Test fairness is a critical issue for any intended test use. When a test is fair, none of the examinee groups should be favored or disadvantaged. Differential item functioning (DIF) or differential testlet functioning (DTF) across subgroups of examinee population may not indicate bias, but should flag up the need for further scrutiny of the item content for any potential item bias. This chapter presents the statistical methods for DIF and DTF analysis as a tool for identifying potentially biased items in language assessment. The focus is on introducing the idea of latent differential functioning analyses in language assessments. The chapter starts with a review of the conventional DIF and DTF analysis methods based on manifest grouping variables. The necessity of latent differential functioning analysis, which borrows strength from both item response theory (IRT) and latent class analysis, is elaborated. An empirical example is used to illustrate different approaches to differential functioning analyses. The chapter ends with a discussion related to challenges and future research for DIF and DTF analyses in language assessments.