 Hello, everyone. Welcome to this workshop, Introduction to Evens, the Evidence for Quality National Survey. I'm Jen Buckley and I'm from the UK Data Service User Support and Training team. And I'm really delighted today to be joined by Professor Nyssa Finney from the University of St Andrews, who is a Director of Evens. And also Joseph Harrison and Michaela Strasna from the University of St Andrews who've been working on the Evens project. We also have Jill in the background today who's helping to make sure everything goes smoothly. And so here's an overview of the session that we have today. So it's in three parts. The first part Nyssa's going to talk us through everything around creating the Evens data. So she's going to be telling us about what was new about Evens and the motivations for the survey and how they went around recruiting and doing their data collection. We'll then move on to look at everything that is about the Evens data, so the sample, the contents of the topics and types of variables that we've got. And then moving on to sort of closing up by looking at how you might use Evens data. So she'll talk through a snapshot of some of the findings and some example research areas. And then I'll talk through how you can go about accessing the data from the UK data service. There'll be time for questions at the end, but we invite you to ask your questions throughout the session and people will be answering them as we go through. So to answer your questions, we ask you to use the Q&A box in Zoom. And these will be picked up as we go through in the Q&A box or answered live as we go through. So that's it for the introduction and I'm going to pass over to Nyssa. Thanks Jen. Good morning everybody and welcome to this very first session about this exciting new dataset, the Evidence for Equality National Survey. So a start-off was just the highlights of what is new about Evens. So Evens is the largest and most comprehensive survey of ethnic and religious minorities in the UK. It has 14,000 participants, including around 10,000 who identify as ethnic minorities. And the data are novel and robust in ways that I'll elaborate as we go through the session. And so it really does provide quite a unique rich resource for understanding experiences of minoritised ethnic groups in Britain. And importantly, and again I'll explain a little bit more about this, Evens uses some quite innovative survey methodology, non-probability methods to improve the representation across a large number of ethnic groups. So to help me and Jo and Michaela as we go through the session, we're now just going to ask you for a little bit about your interests and your experience of survey use before we get underway with telling you a bit more about how we went about collecting Evens. So if you just type in here using the same Mentimeter tool as you used before your main research interest, this can just be a keyword and we should see those start popping up on the screen. And we do have with us today a real range of people in the audience and that's fantastic to see the appeal of Evens. So we've got people from local government, from central government, from voluntary sector advocacy organisations as well as from a large range of academic institutions and a number of disciplines across the social sciences and humanities. And I can see from this dynamic word cloud that we've got quite a lot of you interested in health, health inequalities, education, racism, social justice inequalities. So a lot of these topics we will touch on today and certainly you will be able to research using the Evens data. So health is coming up there as one of the largest words, that means that quite a few of you have put that in there. So as we go through I'll emphasise some of the qualities of Evens for those doing health or public health or patient oriented research. That's great. Thanks very much. Now let's see a little bit about how experienced you are in using survey data. I've never used survey data. I've used it a bit or I use it often and it's part of your day to day work. Okay that's really helpful actually. I think most of you have responded now. So we've got quite a range and I'll try to speak to that as we go through. Most of you have some appreciation of survey data used it a bit but it's not a central part of what you do perhaps day to day. Some of you survey experts and I'll point to other ways that those of you who are very experienced in survey data might get more information and training on Evens and I'll try to be very clear so that those of you who don't have understanding about survey data or have experience will still get a good sense of the capabilities of Evens. Please do, as Jen says, ask your questions in the Q&A box. Jo and Mikaela are on hand to respond to those as we go through and please ask absolutely anything. Any question is a welcome and valid one in the session today. So let's move on to hear a bit about why we went about undertaking the Evens survey. So our first motivation for Evens was the very well-established context of ethnic inequalities in the UK and this had been revealed by research from the Dynamics of Ethnicity as well as many other areas of research across the UK and elsewhere. These very stark and very persistent ethnic inequalities across social realms and this became emphasised, highlighted and a more prominent part of political debates during the COVID-19 pandemic where we saw particularly in terms of health outcomes in those early phases of the pandemic very, very stark ethnic inequalities which are under discussion actually this week in the COVID inquiry. And it had been commented, included by Nazfruh and Bukhara, that the higher COVID-19 related mortality in places that had high ethnic minority