Understanding Society User Support: Issueshttps://iserredex.essex.ac.uk/support/https://iserredex.essex.ac.uk/support/support/favicon.ico?15995719382024-03-13T15:36:15ZUnderstanding Society User Support
Redmine Understanding Society User Support - Support #2075 (Feedback): Using UKHLS to look at trends acro...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> Understanding Society User Support - 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> Understanding Society User Support - Support #2042 (Feedback): Survey Weights for Multi-Wave Pool...https://iserredex.essex.ac.uk/support/issues/20422024-01-29T09:47:05ZLisa Waddell
<p>Hello,</p>
<p>I have constructed an unconventional sample by pooling tab-delineated data files for SN 6614-Understanding Society: Waves 1-13, 2009-2022 and Harmonised BHPS: Waves 1-18, 1991-2009. I request your advice regarding these weights.</p>
<p>Sample Construction: Using the family matrix, I identify everyone in the sample with both a mother and a father pidp identified. Using all waves of data, I keep participants whose mother and father both responded when the participant was aged 10 or younger. I then filter by participants who responded at the age of 21 or older. These two filtering functions leave me with a sample of around ~2000 people between the ages 21-41, from BHPS and USoc samples. Due to my pooling of BHPS and USoc samples, when I follow the steps for constructing a tailored sample weight, I lose a substantial portion of my sample. For example, if I choose a base weight from Wave 1 of USoc, I lose the entire BHPS sample. If I choose a base weight from Wave 2 of USoc, I lose a substantial portion of the USoc sample.</p>
<p>Given how I construct my sample, do you have any advice on how I should be applying survey weights?</p>
<p>All the best,<br />Lisa</p> Understanding Society User Support - Support #2036 (Feedback): Understanding Society - weightshttps://iserredex.essex.ac.uk/support/issues/20362024-01-22T12:50:29ZValentina Di Iasio
<p>Good morning,</p>
<p>After reading the user guide and watch the short YouTube video, I am still confused on which are the correct weights I should select for my pooled cross-sectional analysis using Understanding Society.</p>
<p>I am using waves 6 and 9 for a pooled cross-section analysis. I would therefore being inclined in using the cross-sectional weights. However, when reading the user guide it says that cross-sectional weights should only be used when the analysis includes one wave only. I also read the paragraph on re-scaling the weights to use more waves to conduct cross-sectional analysis. However, I am not sure whether the described procedure would apply to my case since I don't have a year overlapping over the two waves (wave 6 goes from January 2014 to May 2016 while wave 9 goes from January 2017 to May 2019). Therefore I am not sure whether I should simply use cross-sectional weights, re-scale the cross-sectional weights somehow (maybe for the first 6 months of 2016 and 2019 only?), or exclude the first 6 months of the years 2016 and 2019. Or, if I am missing something and I should use longitudinal weights (in that case, since I am doing a pooled cross-section analysis, how should I deal with 0 weights?)</p>
<p>Thank you in advance</p>
<p>Valentina Di Iasio</p> Understanding Society User Support - Support #2031 (Feedback): Cross-Sectional Weighting Questions.https://iserredex.essex.ac.uk/support/issues/20312024-01-18T11:20:26ZIfraz Hussain
<p>Hi, I'm currently working on a cross-sectional study across waves to examine the proportion of children who live in couple-parent families where one parent reports any form of relationship distress.</p>
<p>I have three questions relating to weighting:</p>
<ul>
<li>From this analysis, I've seen changes to weighting across all previous waves and I would like to know what specifically led to the revisions?</li>
<li>Since I'm looking at participants across waves, I'm also interested in whether there is any attempt to mitigate attrition bias (e.g. changes to weighting)?</li>
<li>Given that I'm working with w_psnenui_xw weights for my study, Do you think this weighting is appropriate for examining this area of the USOC Survey data?</li>
</ul> Understanding Society User Support - Support #1894 (Resolved): Weight for unbalanced and merged U...https://iserredex.essex.ac.uk/support/issues/18942023-04-21T14:30:20ZYanan Zhangzhangyanan0918@gmail.com
<p>Dear Sir/Madam,</p>
<p>I hope this message finds you in good health and high spirits.</p>
<p>I am currently working with individual-level data from the merged Waves 1-18 of the BHPS and Waves 1-8 of the UKHLS datasets. I have a couple of questions regarding the use of weights in my analysis. I would appreciate any guidance you could provide.</p>
<p>1. In my study, I am employing fixed effects estimates to analyze the relationship between two variables, x and y. Given this approach, is it necessary to apply weights to the analysis?</p>
