https://iserredex.essex.ac.uk/support/https://iserredex.essex.ac.uk/support/support/favicon.ico?15995719382017-01-13T13:35:24ZUnderstanding Society User SupportUnderstanding Society User Support - Support #700: longitudinal weights for small sub-sampleshttps://iserredex.essex.ac.uk/support/issues/700?journal_id=23532017-01-13T13:35:24ZDavid Bartramd.bartram@le.ac.uk
<ul></ul><p>Correction -- the mean of f_indinus_lw for the sub-sample is 1.0347, not 0.7254. (I had been including the zero values in the calculation of the mean -- ugh...) Using only those in the sub-sample who have non-zero values, the mean is 1.0347.</p> Understanding Society User Support - Support #700: longitudinal weights for small sub-sampleshttps://iserredex.essex.ac.uk/support/issues/700?journal_id=23542017-01-16T09:00:24ZVictoria Nolanvlnolan@essex.ac.uk
<ul><li><strong>Category</strong> set to <i>Weights</i></li><li><strong>Status</strong> changed from <i>New</i> to <i>In Progress</i></li><li><strong>Assignee</strong> set to <i>David Bartram</i></li><li><strong>% Done</strong> changed from <i>0</i> to <i>10</i></li><li><strong>Private</strong> changed from <i>Yes</i> to <i>No</i></li></ul><p>Dear David,</p>
<p>Many thanks for your query. I have passed this on to our weighting team who will look into it for you.</p>
<p>Best wishes, Victoria.</p>
<p>On behalf of the Understanding Society User Support Team</p> Understanding Society User Support - Support #700: longitudinal weights for small sub-sampleshttps://iserredex.essex.ac.uk/support/issues/700?journal_id=23752017-01-19T14:35:19ZPeter Lynnplynn@essex.ac.uk
<ul></ul><p>Hello David.</p>
<p>To my mind the best solution would be for you to use a_indinus_xw as a base weight and then multiply it by an adjustment factor for loss from the sample by w6. You would have to calculate this factor by fitting a model (e.g. logit) based on all wave 1 respondents in your subgroup of interest, in which the dep var is a 0/1 indicator of whether they also responded at w6 (and removing from the base any known to have died or emigrated before w6). Predictor variables can be anything relevant observed at w1. This will give you a predicted probability for every w1 respondent of responding at w6. Call this P. You then need to adjust a_indinus_xw by multiplying it by 1/P for all the cases that can be included in your analysis.</p>
<p>An amended version of your a) might be second-best option. Instead of just taking the mean, take the mean within groups defined by relevant (to your analysis) variables. It sounds like you have 800+ cases with a non-zero weight (and 100 or so with zero?) so you have a big enough sample to divide into a good number of groups (10 to 20?)</p>
<p>Do not use approach b), as that would be very distorting due to the inclusion of the new boost sample in the "ui" weights, but not in your analysis (i.e. all ethnic minorities and immigrants will be greatly weighted down - much more than they should be).</p>
<p>And I wouldn't recommend c) either, as I would doubt that 5 years of attrition is ignorable.</p>
<p>Peter</p> Understanding Society User Support - Support #700: longitudinal weights for small sub-sampleshttps://iserredex.essex.ac.uk/support/issues/700?journal_id=23762017-01-19T14:44:10ZVictoria Nolanvlnolan@essex.ac.uk
<ul><li><strong>Status</strong> changed from <i>In Progress</i> to <i>Feedback</i></li><li><strong>% Done</strong> changed from <i>10</i> to <i>80</i></li></ul> Understanding Society User Support - Support #700: longitudinal weights for small sub-sampleshttps://iserredex.essex.ac.uk/support/issues/700?journal_id=23852017-01-23T12:45:07ZDavid Bartramd.bartram@le.ac.uk
<ul></ul><p>Thank you Peter -- that's a very helpful response.</p> Understanding Society User Support - Support #700: longitudinal weights for small sub-sampleshttps://iserredex.essex.ac.uk/support/issues/700?journal_id=24052017-01-30T11:51:05ZVictoria Nolanvlnolan@essex.ac.uk
<ul><li><strong>Status</strong> changed from <i>Feedback</i> to <i>Closed</i></li><li><strong>% Done</strong> changed from <i>80</i> to <i>100</i></li></ul>