populations were a consequence of social and economic inequalities driven by entrenched structural and institutional racism and racial discrimination. So we were motivated in producing the Evens survey by a concern about racism and discrimination and a concern that we couldn't understand this very well with existing data sources. And indeed there have been debates more generally about data deficiencies around ethnicity and there's fantastic work going on in many research groups and in national statistical offices to try to address this. But it came to a head really during the pandemic when we realised we couldn't evidence the kind of things we wanted to understand about the differential experiences. Because social surveys in the UK tend to represent a rather limited number of broad ethnic groups and the survey sampling favours by design intentionally areas of residential clustering of ethnic minorities which we could think of as being different characteristically from other parts of the country. Crucially general surveys don't have questions on the whole that are bespoke to the concerns of experiences of minority groups. So very deliberately and quite rightly they're designed to catch information about the general population but this doesn't allow us to know about specific experiences including racism and discrimination that might more prevalently affect minority groups. We have fantastic sensors and increasingly administrative data with good population coverage across ethnic groups but that doesn't have good topic coverage. So the data on ethnicity is patchy let's say. And then we had this interest in innovative survey methods to try to better represent ethnic groups and there hasn't been a prior application of this rapidly developing area of survey research non-probability methodology to numerically small populations in the UK. So this was an opportunity to demonstrate and try this approach to social surveys which has implications for other kinds of data production as well. We were motivated by knowing more about the patterning and the mechanisms of ethnic inequalities and to address the ethnicity data gap. How did we go about recruiting and collecting the data for evens? Well the questionnaire is quite a long one it's a 30-minute questionnaire and this is available in the technical reports that General will point out to you later on and also on the evens website and crucially this was developed in collaboration with partners on the project so the content was shaped to some extent by the interests of race equality organisations who we partnered with which I'll come on to in a moment. The survey was administrative administered online through an open web link with also a telephone interview option and it was available in the 14 languages shown here on the slide and importantly we didn't have a sampling frame, we didn't invite specific people or households to take part in evens, we advertised this widely, we said if you consider yourself to be an ethnic minority no matter where in Britain that you live or how you identify, commentate part in the survey and tell us about your experiences so it was open to everybody who considered themselves to be an ethnic or religious minority. Residency in Britain was a requirement for eligibility, we issued an incentive a 10 pound voucher on completion of the survey and data were collected between February and October 2021 so the height of the COVID-19 pandemic and the questionnaire itself which you'll learn a bit more about as we go through some of the examples of what you can do with the data had original questions but also questions borrowed from or developed from existing well-established social surveys in the UK and elsewhere. We went in partnership with Ipsos to deliver the survey and it was conducted with full ethical approval including a number of amendments from the University of Manchester. Now this approach that we talk of non-probability sampling with no sampling frame required us to take a responsive approach to recruitment or what does that mean? Well first of all we set target quotas, we had an aim of a certain number of people in terms of their ethnicity, age, sex and region of residence in Britain and this was to start us off as close as possible to a demographically representative sample. There was an initial registration or screening questionnaire as part of the survey and this ensured eligibility of participants in terms of their residence in Britain and their identification as an ethnic minority. So those whose eligibility was validated were provided with a link to go through to the main questionnaire. So rather than checking who had participated in relation to our sampling flame, for example our list of addresses who we posted invitations to, we monitored who was responding in terms of the demographics in relation to the target quotas and we were doing this on a daily basis throughout the field work and this enabled us to be responsive and adaptive in our approach to recruitment. So where we could see that we were below our target on a certain ethnic age, region, combination we could direct our recruitment efforts working with our partners in that direction to ensure we were meeting our quotas as far as possible. So it was a very intensive field work approach with this daily monitoring of data and then responsive design of the recruitment and promotion methods as a result of that. So what was used to indicate where we needed to target our recruitment efforts were our indicators which are multivariate indicators of representativeness and being advocated in non-probability survey work. We did face many challenges during field work which is always the case in any form of data collection but particularly when you're trying out something new and