<p>2. I have followed the guidelines and used the longitudinal weight provided in Wave 8 of the UKHLS. However, I understand that this weight is applicable only to those who have participated in all waves. Since many individuals have only participated in parts of the waves, I am unsure how to generate weights for these participants. Could you please advise on the appropriate way to handle this situation?</p>
<p>Thanks for your time!</p> Understanding Society User Support - Support #1890 (In Progress): Extracting PSU and Individual-L...https://iserredex.essex.ac.uk/support/issues/18902023-04-11T16:35:50ZLaurence Rowley-Abel
<p>Dear Understanding Society team,<br />I am running a multilevel model using individuals nested within census areas (LSOAs) in Waves 9, 10, 11 and 12. To account for clustering I am using the following levels in my multilevel model: individuals at the first level, PSUs at the second level and LSOAs at the third level. Therefore, from the provided weights, I need to extract separate weights for individuals and for PSUs. Having read your response here [[<a class="external" href="https://iserredex.essex.ac.uk/support/issues/1572">https://iserredex.essex.ac.uk/support/issues/1572</a>]], I am wondering if the below would be the correct approach:</p>
<p>- For the individual level, I would divide l_psnenus_xw by a_psnenus_xd (from the l_indall.dta and the a_indall.dta files respectively)<br />- For the PSU level, I would use a_psnenus_xd (from the a_indall.dta file)<br />- For the LSOA level, I would not be able to calculate a weight as it is not part of the sampling design. I would set this weight to 1 for all respondents.</p>
<p>Would this be correct? Additionally, would this mean I could only include respondents who were included at Wave 1, since I need to use the design weight (a_psnenus_xd) from Wave 1?</p>
<p>Many thanks for your help.</p>
<p>Best wishes,<br />Laurence</p> Understanding Society User Support - Support #1868 (Resolved): Use of weights for analysing job q...https://iserredex.essex.ac.uk/support/issues/18682023-02-25T16:00:04ZThomas Stephenst.c.stephens@lse.ac.uk
<p>Good afternoon,</p>
<p>I have a few questions about the weights to use for some analysis of job quality which I'm carrying out using Understanding Society. I have read another very useful support response on this (see: <a class="external" href="https://iserredex.essex.ac.uk/support/issues/1739">https://iserredex.essex.ac.uk/support/issues/1739</a>), but this still gives rise to some further questions.</p>
I'm planning on carrying out two distinct types of analysis for my research. Although working conditions data is available in every other wave, note that I exclude wave 2 from my analysis, for reasons I expand on below:
<ul>
<li><strong>Descriptive statistics of changes over time across every other wave,</strong> ie comparing Wave 4 vs. 6 vs. 8 vs. 10...;</li>
</ul>
<ul>
<li><strong>Analysis of pooled data from these waves</strong>, to understand the relationship between job quality and various other individual and household characteristics across all of Waves 4, 6, 8, 10.</li>
</ul>
<p>I have the following questions about which weights to use, and the weighting process in general:</p>
<p><strong>1.</strong> Will I have to use two different weights for these two types of analysis? My understanding is that the existing indinub_xw weight (ie just removing the wave prefix) would suffice for the first type of analysis (as per my reading of the Harmonised BHPS user guide, p. 25, <br /><a class="external" href="https://www.understandingsociety.ac.uk/sites/default/files/downloads/documentation/mainstage/user-guides/bhps-harmonised-user-guide.pdf">https://www.understandingsociety.ac.uk/sites/default/files/downloads/documentation/mainstage/user-guides/bhps-harmonised-user-guide.pdf</a>), but that I will have to do weight rescaling for the pooled analysis to avoid under-representing respondents from later waves. Is this correct?</p>
<p><strong>2.</strong> Although I only analyse at every other wave, I have created some new indicators by looking back at data from the wave immediately prior to the respondent's wave (eg I use Wave 3 data to establish whether respondents in Wave 4 have been continuously employed for >1 wave or >2 waves, wave 5 for wave 6, wave 7 for wave 8, etc...). Does this have any bearing on the weight I should rescale to for the pooled analysis?</p>
<p><strong>3.</strong> For the above reason, I exclude wave 2 data, as the relevant questions weren't asked in wave 2. Am I correct in assuming that if my pool starts at Wave 4, this means I need to re-scale to Wave 4 rather than Wave 2, using a variation (albeit in R rather than Stata, as that's what I'm using...) of the code you give here: <a class="external" href="https://iserredex.essex.ac.uk/support/issues/1739">https://iserredex.essex.ac.uk/support/issues/1739</a>? Are there any other issues I need to be aware of?</p>
