In DIF studies, a Type I error refers to the mistake of identifying non-DIF items as DIF items, and a Type I error rate refers to the proportion of Type I errors in a simulation study. The possibility of making a Type I error in DIF studies is always present and high possibility of making such an error can weaken the validity of the assessment. Therefore, the quality of a test assessment is related to a Type I error rate and to how to control such a rate. Current DIF studies regarding a Type I error rate have found that the latter rate can be affected by several factors, such as test length, sample size, test group size, group mean difference, group standard deviation difference, and an underlying model. This study focused on another undiscovered factor that may affect a Type I error rate; the effect of multiple testing. DIF analysis conducts multiple significance testing of items in a test, and such multiple testing may increase the possibility of making a Type I error at least once. The main goal of this dissertation was to investigate how to control a Type I error rate using adjustment procedures for multiple testing which have been widely used in applied statistics but rarely used in DIF studies. In the simulation study, four DIF methods were performed under a total of 36 testing conditions; the methods were the Mantel-Haenszel method, the logistic regression procedure, the Differential Functioning Item and Test framework, and the Lord’s chi-square test. Then the Bonferroni correction, the Holm’s procedure, and the BH method were applied as an adjustment of multiple significance testing. The results of this study showed the effectiveness of three adjustment procedures in controlling a Type I error rate.