Understanding Society User Support: Issueshttps://iserredex.essex.ac.uk/support/https://iserredex.essex.ac.uk/support/support/favicon.ico?15995719382024-03-26T16:09:20ZUnderstanding Society User Support
Redmine Support #2077 (Feedback): Using income variables https://iserredex.essex.ac.uk/support/issues/20772024-03-26T16:09:20ZMhairi Webster
<p>Hello,</p>
<p>I am looking to access the data to use the derived income variables (w_fimnnet_dv). Could you let me know under what access it is under on the UK Data Service as those variables don't appear to be available in the dataset I am using (Understanding Society: Waves 1-8, 2009-2017 and Harmonised <br />BHPS: Waves 1-18, 1991-2009. 11th Edition. UK Data Service. SN: 6614, <a class="external" href="http://doi.org/10.5255/UKDA-SN6614-12">http://doi.org/10.5255/UKDA-SN6614-12</a>).</p>
<p>Many thanks, <br />Mhairi Webster</p> Support #2076 (Feedback): Issues with xx_hadcvvac variables in COVID-19 data collectionhttps://iserredex.essex.ac.uk/support/issues/20762024-03-13T21:01:15ZLaura L
<p>Good evening,</p>
<p>I am currently analysing data from the <em>xx_indresp_w</em> datasets of the COVID-19 data collection, specifically from wave 9 (ci), wave 8 (ch) and wave 7 (cg). From the documentation, the questions <em>xx_hadcvvac</em> (about having received the COVID-19 vaccine in each survey wave) should be asked to respondents that have not already answered that they received 1 or 2 doses of vaccines in previous months (answer codes 1 and 2). However, by cross-tabulating the answers to the <em>xx_hadcvvac</em> questions for wave 7 and 9 for respondents present in wave 9 and 7 (left-joining the datasets by respondent ID <em>pidp</em>, i.e. matching all respondents in wave 9 with those that were also in wave 7):</p>
<p>table(ci_hadcvvac = wave_9$ci_hadcvvac, cg_hadcvvac = wave_9$cg_hadcvvac)</p>
<p>with <em>wave_9</em> the left-joined dataset, I obtain the following table:</p>
<pre><code>cg_hadcvvac<br />ci_hadcvvac -9 -8 -2 1 2 3 4<br /> -8 0 10 0 133 9 492 4835<br /> -2 2 0 2 0 0 0 4<br /> 1 0 0 0 4 1 1 133<br /> 2 0 3 1 <strong>1663 116</strong> 36 2538<br /> 3 0 0 0 0 0 1 5<br /> 4 0 0 0 2 0 3 322</code></pre>
<p>As you can see from the numbers in bold (took as examples), there are some respondents vaccinated in wave 7 that appear to be asked the question again in wave 9. Am I missing some information?</p>
<p>Thank you very much in advance for the support.</p>
<p>Best regards, <br />Laura</p> Support #2075 (Feedback): Using UKHLS to look at trends across calendar months https://iserredex.essex.ac.uk/support/issues/20752024-03-13T15:36:15ZJames Laurence
<p>Hi there,</p>
<p>I am interested in looking at calendar month trends in whether someone wants to move home or not (which is available in every wave): lkmove. Ideally, I would like to look at trends using all waves (1-13). However, if it is easier to look at trends from some other start point, e.g.. 2016 or 2017, then I am flexible. I am also flexible as to whether the BHPS sample is included or not. This will be cross-sectional analysis, so I hope to treat each calendar month as a cross-section (I won’t be doing any longitudinal analysis).</p>
<p>I have been reading the helpful notes on ‘Running analysis on a calendar year or month’ (<a class="external" href="https://www.understandingsociety.ac.uk/documentation/mainstage/user-guides/main-survey-user-guide/how-to-use-weights-analysis-guidance-for-weights-psu-strata/">https://www.understandingsociety.ac.uk/documentation/mainstage/user-guides/main-survey-user-guide/how-to-use-weights-analysis-guidance-for-weights-psu-strata/</a>). However, I just had some questions and was hoping to see if where I’d got to so far looked right.</p>
<p>I have been using the w_month and wave variables to generate a new date variable of year-month. To capture calendar year, I have used the wave and w_month variables in the following manner:</p>
