Confounding is not typically examined in analysis of data because no response data is needed to evaluate confounding. Confounding is determined in the design selection phase and construction phase. Without knowing the specific design you picked, and if you had incorporated other randomization restrictions such as blocking, and the design's resolution it's problematic determining effect confounding. Lastly, typically confounding only arises if there is some fractionation or other unusual design altering that isn't a full factorial pathway going on within the overall design. For example, a 2**(4-1) fractional factorial portion of a central composite design will have confounding present. All of these issues can be explored in the Evaluate Design platform.
Perhaps if you share the specific design, and you can anonymize the factor names if need be, we can help?
