Dear colleagues, I am working with educational data. For this, I am using the classic three-level hierarchical linear model (student, class and school). I'm using Sta version 17 for the analyses. When I perform the residual analysis, the homoscedasticity and normality standards are not met. Here is the adjusted model: xtmixed en_ex_9mat gen_alun rep_alun comp_alun b4.educ_ee gen_prof b1.idad_prof nro_alun_turm b1.sase_esc b1.reg_esc area_esc||id_esc:||id_turm:,mle var Comments: The dependent variable of pt_ex (Exam Score) despite being continuous, has only discrete values ​​(0 to 100). These values ​​are multiples of 4 because the test applied consisted of 25 questions. Regarding the independent variables, with the exception of the variable nro_alun_turm (Number of students in the class), these are nominal/binary categorical. I thought of using a GLMM, ie a Poisson or Negative Binomial multilevel model, but these have infinite support. or could you try a Gamma, since the two assumptions of the Gaussian model are not met? Another question, how do I correct autocorrelated errors at different levels (in this case, at levels 2 and 3, respectively)? Could you please help with this? Thank you very much in advance, Marcos