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About the IAT

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Many scientific studies have shown the limitations of questionnaires and interviews in revealing gender stereotypes in general: respondents often limit themselves to socially acceptable answers. Hence the emergence, over time, of more subtle methodologies to probe the presence of such stereotypes.

The Implicit Association Test (IAT) is one of these more subtle methodologies. In 1998, researchers at the University of Washington and Harvard developed the Implicit Association Test (IAT), designed to measure the strength of associations between objects and concepts in our minds. Since then, the IAT has become a popular tool in social psychology research, used to explore implicit biases that people may hold towards different social groups.

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How it works

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The IAT measures the strength of automatic associations between mental representations of objects and concepts, such as race, gender, and sexuality. Participants complete a series of tasks that measure response times to stimuli that are paired with different categories.

It simply requires people to classify words into categories as quickly as possible with as few errors as possible. At certain points in the test, the classifications to be produced are compatible with the stereotype whose presence we are seeking to detect, while at other times they are opposed to that same stereotype.

In the latter case (opposition phase), classification speed may fall. This usually perceptible but involuntary slowdown reveals the presence of the stereotype in long-term memory.

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How to interpret the test

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The IAT generates a numerical score that indicates the strength of automatic associations between the concepts being tested. A higher absolute score indicates a stronger association, while a lower absolute score indicates a weaker association. The sign of the score indicates the direction of It is important to note that a high score does not necessarily indicate conscious bias, as these associations may be automatic and unconscious.

However, the IAT has been shown to be predictive of behaviour in some cases and can be a valuable tool in exploring implicit biases.

To ensure maximal test validity, we ensured that the conditions for running the test in situ would be optimal (phonic isolation, no visual distractions, on a computer).

We designed both tests with the longer version of the IAT to ensure a significant number of trials for each test.

To ensure maximum validity in both French and English, words have been tested for their usage frequency in both languages. For comparability purposes, we also paired our gender & economic success IAT with the gender & science IAT, known for its robustness.

Data is collected anonymously. It will be leveraged to further research the modalities and intensity with which gender biases manifest themselves regarding perceptions of entrepreneurial and economic success, particularly for people from within the entrepreneurial ecosystem.

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References to dig deeper

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The book What Works : Gender Equality by Design by behavioural economist and Harvard Professor Iris Bohnet covers science-based & action-oriented tools to reduce gender bias in the political and economical realm. Great to have read the next time you want to have a fact-based discussion about quotas 🙂

Research papers kindly provided by Pascal Huguet and Isabelle Régner:

Ambady, N., Shih, M., Kim, A., & Pittinsky, T. L. (2001). Stereotype susceptibility in children: Effects of identity activation on quantitative performance. Psychological Science, 12, 385-390.

Dar-Nimrod, I., & Heine, S. J. (2006). Exposure to Scientific Theories Affects Women’s Math Performance. Science, 314, 435.

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, 17–41.

Huguet, P., & Régner, I. (2007). Stereotype threat among schoolgirls in quasi-ordinary classroom circumstances. Journal of Educational Psychology, 99, 545-560.

Huguet, P., & Régner, I. (2009). Counter-stereotypic beliefs in math do not protect school girls from stereotype threat. Journal of Experimental Social Psychology, 45, 1024-1027.

Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J. & Handelsman, J. (2012). Science faculty’s subtle gender biases favor male students. Proceedings of the National Academy of Sciences, ****109, 16474–16479.

Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). Math = Male, Me = Female, therefore Math ≠ Me. Journal of Personality and Social Psychology 83, 44-59.

Nosek, B. A., Smyth, F. L., Sriram, N., Lindner, N. M., Devos, T., Ayala, A., et al. (2009). National differences in gender-science stereotypes predict national sex differences in science and math achievement. Proceedings of the National Academy of Sciences, 106, 10593-10597.

Régner, I., Smeding, A., Gimmig, D., Thinus-Blanc, C., Monteil, J.-M., & Huguet, P. (2010). Individual differences in working memory moderate stereotype-threat effects. Psychological Science, 21, 1646-1648.

Régner, I., Thinus-Blanc, C., Netter, A., Schmader, T. *, & Huguet, P. Committees with implicit biases promote fewer women when they do not believe gender bias exists. Nature Human Behaviour, 3, ****1171–1179.

Reuben, E., Sapienza, P. & Zingales, L. (2014). How stereotypes impair women’s careers in science. Proceedings of the National Academy of Sciences, 111, 4403–4408.

Uhlmann, E. L., & Cohen, G. L. (2007). "I think it, therefore it's true": Effects of self-perceived objectivity on hiring discrimination. Organizational Behavior and Human Decision Processes, 104, 207–223.

Schmader, T., Johns, M. & Forbes, C. (2008). An integrated process model of stereotype threat effects on performance. Psychological Review. 115, 336–356.

Spencer, S. J., Steele, C. M., & Quinn, D. (1999). Stereotype threat and women's math performance. Journal of Experimental Social Psychology, 35, 4-28.

Valian, V. Why So Slow? The Advancement of Women (MIT Press, 1998).

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