Measurement bias poses a serious threat to test validity, and research has consistently shown that item parameters play a crucial role in generating such bias. The present study aimed to identify the optimal three-parameter method for detecting differential item functioning (DIF) while considering the effects of item parameters. Designed as an applied psychometric investigation, the study simulated data based on independent variables including test type (nonlinear regression, Lord’s three-parameter, and Raju’s three-parameter methods), ranges of parameters (low, medium, and high), and item characteristics. Simulation was conducted by manipulating 20% of items to exhibit DIF with an effect size of 0.5, producing 10 sets of data and a total of 90 datasets. For each dataset, three DIF detection tests were performed, resulting in 270 analyses overall. Findings revealed that the main effect of test type was statistically significant, with nonlinear regression outperforming Raju’s three-parameter method in terms of accuracy. Moreover, DIF detection was more effective when the difficulty parameter was at a medium level compared to low or high levels, while increases in the discrimination parameter consistently enhanced correct detection rates. In contrast, variations in the guessing parameter did not yield significant differences, as test performance remained stable across all levels of guessing. These results underscore the importance of focusing on difficulty and discrimination parameters in DIF detection and suggest that nonlinear regression can serve as a practical and efficient alternative to item response theory (IRT)-based methods, particularly in contexts where guessing effects are present. Overall, the study contributes to psychometric methodology by highlighting parameter-specific influences and offering evidence for the utility of nonlinear regression in improving DIF detection.