# Ordinal regression vs. summated score

It seems quite common to see scientific researches using a summated score to represent an overall scale that consists of many individual scale items (ex. Likert scale items). I assume that regular statistical tools (ex. ANOVA, linear regression, etc.) can be used on these data, which are treated as quantitative data.

However I've came upon another tool when dealing with ordinal data - ordinal regression (or ordered logistic regression). My initial question was: would it be better if I just used ordinal regression rather than calculating summated score first then use multiple regression? But I found out that ordinal regression seemed applicable only when all individual scales were included to fit a dependent variable. In cases where scales are made up of subscales, then generating summated scores is a must (or perhaps not?), meaning that data are treated as quantitative already.

I'm not sure if what I understand here is correct or not. Verification is greatly appreciated.

• If the resulting scale is not an interval scale, that is, the meaning of a particular gap is not the same across the scale (e.g. is the difference between 1 and 3 the same as the difference between 101 and 103?) then treating it as a continuous measurement does not make any sense. So, this really translates to a question about whether or not you can make a rational argument that your outcome is an interval scale - if so, you can treat as you would any other continuous variable. Jan 6, 2013 at 22:11
• Treat as ordinal regression, as at stats.stackexchange.com/questions/64788/… Nov 3, 2021 at 11:38