Implicit association tests for all: Using iatgen for non-English and offline samples

Area of Science:

• Psychology
• Social Sciences
• Human-Computer Interaction

Background:

• The Implicit Association Test (IAT) is a widely used tool for measuring automatic associations.
• Existing IAT software often requires technical expertise, lacks online deployment ease, and has limited data analysis features.
• Current IAT tools are predominantly English-only, hindering cross-cultural research and increasing reliance on translated templates.

Purpose of the Study:

• To introduce iatgen's new translation functionality for creating non-English IATs.
• To present a method for deploying Qualtrics-based IATs in offline environments.
• To facilitate cross-cultural research and reduce reliance on WEIRD samples.

Main Methods:

• Utilized a web-based iatgen Shiny app and the tr.iatgen R package for non-English IAT development.
• Implemented a translation repository on GitHub for community contributions and reviews.
• Developed a method for offline deployment of Qualtrics-based IAT studies.

Main Results:

• Successfully introduced translation functionality, enabling the creation of IATs in multiple languages.
• Established a collaborative platform for IAT translation contributions and quality control.
• Provided a viable solution for conducting IAT research in offline settings.

Conclusions:

• Iatgen's enhanced features, including translation and offline deployment, significantly improve accessibility for international researchers.
• These advancements aim to broaden the scope of IAT research, making it more inclusive and representative.
• The developed tools support the creation of culturally sensitive and globally applicable psychological measures.