<p><strong>4.</strong> I won't be analysing changes based on calendar years; I'll be keeping respondents in their waves. My reading is that I therefore don't have to carry out the adjustments you outline in p. 10 of your weighting FAQs: <a class="external" href="https://www.understandingsociety.ac.uk/sites/default/files/downloads/documentation/user-guides/mainstage/weighting_faqs.pdf">https://www.understandingsociety.ac.uk/sites/default/files/downloads/documentation/user-guides/mainstage/weighting_faqs.pdf</a>. Is this correct?</p>
<p><strong>5.</strong> I haven't seen any discussion of seasonality in the user forum or weighting FAQs. Ie if one wave happens to over-represent people interviewed in later seasons where labour market statistics might be different. Could I check whether your weights account for this?</p>
<p>Many thanks in anticipation.</p>
<p>Best wishes,</p>
<p>Tom</p> Understanding Society User Support - Support #1865 (Resolved): Changes to USOC wave data download...https://iserredex.essex.ac.uk/support/issues/18652023-02-23T16:42:31ZWilliam Shufflebottom
<p>Hi,</p>
<p>QUESTIONS</p>
<p>Q1: indscub_xw weight from wave 6 of USOC is present in our historical download of the wave 6 data but appears to be missing in the version of wave 6 we downloaded from UKData Service a few months ago and is also not listed as being in wave 6 on the USOC variable search page - can we confirm why only the indscui_xw weight is in the latest Wave 6 version, confirm it was in the original release, and if/when (and if so why) it was removed?</p>
<p>Q2: Our estimates run on the latest download of wave 1 to 12 of USOC are producing different numbers from the estimates we ran at the time of the previous wave's releases. Has there been a change to the data or weights (beyond wave 6 having a different weight) or how the weights work that could explain the difference we are seeing for all waves (bar wave 1 and wave 12) in a recent download of the data from all the waves. We are using the same weight (bar wave 6) and the same variable (sclfsat_7 in this case - but we use a range of USOC variables in our analysis).</p>
<p>BACKGROUND</p>
<p>We are producing estimates for the OECD and just discovered some differences for the estimates and CIs for the sclfsat7 variable when we re-ran historical estimates for all USOC waves 1 to 12. We run breakdowns for this variable (and others) by various domains when we update our publications and a new USOC wave has been released so we have the estimates from previous runs made at the time of USOC wave data release. We only ran the sclfsat7 variable again recently so there may be other changes.</p>
<p>We have a document for the weights to use for each variable which states that the indscub_xw weight is the correct weight to use for the sclfsat_7 variable in wave 6 but we noticed it was "missing" in the wave 6 data we downloaded around November from UK Data service (instead indscui_xw is present). As we are getting differences in our estimates and CIs for all waves (bar wave 1 and 12), this has prompted us to check with you if there have been changes made to the versions of the USOC main study wave data currently on the UK Data Service compared to what would have been available at the time each wave's data was released which could explain the differences we are seeing.</p>
<p>Your help is greatly appreciated as this has the potential to impact a lot of our publications and the current ad hoc we are working on</p> Understanding Society User Support - Support #1852 (Resolved): Select the correct weighting valueshttps://iserredex.essex.ac.uk/support/issues/18522023-02-07T17:53:18ZYushi Bai
<p>Dear colleagues,</p>
<p>I'm a post-doc research associate at the University of Manchester. We're currently planning an analysis investigating how mental health problems spread within a family network using your data (thank you for providing such an excellent dataset!). However, we're confused about how to create the correct weighting on our data even after reading all the tutorial materials. So I sincerely hope we can have your support for our analysis. I will first brief you on our initial analytical plan:</p>
<p>1. Formulate an initial participant pool consisting of all data in waves 1, 3, 5, 7, 9, and 11, because the Strengths and Difficulties Questionnaire (SDQ) data are available in those waves.<br />2. Within this initial pool, compare the data quality for each family across the waves (e.g. compare the quality of SDQ data for family A in waves 1, 3, 5, 7, 9, and 11).<br />3. Select a particular dataset for each family if the dataset has the fewest missing values across the waves, and formulate a large cross-sectional dataset. For example, if SDQ data have the fewest missing values for family A in wave 1, and for family B in wave 3, we use data for family A from wave 1, and data for family B from wave 3 to formulate a cross-sectional dataset.</p>
<p>By doing so, we hope we can boost our sample size and the quality of the data. This is because our analytical approach (network analysis) requires highly on data quality. However, we're aware that this participant selection approach may introduce bias. Therefore, we're wondering whether you can suggest whether our participant selection plan is reasonable in the light of your research design, and if so, what materials we can use to create the correct weighting values for our data?</p>
