It throws away information, and that’s rarely a good thing to do.ĭo ordinal logistic regression and test the assumptions. Multinomial logistic is even harder to understand, and is a very complex model, with many parameters to estimate.Ĭollapsing the variable will only very rarely be correct. Ordinal logistic is more likely to be correct, but is less known and harder to understand.

The great advantages of linear regression are its ease of interpretation and its familiarity. Since those are relatively unusual methods, some people just collapse the categories into (say) “severe'” or “excruciating” vs.

That assumption might be violated, in which case, you might want to use multinomial logistic. Technically, if you follow Steven’s categories of nominal, ordinal, interval, ratio, your DV is ordinal, and should be analyzed with some form of ordinal logistic regression.īut the most common type (by far) of ordinal logistic regression is the proportional hazards model, which assumes proportional hazards. Is the same as that between (say) “moderate” and “severe”. But you’d be hard pressed to prove that the difference between “none” and “barely noticeable” If one is strict about it, linear regression requires a continuous DV – and we do not have one, at least as we’ve measured it, although it could be argued that there is a latent underlying variable here that is continuous. How much pain are you in on a typical dayĪnd your independent variables are things like age, sex, injury status, time since injury and so on. Suppose you are a doctor studying back pain, and suppose your DV is response to a scale: There are no routine statistical questions, only questionable statistical routines The short answer to this is to quote Sir David Cox

And yet others will do things such as multinomial logistic regression, or collapsing the DV into two categories, and then doing binary logistic. Others routinely say this is incorrect, and that you should use ordinal logistic regression. A lot of people routinely use linear regression (often simply called regression). There are three broad categories of regression models that might be applicable. And suppose you want to regress that on several independent variables. That is, it’s some sort of rating, from 1 to 5 or 1 to 7 or some such. Suppose your dependent variable (DV) is a Likert scale or something similar.

Spss code dependent