How to determine greatest effect for ordinal logistic regressions

[#1]

Hello,

I am testing the effects of four variables on a 6-point index using separate ordinal logistic regressions. Three of my variables show significant results but I am having trouble assessing which of these has the greatest effect on the probability of achieving higher ranks on my index. 

I am posting my effect summaries for reference. My variable (LOG MBL Balances) exhibits the highest log-worth and highest pseudo r-squared but a slightly lower coefficient estimate than the other two variables. My other two variables (LOG assets and LOG membership) exhibit very similar results and both have a higher absolute value for avg log-likelihood than MBL balances.

Am I able to say that since LOG MBL exhibits a better fit that it would be the variable with the greatest effect on my DV despite having a lower coefficient and a lower absolute value on the avg log-likelihood? Or is it better to say that all three variables exhibit similar, modest effects on my DV?

P.S. I have run my regressions in both JMP 14 and Eviews 11. Can anyone tell me why the coefficients are reported as negative in JMP while in Eviews they are reported as positive? 

Thank you

[#2]

I conducted factor analysis on the PCA and ran an ordinal logistic model using three factors. I've come across very interesting results.

It appears as though assets and membership are coupled as the highest loading values on factor 1 (with 47% variation explained), average balances is the highest on factor 2 (with ~25% explained) , and lending portfolios is the highest on factor 3.

Although factor 3 provides the lowest explanatory value (at ~12%) it appears to have the greatest practical significance in the model. Would I be correct in interpreting factor 3 as having the greatest practical effect on my DV regardless of the fact that it only explains 12% of the variation?

[#3 · Accepted solution]

The PCA is a very helpful tool in multivariate analysis of real data. Correlations are difficult to avoid. Hopefully the PCA alone provides some insight, such as assets and membership possibly the expression of some latent variable.

I assume that by "although factor 3 provides the lowest explanatory value (at ~12%)" you mean 12% of the variance in the PCA. You are correct about factor 3. The fact that factor 1 represents the most variation in the original variables in no way implies that it is the most important predictor. Using all three factors in the model provides a strong analysis for separating and testing the effects.