Hi there,

I wish to run a nested regression to see the relationship between IVs (deprivation, gmfcs, age and sex) on total score (DV). When researching I found two syntax options:
1) nestreg, lr: regress totalR (i.deprivationquintile) (i.gmfcs) (i.epilepsy) (i.AGEgroup) (i.sex)
2) asdoc reg totalR i.deprivationquintile i.gmfcs i.epilepsy i.sex i.AGEgroup, nested

Do both of these options allow me to test the associations between the outcome (totalR) and deprivation first? Then the outcome and gmfcs while controlling for deprivation? Then the outcome and epilepsy while controlling for deprivation and gmfcs? and so on? Or might the first option be most appropriate?

Also, in the above examples might it be best to look at the standardised coefficients when reporting the regression results?

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input int totalR byte(deprivationquintile gmfcs) float epilepsy byte(sex AGEgroup)
 59 . 1 1 1 3
 57 3 1 0 1 4
 75 4 2 1 1 2
 72 4 1 0 1 1
 64 4 2 0 2 3
 85 1 2 1 1 2
 61 . . 1 2 1
 76 5 . 0 1 1
 75 4 5 0 1 4
102 3 4 1 2 3
 61 5 4 1 1 2
 54 1 1 0 2 2
 52 1 5 0 1 1
 60 4 4 0 1 1
 90 3 2 1 2 1
 68 3 4 0 1 2
 51 4 1 1 1 1
 71 4 5 0 2 2
 60 4 4 0 1 2
 73 5 1 0 1 1
 65 1 5 0 1 2
 85 4 4 0 1 2
 71 5 5 1 1 3
 73 1 4 1 2 2
 59 2 2 1 2 3
 72 2 5 0 1 4
 57 4 2 0 2 4
 53 5 1 0 2 2
 77 4 2 0 1 1
 85 3 1 1 2 1
 60 . 1 0 1 1
 61 4 5 0 2 1
 91 1 4 0 1 2
 64 5 4 0 2 1
 62 1 2 1 2 4
 57 1 3 0 1 4
 79 4 1 0 1 4
 71 1 4 1 1 3
 65 2 4 1 1 3
 58 2 4 0 1 2
 83 5 4 1 2 2
 92 3 4 1 1 1
 55 4 1 1 2 1
 61 5 1 0 1 1
 79 5 4 0 1 1
 88 1 1 0 1 3
 57 2 3 0 2 3
 61 3 2 1 1 4
 77 . 3 0 1 1
 87 2 4 0 2 1
 83 4 4 1 1 2
 63 2 4 0 1 1
 68 5 1 0 2 4
 63 3 1 1 1 2
 87 1 4 1 1 2
 52 5 5 0 2 1
 68 2 1 1 1 1
 75 5 4 1 1 3
 51 1 2 0 1 1
 68 5 3 0 1 1
 76 4 4 0 1 3
 80 1 5 0 2 1
 93 2 2 1 1 1
 83 4 2 0 2 4
 59 3 1 1 1 4
 64 3 4 0 1 1
 71 3 1 0 1 4
 55 5 2 1 2 3
 90 2 4 1 1 1
 53 1 1 1 2 3
 70 3 4 0 1 1
 56 4 1 1 1 1
 84 4 5 0 2 1
 66 3 5 0 1 2
 73 2 4 0 2 3
 78 1 1 0 2 1
 70 . 4 0 2 2
 56 5 4 0 2 2
 60 1 4 0 2 1
 65 4 4 1 1 1
 79 4 4 0 1 1
 78 3 4 0 1 2
 33 1 5 0 1 3
 29 5 5 0 2 3
 43 3 2 0 1 1
 50 4 5 1 2 4
 34 5 . 0 2 1
 47 4 3 0 2 1
 50 . 4 1 2 1
 45 4 3 0 2 4
 36 . 5 1 2 1
 49 2 1 1 1 4
 45 1 1 1 1 2
 42 4 5 0 1 3
 41 5 4 0 1 4
 47 1 3 0 1 1
 39 4 2 0 1 1
 44 1 4 0 1 1
 43 4 4 0 1 2
 47 4 3 1 1 1
end
Many thanks,
Mary-Elaine.