Differential item functioning (DIF) analysis is important in terms of test fairness. While DIF analyses have mainly been conducted with manifest grouping variables, such as gender or race/ethnicity, it has been recently claimed that not only the grouping variables but also contextual variables pertaining to examinees should be considered in DIF analyses. This study adopted propensity scores to incorporate the contextual variables into the gender DIF analysis. In this study, propensity scores were used to control for the contextual variables that potentially affect the gender DIF. Subsequent DIF analyses with the Mantel-Haenszel (MH) procedure and the Logistic Regression (LR) model were run with the propensity score applied reference (males) and focal groups (females) through propensity score matching. The propensity score embedded MH model and LR model detected fewer number of gender DIF than the conventional MH and LR models. The propensity score embedded models, as a confirmatory approach in DIF analysis, could contribute to hypothesizing an inference on the potential cause of DIF. Also, salient advantages of propensity score embedded DIF analysis models are discussed.