Hi, I have a model I am working with to estimate 6 grade test scores from a sample of students who were in a school district from K-6. I am using both Stata and MLWIN to compare results. The ICC from the random intercept model is around .15, so there is school level variance to explain. I have school size, school free/reduced rate, school mobility and student teacher ratio as school level variables. When I add these my school level variance goes away and the residual plot of the school residuals shows no difference in school means. Very nice. Now when I add student level variables, such as school choice decision (neighborhood school/magnet school), gender, stayed in same school all 7 years, students free reduced status and ethnicity, the coefficients on my Level 2 variables are all insignificant now, my level 2 variance is still low, and I get significant coefficients on the level 1 variables. Should I just drop the level 2 variables now since they are really composites of the level one variables, for the most part? Cross level interactions don't seem to help. What is a good explanation of why the coefficients are changing on the level 2 variables (other than lack of independence between levels!?) Thanks so much