Hi.
I use growth model for examining the relationship between discrimination and mental health among older adults.
In my model, I used centering age(grand mean centering). However, I have an issue about centering age.
Here are my code.
I think upper code is very common code for centering age. But this r(mean) refers the grand mean of the data set.
So I think I have to use the mean of sample included my model.
For example,
However, if I use the sample mean as an grand mean, the coefficient and significance is differed.
So my question is...
Can I use the grand mean of raw data set? or Should I use the grand mean of sample (only included model)?
If you have an idea about solution, Please share your thoughts.
Thank you for reading my text!
Best,
Gayoung
I use growth model for examining the relationship between discrimination and mental health among older adults.
In my model, I used centering age(grand mean centering). However, I have an issue about centering age.
Here are my code.
Code:
*age centered summarize age, meanonly gen c_age = age - r(mean)
So I think I have to use the mean of sample included my model.
For example,
Code:
mixed cesd ($indi $health i.infm_v2 i.earlyret##i.fm_v4)##c.c_age || pid: c_age, cov(un) gen sample==e(sample) sum age if sample ==1 *average mean of age in sample is 65 gen c_age = age - 65
However, if I use the sample mean as an grand mean, the coefficient and significance is differed.
So my question is...
Can I use the grand mean of raw data set? or Should I use the grand mean of sample (only included model)?
If you have an idea about solution, Please share your thoughts.
Thank you for reading my text!
Best,
Gayoung
Comment