# How to adjust continuous and categorical variables for categorical variable?

I am performing a metaanalysis where I am trying to find predictors for an ordinal response variable. Additionally, I want to perform pair-wise correlations on some of the variables. I have a confounding categorical variable that I want to remove.

In my data, there are 4 levels of variable RV, and 40 levels of the confounder CON1. The data looks like this, except I have thousands of cases.

VARNAME RV      CON1        IV1         IV2         IV3        IV4
VARTYPE Ordinal Categorical Categorical Categorical Continuous Continuous
CASE1   1       1           M           R           49         476.23
CASE2   1       2           F           S           31         465.11
CASE3   2       2           M           R           37         411.20
CASE4   3       7           M           X           41         407.33


I don't think I can adjust means because the response variable is ordinal, so no linear regression.

Proportional odds logistic regression might be a good choice, but I don't think the output will produce "standardized" variables, that I can perform the pairwise correlations on.

How do I remove the effect of the confounder variable?