I have limited statistical experience from my coursework in undergrad running simple linear regressions and performing chi-square tests. I have some data, ~5000 survey results on individuals, each with a score from a scale of 1-12 on how security conscious they are (determined by their answers to previous security related questions) and we also asked multiple choice questions on income (USD 0-USD 19,999; USD 20,000-USD 39,999; etc.), age (21-30, 31-40, etc.) and level of education (High School, Undergraduate, Masters, and Doctoral). I wanted to know how I would set this up to determine which is the biggest factor in determining their security consciousness with statistical significance. Here is a pivot table from my Excel file with random data. I have all the individual responses as well.
Should I be using dummy variables (one for each category within a group). I also tried using the average number if the number was a range (USD 10,000 for USD 0- USD 19,999 OR 26 for age 21-30) but still not sure how that would work for education. I have run some tests and am unsure what the results entail (i.e. regression of security consciousness against each of the income brackets but this doesn't seem to make sense since no one can be in more than one and each bracket was given a correlation coefficient). I am fairly certain my chi-square test makes sense and tells me that income, age and education all play a "significant role" in the variation of security consciousness (all the p-values were well below .05, around .001-.003). But how do I quantify this "significant role" within the variation?
Can anyone let me know how to best go about forming the correct conclusions (i.e. "income is the largest factor in determining level of security consciousness" or "age has no statistical significance in determining security consciousness")? How do I use my categorical explanatory variables in a multiple regression with my security consciousness ordinal "score" response variable?