Understanding Society User Support: Activityhttps://iserredex.essex.ac.uk/support/https://iserredex.essex.ac.uk/support/support/favicon.ico?15995719382024-03-15T15:38:08ZUnderstanding Society User Support
Redmine Support #2075: Using UKHLS to look at trends across calendar months https://iserredex.essex.ac.uk/support/issues/2075#change-86522024-03-15T15:38:08ZJames Laurence
<p>Hi Olena,</p>
<p>Thank you for the detailed reply. It's really helpful. Apologies for mixing up calendar month and sample month but you are absolutely right that I will be using sample month.</p>
<p>Best wishes,</p>
<p>James</p> Support #2076: Issues with xx_hadcvvac variables in COVID-19 data collectionhttps://iserredex.essex.ac.uk/support/issues/2076#change-86512024-03-15T14:03:57ZLaura L
<p>Understanding Society User Support Team wrote in <a href="#note-2">#note-2</a>:</p>
<blockquote>
<p>Hello Laura</p>
<p>The question routing is as follows: If ff_hadcvvac = 3, then ask if the respondent has not reported in a previous month that they had a coronavirus vaccine.</p>
<p>When tabulating (tab ci_hadcvvac ci_ff_hadcvvac), we see responses only when ci_ff_hadcvvac = 3, which is expected.</p>
<table>
<tr>
<th>Had covid-19 vaccine </th>
<th>1 Yes, first vaccine only </th>
<th>2 Yes, both vaccinations </th>
<th>3 No </th>
<th>Total </th>
</tr>
<tr>
<th>-8 inapplicable </th>
<td> 5,917 </td>
<td> 175 </td>
<td> 24 </td>
<td> 6,116 </td>
</tr>
<tr>
<th>-2 refusal </th>
<td> 0 </td>
<td> 0 </td>
<td> 10 </td>
<td> 10 </td>
</tr>
<tr>
<th>1 Yes, first vaccination only </th>
<td> 0 </td>
<td> 0 </td>
<td> 201 </td>
<td> 201 </td>
</tr>
<tr>
<th>2 Yes, both vaccinations </th>
<td> 0 </td>
<td> 0 </td>
<td> 5,972 </td>
<td> 5,972 </td>
</tr>
<tr>
<th>3 No, but I have an appointment </th>
<td> 0 </td>
<td> 0 </td>
<td> 13 </td>
<td> 13 </td>
</tr>
<tr>
<th>4 No </th>
<td> 0 </td>
<td> 0 </td>
<td> 506 </td>
<td> 506 </td>
</tr>
<tr>
<th>Total </th>
<td> 5,917 </td>
<td> 175 </td>
<td> 6,726 </td>
<td> 12,818 </td>
</tr>
</table>
<p>These observations might be related to booster doses, which began in September 2021, six months after their second vaccine dose, to members of the following groups: people over the age of 50, vulnerable people over 16, health and social care workers, adult members of the households of immune-suppressed individuals</p>
<p>If you check the age of the 1,663 (cg_hadcvvac=1 & ci_hadcvvac==2 ) and 116 (cg_hadcvvac=1 & ci_hadcvvac==1) observations, we find that over 80% are above 50 years old, which were the first group receiving the booster does.</p>
<p>I hope this information is helpful.<br />Best wishes,<br />Roberto Cavazos<br />Understanding Society User Support Team</p>
</blockquote>
<p>Dear Roberto,</p>
<p>Thank you very much for your kind answer.</p>
<p>I hope I am not bothering you, but this is still not fully clear to me. Specifically, how are the routing variables ff_hadcvvac constructed? <br />For instance, from what I understand ch_ff_hadcvvac should be derived by respondents' answer to cg_hadcvvac (except of course for new respondents). However, by tabulating table(cg_hadcvvac = check$cg_hadcvvac, ch_ff_hadcvvac = check$ch_ff_hadcvvac) for respondents in wave 7 (check is the dataset containing the wave 7 respondents), this is what I get:</p>
<pre><code class="r syntaxhl"><span class="w">
</span><span class="n">ch_ff_hadcvvac</span><span class="w">
</span><span class="n">cg_hadcvvac</span><span class="w"> </span><span class="m">-8</span><span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="m">2</span><span class="w"> </span><span class="m">3</span><span class="w">
</span><span class="m">-9</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">2</span><span class="w">
</span><span class="m">-8</span><span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">15</span><span class="w">
</span><span class="m">-2</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">5</span><span class="w">
</span><span class="m">1</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">2078</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">31</span><span class="w">
</span><span class="m">2</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">145</span><span class="w"> </span><span class="m">3</span><span class="w">