<p>gen year = 2009 if wave==1 & (month>0 & month<13)<br />replace year = 2010 if wave==1 & (month>12 & month<25)<br />replace year = 2010 if wave==2 & (month>0 & month<13)<br />replace year = 2011 if wave==2 & (month>12 & month<25)<br />replace year = 2011 if wave==3 & (month>0 & month<13)<br />…<br />replace year = 2021 if wave==13 & (month>0 & month<13)<br />replace year = 2022 if wave==13 & (month>12 & month<25)</p>
<p>To measure calendar month, I have recoded the w_month variable, combining the two monthly measures into one. So, in the w_month variable, it tells us whether someone was sampled in January in the year 1 sample or January in the year 2 sample. I’ve now combined these into a single category of whether someone was sampled in January. For example, ‘jan yr1’ and jan yr2’ are now just ‘jan’; ‘feb yr1’ and ‘feb yr2’ are now just ‘feb, etc.</p>
<p>With these new calendar year and calendar month variables, I have now created a new measure of calendar year-month, which looks like this (I hope this is correct so far):</p>
<pre><code>2009 Jan = 1<br /> 2009 Feb = 2<br /> 2009 Mar = 3<br /> 2009 Apr = 4<br /> 2009 May = 5<br /> 2009 June = 6<br /> 2009 July = 7<br />…<br /> 2022 June = 162<br /> 2022 July = 163<br /> 2022 Aug = 164<br /> 2022 Sep = 165<br /> 2022 Oct = 166<br /> 2022 Nov = 167<br /> 2022 Nov = 168</code></pre>
<p>I understand that whatever weight I choose to use I need to correct it due to Northern Ireland only being sampled in issue month 1-12 (and not 13-24). Therefore, I will apply the following adjustment to the weight (gen adj=1, replace adj=0.5 if w_country==4, gen weight=w_xxxyyus_lw*adj 8) as outlined in the online notes.</p>
<p>However, where I’ve become a little lost is what weights to initially use. In the notes, it states due to exceptions in sample selection ‘we recommend use of the us_lw weight in analysis’. Given my intention to look at calendar months up to wave 13, does this mean I should use the m_indpxus_lw weight? Is this the case, even if I just want to look at the data cross-sectionally (treat every calendar month as a cross-sectional picture of lkmove)? Because it seems that if I use m_indpxus_lw then it substantially reduces the sample size (due to these longitudinal weights requiring someone to have participated in every wave). Is it possible to use the cross-sectional weights for my aims, while excluding the BHPS and IEMB, as is suggested that one needs to do for this kind of calendar month analysis in the online notes? Or, do I need to use longitudinal weights for my intended analysis?</p>
<p>I was also just trying to get my head around the issue of scaling discussed in the online notes: ‘The weights provided are not designed directly for pooling data across waves as they are scaled to a mean value of 1.0 within each wave, and therefore produce different weighted sample sizes in each wave’, under the section ‘Pooling data from different waves for cross-sectional analysis.’ Firstly, I just wanted to confirm this applies to my case of doing monthly trends?</p>
<p>And secondly, if so, from what I can see, the syntax kindly provided is intended to produce an accurate weight to look at the variable jbstat for the calendar year 2011, using months 13-24 of wave 2 and 1-12 of wave 3. At the end, we get the weight variable weight2011, to use for weighting calendar year 2011. In my situation, I would like to do a longer running trend of values of lkmove by months. Would I need to create these weights for each calendar year I look at? So, for 2014, I would need to create a new cross-sectional weight using e_indpxub_xw and f_indpxub_xw (waves 5 and 6). For 2015, I would need to create a new cross-sectional weight using f_indpxub_xw and g_indpxub_xw (waves 6 and 7). For 2016, I would need to create a new cross-sectional weight using g_indpxub_xw and h_indpxub_xw (waves 7 and 8). And to follow this all the way to my last calendar year. Then, to look at monthly trends, treating the data as pooled cross-sectional, I would have my data in long-format and have a new weight variable made up of all these new calendar year weights I’ve created?</p>
