# Ordinal regression model in R - multiple coefficients appearing for only one dependent variable. Can someone help me analyze?

I am running an ordinal logistic regression model in R (with an ordinal dependent variable). For my model I am also only including one primary independent variable (which is also ordinal). However, when I run my model, three coefficients and correspondingly three intercepts appear. This is strange as when I see this exact code executed online for different datasets only one coefficient and intercept appear per variable. My thought process is that this problem may inadvertently coincide with the 4 levels coded on my dependent variable. Can someone perhaps tell me whether I am doing something wrong or how I can interpret the output of my code?

I have copy-pasted my input and output here: Input:

IV<- as.factor(DissData_2$nucsym) DV<- as.factor(DissData_2$Cenviosy (0-3) DV)

Model_1 <- polr(DV ~ IV, data=DissData_2)

Model_1

Output: Call: polr(formula = DV ~ IV, data = DissData_2)

Coefficients: IV1 IV2 IV3 -0.16796685 0.04071596 0.45894483

Intercepts: 0|1 1|2 2|3 -0.6393510 -0.3063191 1.0996081

Residual Deviance: 663.2484 AIC: 675.2484

• Welcome to CV. Since you’re new here, you may want to take our tour, which has information for new users. R creates $k-1$ dummy variables for factors (as in your case) by default, where $k$ is the number of categories of the factor variable. And since you use ordinal logistic regression (OLR), the model estimates (as you guessed) three thresholds. There is nothing wrong (based on the output). You can see here how to interpret OLR models: Interpretation of ordinal logistic regression – T.E.G. Feb 5 at 6:45