# What to do with ordered logistic regression when I have correlated predictors

I am currently trying to analyze a research project and I have very little experience dealing with this type of data and I am looking for a bit of help/suggestions.

The project revolves around frog mating behavior, specifically investigating the influence of length, weight, condition, and age on a female frogs willingness to travel certain distances (simulated by altering the volume of a males mate attraction call in a sound chamber playback experiment) to mate with an attractive male vs an unattractive (but nearby) male.

The response variable is the distance a given female is willing to travel to choose an attractive mate. This response variable is categorical and has 4 levels corresponding to 4 different volume settings (essentially simulating distance going from short distance to long distance).

The predictor variables I am interested in are length, weight, condition (which is calculated using the residuals of a length/weight regression), and age. Length, weight and condition are all continuous, while age is a factor (age is measured in years and all individuals are either 2 or 3 year olds).

From what I have read ordered logistic regression sounds like it might be a way to analyze this data since my response variable is categorical and has a natural ordering to the responses.

My problem is that from what I have read ordered logistic regression requires that the predictor variables are not correlated.

I know that my predictor variables are definitely correlated. For example if I were to run an anova or t-test with age and length (or age and weight) I find that the older females are significantly longer/heavier. Additionally length and weight are certainly strongly correlated (as you would expect).

Unfortunately I have not been able to find any good suggestions for what to do in this scenario where I have correlated predictors. Is there any way for me to tease apart these correlated predictors? Can I still use ordered logistic regression or would some other test be more appropriate?

Thanks!

• Welcome to crossvalidated. – Jeremy Miles Oct 16 '15 at 3:24
• This is not a problem. It's kind of the point of regression. The only way to get uncorrelated predictors is (usually) to assign individuals to conditions yourself. Random numbers will give you correlated variables. – Jeremy Miles Oct 16 '15 at 3:25

$length = \beta_0 + \beta_1 weight + \underbrace{\varepsilon}_{condition}$