an issue that I'll raise here which doesn't affect your use of the data but I think is really important methodologically as we think about how do we collect data is a challenge of survey fraud and this came about for us because of this combination of an open web web link with the voucher being provided. So anybody could get to the web link it was widely advertised and people who completed the survey knew that they would receive a £10 voucher so that combination provides potential for people to complete the survey not with a view to contributing to our valid data collection but with a view to financial benefit for themselves. So in March 2021 several weeks after the launch of the survey the daily monitoring that we and the evens team together with Ipsos were undertaking revealed a spike in survey completion and completion via snowballing which I'll come on to say a little bit about in a moment and there were some aspects of these completions during this spike which caused concern for us including clustering in certain language and ethnic groups, non-standard questionnaire timing so it took people either longer or shorter to complete the questionnaire and they were doing it at times of day that were extraordinary given that this was supposed to be for UK for British residents. Use of fake postcode information suspicious open-ended responses to those questions that required writing answers, sufficient IP addresses and use of suspicious email addresses which participants had to provide for the receipts of the voucher and so these patterns in the monitoring suggested that the completes in this phase were largely coming from survey farms or from digital bots so automated responses meaning that we could not trust those data. We made the difficult decision to pause data collection at that point so that we could instigate a number of data quality initiatives and this is all documented in quite some detail in the technical report. We sent follow-up emails to the the cases we had identified as being suspicious and when it was confirmed that these were indeed not valid cases voucher payments were not made so we did not in the end lose money through this and we're quite confident in the quality of the data that is now available to you through the UK data service and that's because we implemented a large number of additional quality assurance measures during that period of a couple of weeks when we paused the field work. We brought in additional digital fingerprinting. We introduced a recapture type question at the beginning of the survey to mitigate the use of bots. We added an extra validation of survey respondents before supplying any snowball links to them. We revised our method for daily checks and brought in additional validation checks for those daily checks on the completions and crucially we switched from digital provision of the vouchers to postal delivery of the vouchers and this we think was really very important in ensuring that we have very high quality data because people who completed it had to have a valid residential address in Britain. Of course this has implications for the operation of the survey for the resourcing and the timing and that was certainly worthwhile in terms of improving the quality of the data. So we overcame the challenges. We prepared this questionnaire. How did we actually get people to know about the survey and to take part in evens? So key to this were promotion and partnerships. So from the outset at the very start of the project we partnered with these organisations shown here who are leading race equality organisations in the UK and they represent a number of regions of the UK and in some cases specific minority groups. So we worked with ethnic minorities and youth support team Wales, business in the community, the Muslim Council of Britain, friends, families and travellers, the Stuart Hall Foundation, the Runnymede Trust, the Ubele Initiative, BMIS in Scotland, Institute for Jewish Policy Research, the Race Equality Foundation, Operation Black Vote, the NHS Race and Health Observatory and Migrants Rights Network. And all of these partners were heavily involved in designing the questionnaire feeding back on the questionnaire but mainly on publicising the questionnaire, publicising the survey to their constituencies through a large number of methods. And this meant that we had a number of routes into the even survey. So the final data come through different participation starting points if you like which are shown in the colour boxes on the left hand side of this slide. So we had the open promotion with the voluntary community sector partners. We had snowballing which is when somebody completes the questionnaire, they were asked if they wanted links to the questionnaire that they could pass on to their friends and family. We had direct activity of the community organisations which includes their email lists and regular targeting through their distribution networks and newsletters and other activities through media and radio newspapers and so forth. We also made use of online panel surveys and this was the way that we generated the general population sample or the white British sample for evens. So all of the recruit that I'm talking about is for the ethnic minority part of the sample which is just under 10,000 people in total. Different from many surveys that advertising the promotion around evens was really quite crucial. This is just an example of some of the marketing materials. We held events. You can see an example here with representatives from many of the partner organisations. We had large social media campaigns across multiple platforms, Facebook, Instagram, Twitter. We had strong online presence including on the front pages of all the partner websites. We