I am examining how several different measurements predict academic achievement. The participants included students from six different classrooms. A reviewer asked if the classrooms were equivalent. After running some analyses, I came to the conclusion that the classrooms were not equivalent. I previously was utilizing a regression procedure for this analysis, but I am unsure of how to proceed. I believe that I want to examine my data from a nested, HLM, MLM perspective? I attempted to include the class into the regression analysis; however, the class is a nominal variable with six levels. From my searching, regression cannot handle more than two levels of a nominal variable without dummy coding. I completed dummy coding and attempted to run the analysis and the results became very confusing to me because I was now comparing the results of five classes against one class and to be honest, I wasn't really sure what I had anymore. I spoke with a statistician who recommended that I use an ANCOVA design with the class as the factor and predictors as covariates and a custom model to examine the main effects of all variables. I did this and have run analyses in two separate ways. (1) I ran this with the class variable entered as the factor and (2) with the 6 dummy variables of the class variable as the factor. Which one is correct? The results are wildly different and I am not sure if I am answering my research question anymore. I hope this made sense, but if not, I will try to clarify. Thanks in advance.