<p>Thank you in advance for your time and help, and we're looking forward to hearing from you.</p>
<p>Kind regards,<br />Yushi</p> Understanding Society User Support - Support #1696 (Resolved): random effects logistic regression...https://iserredex.essex.ac.uk/support/issues/16962022-05-09T12:57:51ZZohra Ansari-Thomas
<p>Hello,</p>
<p>I am attempting to run a random effects logistic regression model using waves 1-10 of the UKHLS, and am running into some issues with how to take into account the longitudinal weighting, strata, psu, as well as clustering by PIDP or allowing for random intercepts by PIDP to account for the longitudinal design of the study. I am using Stata</p>
<p>I can svy set my data to account for the longitudinal weights (indinus_lw), the psu, and the strata, but I am not sure how to account for the clustering by PIDP. I am using the svy: melogit command.</p>
<p>Any advice would be much appreciated, thank you!</p> Understanding Society User Support - Support #1667 (Resolved): Youth self completion longitudinal...https://iserredex.essex.ac.uk/support/issues/16672022-03-14T17:20:00Zjennie parnham
<p>Hello,</p>
<p>I was looking to check if I have correctly understood the weight I needed for my analysis .</p>
<p>My analysis is a longitudinal using the youth self-completion data from waves 7-11, following them into young-adults, if applicable. However, I do not need the participant to have participated in every wave between 7-11, I just need them to have participated in at least two waves, it doesn't matter which.</p>
<p>I have read the responses to the following similar queries, and wanted to check if what I have understood is correct.<br /><a class="external" href="https://iserredex.essex.ac.uk/support/issues/1091">https://iserredex.essex.ac.uk/support/issues/1091</a> <br /><a class="external" href="https://iserredex.essex.ac.uk/support/issues/1323">https://iserredex.essex.ac.uk/support/issues/1323</a><br /><a class="external" href="https://iserredex.essex.ac.uk/support/issues/1585">https://iserredex.essex.ac.uk/support/issues/1585</a></p>
<p>Would it be correct to use the last applicable sub-optimal weight for the wave that that individual participated in, and make a weight which is a combination of these?</p>
<p>For example:<br />IF last wave of data collection is Wave 11 (Young adult) then weight = k_indscui_lw<br />IF last wave of data collection is Wave 11 (Youth) then weight = k_psnenui_lw<br />IF last wave of data collection is Wave 10 (Young adult) then weight = j_indscui_lw <br />IF last wave of data collection is Wave 10 (Youth) then weight = j_psnenui_lw <br />IF last wave of data collection is Wave 9 (Young adult) then weight = i_indscui_lw (and so on ...)</p>
<p>So the weight variable that I use in the analysis is a combination of different weights, specific to their age at the last wave of participation. Or is it incorrect to take from different weight variables in this way?</p>
<p>I am concerned that if i just use the longitudinal enumeration weight for the last wave in the analysis (wave 11), I will end up excluding many participants.</p>
<p>Many thanks for your help</p> Understanding Society User Support - Support #503 (Closed): Inconsistencies in self-completion mo...https://iserredex.essex.ac.uk/support/issues/5032016-02-15T14:34:25ZTill Hoffmann
<p>In wave three of the Understanding Society survey, there are six entries for respondents who have refused the self-completion part of the interview but have positive self-completion interview weights. In particular, c_csac is 4 (refused) or 5 (not able to complete) (see <a class="external" href="https://www.understandingsociety.ac.uk/documentation/mainstage/dataset-documentation/wave/3/datafile/c_indresp/variable/c_scac">https://www.understandingsociety.ac.uk/documentation/mainstage/dataset-documentation/wave/3/datafile/c_indresp/variable/c_scac</a>) but c_indscub_xw is nonzero (see <a class="external" href="https://www.understandingsociety.ac.uk/documentation/mainstage/dataset-documentation/wave/3/datafile/c_indresp/variable/c_indscub_xw">https://www.understandingsociety.ac.uk/documentation/mainstage/dataset-documentation/wave/3/datafile/c_indresp/variable/c_indscub_xw</a>).</p>
<p>Similarly, there are three entries indicating that the self-completion part of the interview was refused but the response to the "What is the sex of your first friend?" question contains valid data even though the question is in the self-completion module.</p>
<p>Am I missing something?</p> Understanding Society User Support - Support #448 (Closed): weightshttps://iserredex.essex.ac.uk/support/issues/4482015-11-16T00:17:28ZVernon Hedgevernonhedge@hotmail.co.uk
<p>I am looking at data exclusively at Wave C Understanding Society c_indresp.sav. I am planning to employ model based inference which may (as needs be) incorporate weight, strata and PSU into the model.
