic, making them challenging to measure using traditional self-report methods (Cooper et al., 2015). This series of blog-posts will critically review the implementation of the Implicit Association Test (IAT) as a tool for assessing attitudes, with reference to relevant theory and research. Additionally, an evidence- based recommendation will be made about whether this practice should be rolled out across the whole country.

What is the Implicit Association Test (IAT)?

The Implicit Association Test (IAT) is a widely-used measure of implicit attitudes and associations, particularly those related to social and racial biases. Developed by Greenwald, McGhee, and Schwartz in 1998, the IAT measures the strength of an individual’s automatic association between mental representations of objects in memory by measuring reaction times to categorize stimuli into various categories. The IAT has been used to investigate various implicit biases, such as those related to race, gender, sexual orientation, and age.
For example, studies using the IAT have found evidence of implicit biases against Black individuals in various domains, including healthcare, education, and criminal justice. One study found that medical professionals who scored higher on the IAT for implicit bias against Black individuals were less likely to recommend thrombolysis (a clot-busting drug) to Black patients with acute coronary syndrome than to White patients with the same condition (Blair et al., 2014). In education, studies have found that implicit biases can affect teachers’ expectations and perceptions of students, leading to differential treatment and outcomes. For instance, teachers who scored higher on the IAT for implicit bias against Black individuals were more likely to rate Black students’ behaviour as problematic and to recommend disciplinary action than White students with the same behaviour (Okonofua et al., 2016).