# Regression model for ordinal dependent variable and categorical independent variables

If I'm using R, which regression model should I use for my dataset? (I need to get the R-squared value.) I have 1 dependent variable and 6 independent variables as follows:

1 dependent variable:

• concern {-2, -1, 0, 1, 2}

6 independent variables:

• org { scl_msg, scl_pg, fin}
• type_d { prsnl, activ, log}
• type_f { x-t, user-x, t-x}
• gender { male, female}
• age { 18-25, 26-30 , 31-35, 36-40, 40+}
• awareness { fully-aware , partially-aware, not-aware}
• Are you saying that your dv can only take the values -2, -1, 0, 1, 2? Oct 16 '14 at 2:33
• yes it is only {-2, -1, 0, 1, 2}
– sdj
Oct 16 '14 at 14:25
• Then your dependent variable isn't continuous. Eg, you can't have a 1.5 or a -3, etc. Do you have good reason to believe that the difference between 2 & 1 is the same as -1 & -2? These look like ordinal data to me. Oct 16 '14 at 15:27
• it is representing concern levels , so -2 means extremly concened, 0 means neutral and 2 means not concerned. in this case if it ordinal do you think logistic regression model should be the corect model to use ? or do you have another seggastions ?
– sdj
Oct 16 '14 at 15:56

You will be best off using ordinal logistic regression. There are at least four ways to do this in R (meaning different functions in different packages). The uniformly excellent UCLA statistics help site has a fairly comprehensive tutorial (albeit using only polr in MASS) here. There is a nice overview of the different possibilities here (it is primarily code you can run, with less explanation).
Note that there isn't really such a thing as R-squared for generalized linear models such as ordinal logistic regression. There are a number of so-called pseudo R-squareds, but it is important to understand what each one measures (there is a nice guide here), and their value is debatable (for an overview of the issues, see this excellent CV thread: Which pseudo $$R^2$$ is the one to report for logistic regression).