Hi Mkeintz,   I'm getting closer. I did not know about the dif function, so that is very helpful! I needed to adapt your code, because I was simplifying my data for the purpose of my question, and I actually have 24 correlations. Here is my code now:   data results_cumcorr; set results; cum_y + y; cum_ysq + y**2; array x x01 - x24; array cum_x cum_x01 - cum_x24; array cum_xsq cum_xsq01 - cum_xsq24; array cum_cov cov_01 - cov_24; array cum_r cumr_01 -cumr_24; array con con_01 - con_24; do over x; cum_x + x; cum_xsq + x**2; cum_cov + y*x; cum_r = ((_n_*cum_cov)-(cum_x*cum_y))/sqrt(((_n_*cum_xsq)-cum_x**2)*((_n_*cum_ysq)-cum_y**2)); if _n_>1 and dif(cum_r)<=.01 then con=1; else con=0; end; run; With this new line of code, I have 24 new variables that show me whether convergence has been met (1) or not (0). I thought about doing a proc freq to figure out when convergence has been met for each variable, but the problem is that it doesn't necessarily converge the first rep for which con=1. For example, in this instance, it appeared that convergence had been met but then the change in correlation went above .01 for rep 203. If I had just asked for it to stop when con=1, then it would not have caught that instance. (And I'd have the same issue with doing a proc freq)   rep con 200 1 201 1 202 1 203 0 204 1   Is there a way for me to ask SAS to output the last rep for which con=0?   Thank you! ... View more

So, you have students nested within schools. X2RTHETK1, X2MTHETK1, and x1ageent are student-level variables. S2NMRETK  is a school-level variable. I presume Retained is binary.   If your clusters (schools) are independent, then there is no need "to allow the cluster-level residuals to be correlated". I think what you might be thinking is to allow nonzero covariances among the random intercept and random slopes for X2RTHETK1_cwc and X2MTHETK1_cwc which would be accomplished by    random intercept X2RTHETK1_cwc X2MTHETK1_cwc/ subject=s2_id type=un; The "Estimated G matrix is not positive definite." message occurs because one or more variances/covariances have been set to zero, which might be due to the (co)variance being very small or to inadequate data support for its estimation or to an estimation method that is less optimal (binary response data can be problematic). You could try various adjustments to the model such as using Laplace estimation; see the papers by Kiernan, Tao, and Gibbs (2012) and Tao, Kiernan and Gibbs (2015) for this and other ideas.   With binary response data, you will not need a random _residual_ statement.   If you have not already done so, you would probably find value in reading in detail about multilevel modeling; there is an extensive list or resources here.   I hope this helps.   ... View more