# Can I categorize the factor scores to use them as predictors of an ordinal logistic regression?

I was wondering if I can categorize the factor saved scores by taking their quartiles (or some other measures, I am not sure what should I use!) as cut points and use them as predictors in an ordinal logistic regression.

The reason I am thinking of doing so is that, the number of empty cells increases to a lot extent if I use the factor saved scores as they are (continuous in nature).

I have no particular reason behind choosing quartiles as the cut points. The factor scores were obtained from an optimal scaling of the variables (some variable were continuous, some were ordinal, some were nominal) and then factor analysis of the optimally scaled variables using varimax rotation. I am just thinking if I can categorize the factor scores by some means and try to name them as, probably, "lowest scores", "lower scores", "higher scores" and "highest scores" etc. and use them in an ordinal logistic regression as independent variables so that I can comment on the different levels of scores compared to the reference category ("lowest scores") of that factor.

I am not sure what should be used as the cut points. Even I am not sure if this is a right procedure. Can anyone please suggest?

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Continuous IVs are fine for ordinal logistic regression. True, they create empty covariance pattern cells which makes it problematic to rely on goodness-of-fit measures such as chi-square. But think, do you really need to know goodness-of-fit in your case? Anyway, of course you can categorize factor scores, like any continuous variable. – ttnphns Jan 11 '13 at 1:30
@ttnphns Thank you. I actually ran an ordinal logistic regression with the factor scores as independent variables. But the coefficients are not coming significant although I was hoping them to come significant. As the factors are obtained using a varimax rotation, I can expect that the problem is not multicollinearity. So I thought I better categorize the factor scores and reduce the number of empty cells, hoping that it might give a better result! May I know your thoughts about it? – Blain Waan Jan 11 '13 at 6:28
Categorizing won't make your coefficients higher or more significant, is unlikely to improve your model. The only beneficial effect of it is that you may now believe the goodness-of-fit measure is computed reliably. – ttnphns Jan 11 '13 at 10:02
Thank you so much for your kind advice. – Blain Waan Jan 11 '13 at 10:06