Thank you, thank you, for this helpful response! I do have a follow-up question for clarification, and my apologies if this reveals some misunderstanding on my part as I'm still learning about mixed models. When I IGNORE the twin type and just include a random intercept reflecting that each person in the study is part of a "family cluster" (as below): proc mixed method=ml covtest noclprint; class FAMILYID; model DEPENDENT = INDEPENDENT/solution; random intercept/sub=FAMILYID type=un gcorr; run; I get a covariance parameter estimate for the "FAMILYID" as well as a residual term. My understanding is that this decomposes the variability into "within-cluster" variability (residual estimate) and "between-cluster" variability (FAMILYID estimate). I get the intraclass correlation coefficient between my family clusters by computing: (FAMILYID estimate) divided by (FAMILYID estimate + residual estimate). When I use the syntax you suggested proc mixed method=ml covtest noclprint;<br> class FAMILYID; <br> model DEPENDENT = INDEPENDENT/solution;<br> random intercept/sub=FAMILYID group=TWINTYPE type=un gcorr;<br> run; and look at my covariance parameters, I get one FAMILYID estimate for fraternal twins, one FAMILYID estimate for identical twins, and one residual term. So -- this means that I have told my model to make different "between-cluster" estimates for identical and fraternal twins -- but isn't it still disregarding twin type when it calculates the residual term? And if the residual term reflects the "within-cluster" variability, isn't it a problem that it doesn't take into consideration that there is likely to be LESS variability within an identical twin pair on the outcome variable and MORE variability within a fraternal twin pair? So I guess my follow-up question is: is there syntax that would leave me with a between-cluster estimate for identical twins and fraternal twins, and then a residual term for identical twins and fraternal twins? OR, am I confused as to why this would be needed? Additional help/clarification much appreciated. THANKS!
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