Hi,

I need to clean the variable sp016_* which presents 4 alternatives: "less often", "monthly", "weekly", "daily".
I need to do an OLS and Fixed Effects regression on the data. I want a balanced dataset (because unbalanced is too complex), therefore I selected the individuals with duplicate IDs and who have responded to all the variables.
In this case, is a multiple response where each individual replies regarding the amount of care they give to each grandchildren. So if the max grandchildren is 4, then the other 6 cases are not going to be selected, and hence not missing (just not selected because there weren't more grandchildren).
Even though there are about 20 000 missings in Child 1, 25 000 missings in child 2, 30 000 missings in child 3 and so forth until some variables with 100% missings.

If I drop all missings from all variables I am left with about 200 people.

How would you clean this? Or what command would you use to get a dummy variable for :
1/0 less often , 1/0 monthly, 1/0 weekly, 1/0 daily and at the same time taking the missings into account.

I have tried mrtab, but that generates frequencies. I need individuals, not frequencies.
I have tried "anymatch" but I am not sure if it exactly the command I need. The dummy doesn't seem correct.
This is the third time I ask something like this, and I don't know who else to ask.

This is my data set

Code:
clear input byte(sp016_1 sp016_2 sp016_3 sp016_4 sp016_5 sp016_6 sp016_7 sp016_8 sp016_9 sp016_10) 3 . . . . . . . . . 2 . . . . . . . . . . 1 . . . . . . . . 2 2 . . . . . . . . 4 . . . . . . . . . 2 . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 2 . . . . . . . . . . 2 . . . . . . . . . 2 . . . . . . . . 2 . . . . . . . . . . . . 4 . . . . . . 1 . . . . . . . . . . . . . 3 . . . . . . 2 . . . . . . . . . 4 . . . . . . . . 1 . . . . . . . . . 2 . . . . . . . . . 4 4 . . . . . . . . 2 . . . . . . . . . 4 . . . . . . . . . 1 . . . . . . . . . 3 . . . . . . . . . 2 . . . . . . . . . 1 1 . . . . . . . . . . 2 . . . . . . . 2 2 . . . . . . . . 2 . . . . . . . . . 2 . . . . . . . . . 3 3 . . . . . . . . 2 . . . . . . . . . 1 3 . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . . . 1 . . . . . . . 2 . . . . . . . . . 1 . . . . . . . . . 1 . . . . . . . . . 4 . . . . . . . . . . 3 . . . . . . . . . 1 . . . . . . . . . . 4 . . . . . . . . 1 . . . . . . . . 4 . . . . . . . . . 3 . 2 . 2 . . . . . 1 1 . . . . . . . . . 4 . . . . . . . . . 2 2 . . . . . . . 1 . 1 . . . . . . . 3 . . . . . . . . . 2 . . . . . . . . . . 1 . . . . . . . . 4 . . . . . . . . . 4 . . . . . . . . . . 4 . . . . . . . . 2 . . . . . . . . . 1 . . . . . . . . . 3 3 . . . . . . . . . 2 . . . . . . . . 4 . . . . . . . . . 1 . 1 . . . . . . . 2 . . . . . . . . . . . 3 . . . . . . . 1 . . . . . . . . . . . 4 . . . . . . . 1 . . . . . . . . . . . 4 . . . . . . . . 2 . . . . . . . . 1 1 . . . . . . . . 2 . . . . . . . . . 1 . . . . . . . . . 2 . . . . . . . . . 2 4 . . . . . . . . . . 2 . . . . . . . 4 4 4 4 . . . . . . 4 . . . . . . . . . 2 . . . . . . . . . 4 . . . . . . . . . 2 2 . . . . . . . . 2 . . . . . . . . . 2 2 . . . . . . . . 4 2 . . . . . . . . . 4 . . . . . . . . . 4 . . . . . . . . 3 . . . . . . . . . 3 3 . . . . . . . . . 2 . . . . . . . . 1 1 . . . . . . . . 2 . . . . . . . . . 2 2 . . . . . . . . 2 . . . . . . . . . 2 . . . . . . . . . 4 . . . . . . . . . . . 2 . . . . . . . 3 . . . . . . . . . 4 . . . . . . . . . 2 . . . . . . . . . . 4 . . . . . . . . end label values sp016_1 howoftensp label values sp016_2 howoftensp label values sp016_3 howoftensp label values sp016_4 howoftensp label values sp016_5 howoftensp label values sp016_6 howoftensp label values sp016_7 howoftensp label values sp016_8 howoftensp label values sp016_9 howoftensp label values sp016_10 howoftensp label def howoftensp 1 "About daily", modify label def howoftensp 2 "About every week", modify label def howoftensp 3 "About every month", modify label def howoftensp 4 "Less often", modify
thank