<p>I was also wondering if it would be possible to include monthly lkmove data from the calendar year 2022 (using wave 13 of the UKHLS mainstage). As I understand things, previous calendar years (e.g., 2018) are composed of samples from two waves (waves 9 and 10 of the mainstage). However, for the calendar year of 2022, it is only composed of the sample from wave 13. Is it still possible to look at calendar month trends in lkmove for 2022? If so, would I need to make other sample restrictions to the other calendar years, for example, drop the IEMB sample from the trends? And would I need to make other adjustments to the weights? Or, is it not possible yet to look at monthly trends until wave 14 comes out)? I think from the online notes this is mentioned: ‘The analysis sample is only representative when all 24 monthly samples are combined in equal measure.’ Does this point refer to my question?</p>
<p>I am also interested in potentially looking at quarterly trends (Jan-Mar, Apr-Jun, etc.), instead of monthly trends (using the x_quarter variable). To do so, can I take the same approach as above? So, create a new time variable which is years divided into quarters (e.g., 2013 Jan-Mar, 2013 Apr-Jun, 2013 July-Sep, 2013 Oct-Dec, 2014 Jan-Mar, 2014 Apr-June…2022 Jul-Sep, 2022 Oct-Dec). Do I need to do anything different with the weights?</p>
<p>I hope this all makes sense.</p>
<p>Thanks so much in advance.</p>
<p>James</p> Support #2074 (In Progress): Longitudinal weights https://iserredex.essex.ac.uk/support/issues/20742024-03-09T16:03:06ZJoe Mattock
<p>Hi,</p>
<p>I'm conducting an analysis specifically over waves 2, 3, 6 and 9 for Understanding Society, as relating to the voteintent variable which is only included in these waves. I would just like to ask about the weighting procedure for this case. I am examining how an independent variable (gentrification, as measured by an index) affects voting intention at the LSOA-level.</p>
<p>My understanding is that I need to take the longitudinal weight from the final wave used in my analysis and apply it to all respondents (i_indscub_lw - I believe). However, given that my dependent variable of interest is not observed in consecutive waves, I wanted to ask whether this principle applies in the same way.</p>
<p>I also wanted to ask how this weighting would be applied in practice. I am slightly confused about the order of things. For example, would you remove all wave-specific prefixes, merge LSOA indicators with the Understanding Society data, and then apply the relevant weight for each respondent?</p>
<p>Much appreciated,</p>
<p>Joe</p> Support #2060 (Resolved): Design weights taken account of in enumeration weights?https://iserredex.essex.ac.uk/support/issues/20602024-02-27T13:21:37ZRosie Cornish
<p>I think the answer to this is yes, but can you confirm that the household enumeration weights (e.g. a_hhdenus_xw) take account of the design weights - i.e. they are the product of the design weight and a household response weight?</p> Support #2058 (Resolved): Using longitudinal weights when combining Covid-19 waves and mainstage ...https://iserredex.essex.ac.uk/support/issues/20582024-02-22T16:48:24ZJames Laurence
<p>Hi there,</p>
<p>I was just hoping to get some more advice regarding correctly weighting my analysis combining the mainstage and Covid-19 waves of the UKHLS. You kindly helped with a previous weighting issue I had for treating the data as repeated cross-sections. However, I am also hoping to conduct some fixed effects panel data analysis of the combined mainstage and Covid-19 waves (web survey only).</p>
<p>As a basic set-up, I am combining wave 9 of the UKHLS mainstage survey (the last mainstage survey that doesn’t cover the pandemic) with waves 1 to 9 of the COVID-19 survey. The data are in long format. As I would like to do some fixed effects longitudinal analysis, I believe I need to use the longitudinal weights. From my reading, I need to choose the longitudinal weight from the last wave of the survey I will be using – in this case wave 9 of the Covid-19 survey: ci_betaindin_lw</p>
