Interpretation of grand mean centered versus original form level-2 variables in multilevel logistic regression

[Deepali godha]

Hello,

I have a random intercept multilevel logistic regression model with cross-level interactions. The final sample size is 14489 with 25 groups (average size of 590) and an ICC of .109. I have used group mean centered level-1 variables and grand mean centered group means. To interpret the impact of a variable at both levels (age here), I see two ways of using the margins command:

1.

margins , pred(pr) at (gm1_c=(-2.073019    -1.264275    -1.089293    -.5425472    -.091465    2.29e-07  .6355743    .9508991  1.047394  1.649328)) over(agegroup) vsquish post nose

Here gm1_c is the group mean for age that has been grand mean centered. Instead of using the group mean centered individual age(level 1), I have opted to use age-group. Marginsplot gives the following graph


2.

margins , pred(pr) at (gm1=(25.67114    26.47989  26.65487  27.20161  27.65269  27.74416  28.37973  28.69506  28.79155  29.39349)) over(v013) vsquish post nose

Here gm1 is the group mean for age. Marginsplot gives the following graph


Looking at them side by side, the difference between the two graphs is probably due to the difference in scale, if I am assuming right. This will mean that both graphs are right.

However, the clusters represented at the left end of graph 1 (where average cluster age is lower than average age across clusters i.e. relatively younger clusters) are the same as those represented at the left end in graph 2 (clusters with low average age i.e. relatively younger clusters). This means that the interpretation changes, though it should not.

I will request your guidance in clarifying this issue. What am I doing wrong?

Thanks
DG

[Deepali godha]

Hello,

I finally decided to use the exact scale used in the model- group mean centered level-1 and grand mean centered level-2 variables in my margins command.

Thanks