Data analysis began with a slow start on Sunday morning the day after we have finished coding our data set. After running a few frequency distributions and cross tabs which I remembered how to do from the lessons taught during the course, I began to realize that I would be unable to complete the data analysis from memory and would need a bit of direction to begin. Therefore, on Monday during class I asked LTC Sanborn for a few suggestions on conducted the analysis. He first suggested examining the data collected using a Cross tab to determine if there were significant differences among the classes in spending habits and the source of spending. What we saw was that the modal category for the 1st class cadets was the highest category. Meanwhile for the 2nd class cadets it was the median category and for the 3rd class and rats it was the 2nd lowest category. The data on the source of income also correlated strongly with class as two trends clearly emerged. First, as cadets advanced through VMI they were more likely to have an ROTC stipend as their primary source of income and less likely to receive the majority of their money from their parents. Also, summer employment logically peaks before 3rd class year as it is the summer after matriculation least likely to impacted by summer military training. Because of these distinct differences in both spending habits and source of income among the classes, it was advised by LTC Sanborn they we create new variables for each Class and Source of income. Summer jobs and on post jobs were collapsed into one category when the new variables were drafted. 12 new variables were created as there were four classes and three possible primary sources of income (ROTC, job, or parents/family) These were coded as dichotomous. For example variable “S1_ROTC_18” was a “yes no” variable that showed whether a cadet in the class of 2018 had listed a ROTC stipend as their primary source of income.