</span><span class="m">3</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">604</span><span class="w">
</span><span class="m">4</span><span class="w"> </span><span class="m">9</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="m">9075</span><span class="w">
</span></code></pre>
<p>How are some respondents assigned to category 3 if they answered that they got the vaccine in wave 7? Also, what does the -8 value mean in the context of routing variables ff_hadcvvac? Lastly, you were telling me that the observations for which cg_hadcvvac=1 & ci_hadcvvac==2 and cg_hadcvvac=1 & ci_hadcvvac==1 might refer to booster doses. However, the question options are: </p>
<pre><code>Have you had a coronavirus vaccination?<br />1. <strong>Yes, first vaccination only</strong><br />2. <strong>Yes, both vaccinations</strong><br />3. No, but I have an appointment<br />4. No</code></pre>
<p>How can they refer to boosters given the wording?</p>
<p>Thank you again for your support and prompt response.<br />Best regards, <br />Laura</p> Support #2076 (Feedback): Issues with xx_hadcvvac variables in COVID-19 data collectionhttps://iserredex.essex.ac.uk/support/issues/2076#change-86492024-03-15T09:18:07ZUnderstanding Society User Support Teamusersupport@understandingsociety.ac.uk
<p>Hello Laura</p>
<p>The question routing is as follows: If ff_hadcvvac = 3, then ask if the respondent has not reported in a previous month that they had a coronavirus vaccine.</p>
<p>When tabulating (tab ci_hadcvvac ci_ff_hadcvvac), we see responses only when ci_ff_hadcvvac = 3, which is expected.</p>
<table>
<tr>
<th>Had covid-19 vaccine </th>
<th>1 Yes, first vaccine only </th>
<th>2 Yes, both vaccinations </th>
<th>3 No </th>
<th>Total </th>
</tr>
<tr>
<th>-8 inapplicable </th>
<td> 5,917 </td>
<td> 175 </td>
<td> 24 </td>
<td> 6,116 </td>
</tr>
<tr>
<th>-2 refusal </th>
<td> 0 </td>
<td> 0 </td>
<td> 10 </td>
<td> 10 </td>
</tr>
<tr>
<th>1 Yes, first vaccination only </th>
<td> 0 </td>
<td> 0 </td>
<td> 201 </td>
<td> 201 </td>
</tr>
<tr>
<th>2 Yes, both vaccinations </th>
<td> 0 </td>
<td> 0 </td>
<td> 5,972 </td>
<td> 5,972 </td>
</tr>
<tr>
<th>3 No, but I have an appointment </th>
<td> 0 </td>
<td> 0 </td>
<td> 13 </td>
<td> 13 </td>
</tr>
<tr>
<th>4 No </th>
<td> 0 </td>
<td> 0 </td>
<td> 506 </td>
<td> 506 </td>
</tr>
<tr>
<th>Total </th>
<td> 5,917 </td>
<td> 175 </td>
<td> 6,726 </td>
<td> 12,818 </td>
</tr>
</table>
<p>These observations might be related to booster doses, which began in September 2021, six months after their second vaccine dose, to members of the following groups: people over the age of 50, vulnerable people over 16, health and social care workers, adult members of the households of immune-suppressed individuals</p>
<p>If you check the age of the 1,663 (cg_hadcvvac=1 & ci_hadcvvac==2 ) and 116 (cg_hadcvvac=1 & ci_hadcvvac==1) observations, we find that over 80% are above 50 years old, which were the first group receiving the booster does.</p>
<p>I hope this information is helpful.<br />Best wishes,<br />Roberto Cavazos<br />Understanding Society User Support Team</p> Support #2073 (Feedback): Data filehttps://iserredex.essex.ac.uk/support/issues/2073#change-86482024-03-14T13:43:27ZUnderstanding Society User Support Teamusersupport@understandingsociety.ac.uk
<p>Hello Luisa</p>
<p>Wave 13 main stage Study covers years 2021 and 2022. The interviews span from January 2021 to May 2023. Around 2% of the interviews were done in 2023 which are usually the late respondents. <br />To compare monthly information please refer to <a href="https://www.understandingsociety.ac.uk/documentation/mainstage/user-guides/main-survey-user-guide/how-to-use-weights-analysis-guidance-for-weights-psu-strata/" class="external">How to use weights – Analysis guidance for weights, PSU, Strata</a> section, and particularly to examples “Running analysis on a calendar year or month” and “Pooling data from different waves for cross-sectional analysis”.</p>
<p>I hope this information is helpful.</p>
<p>Best wishes,<br />Roberto Cavazos<br />Understanding Society User Support Team</p> Support #2066 (Resolved): Code creator E-Mailhttps://iserredex.essex.ac.uk/support/issues/2066#change-86472024-03-14T13:31:36ZUnderstanding Society User Support Teamusersupport@understandingsociety.ac.ukSupport #2069: Match children information with parental informationhttps://iserredex.essex.ac.uk/support/issues/2069#change-86462024-03-14T13:30:59ZUnderstanding Society User Support Teamusersupport@understandingsociety.ac.uk
