In response to public concern over fairness in testing, conducting a differential item functioning (DIF) analysis is now standard practice for many large-scale testing programs (e.g., Scholastic Aptitude Test, intelligence tests, licensing exams). As highlighted by the Standards for Educational and Psychological Testing manual, the legal and ethical need to avoid bias when measuring examinee abilities is essential to fair testing practices (AERA-APA-NCME, 1999). Likewise, the development of statistical and substantive methods of investigating DIF is crucial to the goal of designing fair and valid educational and psychological tests. Douglas, Roussos and Stout (1996) introduced the concept of item bundle DIF and the implications of differential bundle functioning (DBF) for identifying the underlying causes of DIF. Since then, several studies have demonstrated DIF/DBF analyses within the framework of “unintended” multidimensionality (Oshima & Miller, 1992; Russell, 2005). Russell (2005), in particular, examined the effect of secondary traits on DBF/DTF detection. Like Russell, this study created item bundles by including multidimensional items on a simulated test designed in theory to be unidimensional. Simulating reference group members to have a higher mean ability than the focal group on the nuisance secondary dimension, resulted in DIF for each of the multidimensional items, that when examined together produced differential bundle functioning. The purpose of this Monte Carlo simulation study was to assess the Type I error and power performance of SIBTEST (Simultaneous Item Bias Test; Shealy & Stout, 1993a) for DBF analysis under various conditions with simulated data. The variables of interest included sample size and ratios of reference to focal group sample sizes, correlation between primary and secondary dimensions, magnitude of DIF/DBF, and angular item direction. Results showed SIBTEST to be quite powerful in detecting DBF and controlling Type I error for almost all of the simulated conditions. Specifically, power rates were .80 or above for 84% of all conditions and the average Type I error rate was approximately .05. Furthermore, the combined effect of the studied variables on SIBTEST power and Type I error rates provided much needed information to guide further use of SIBTEST for identifying potential sources of differential item/bundle functioning.