<p>Applying this weight [ci_betaindin_lw] will give me a balanced panel, restricting the sample to everyone who participated in all 9-waves of the Covid-19 survey. However, I would also like to analyse wave 9 of the mainstage survey as part of a longitudinal, fixed effects analysis covering mainstage wave 9 and Covid survey waves 1-9. Is this possible? If so, is one approach to feed back the ci_betaindin_lw weight so that the people who were in wave 9 of the mainstage survey who were also present in all 9-waves of the Covid-19 survey have the weight value of ci_betaindin_lw? Therefore, the ci_betaindin_lw weight would cover the mainstage wave 9 sample and the Covid-19 sample.</p>
<p>In case it’s not clear, to make-up an example of the data in long-format, which contains wave 9 of the mainstage survey and waves 1-9 of the Covid survey. Pidp no. 111111 was present in wave 9 of the mainstage sirvey and all 9 waves of the Covid survey and had a value of 1.5 for their longitudinal weight at wave 9 of the covid survey (ci_betaindin_lw). So, my data would just look like this:</p>
<p><strong>[PIDP]</strong> <strong>[WAVE] [Value of ci_betaindin_lw]</strong><br />111111 Mainstage wave 9 <em>Missing Value</em><br />111111 COVID wave 1 1.5<br />111111 COVID wave 2 1.5<br />111111 COVID wave 3 1.5<br />111111 COVID wave 4 1.5<br />111111 COVID wave 5 1.5<br />111111 COVID wave 6 1.5<br />111111 COVID wave 7 1.5<br />111111 COVID wave 8 1.5<br />111111 COVID wave 9 1.5</p>
<p>Is just feeding back the value of ci_betaindin_lw (1.5) what I need to do? So, it would now look like:</p>
<p><strong>[PIDP]</strong> <strong>[WAVE] [Value of ci_betaindin_lw]</strong><br />111111 Mainstage wave 9 <strong>1.5</strong><br />111111 COVID wave 1 1.5<br />111111 COVID wave 2 1.5<br />111111 COVID wave 3 1.5<br />111111 COVID wave 4 1.5<br />111111 COVID wave 5 1.5<br />111111 COVID wave 6 1.5<br />111111 COVID wave 7 1.5<br />111111 COVID wave 8 1.5<br />111111 COVID wave 9 1.5</p>
<p>If so, could this method apply if I wanted to include more mainstage waves of data? So, if I wanted to include waves 6, 7, 8 and wave 9 of the mainstage survey alongside waves 1-9 of the Covid survey - would I just feed back an individuals' weight value for ci_betaindin_lw back so the individual have that weight value for mainstage waves, 6, 7, 8 and 9?</p>
<p>I may be completely misunderstanding how to use the longitudinal weights, or have missed something crucial meaning you can't applying the Covid longitudinal weights to the pre-Covid survey mainstage waves. If so, apologies in advance and any advice would be hugely appreciated.</p>
<p>Best wishes,</p>
<p>James</p> Support #2052 (Resolved): Vote Choice: display all partieshttps://iserredex.essex.ac.uk/support/issues/20522024-02-09T15:52:16ZTarek Jaziri-Arjona
<p>Hi,</p>
<p>In the questions regarding voting behaviour. Is it possible to get the all the vote choices of the respondents? Concretely, is it possible to get all the possible parties that people named when answering those questions?</p> Support #2051 (Resolved): Local Authority variable name in Special Licence dataset (SN 6666)https://iserredex.essex.ac.uk/support/issues/20512024-02-08T16:56:41ZHannah Chappell
<p>Hello,</p>
<p>I am struggling to find the variable name for Local Authority indicators in the Special Licence Access version SS 6666. I need this information in order to complete my application for access.</p>
<p>Can someone please tell me what the naming convention for this variable is?</p> Support #2050 (Resolved): Linking BHPS and US Dataset for special license data (6931)https://iserredex.essex.ac.uk/support/issues/20502024-02-07T22:00:17ZMax Bradley
<p>I am attempting to merge individual files from harmonised bhps and ukhls into long format. The datasets I am using are the special license datasets 6931. In the syntax provided in the documentation page, it uses the age_dv (i.e., derived variable). However, it seems that these dervide variables are not present in the BHPS indresp stata files. Could someone enlighted me as to whether I am missing something? Or perhaps, how to derive such variables myself?</p>
