Implicit association tests for all: Using iatgen for non-English and offline samples - João O. Santos; Emerson Do Bú; Tomohiro Hara; Cristina Mendonça; Sara Hagá; Ruth Pogacar; Michal Kouril (PLoS ONE, 2026)
The Implicit Association Test (IAT) has become an invaluable tool for researchers in many fields. The IAT is a sorting task that measures the strength of automatic associations between targets (e.g., flowers / insects) and attributes (e.g., pleasant / unpleasant). Several programs exist to create and run IAT studies, and each has unique advantages and disadvantages. Yet most share the same limitations: being general-purpose data collection tools that require time to master, requiring extra steps to run online (e.g., deploying a web server), and having no IAT-data analysis features. This increases researcher reliance on pre-made templates that typically operate only in English and are difficult to translate. Iatgen addresses some of these issues by allowing researchers to design and analyze IATs through a simple web-interface, to easily combine IATs with experimental manipulations or other measures in Qualtrics, and to analyze data using the same web-interface. However, until recently, the problem of monolingual, English-only capability remained. In this paper, we introduce iatgen’s new translation functionality, which allows users to create non-English IATs using the web-based iatgen Shiny app and the tr.iatgen R package. Users are invited to contribute to the translation repository in GitHub by submitting and reviewing IAT translations. We also describe a method for deploying Qualtrics-based IATs in offline environments. We hope this increased functionality will facilitate cross-cultural research and reduce the negative effects of disproportionately Western, educated, industrialized, rich, and democratic (WEIRD) samples.