Understanding Society User Support

Hi Understanding Society team,
I am conducting a basic analysis of health outcomes across combinations of ethnic groups and age groups using the Wave 10 data. My issue is that when I produce things like cross-tabulations, once I apply weights, and break down the sample by both broad ethnic group and age group, I end up with a small N in many cells (ie: < 10) and in some cases 0 counts for a cell and so I am ending up with very large confidence intervals. Having read the general User Guide and the Ethnicity and Immigration User Guide, my understanding is that one of the reasons the ethnic minority boost samples were included was to try to tackle this issue of small sample sizes in marginalised subgroups, so I wanted to check that I am not missing something. I think my main issue is that once I apply a weight (such as j_indinui_xw), the N for the ethnic minorities is being scaled down in order account for the oversampling of these groups, and since the weighted N (rather than the unweighted N) is being used to calculate confidence intervals by R, I end up with very large confidence intervals.

As a quick illustration: I'm using the j_indresp.dta file and have recoded the j_ethn_dv variable into Asian, Black, Other, White. In the Black category, there are 1314 respondents, but when I apply the weight j_indinui_xw and tabulate by ethnicity using the svytable function in R, this is scaled down to only 458.3841. If I then break this down by 10-year age group and my health outcome variable, I end up with very small Ns (or zeros) which means when using a function such as svyciprop in R, I get very large confidence intervals.

This may simply be an unavoidable problem, but given that Understanding Society has put lots of effort into including these extra ethnic minority samples, I wanted to make sure I was making best use of them. And just to double check - I can simply use the normal indresp file in order to draw on this ethnic minority boost sample?

Many thanks for your help and the amazing resources you provide!

Best wishes,
Laurence Rowley-Abel