I am doing a logistic regression analysis with random intercept in the model to account for within cluster correlation.There are 3 levels in my data: Level 1: individual subject Level 2: Family (some subjects from the same family), the variable is fam_num Level 3: Village, the variable clu_num   The model included country, age, gender, education and marrital status.   My codes are:   proc glimmix data=work0 NOCLPRINT; class  country (ref='Pakistan') clu_num  fam_num age4g(ref='40~49') gender edu2g mar2g ; model comb2g(ref='0')= country age4g gender edu2g mar2g /solution Link=logit dist=binary random int /sub=clu_num; random int /sub=fam_num (clu_num) ; COVTEST GLM; run;   I used 'COVTEST GLM' to check if the outcome is independent or not within clusters. The results I got are as follow: The P value is 0.4115, suggesting the clustering effects are not statistically significant. In such case, can I remove the random effect from model, and use standard logistic regression? Thanks   Covariance Parameter Estimates Cov Parm Subject Estimate Standard Error Intercept clu_num 0.01724 0.02710 Intercept fam_n(clu_nu) 0.01920 0.1512   Tests of Covariance Parameters Based on the Residual Pseudo-Likelihood Label DF -2 Res Log P-Like ChiSq Pr > ChiSq Note Independence 2 10507 0.58 0.4115 MI ... View more