Hello! I am applying logistic regression on an independent variable gender and another dependent binary variable. The result of ordinal logistic regression as well as binary logistic regression are showing significant relationship between variables but it is not showing fitted model and the values of Cox n Snell and Nagelkerke are too loo. The sample size is 450. Why is this happening?
Have you seen this FAQ?
https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-what-are-pseudo-r-squareds/
Hello Zoiya,
In addition to the responses given above, if a single dichotomous IV fails to account for a lot of the differences in a single dichotomous DV, that simply means that the IV and DV are not strongly related, at least for your sample.
So, how could this happen?
1. There truly is no substantial relationship between the two (gender and whatever the DV is) in the target population.
2. There truly is a substantial relationship, but your sample is unrepresentative of the target population.
Good luck with your work.
I'll second the suggestion by Bruce Weaver . Specifically, there's Efron's pseudo r-square and Adjusted Count pseudo r-square, that are much easier to understand than are Cox and Snell or Nagelkerke.
But for simple and multiple logistic regression, I find that all of McFadden, Cox and Snell, Nagelkerke, Efrons, and Adjust Count usually have results in the same ball park. That may not always be the case, tho.
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