I have data in which the response variable (attitudes towards tourism) is scaled in nature ranging from -10 to +10 (calculated from the summation of scores of a few questions related to tourism). The independent variables are continuous, categorical (3-4 categories), and binary (Yes/NO) in nature. I can categorize the response (attitude) into Negative, Neutral, and Positive and run an ordinal logistic regression (not very sure even this is the correct way). But I have come across some literature relevant to my study where they have used multiple linear regression even when their independent variable was Categorical (Negative, Neutral and Positive) and some cases when the independent variable was scaled like mine. Can I use multiple linear regression in this case?? IF not, what about ordinal logistic regression?
With a response variable on an ordinal scale of -10 to +10 this is a valid reason for treating it as numeric and running a standard multivariable regression. You will need to inspect residual plots in order to assess whether they are plausibly normally distributed if you are going to make certain inferences.
This will likely be much better than categorizing into 3 levels as this will result in a lot of information loss.