Understanding Society User Support: Issueshttps://iserredex.essex.ac.uk/support/https://iserredex.essex.ac.uk/support/support/favicon.ico?15995719382021-12-02T21:36:26ZUnderstanding Society User Support
Redmine Understanding Society User Support - Support #1614 (Resolved): jbstat Wave 11https://iserredex.essex.ac.uk/support/issues/16142021-12-02T21:36:26ZTheocharis Kromydas
<p>Hi there</p>
<p>Jbstat variable in Wave 11 has two additional values (12 and 13) that are not label and also they don't exist as labels in the relevant pdf questionnaire materials. Could you please advice?</p> Understanding Society User Support - Support #1575 (Resolved): _jk mainstage survey and COVID-19 ...https://iserredex.essex.ac.uk/support/issues/15752021-08-19T11:24:50ZTheocharis Kromydas
<p>Hi there.</p>
<p>I am conducting a study where using GHQ-12 variable as my outcome variable. I am using the mainstage survey (_jk - Waves 10/11) for pre-pandemic period and six COVID-19 surveys (April, May, June, July, September and November) for during pandemic period. However, I have allocated nineteen individuals who appear to have responded in Wave 10 or 11 questions in April and May 2020 and this means that they might have responded for pre-pandemic period after they responded for the during pandemic period. Looking at the surveystart and surveyend variables it seems that there are cases where respondents from Wave 10/11 actually responded after they have responded to COVID-19 surveys. Could you please let me know if indeed there are cases and individuals who are in the state I described above?</p>
<p>On a rather different mode, there is a variable called "scac" with the description self-completion accepted and I am quite unsure what this variable refers for. I noticed that individuals who appear as inapplicable in the scac variable they also have valid data on the scghq1_dv variable. Is this possible?</p>
<p>Many thanks,<br />Harry</p> Understanding Society User Support - Support #1514 (Resolved): Covid Longitudinal weightshttps://iserredex.essex.ac.uk/support/issues/15142021-02-23T12:09:58ZTheocharis Kromydas
<p>Hi</p>
<p>I recently realised that Understanding Society weighting team has now included a longitudinal weight (explained on page 38 on the Guide 6.0 document) in the Covid datasets. So, each individual is assigned with a different longitudinal weight value in each Wave. I want to run some analysis that includes Waves 9 to up to November Covid Wave. So, I guess we would need to find the longitudinal weight value for the September Covid wave and assign the same value backwards until Wave 9, given that an individual has participated to all pre and post Waves.</p>
<p>However, because we are not getting the power needed to do some stratification by sex or specific age groups I was trying to find a way adding more individuals and observations, including individuals that stopped participating before the November Covid Wave. So I came up with a method <del>I am not really sure is legit</del>, using again a similar backward assignment using the weight value that corresponds to the last Wave they participated in. So if someone participated in Waves 9 to 13, then that individual will still be included in the analysis sample (even though they did not participate in Waves 14 & 15). In this case, the weight assigned to all previous Waves will be the Wave 13 relevant value. So in a nutshell, I would be including all individuals that have participated in pre and post waves, given that they have participated to all pre (i.e. waves 9 & 10-11) and at least one of the post Covid Waves.</p>
<p>As far as I can understand from the guide document, Covid Wave 3 weight, for example, is conditional on Covid-wave 1 and Covid-wave 2 and Wave 9 responses. But if I am right an individual is weighted based on previous weights, so at Covid-Wave 2 the weight is conditional on Covid-Wave 1 weight and Wave 9 responses, so if that individual stopped participating in Covid Wave 2 (May) s/he would be accounted for at Covid Wave 3 longitudinal weights, but not at Covid Wave 4 weights as they are conditional on Covid waves, 1,2 and 3 plus Wave 9 responses. So the probability an individual drops out at Wave 2 is included in Covid Wave 3 weights but not in Covid Wave 4 weights. So, it seems to me that the weighting model compares those with full responses, say at Wave 5 as compared to those who have just missed Wave 5 but had full responses up until Wave 4. So those who stopped at Wave 3 are included only in Wave 4 but not in Wave 5. That is why, I think, those who have missed one wave and then return back in the next one have zero weights. Based on my strategy explained above I am including those individuals but treat them as not returning two Waves after and assign them with their weight value at the last Wave they have participated in a consecutive way-before they drop out and come back again two waves after.</p>
<p>I hope this makes some sense. I am very looking forward hearing your thoughts on this.</p>
<p>Best,<br />Harry</p> Understanding Society User Support - Support #1473 (Resolved): keyworksector variable missing fro...https://iserredex.essex.ac.uk/support/issues/14732021-01-05T16:35:09ZTheocharis Kromydas
<p>Hi there. I think the variable keyworksector is missing from the "8644_ukhls_covid19_sept20_questionnaire_v2.0" pdf document.</p> Understanding Society User Support - Support #1427 (Resolved): i_hidp and hidp missing from jk da...https://iserredex.essex.ac.uk/support/issues/14272020-10-14T17:25:22ZTheocharis Kromydas
<p>Hi there</p>
<p>I realised that there are only the j_hidp k_hidp and jk_hidp variables available in the new "mainstage_data_2019" release (jk_indresp_cv & jk_hhresp_cv). Does this mean that these are new households that cannot be linked back to previous waves using the original hidp or i_hidp (Wave 9) variables?</p>
<p>Many thanks<br />Harry</p> Understanding Society User Support - Support #1408 (Resolved): Family structure https://iserredex.essex.ac.uk/support/issues/14082020-09-11T16:31:07ZTheocharis Kromydas
<p>Hi there</p>
<p>I am trying to construct a family structure variable composed by 4 categories: Single, no children, Coupled no children, Coupled with children and lone parents using the hhtype_dv, nkids_dv marstat and nonepar_dv variables but I think there is some error with some of these variables. When I cross tabulate hhtype_ with nkids_dv I am getting some conflicting results - please see spreadsheet attached. Just to mention here that I have already pooled all 9 waves together and also integrate data from the household to the individual level dataset. Could you please help me with this?</p>