Understanding Society User Support

Okay, thank you so much.

As a follow-up, if I xtset the data with pidp and wave as my time variable and add dummies for intdatey (2016, 2017, 2018, 2019), would that be incorrect?

Further, for the purpose of graphing, instead of creating calendar year based cross sections and then computing real wage averages, would the same result be achieved simply by the following:-

by intdatey, sort: summarize rminc [where rminc is real monthly household income] and then taking the reported means?

Hello Vikram,

Our remit is to answer questions related to the survey & its details and the datasets (incl data management). Due to staff time constraints we are not able to answer questions related to analysis and syntax (unless related to data management or weighting).

There are multiple ways of creating means in Stata, the method you have suggested will work but you have not specified the weights or the sample design. You can do that by using svyset:
svyset psu [pw=weight variable], strata(strata)
svy: mean rminc, over(year variable)

Best wishes,
Understanding Society User Support Team

Vikram,

Just to add, if you use a calendar year you will need to use three weights (see below an extract from FAW that explains this).

You can of course use wave as your time variable but I wouldn't include intdatey in your model. This variable is a processing variable and is related to the likelihood of response - I am not sure this has any meaning for your substantive model.

from FAQ, question 11

Let’s say you are interested in studying December 2014. Your optimal option with the
largest sample size will be to combine all interviews carried out in December of 2014
from the following samples:
- Wave 5 sample months 21, 22, 23 and 24
- Wave 6 sample months 9, 10, 11 and 12
- Create a new variable that equals e_xxxxxus_zz weight for the wave 5
interviews and f_xxxxxus_zz weight for wave 6. No Northern Ireland
adjustment is needed. No extra nonresponse adjustment is needed as late
respondents in the month 24 sample are compensated for by bringing in the late
respondents from previous sample months. But you will need a scaling factor
(see Q12).
- Use psu and strata variables from xwave.dat to take into account clustering and
stratification.
Note if you want to study January 2014 for example, the information will come from 3
waves, because to compensate for missing of late respondents from wave 5, sample
month 1, you will need to include January respondents from wave 4, sample months 22-
24. The rest will follow the above example.
If you use respondents from calendar months / year just from one wave you will need an
extra adjustment for Northern Ireland and potentially also for late respondents (if your
period of interest includes sample months 1, 2 or 3).

Dear Olena,

Thank you so much for your response!

I apologize for asking something that did not fit with the remit of this user forum but thanks so much again for your response

If I may have just one more follow-up when I perform svy: mean (rminc), over(wave), STATA gives the output along with a message stating 'missing standard errors because of stratum with single sampling unit'.

Is there any way to address this?

Best,
Vikram