<p>Many thanks</p> Support #2049 (Resolved): Linking BHPS and US Dataset for special license data (6931)https://iserredex.essex.ac.uk/support/issues/20492024-02-07T15:44:08ZMax Bradley
<p>I am attempting to merge individual files from harmonised bhps and ukhls into long format. The datasets I am using are the special license datasets 6931. In the syntax provided in the documentation page, it uses the age_dv (i.e., derived variable). However, it seems that these dervide variables are not present in the BHPS indresp stata files. Could someone enlighted me as to whether I am missing something? Or perhaps, how to derive such variables myself?</p>
<p>Many thanks</p> Support #2048 (Resolved): Code Creatorhttps://iserredex.essex.ac.uk/support/issues/20482024-02-07T09:42:14ZMartha Tindall
<p>Hi</p>
<p>I am having issues when trying to use the code creator on your website. When I click save the save it does nothing and doesn't save the variable to the code creator on the right of the screen. I have tried different browsers, clearing my cookies and different variables but have no luck.</p>
<p>Thanks <br />Martha</p> Support #2047 (Resolved): Council Taxhttps://iserredex.essex.ac.uk/support/issues/20472024-02-06T15:00:26ZElaine Robinson
<p>Where can I find the amount of council tax paid by a household?<br />The variable look up suggests I need ficountax_dv and it is listed as present the hhresp file in waves 1 to 13 of Understanding Society:<br /><a class="external" href="https://www.understandingsociety.ac.uk/documentation/mainstage/variables/ficountax_dv/">https://www.understandingsociety.ac.uk/documentation/mainstage/variables/ficountax_dv/</a><br />Yet, when I browse the data in the hhresp file, it seems that this variable is not present.</p> Support #2046 (Resolved): Unable to locate org variable used in the pasthttps://iserredex.essex.ac.uk/support/issues/20462024-02-05T11:23:20ZMel Cairns
<p>Hello, We have previously used a variable which we noted the name of as 'org' which was about being a member of one of (or at least one of) a list of organisations such as WI, trade union, religious organisation etc. The last data we have for this variable was from 2016. Now when I search for 'org' in the variable list it doesn't come up. It looks like it was one of the questions listed here: <a class="external" href="https://www.understandingsociety.ac.uk/documentation/mainstage/questionnaire-modules/groupsandorganisations_w12/">https://www.understandingsociety.ac.uk/documentation/mainstage/questionnaire-modules/groupsandorganisations_w12/</a> <br />but either these are from the BHPS which finished well before 2016, or when I search for them in the mainstage variable search they don't come up.</p>
<p>So I'm wondering how I can find the latest data for this variable? Is it in a different section (not the main stage survey)? And if so is that comparable to the data from 2016? Or is there something else I'm missing?</p>
<p>Many thanks for any help you can give.<br />Mel</p> Support #2045 (Resolved): Winter Fuel Paymentshttps://iserredex.essex.ac.uk/support/issues/20452024-02-01T14:06:26ZElaine Robinson
<p>There are a range of benefits for those of pension age to help with fuel bills, including the Winter Fuel Payment and the Cold Weather Payment.<br />Are they included as income or benefits in Understanding Society?</p> Support #2044 (Resolved): Old Westminster constituecieshttps://iserredex.essex.ac.uk/support/issues/20442024-01-31T13:32:18ZMargherita Negri
<p>Hello, I am trying to link survey responses to electoral results for the Westminster constituencies. I have obtained the information about the Westminster constituencies of the respondents. However, these seem to be the most recent constituencies. For elections before 2005, I am unable to assign some constituencies to individual responses because these constituencies only existed up to 2005. Would it be possible to obtain more accurate information about the exact Parliamentary constituency where the individual lived <strong>at the time they responded</strong>? Thank you in advance.</p>