I'm doing an ordinal regression (cumulative logit model), with a 4-point, self-report health assessment measure as my outcome.
My sample size is 8,070, and so far the model has 15 predictors: 8 binary/categorical and 7 continuous.
A few of the continuous variables however are scores (e.g. psychosocial risk & resilience measures), and can be broken down further into their individual subscales.
I'm wondering then how many variables would be too many for my model? The sample size shouldn't decrease, but currently there are only 169 respondents in the lowest level of the outcome variable.
tl;dr – Is there any rule of thumb that tells you how many predictors you can use in an ordinal regression? Thank you.
In addition to sample size you also need to look at the number of observations. so-called "rule of 10" . For instance if you have 8, 000 observations. the maximum number of predictors are 80. However, the rule is for linear regression. In Logistic regression it is often said that higher number of predictors bring a better stability. One study suggests that we need to user rule of 25. However, it is most common to use rule of 15. In your case having $8,070 / 15 = 538$ variables afford good stability. You may find this post useful.
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