Recent research has demonstrated an intersectional approach to the study of differential item functioning (DIF). This approach expands DIF to account for the interactions between what have traditionally been treated as separate grouping variables. In this paper, we compare traditional and intersectional DIF analyses using data from a state testing program (nearly 20,000 students in grade 11, math, science, English language arts). We extend previous research on intersectional DIF by employing field test data (embedded within operational forms) and by comparing methods that were adjusted for an increase in Type I error (Mantel-Haenszel and logistic regression). Intersectional analysis flagged more items for DIF compared with traditional methods, even when controlling for the increased number of statistical tests. We discuss implications for state testing programs and consider how intersectionality can be applied in future DIF research.