MODEL geno age_group sex geno*age_group geno*sex age_group*sex geno*age_group*sexĬode.

PLAN FILE = '/Users/Kathleen/Desktop/Survey Plan.csaplan' PLAN FILE='/Users/Kathleen/Desktop/Survey Plan.csaplan' Svy: glm id ib3.geno#ib3.age_group#ib1.sex Replace geno = 0 if weight >= 100 & weight = 150 & weight = 200 Replace age_group = 1 if height >= 350 & height = 400 & height = 450 This same issue occurs with my real dataset as well. The degrees of freedom are correct, but I cannot figure out how to get the t and corresponding p values to match the SPSS output. I can get some of the testparm commands to replicate the SPSS output, but others are way off. For instance, if the genotype x age group interaction had a significant p value in the 'Test of Model Effects,' then I proceeded to do the polynomial contrasts. To follow up, I was using this overall Wald F to determine whether or not I should perform follow-up contrasts on each interaction. Is there a way to get similar output from glm in Stata? This included a Wald F test for the overall interactions (followed by the GLM output that tested the interactions at each level of the factors). When running this model in SPSS, the (CSGLM) output included a 'Test of Model Effects' section first (see attached photo although this photo has different variable names). I am interested in testing polynomial contrasts to examine genotype x age group effects: contrast effects Here is my model: svy:glm cogScore ib2.geno#ib3.age_group#ib2.sex However, is there anything analogous to the 'test of model effects' that can be obtained in Stata glm output?

I am able to get the same output for my model in both programs.

Unianova spss code