Differential item functioning (DIF) is a phenomenon when two respondents with the same underlying latent trait (such as quality of life, stress, pain, attitudes, knowledge, or ability) but from different social group have different probabilities to endorse an item in multi-item measurement. Many methods for DIF detection are derived from comparison of regression curves of reference and focal group, however, most of them are limited to detection of DIF caused either by difference in difficulty or discrimination parameters (inflexion point and slope of the probability curve). Possible methodological gap can be filled by nonlinear regression models for DIF detection (Drabinová & Martinková, 2017) implemented within difNLR R package (Hladká & Martinková, 2019). These models offer possibility to account for guessing or inattention (guilt or shame in health-related outcome measures) when answering and moreover to test whether these item characteristics differ between groups. Another approach which can detect such differences is a newly proposed nonparametric comparison of regression curves. This method can be useful for example in situations of small sample sizes or when no specific true model is expected. A special type of DIF is differential distractor functioning (DDF), situation when two respondents with the same underlying trait but from different group have different probability to select given distractors. DDF can be modelled by nominal models among ordinal and nominal data. Similarly, cumulative logit, or adjacent category logit may be used to describe DIF in ordinal items. Other topics in DIF detection cover item purification and multiple comparison corrections. In this presentation, we introduce several approaches for DIF and DDF detection, show results of the simulation studies and illustrate usage and implementation of the methods with real and simulated examples.