Understanding Society User Support

Dear Understanding Society team,

I would like to check the effect of the self-completion weights for my particular analysis (estimating prevalence and correlates of loneliness in the last year of life), as when applied they increase rather than decrease the weighted count from my original sample (this query is separate to, but related to query number 2154). For background I have pooled data from waves 9-13 for people who were identified as having died and had a valid interview in the wave before. I have pooled the weights without scaling as previously advised. To identify my sample, I exclude people who had missing loneliness questions and covariates. The weights work as expected in the pooled data until I applied them to my final selected sample. After removing anyone with missing loneliness and covariates, the sample size is 743 people but the weighted count is 879. People who died but were missing loneliness and covariates had a lower mean weight:
240 people with missing covariates, mean weight 0.4042998 (sd 0.605)
743 people with full data, mean weigh 1.182772 (sd 0.737).

One reason I can think for this unusual effect is that my sample of people who died may have a high proportion of characteristics common to non-responders, so they are upweighted but I am nervous to apply the weight without advice.

A further difficulty is that the questions on loneliness weren’t included in the first 6 months of wave 12 due to COVID, so for wave 12 the sample excludes people interviewed in those months. In addition to the weight reducing the sample for NI (because NI sample only collected in the first half of each wave), I'm not sure if this affects the appropriateness of using weights in other ways e.g. for pooled analysis, the FAQs indicate an even number of 24 month samples should be pooled.

I'd be very grateful for advice on all the above,
Thanks,
Emma