# Thread: Multinomial Regression - predicting probabilities

1. ## Multinomial Regression - predicting probabilities

Hi All,

I have responses to a questionnaire on patient experience. My aim is to add a component of this questionnaire (on communication) to an already established questionnaire (so adding a domain).

The responses to the original questionnaire I have are in the following format:

I had a good experience with the consultant = agree
I felt I was listened to = agree completely
The communication experience was positive = agree
I felt I was taken care ok = so-so

The possible responses for each of the 4 questions within the communication domain are agree completely, agree, so-so, disagree, disagree completely. I therefore have for each patient a combination of the 4 responses. For a sub set of patients I also showed them a health state (as described by the 4 statements above) and asked them, to rate this as very poor, poor, average, good or very good.

I therefore have a subset of the health states as described by the 4 statements, with an overall rating for this health state. I ran multinomial regression on the data in order to determine whether the responses from the questionnaire could be used to predict an overall rating for the health state.

I need to be able to predict a rating for every combination of responses (54). I ran the analysis in SPSS. I have been trying to work out the equations behind the multinomial regression and co-efficients from the multinomial regression. However, the equation’s seem very complicated.

Can anyone advise, or does anyone know of some existing SPSS coding which can be applied?

Many thanks

VicB

2. ## Re: Multinomial Regression - predicting probabilities

Hey VicB.

Are you aware of regression models for categorical variables? Basically you use a tonne of dummy variables that take on a 0 or 1 value and if you only have categorical variables, then you do what is known as a logistic regression.

If you want to know more details then do a search in google for logistic regression, its derivation, and the estimation of parameters and what they mean.