Heterogeneity of variance in one-way ANOVA design has been frequently studied. Zimmerman (2004) concluded a separate-variances test should be used "unconditionally whenever sample sizes are unequal." However, has been little attention to heteroscedasticity in Factorial ANOVA (FANOVA). Statisticians at SAS (Littell et al., 2006) have suggested the use of approximate degrees-of-freedom (ADF) tests based on Kenward & Roger (1997). Recently, Zhang (2011) propose an ADF test for FANOVA models that is invariant under different choices of contrast matrices. There has also been a recent focus on the performance of Heteroscedastic Consistent Covariance Matrix (HCCM) estimators in complex regression models. To date, the performance of these approaches in FANOVA models has not been directly addressed. Based on simulation conditions from previous research, we will examine the Type 1 and 2 error rates of the methods for detecting main effects and interactions when heteroscedasticity is function of various patterns of cell variance heterogeneity in unbalanced FANOVA models. Preliminary simulations have confirmed the poor performance of classical F-tests under heteroscedasticity.