One question again to be clarified: Can I use the variables as noted below [(3) a,b,c etc] as continuous variables in my logistic regression and if so what will be my explanation in the paper that I am writing.
I have the following sets of variables:
- A Categorical (binary) variable Ayurveda and Allopathy
- Test variable (binary) "Spirituality is a scientific subject": Agree and Disagree
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Then I have a number of participant perspectives/characteristics such as:
- (a) Do you believe there is life after death: 1) yes, 2) no, 3) not sure
- (b) To what extent do you consider spiritual 1) Very 2) moderate 3) slightly 4) not at all
- (c) How often would you say the experience of illness increase patients’ awareness of and focus on R/S: 1) Rarely 2) Never 3) sometimes 4) Often 5) Always 6) Not apply
- (d) etc= several more such variables with multiple choices as above
Please advise.
Best Answer
I would say no. Including these categorical variables as continuous regressors assumes that a one unit change in any of the multiple choice variables results in the same effect on the outcome. For example, you are assuming that going from [1)Very] to [2)moderate] has the same marginal effect as going from [3)slightly] to [4)not at all].
To me this is an overly restrictive assumption. Thankfully, it is straightforward to estimate this model without this assumption: include the categorical regressors as factor variables. This breaks each of the categorical regressors down into a series of dummy variables. To do this with R you can use the factor() function inside the glm function:
glm(y~factor(x),binomial())
or in stata use the xi and i prefixes:
xi:logit y i.x