Understanding Society User Support

Hello,

I am conducting a comparative study on household conditions in London and the South West, using the calendar year 2022 dataset available via the UK Data Service (Open Access version). I would like to double-check that I have correctly implemented the merging and survey weighting procedures to ensure a representative sample.

My unit of analysis is the individual, but I also need to incorporate household income to so I attempted to merge the indresp and hhresp files in R using the below:

merged_data <- merge(individual_data, household_data, by = "lmn_hidp")

I then constructed the survey design object in R using the survey package as follows:

design <- svydesign(
id = ~lmn_psu, #this is to account for clustering
strata = ~lmn_strata, #stratification
weights = ~lmn_inding2_xw, #the only cross sectional weight I found for the main individual interview
data = mydata,
nest = TRUE
)

I would be grateful if you could confirm:

• Is this the correct approach for merging the files when conducting individual-level analysis that includes household-level variables?

• Is the use of lmn_inding2_xw appropriate for generating representative estimates for calendar year 2022? This weight is only for wave 14 eventhough the calendar year 2022 includes wave 13 and a few respondents from wave 12.

• Can I assume that the results produced using svytable() or svymean() with this design object are representative of the UK population for 2022?

As an example, I am using the following line to get the weighted sample distribution across regions (with regional_breakdown being a recode of lmn_gor_dv):

svytable(~regional_breakdown, design)

I appreciate any feedback you can provide. Thank you in advance!

Understanding Society User Support Team wrote in #note-2:

Hello,

The approach you described sounds correct. About cross-sectional weights - there are 3 _xw waves available in the calendar year 2022 indresp file: lmn_indpxg2_xw, lmn_inding2_xw, lmn_indscg2_xw. When you want to include proxies in the analysis use lmn_indpxg2_xw (other two exclude proxies altogether), if your analysis includes questions that come from the self-completion questionnaire and the main questionnaire use lmn_indscg2_xw. If you're using only questions from the main questionnaire with no self-completion questions use lmn_inding2_xw. In principle, the same rules as described below apply to picking the weights for the calendar year dataset: https://www.understandingsociety.ac.uk/documentation/mainstage/user-guides/main-survey-user-guide/selecting-the-correct-weight-for-your-analysis/.

Best wishes,
Piotr Marzec
UKHLS User Support

Hello Piotr,

Thank you very much for you response. Yes, I am using the main questionnaire so I'll depend on lmn_inding2_xw. Can I now assume, that following this weighting process, the results I obtain from the analysis constitute a representative sample of the UK population for the calendar year 2022?

(I am bit new to working with weights and want to make sure I did not miss anything else)

Hello,

Yes, the results will be representative of the UK population. See the User Guide, section 4.1 (https://doc.ukdataservice.ac.uk/doc/9333/mrdoc/pdf/9333_main_survey_calendar_year_user_guide_2022.pdf)

Best wishes,
Piotr