had media campaigns with TV, a partnership with Sky. We had some promotion through local community radios including some discussion events and mini series. We had podcasts and other efforts to really raise the profile of the survey and make people aware that this was going on and the messaging here was really very important as well in order to appeal to different groups that we were trying to engage. So at the bottom left for example here we see one of the examples of promotion that went out through friends, families and travellers trying to engage Roma and traveller communities and you can see in the text here that there's an emphasis on confidentiality, that the data, the information will be fully projected, protected, responding to concerns in that community in particular and also an emphasis here that there's a free phone telephone number. The advert at the bottom right is an example of one that went out with Institute for Jewish Policy Research and here you can see the emphasis is on recognising Jewish populations as an ethnic and religious minority. So there was bespoke communication through the different channels. However we still were seeing in our monitoring that some of the ethnic groups that we really wanted to engage in the survey were not participating as much as we would have liked and this was particularly the case with the Roma and Gypsy traveller communities which is not surprising. Anyone who has done research with these groups will know that this can be a particular challenge and so we took quite a different approach to the recruitment of the Roma and particularly the traveller communities in evens. For the focus groups that we ran via friends, families and travellers in the early months of the survey when we noticed this low participation revealed concerns amongst those communities around trust in us as academics and as data collectors, concerns about confidentiality that the data may somehow be revealed that their individual information might become known and there were issues of digital literacy amongst some of these communities. So in partnership with friends, families and travellers we trained six community interviewers and employed them to undertake face-to-face interviews to complete the evens online questionnaire. So they went out into communities and with individuals went through the questionnaire with them face-to-face using the online web entry to the survey. This took place in July and August in 2021 as part of the reason why the field work period was extended through to the autumn and we recruited 309 participants via this method giving us around 350 Roma and traveller participants in the survey overall. So this brings me to the end of the section that I was going to cover in terms of the collecting the data. So I want to pause at this point to see if there are any questions, queries that we might address at this point. But let me tell you about the data. What do you get after all this recruiting and overcoming some challenges? What do you get from us? Let's think about the sample of evens who is in it in the end. So the summary is we have just over 14,000 people just under 10,000 identified as an ethnic or religious minority and we have larger sample sizes and crucially more ethnic groups represented than any other UK social survey. So those of you familiar for example with Understanding Society, fantastic UK household survey does have an ethnic and immigrant boost but really is useful for looking at broad ethnic groups with a starting sample of around a thousand for those broad ethnic groups. You can see here we have pretty high numbers for survey data for quite a lot of ethnic minority groups, lower for others, for example the white Irish and the white Roma towards the bottom. I have to stress here that these are the unweighted sample sizes. So in doing analyses you would very rarely use these numbers might not even look at these kinds of numbers. This is actually a number of people but when you use the data you need to use them in such a way so that you can consider them to be representative of the population and the adjustments we've made in the data, the weights we provide, allow you to do this and I'll come on to this in just a moment. What about breakdowns of the data in other respects? These are the numbers for religious groups so we're really successful in the partnership with Muslim Council of Britain for example and getting really high number of participants who identify as Muslim and pretty good participation across the UK and we're particularly pleased to have high numbers in Wales and Scotland allowing us to compare those and other regions of England with one another. The age distribution is there you'll notice that this is somewhat skewed, somewhat larger numbers in the younger age groups that are common finding in survey participation and something to be aware of if you're using these data. You can find lots more detail about the sample in the technical report and in the evens book as well. This is a table from that book which shows for each of these 21 ethnic groups that we have in evens the proportion that a male and female, the proportion in each age group and the proportion in each region and here this is all weighted percentages and you have the weighted number of people at the right hand side in that final column and you can see the weighted number for most of the groups is smaller than the actual number of people we spoke to because of course if we're making our sample look like the general population of Britain then we have to add weight to, we have to multiply the numbers in the white British group because in the oval population they do account for three quarters of the population. So what did the weighting involve? This is actually