Implicit Associations Test (IAT)

The Implicit Associations Test (IAT) was originally developed in the United States by Greenwald, McGhee, and Schwartz (1998) to measure implicit cognitions and overcome some of the shortfalls of self-report measures. Whilst self-report measures undoubtedly provide clinicians and researchers with useful information, they are hampered by a lack of self-awareness and various response biases such as social desirability bias (Greenwald, Poehlman, Uhlmann, & Banaji, 2009). The IAT aims to overcome this by measuring response latencies (e.g. reaction times) rather than relying on deliberate responses (e.g. self report measures) (Baron & Banaji, 2006).

The IAT is administered via a computer program that delivers a sequence of discrimination tasks to participants and their reaction times to each are recorded (Greenwald et al., 1998). Similar to evaluative priming methods, the program measures relative strengths of four associations using two sets of contrasted concepts (e.g. male-female and weak-strong) (Nosek, Greenwald, & Banaji, 2005). Participants sort the computer-generated words by rapidly pushing a key on either the right or left side of the keyboard. Faster reaction times between a concept (e.g. male) and an attribute (e.g. evaluation words such as “strong”) indicates a stronger implicit association (Greenwald & Farnham, 2000). Examples of IAT stimuli can be found in the Appendix. One of the main strengths of the IAT is that it can be adapted to assess a multitude of constructs such as self-esteem and self-concept (Greenwald & Farnham, 2000), personality (Grumm & von Collani, 2007), implicit bias in healthcare practitioners (Blair et al., 2012; FitzGerald & Hurst, 2017), development of racial bias (Baron & Banaji, 2006) and so forth.

The IAT has been used for both adolescent (Nock & Banaji, 2007; Thush & Wiers, 2007) and adult populations (Rachlinski, Johnson, Wistrich, & Guthrie, 2009; Teachman, Smith-Janik, & Saporito, 2007). More recently the IAT has also been used with children as young as 5 years old via the Child-oriented version of the IAT (ChildIAT), which incorporates child-friendly elements such as larger keys and verbal recordings of words to account for varied reading ability (Baron & Banaji, 2006). The IAT has been the subject of some controversy regarding psychometric soundness, particularly surrounding its construct validity (Nosek et al., 2005) and predictive validity (Greenwald et al., 2009). For example, some critics argue that the IAT is tapping into familiarity rather than implicit bias. However, research has indicated that similar effects can be seen when controlling for familiarity (Dasgupta, McGhee, Greenwald, & Banaji, 2000). The IAT has shown good convergent & discriminant validity (Gawronski, 2002; Grumm & von Collani, 2007) and good test-retest reliability (Greenwald & Farnham, 2000). The reliability and validity varies depending on which stimulus is being used however, overall the IAT appears to be a relatively psychometrically sound instrument compared to other implicit measures (Nosek et al., 2007).

The software and stimuli for the tool are freely available to download via http://www.millisecond.com/download/. A wide variety of stimuli are also available for download along with personalised packages via http://projectimplicit.net/nosek/papers/pcias/ (Nosek et al., 2007). Permission to use the tool is not necessary however, Nosek et al. (2005) have suggested guidelines surrounding proper use of the IAT. The IAT was originally developed and probably most suited at this point for research purposes (Nosek et al., 2007). However, there is some potential for this tool to be used in conjunction with self-report measures to gain a more in depth understanding of a clients functioning and self-awareness in a clinical setting (Nock & Banaji, 2007). Further information and tests for personal use are available via Harvard’s Project Implicit website at http://implicit.harvard.edu. Although more research and some modifications are necessary, the IAT offers a promising avenue to assess implicit cognitions that people may be either unable or unwilling to express (Greenwald et al., 2009).

References

Baron, A. S., & Banaji, M. R. (2006). The Development of Implicit Attitudes. Psychological Science, 17(1), 53-58.  doi:10.1111/j.1467-9280.2005.01664.x

Blair, I. V., Havranek, E. P., Price, D. W., Hanratty, R., Fairclough, D. L., Farley, T., . . . Steiner, J. (2012). Assessment of Biases Against Latinos and African Americans Among Primary Care Providers and Community Members. American Journal of Public Health, 103(1), 92-98. doi:10.2105/AJPH.2012.300812

Dasgupta, N., McGhee, D. E., Greenwald, A. G., & Banaji, M. R. (2000). Automatic Preference for White Americans: Eliminating the Familiarity Explanation. Journal of Experimental Social Psychology, 36(3), 316-328. doi:10.1006/jesp.1999.1418

FitzGerald, C., & Hurst, S. (2017). Implicit bias in healthcare professionals: a systematic review. BMC Medical Ethics, 18(1), 19. doi:10.1186/s12910-017-0179-8

Gawronski, B. (2002). What Does the Implicit Association Test Measure? A Test of the Convergent and Discriminant Validity of Prejudice-Related IATs. Experimental Psychology, 49(3), 171-180. doi:10.1026//1618-3169.49.3.171

Greenwald, A. G., & Farnham, S. D. (2000). Using the Implicit Association Test to measure self-esteem and self-concept. Journal of Personality and Social Psychology, 79(6), 1022-1038. doi:10.1037/0022-3514.79.6.1022

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.

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.

Grumm, M., & von Collani, G. (2007). Measuring Big-Five personality dimensions with the implicit association test – Implicit personality traits or self-esteem? Personality and Individual Differences, 43(8), 2205-2217. doi: 10.1016/j.paid.2007.06.032

Nock, M. K., & Banaji, M. R. (2007). Assessment of Self-Injurious Thoughts Using a Behavioral Test. American Journal of Psychiatry, 164(5), 820-823. doi:10.1176/ajp.2007.164.5.820

Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2005). Understanding and using the Implicit Association Test: II. Method variables and construct validity. Personality & Social Psychology Bulletin, 31(2), 166-180.

Nosek, B. A., Smyth, F. L., Hansen, J. J., Devos, T., Lindner, N. M., Ranganath, K.A., Smith, C. T., Olson, K. R., Chugh, D., Greenwald, A. G., & Banaji, M. R. (2007). Pervasiveness and correlates of implicit attitudes and stereotypoes. European Review of Social Psychology, 18, 36-88.

Rachlinski, J., Johnson, S., Wistrich, A., & Guthrie, C. (2009). Does Unconscious Racial Bias Affect Trial Judges. Notre Dame Law Review, 84(3), 1195-1246.

Teachman, B. A., Smith-Janik, S. B., & Saporito, J. (2007). Information processing biases and panic disorder: Relationships among cognitive and symptom measures. Behaviour Research and Therapy, 45(8), 1791-1811. doi: 10.1016/j.brat.2007.01.009

Thush, C., & Wiers, R. W. (2007). Explicit and implicit alcohol-related cognitions and the prediction of future drinking in adolescents. Addictive Behaviors, 32(7), 1367-1383. doi: 10.1016/j.addbeh.2006.09.011