# Recode Binary to scale Variables

I'm looking to find information to recode a set of dichotomous response variables to preferable a 1-5 scale.

I have several response variables which when combined with PCA clearly depict certain strategies, ie.

1,0,0,0,0 = Accomodate / 1,1,0,0,0 = Accomodate / 0,0,1,0,0 = Retaliate / 0,0,1,1,0 = Retaliate / 0,0,0,0,1 = Reposition / 0,0,0,0,0 = Ignore

Discriminant analysis proves this model with 97% certainty.

Now, I would like to correlate these response strategies (seeing them as dependant variables) based on two scales I have created, being: Entry Barriers & Expected Retaliation (both measured on a 1-5 likert scale).

If I keep the response variables as 0-1 or even 1-5 I do not get any significant correlations, logically enough. However if I play around a little, ie. High Expected retaliation score translates to a 5 on Retaliation strategy, whilst low expected retaliation score translates to a 1-2 on retaliation strategy I get clear correlations with significance **.

I do not want to play around with data, I want to find a way to recode the binary variables to 1-5 scale variables for my correlations, this has led me to:

How to transform ordinal data from questionnaire into proper interval data?

Which leads me to IRT and Rasch model, which throws me off the bus, it is above my knowledge. I am guessing it essentialy assigns a sort of weighting to variables, but I cannot for the life of me figure it out.

This question is quintessential to my masters degree, hence I am looking for an answer desperately.

Any ideas? Or is this just impossible?