quite important and this might get a bit technical here for many of you who are not particularly interested in the weights but it is important for me to outline this because it does affect how you use the data. The main thing is you have to use the weights but let me say a little bit about it for those of you who do have an interest in how we did this with this non-probability sample. So what do the weights do? They enable you to use this data as if it were representative of the British population. This is standard all surveys and many other kinds of data have weights in them to adjust what we have in the data so that it looks like the population overall so that we can use it in a way that allows us to make comments about the general population. So our weights account for both coverage errors and selection bias. So coverage error that means if we don't get enough older people we don't get enough people from the northeast, we don't get enough people in the black African ethnic group. In the end we need to adjust to make the numbers in our sample look approximate those in the population overall in terms of those key demographic characteristics. So we did that in terms of ethnic group by age group, sex and region and I'll say a bit about the data we used in a moment for these calibrations. So the selection bias which is the more complex aspect of the weighting and what this does is says that okay well some people are more interested in surveys than others some people are more likely to take part in this survey and some people are more likely to take part in surveys in general and quite a lot is known about what makes people take part or want to take part in surveys and this is known specifically about how this might vary across ethnic groups but there's large literatures that tell us about this likelihood of taking part but we wanted to adjust for this because the answers of those taking part are likely to be skewed in a certain direction and we wanted to take account for that in the data that we publish and make available to you. So what we've used is a propensity score approach, a quasi-randomization which links evens participation to that from a reference probability sample, don't worry at all if this is all gobbledygook to you it doesn't prevent you from using the data. So we take questions that have been asked in both the evens data and that was part of the design and in reference datasets that also have ethnicity in and we look at the responses in the two datasets and we match them up to adjust our evens data so that we're confident that it accounts for some of this bias in who's likely to take part and we adjusted this on the basis of people's voting eligibility, their interest in politics, their subjective general health, their participation in religious events, their religiosity, their citizenship, their trust in parliament and trust in police. So these are all aspects of people's experience and characteristics that are known through prior studies to be associated with either greater or lesser likelihood of participating in surveys. So we took this information, we borrowed from this knowledge about the participants in our survey and participants in other survey to adjust for any bias there may be in terms of who did participate in evens in the end. So to make these adjustments we used a number of reference datasets, we used the censuses from 2011 for England's Wales and Scotland and for England and Wales censors 2021. We used the ONS annual population survey, we used the F-POP estimates from University of Leeds and we used the European Social Survey but the key message here is this complicated work all leads to it's really vital to use the weights when using the evens data. We provide several weights in the dataset but for general use this is the weight variable that we recommend that you use. And for those of you who are really interested in knowing more about the statistical approaches, about the detail of this weighting process and indeed the representative measures and such like there will be a follow-on session to this coming soon on non-probability survey design that will be delivered by UKDS together with Professor Natalie Schlomo from the University of Manchester who was the statistical lead on the evens survey. So you've got your sample, you've got decent numbers of people across ethnic groups more than in any other survey, you've got the weights that allow you to use it as if it's representative but what can you say? What kind of things can you talk about with evens? We have 679 variables covering a number of topics. Socioeconomic and financial circumstances what is particularly useful I think and interesting here is a lot on COVID-specific circumstances and changes. So receipts of benefits, furlough, changes in working hours, need to home school, need to provide care, so very many indicators of what we might generally refer to as socioeconomic precarity in 2021. We have a whole module on ethic and racial identity, I'll give some detail on that in a moment. We have information on housing and demographics and this is largely I would say can be used as background, control, contextual information to use in combination with other variables. There's a lot on health, so those of you who here are interested in health research, we have a wide suite of variables on both mental health conditions, individual variables on a number of mental health experiences and physical health conditions and prevalence of very specific diseases and conditions. I'm sure Jo and Michaela can give some examples if you have some questions on that please put them in the Q&A. We have a number of questions on Black Lives Matter, what did this movement in support of anti-racism, what did people think about that