The project implicit international dataset: Measuring implicit and explicit social group attitudes and stereotypes...

Area of Science:

• Social Psychology
• Cross-Cultural Psychology
• Data Science

Background:

• Understanding worldwide variation in social group attitudes and stereotypes is crucial for social science research.
• Previous research lacked comprehensive cross-country data on both implicit and explicit measures.
• Identifying correlates of attitude and stereotype variation across places remains a key objective.

Purpose of the Study:

• Introduce the Project Implicit:International (PI:International) dataset, the first cross-country compilation of implicit and explicit attitudes and stereotypes.
• Provide a robust dataset for investigating global patterns and variations in social perceptions.
• Facilitate novel discoveries on the worldwide nature of implicit and explicit biases.

Main Methods:

• Utilized 2.3 million Implicit Association Test (IAT) and self-report measures across seven topics (race, sexual orientation, age, weight, nationality, skin-tone, gender-science stereotypes).
• Collected data continuously from 2009-2019 in 34 countries, translated into native languages.
• Validated IAT data for internal consistency, convergent validity, and known groups validity.

Main Results:

• The PI:International dataset demonstrates adequate reliability and validity for implicit association test data.
• Basic descriptive statistics on global implicit and explicit attitudes and stereotypes are summarized.
• The data reveal the overall strength and variability of these perceptions worldwide.

Conclusions:

• The PI:International dataset is a valuable, openly accessible resource for social science research.
• It enables extensive cross-country analysis of implicit and explicit social attitudes and stereotypes.
• The dataset will propel new insights into the global psychological landscape of social biases.