Hello all

I am trying to fit a model using JMP 10, and the results confuse me, I thought perhaps you could assist.

I have an ordinal dependent variable, with 3 categories. My main independent variable of interest is discrete, with not less than 5 categories ! In addition to this IV, I have several other IV's to examine, some are discrete and some continuous.

I ran the model using the Fit model module. First, I tested only the main IV of interest, and something weird has happened. JMP have created dummy variables, leaving the last category as control, fine. The model was not significant, with P=0.1134 (whole model test), however, one dummy variable was actually significant (P=0.036). The generalized R square was 0.01, the entropy R square was 0.009, and the training misclassification rate was 35% (so any testing set would be poorer). Every indication show that the results are week, but how can I explain a whole model being non-significant while one IV (in this case a level of dummy), being significant ? There isn't a correlation, since all IV's are levels of the same dummy !

Then I tried adding a couple of continuous IV's. Both were very significant (P=0.0015, P=0.0126). However, the generalized R square is still around 5% only. The odds ratios are 1.16 and 0.9. When I have changed the roll play, and created a couple of box-plots of these IV's group by my DV, it didn't take an expert to say that there is not an interesting difference. My sample size is 411, can it be that the large sample size caused the low P-values, or is it a type I error ? How can I explain very significant results when I get the sense that they have no meaning ?

Thank you !