Methods for detecting differential item functioning (DIF) and item bias are typically used in the process of item analysis when developing new measures; adapting existing measures for different populations, languages, or cultures; or more generally validating test score inferences. In 2007 in "Language Assessment Quarterly," Zumbo introduced the concept of Third Generation DIF. In the current article we introduce a new methodology, latent class logistic regression, for Zumbo's Third Generation DIF, whose foundation is a novel ecological model of item responding. The ecological model and the new statistical methodology are introduced, and a proof-of-concept is provided, in the context of an example of an international reading test focusing on DIF due to testing language. The new DIF framework is described and contrasted with other methods, "Mplus" code is provided, and the new method is shown to have potential for application in assessment.