<p>Hello Giovanni,</p>
<p>You would need first to identify children that have left the parental house. I think the best approach will be using file xhhrel (family matrix) which identifies all family relationships across the Study even if they are not co-residents.</p>
<p>The xhhrel file creates an individual level cross-wave file of all sample members (those who were ever enumerated as part of the study) that contains familial relationship identifiers reported over the survey period for each sample member. This file also contains an origin household identifier variable (osm_hh) which identifies the household they come from, so that sample members who are connected can be identified (either because they were co-resident at some point or were co-resident with individuals who were co-resident with each other).<br />For further details please refer to <a href="https://doc.ukdataservice.ac.uk/doc/6614/mrdoc/pdf/6614_family_matrix_xhhrel_user_guide.pdf" class="external">Family Matrix User Guide</a></p>
<p>I hope this information is helpful.</p>
<p>Best wishes,<br />Roberto Cavazos<br />Understanding Society User Support Team</p> Support #2041 (Resolved): w_outcome in COVID-19 survey waveshttps://iserredex.essex.ac.uk/support/issues/2041#change-86442024-03-14T12:14:19ZUnderstanding Society User Support Teamusersupport@understandingsociety.ac.ukSupport #2075: Using UKHLS to look at trends across calendar months https://iserredex.essex.ac.uk/support/issues/2075#change-86432024-03-14T12:03:10ZOlena Kaminskaolena@essex.ac.uk
<p>James,</p>
<p>Thank you for your question. From your description I find that you are technically not using 'calendar' month but 'sample' month, which makes it much easier for weighting. This means that while some people (late respondents) answer interviews later, you are including them in January months if they are sampled in January month. Also, you are conducting cross-sectional analysis. In this situation:</p>
<p>1. You should always combine two sample years (soe January in year 1 with January in year 2). In this situation you won't need correction for NI (so don't divide it by 2), and you can use IEMB and BHPS sample. So basically use our data as it is (don't do any selection on samples), and use one of our weights. You can use ub or ui weights (but the same weight for both sample years).<br />2. Technically you could use longitudinal weights, but there is no such need for your analysis. I suggest xw weights.<br />3. On scaling. Scaling is important if you are analysing all data in one model, e.g. multilevel model. In such situation you want to avoid some years contributing more than others, and scaling is necessary. But scaling is not needed if you just want to graph a time trend, i.e. if you want to estimate separate proportions for each time period. Scaling won't make any difference in this situation and won't be needed.<br />4. Calendar year 2022 should be find as (I think) uses wave 12 year 2, and wave 13 year 1. I believe wave 13 is released. But calendar year 2023 is trickier in terms of weighting. I suggest you use calendar year release (which we are working on at the moment), and this will be shaped and have weights ready for you. Calendar year release precedes full mainstage release, but includes questions only from core questionnaire. Weighting is tricky if you want to include calendar months based on year 1 only, due to uneven sampling in year 1. Correction for NI, and potential exclusion of some samples may be advisable in this situation.<br />5. Quarterly trends will follow the same logic, and if you base it on sample months weighting is much more straightforward, and everything mentioned above applies.</p>
<p>Hope this helps,<br />Olena</p> Support #2041: w_outcome in COVID-19 survey waveshttps://iserredex.essex.ac.uk/support/issues/2041#change-86422024-03-14T10:36:07ZLaura L
<p>Understanding Society User Support Team wrote in <a href="#note-5">#note-5</a>:</p>
<blockquote>
<p>Hello Laura,</p>