<p>I am having difficulty finding out how the weights were computed. I was hoping to use include the variables by which the weights were calculated within the model and specify PSU as level 2 random effects. I just cannot seem to find how the weights were calculated from Understanding Society documentation.</p>
</p>
<p>All the variables are from the c_indresp file. The 12 are listed here as name, “label”, [position number in variable view of c_indresp.sav]</p>
<p>c_sex_cr “sex (corrected)” [2292],<br />c_age_cr “age (corrected)” [2294],<br />c_birthy “year of birth” [2771], <br />c_big5c_dv “Conscientiousness” [2896],<br />c_big5o_dv “Openness” [2899],<br />c_hiqual_dv “Highest qualification” [2904], <br />c_gwri_dv “Cognitive ability: Immediate word recall: Number of correct items” [2915], <br />c_cgvfc_dv “Cognitive ability: Verbal fluency: Count of correct answers” [2932],<br />c_cgna_dv “Cognitive ability: Numeric ability: Count of items answered correctly”[2935], <br />c_jbnssec8_dv “Current job: Eight Class NS-SEC” [2947],</p>
<p>I am also having difficulty identifying which weight variable would be most appropriate to my analysis according to the w_xxxyyzz_aa scheme (p67 of the User Manual).</p>
<p>I can fill in this much c_indyyzz_xw – i.e., I know I am dealing with wave c only (so c_ and xw) and only with adult (16+) respondents (so ind).</p>
<p>I have identified 4 weight variables relevant to a cross-sectional design in the c_indresp file,</p>
<p>1. c_indpxub_xw “combined cross-sectional adult main or proxy interview weight” [3002], <br />2. c_indinub_xw “combined cross-sectional adult main interview weight” [3003], <br />3. c_indscub_xw “combined cross-sectional adult self-completion interview weight” [3004], <br />4. c_ind5mus_xw “cross-sectional extra 5 minute interview person weight” [3005].</p>
<p>The yy component must be either px, in, sc, or 5m. I think I can exclude 5m, as none of the variables on my list is on the list on Table 25 (p56) of the User Manual. Likewise, viewing Table 24 (p53), I think sc can be excluded.</p>
<p>As for the zz component it is tempting to just use "us" (for “understanding society”?). The user guide advises me that the "us" designation refers to “GPS [General Population Sample] and EMB samples” – is this what is meant by “Mainstage”?</p>
<p>Looking at the “Levels of Analysis” in Table 28 (p62), I think I can exclude level 4 “Adult or youth self-completion”. I cannot, however seem to find information on whether the c_indresp variables I am using are level 3 “Adult proxy and main interview” or level 2 “Adult main interview only (no proxy)”. Using the Understanding Society website to search each variable name they all return “Mainstage Variable”. I cannot tell from this which level of 1 to 4 is the most appropriate to select a weighting variable.</p>
<p>So the two problems I have are 1) identifying which variables were used to calculate survey weights and 2) identifying which ”xw“ survey weight variable is most appropriate to my analysis.</p>
<p>I would be enormously grateful for any clarification.</p> Understanding Society User Support - Support #253 (Closed): general population samplehttps://iserredex.essex.ac.uk/support/issues/2532014-03-28T16:09:03Zpeter tammes
<p>Dear sir /madam,<br />We would like to use only the General Population Comparison sample. Which of the weight variables should we use in our analysis?<br />Thank you <br />Peter</p>