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Differential item functioning (DIF) uses traditional methods that consider test taker characteristics at the singular level, eliminating the possibility of any multiplicative evaluation of different variables. One of these methods, the Mantel-Haenszel, uses single categories such as male and females to create groups. Intersectionality theory posits that individuals are more than their binary distinctions, consisting of multiple identities that overlap (Crenshaw, 1990). Intersectional DIF aims to recognize these identities by creating reference and focal groups that are reflective of theoretical inquiry. Using both the logistic regression and Mantel-Haenszel methods, we examined items from a large-scale state science standardized assessment. We found that the intersectional method led to an increase in items being flagged for DIF.