<p>Here is the feedback from our Survey team.<br />1. Yes, if they start the interview but stop before a point around two-thirds of the way in, they are coded as “refusal during the interview”. If they reach that two-thirds point, they are partial interviews.</p>
<p>2. An office refusal is when someone contacts us or the fieldwork agency before the interviewer is allocated to the point. So, the interviewer has not had a chance to call. A proxy interview is where someone refuses on behalf of someone else (e.g., someone says “my partner doesn’t want to do it this year”), so we don’t get the refusal directly from the sample member.</p>
<p>3. An adult in an “other non-responding household” is where the household didn’t take part because everyone was ill, or away from home, had language difficulties etc. The “ioutcome” is an individual-level outcome but for those who were not interviewed because no one in the household was interviewed, we don’t know their specific reason for non-interview, and so they are in “other non-responding household”. In a household where at least one person takes part, we probably know the reason for the individual non-response, but we don’t in households where no one takes part.</p>
<p>I hope this information is helpful.</p>
<p>Best wishes,<br />Roberto Cavazos<br />Understanding Society User Support Team</p>
</blockquote>
<p>Dear Roberto,</p>
<p>Thank you very much, everything is clear now.</p>
<p>Best regards, <br />Laura Leone</p> Support #2076 (In Progress): Issues with xx_hadcvvac variables in COVID-19 data collectionhttps://iserredex.essex.ac.uk/support/issues/2076#change-86412024-03-14T09:45:10ZUnderstanding Society User Support Teamusersupport@understandingsociety.ac.uk
<p>Many thanks for your enquiry. The Understanding Society team is looking into it and we will get back to you as soon as we can. We aim to respond to simple queries within 48 hours and more complex issues within 7 working days.</p>
<p>Best wishes,<br />Understanding Society User Support Team</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 (In Progress): Using UKHLS to look at trends across calendar months https://iserredex.essex.ac.uk/support/issues/2075#change-86402024-03-13T16:11:03ZUnderstanding Society User Support Teamusersupport@understandingsociety.ac.uk
<p>Many thanks for your enquiry. The Understanding Society team is looking into it and we will get back to you as soon as we can. We aim to respond to simple queries within 48 hours and more complex issues within 7 working days.</p>
<p>Best wishes,<br />Understanding Society User Support Team</p> Support #2075 (In Progress): 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 #2069: Match children information with parental informationhttps://iserredex.essex.ac.uk/support/issues/2069#change-86392024-03-11T11:53:08ZGiovanni Greco
<p>Dear Roberto,<br />Thank you very much for your help.<br />I need to merge parental data into children's information also when children have left the parental house. I had a look at the syntax you suggest. In this regard, I wanted to ask whether there is a way to modify this syntax in order to allow matching also information for children that are no longer living in the same household of the parents. Otherwise, is there another syntax for this specific goal?<br />Thank you very much.</p>
<p>Best wishes,<br />Giovanni Greco</p> Support #2070: Creating Chronology when using COVID-19 and main panel datahttps://iserredex.essex.ac.uk/support/issues/2070#change-86382024-03-11T11:47:21ZUnderstanding Society User Support Teamusersupport@understandingsociety.ac.uk
<p>Hello Isaac</p>
<p>As was mentioned COVID-19 survey data can be linked to the main UKHLS annual survey data using the variable PIDP, which is included in all data files. Please refer to sub-section 10.7 in the <a href="https://understandingsociety.ac.uk/wp-content/uploads/documentation/user-guides/8644-user-guide-covid-19.pdf" class="external">User Guide</a> for more details.</p>
<p>The interview date variables w_istrtdaty, w_istrtdatm and w_istrtdatd are the same on main stage survey. Keep in mind that information for a calendar year may be spread across up to three different waves.</p>
<p>I hope this information is helpful.</p>
<p>Best wishes,<br />Roberto Cavazos<br />Understanding Society User Support Team</p>