Thanks a lot for your reply! It seems like with proc logistic is getting a bit challenging. I will try Proc FREQ as you suggested. What about using proc genmod, specifiying a multinomial distribution? I dont know much, so Im just asking.    I tried your code for using the unequalslopes test for Harvest and this is what I got: Type 3 Analysis of Effects Effect DF Wald Chi-Square Pr > ChiSq Harvest 0 . . U_Harvest 0 . . Variety 2 14.4212 0.0007   Analysis of Maximum Likelihood Estimates Parameter   PeelColor DF Estimate Standard Error Wald Chi-Square Pr > ChiSq Intercept   2 1 -2.0236 0.5433 13.8708 0.0002 Intercept   3 1 -1.7612 0.5217 11.3981 0.0007 Harvest 1   1 0.6247 0.5672 1.2128 0.2708 Harvest 55   0 0 . . . U_Harvest 1 2 1 0.5878 0.5562 1.1166 0.2906 U_Harvest 1 3 0 0 . . . U_Harvest 55 2 0 0 . . . U_Harvest 55 3 0 0 . . . Variety 465418_9   1 2.4091 0.6456 13.9251 0.0002 Variety BL516   1 1.3044 0.5592 5.4415 0.0197 Variety Hass   0 0 . . .   Which p-value do I need to check?   Thank you so much StatDave!!     ... View more

I guess because in my case the variable (color) is not ordinal. ... View more

The steps were very helpful in order to simplify a model, thank you so much for your great support! I apologize that I was using other data for this code, sorry for the confusion. Now I have two questions: 1. By reducing the number of levels from 8 to 3 in the dependent variable, am I not loosing information and variation? 2. How do I use the unequalslopes option to test equality assumption? proc logistic data=one; class Harvest Variety/param=glm; model PeelColor=Harvest Variety/link=glogit unequalslopes=??; run;   Thanks a lot!!       ... View more

Thank you so much for your support StatDave.   I tried the new model you suggested:   data one; set one; PeelColor=int(PeelColor); if PeelColor=5 then PeelColor=4; run; proc logistic data=one; class Harvest Variety/param=glm; model PeelColor=Harvest Variety Wks/ link=glogit ; run;   but I get these two warnings (for most of my dep variables):  WARNING: There is possibly a quasi-complete separation of data points. The maximum likelihood estimate may not exist. WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based on the last maximum likelihood iteration. Validity of the model fit is questionable.   I have two concerns with this new model:   1. For the statement PeelColor=int(PeelColor); if PeelColor=5 then PeelColor=4;  This dep var goes from 1 to 6, so Im not sure about this.    2. I understand the need of symplifying the model and thus just include the main factor effects, but what if I really need to know the ffects of the interactions of harvest*variety and variety*weeks on the dep var?   Thank you very much!   ... View more

Thank you very much for your feedback Ksharp! I tried StatDave approach.    Thanks Caroline ... View more

Thank you so much for your great support StateDave! I liked your suggestion, just wanted to ask you why do you consider the variable Weeks as continuous? I would consider it categorical (ordinal) since it can only take values of 1, 3, or 6 weeks (number of weeks that the fruits were storaged in a coldroom).   I tried the code you suggested and I got results for the var PeelColor, although I got this warning: matrix is singular and thus the convergence is questionable.   I would greatly appreciate your feedback on this.   Thank you! Caroline ... View more

Thank you Paige for your feedback, I think I will follow the suggestion of StateDave.   Thanks Caroline ... View more

Thanks for your quick reply Page. Im using catmod because my data is not normal, and it is nominal (scale from 1 to 6). So if I only have fixed effects should I then use glm instead? Thank you! ... View more

Great, thank you so much for all your support, it has helped me a lot! ... View more

That was very helpful, thank you so much! So for an ANOVA model to check for the interactions between factors I could use catmod or proc logistic as well for ordinal or non-ordinal data. Both would be appropiate right? Thanks! Caroline ... View more

Thank you so much for your great suggestions!   So since I have different types of response variables, the most appropiate procedures will be as follow:   For continuous (measures) I would use proc genmod (normal or gamma dist). For binary (0/1 or 0/100) I would use proc logistic. For multinomial  (scales; 0, 1, 1.5, 2, 2.5, 3) I would use proc logistic.  For ordinal (fruit browning; 0=no browning, 1=mild, 2=severe) I would use proc catmod (is this procedure only for ordinal data?).   Does it sounds reasonably to you? Also, how can I specify in proc logistic if my response var is binary or multinomial?   Thank you very much! Caroline ... View more

Ok, thank you so much for your reply! What if my dependent var is an ordinal scale that goes from 0 to 5 but where I also have half values of 1.5 for example? Can I still use the multinomial distribution?   Thank you!   ... View more

Thank you Ksharp! So do think the gamma distribution will be more appropiate for values of 1 to 5  with half values like 1.5?   Thank you! Caroline ... View more