at this time of heightened health and social inequalities? We ask about social cohesion and belonging, attitudes towards the police, compliance with COVID-19 initiatives and recommendations, trust in government at various different levels and a module on racism and discrimination which I'll come to in a moment as well. So really rich datasets with some quite unique variables, some that allow you to cross-reference with other data and some that are just really useful to have in there as context and background and quite a lot that are very specific to the COVID pandemic and probably one of the most detailed sources of that kind of information particularly if you're interested in this large range of ethnic groups that evens offers. So ethnic identity, a lot of you are interested in how we went about this. We collected information about ethnic identity a number of ways. First of all we asked people we're often asked to record ethnicity, how would you describe your ethnic background in your own words? So we have a write-in so we have 14,200 write-in responses of how people consider their own ethnic identity. We also ask people to do the categorical tick boxes which you'll no doubt be familiar with from censuses and various administrative data collection using the census 2021 ethnic group categories. We asked religion in the same standard categorical way for people identifying as Jewish we asked about membership of synagogues. Then we asked about some questions about the meaning of ethnic and religious background, how important is your ethnic background or religious background to the sense of who you are and there are a number of questions about activities relating to ethnic and religious identity. We also have country of birth of the respondent and of the respondents parents so there is some capability to look at migration, immigration and migrant generation with these data and in the next part I'm going to give some flavors of some of the results from the data and bring some of these variables to life a little bit. On the racism and discrimination this is a very large and unique set of information. We ask about different forms of racism and discrimination. Have you been insulted for reasons to do with ethnicity, race, color or religion? Has property been damaged? Have you experienced physical attack? Have you been treated unfairly? And we ask about that one in a number of domains in education and your job out in public by friends or family in housing in general or in some other context. We ask if neighbors have ever made life difficult for you or your family. We ask if people worry about being harassed and we ask if there has been any change in unfair treatment since the outbreak of the pandemic. And for all of these questions about insult, damage, unfair treatment and physical attack we ask people to say whether they've experienced that in the past year, the past five years, the past 10 years or over 10 years ago. So although this is a cross sectional survey where you just collect data at one point in time, we do have the capability to have some time dimension to our description of the experiences of racism. We also ask how people responded to experiences of discrimination or unfair treatment. Did they try and do something about it? Did they accept it? Did they work harder to prove people wrong? Did they talk to someone, express anger? Did they pray about it? Really interesting set of information about responses to experiences of racism and discrimination. I'd like to highlight a few other variables that I think are really interesting in their evens data. We ask about type of accommodation and for those of you interested in travel communities there are a couple of variables in there, the categories in their responses that you don't usually get in information about accommodation. And this was a result of working in partnership with friends, families and travellers that we had those questions in there. Similarly through that partnership we had questions in there about access to sanitation and water services. We asked about outdoor space, which was a big topic of debate during the pandemic. Homeworking, working arrangements, financial circumstances and benefit receipts, loneliness and social isolation, specific mental health conditions, specific physical health conditions, receipt of care, experiences of having the COVID virus, household income and immigration status. So all sorts of information that you can use in combination with this wide range of ethnic groups to really investigate quite a lot of novel areas that we don't have a good understanding of so far. There are some sensitive variables from evens including gender identity and sub-national geographical indicators, which will publish in terms of area classification such as deprivation, urbanness and other aspects of local place. These are going to be made available in a further data set. These aren't in the safeguarded data set that you can now access. These will be made available in a special license data set. They have to undergo some other processes of disclosure control to ensure that it's not possible to identify individuals in these data. I think I'm going to move on now to ensure that I can show you a bit about what the data show in the time. I realise there are still some questions that I haven't addressed here, but we'll hopefully have a bit of time at the end to come back to that. Let's see a little bit about the data. What kind of things can you do with it? I'm going to present here a real snapshot of some other findings, just indicative of what you can do with the evens data. I've taken most of these from the evens book, which was published earlier this year. This is free as an e-book from the evenservice.co.uk website or from