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Support #2325

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Joining and weighting youth, adults and households

Added by Akansha Naraindas about 12 hours ago. Updated about 6 hours ago.

Status:
In Progress
Priority:
Normal
Assignee:
Understanding Society User Support Team
Category:
Weights
Start date:
01/28/2026
% Done:

20%

Description

Hi,

I have a somewhat complex merge and would appreciate some guidance on weighting. My analysis examines how household-level food insecurity at Wave 13 relates to dieting behaviour and appearance concerns among youth at Wave 15. I am also including parent-level covariates from the Wave 13 individual questionnaire and youth-level covariates from the Wave 13 youth data.

Given that the outcomes are measured at the youth level in Wave 15, am I correct in assuming that the appropriate weights would be the Wave 15 youth longitudinal weights? I am not using data from Wave 14.

Additionally, should the weights be applied after merging all relevant datasets into a single analysis file, or should they be applied prior to merging?

Many thanks for your help,

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 #1

Updated by Akansha Naraindas about 10 hours ago

i now realise there arent any youth longitudinal weights, i do not think id have the capabilities to create my own weight given my time constraints. Could you please suggest another alternative? would i be able to use the cross sectional youth weight? or perhaps the household longitudinal weight as that is the exposure

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 #3

Updated by Akansha Naraindas about 6 hours ago

Thanks very much for this. I looked through the previous queries and it seems that when a custom weight is not created, the longitudinal enumeration weight (psnenus_lw) is usually recommended. Would this be suitable for analyses focused on youth outcomes? I understand that it adjusts for attrition across waves, but I am unsure whether it is designed specifically for youth samples? in practice, do researchers typically use this weight for youth-focused analyses?

Attrition is something i want to account for issue in this study, and I believe psnenus_lw accounts for general dropout over time. However, I am not sure whether it captures the structural missingness in the youth files as participants age out of eligibility?

Finally, if psnenus_lw is used, should it be applied to the final merged dataset after linking the household, adult, and youth files?

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