Understanding Society User Support

Isabelle,

My understanding is that while your model allows for missingness, it does not correct for it. Is this right?

If the model corrects for missingness / or makes sensible assumptions about it, with which you are happy, you don't need to worry about nonresponse part due to attrition, and can start with wave 1 weight (or wave 2 weight for bigger sample size). The attrition correction will then be assumed to be accounted for by the model itself. No further adjustment would be needed.

If the model can't correct for attrition, you will have to do it yourself. My suggestion would be to use a tailored weight, and correct for attrition from wave 1 (or wave 2 for larger sample size) where 'response' will be all people who are included in your model. Did you look into our course already?: https://www.understandingsociety.ac.uk/help/training/creating-tailored-weights/

Hope this helps,
Olena

Hi Olena!

To my understanding, the multilevel model can handle missingness under the assumption that its missing at random (MAR). Thank you for your input. Just to clarify, my study period begins at Wave 6. You wrote that I should correct for attrition from Wave 1 or Wave 2, is this correct even when my study period starts at Wave 6 and ends at Wave 13? In which case, I should choose a cross-sectional weight as my start weight and adjust for mortality and emigration, correct?

Second, when you write that my "response variable" will be everyone included in my model, would that entail everyone in my final estimation sample? Meaning that the response variable would take the value of 1 if my sample equals 1 and zero otherwise?

I have looked at the course material, and it seems rather straight-forward how one corrects for attrition and non-response between specific waves, using inverse probabities. For instance, having the response variable being equal to 1 if respondents have zero weights at the start of the observation and have given complete interviews at the waves of interest (e.g., a, d, and g). In my case, the definition of the response variable becomes more tricky. I could have a response variable being equal to 1 if respondents have zero weights in wave 6 and have given full interviews at wave 7,8,9,10,11,12 and 13. But I do not see how this weight would be any different from using the existing longitudinal weight for wave 13, m_indinui_lw.

Aside attrition, if I want to correct for unequal selection probabilities, can I use the UKHLS design weight psnenus_xd?

Isabelle,

I suggest you start with wave 6 weight as it includes IEMB sample that has many immigrants and model from there. Starting with design weights would not be advisable unless you limit your analysis to GPS+EMB sample. You can then simply follow our example in creating your own tailored weight. Please always use longitudinal weight as your base weight. Cross-sectional weights are not suitable for this.
For response variable everyone if you estimation model will have a value of 1 (respondent) and 0 otherwise. My understanding is that you model does not require response in each wave, only some, so you should have larger sample with your tailored weight.

Hope this helps,
Olena