I am writing my bachelor's thesis that aims to investigate how a liberal outlook affects one's income. I am using a probit analysis with a dummy variable as my dependent variable and 16 independent variables that include a mix binary dummy variables and other categorical variables of which most are ordered on the Likert Scale (strongly agree, agree, neither agree nor disagree, etc). i am confused as to how i can interpret the marginal effects for the variables with more than 2 categories. this is the probit model with the variables i plan on using -

probit highinc whiterace female age educ location marry child Eng SocSpend Transopin Homosexual bothinc gendwork reduccar relsty liberauth

Here highinc is a dummy with 1 = the individual is in the high income band and 0 = he is not. Similarly i have dummy variables for being caucasian (whiterace), female (female), marital status (marry), having children (child) and living in England (Eng). the remaining variables are all ordinal in nature. the variables i am quite confused about are the latter ones in the model, namely the variables SocSpend, which measures people's attitudes towards social spending and whether it should be increased. this is ordered on the likert scale with 1 = strongly agree and 5= strongly disagree . similarly Transopin and Homosexual measure Transphobia and Homophobia in the same way and reduccar measures environmentalist attitudes on the Likert Scale. Another variable i am confused by is gendwork that checks for gender stereotyping of jobs with 1 = no jobs are equally suited to men and women and 7= all jobs.

i am not too sharp with statistics and would really appreciate the help. Thank You