the policy press website, so go ahead and download that after the session and have a good flick through. Each chapter has a summary of the key points and there's lots of what I hope are quite accessible figures to illustrate the findings. First of all, I think this is a really powerful message actually. I think minorities have had a higher likelihood than the general population of having been recently bereaved, particularly in relation to COVID-19. So if you're an ethnic minority, have a higher likelihood of losing someone, someone very close to you dying recently. All of the ramifications and implications of that. That is shown by the charts on the left-hand side. Let me explain these a little bit because there are a few that look like this. So we have a dot for each of the 21 ethnic groups in the evens survey. The further to the right that dot is, the higher the likelihood of people in that ethnic group being bereaved. The chart at the top is COVID-19 related bereavement. The chart at the bottom is bereavement of any kind. So if we take that chart at the top, any dot that is to the right-hand side of the red line indicates a higher odds of bereavement compared to the white British population, which is indicated by that red line. And what's immediately obvious even without looking at any detail of the numbers is the number of those dots for the ethnic minority groups that are right of that red line. The number of ethnic minority groups who had a higher likelihood of being bereaved in 2021 or just prior to that compared to white British. I think minorities also have a higher rates of experience in financial difficulties than here we presented during the pandemic than the white British. So the same kind of chart. Red line again is indicating where the figure is for the white British population. Dots to the right-hand side of that white line indicate higher rates of financial difficulty. And you can see that this is the case for many ethnic groups, particularly for Arab towards the bottom there. Also some of the mixed groups, white and black Caribbean. This is adjusted for age. Those of you who might be thinking about is that because the mixed groups are particularly young. So these are age adjusted rates. Also the Pakistani group, higher rates of experience in financial difficulties than the white British. So you can see what's really powerful in these evens data is the ability to talk about a whole range of ethnic groups to compare across these 21 groups. But interestingly across Britain, ethnic minorities have higher levels of trust in UK Parliament than the white British. So we've aggregated ethnic groups here, which of course you can also do. So ethnic minority people in the green bars, white British population in the yellow bars. And we have the proportion of people who expressed a great deal or a fair amount of trust in UK Parliament. And this is actually management of the coronavirus pandemic. So trust in Parliament in relation to COVID-19. In all of these constituent countries of Britain, we see minorities have a higher proportion who trust the UK Parliament management of the pandemic. And this is particularly evident in Wales. And our artists had this higher rate of trust than the overall the general white British population. I find the questions on local belonging really interesting as a geographer. And here we see quite clearly that people identifying as Indian, Pakistani and Bangladeshi have notably strong sense of local belonging. So this is the likelihood, the odds of feeling a strong sense of local belonging. Again, to the right of the line, it indicates particularly high levels. And you can see those dots to the right of the red line for the Indian, Pakistani and Bangladeshi participants in evens. The Bangladeshi approaching three times are slightly had to have a strong sense of local belonging compared to the white British population. And on this theme of belonging again, most ethnic groups have very strong feelings of being part of British and English society. So slightly different chart here. This is the the probability of feeling parts of British and English society, British in the left hand panel and English in the right hand panel. And white British is included here. That's at the bottom row of this chart. So here we don't have this reference, this comparison to white British. This is this is the probabilities for all ethnic groups. And to point out here that it's really hard, like most people, no matter what your ethnic group, feel a strong part of society with the exception of the Roma population here. And I want to present hot off the press two sides with very current research that we're undertaking in the evens team to try to spark your interest in some of the things and some that you might be able to do with it. And this is on the topic of ethnic identification. So here what we've done is taken the responses to that writing question where we asked people to describe in their own words, what how do they describe their ethnic background. And we've analyzed all of those 14,000 responses to that question, the the text answers to that question. In terms of, did they use standard concepts and language, as in the concepts and language used in the official office for national statistics for national records of Scotland ethnic group classifications? Or do they use some other ideas about ethnicity that aren't captured in current measurements? And we've turned these this use of other ideas, complex articulations of ethnicity. And that's showed in the yellow segments of the bars here. So for each each ethnic group has a bar. And then the proportion in that ethnic group that had a complex articulation of ethnicity is indicated by the yellow segment of the bar. So people who, when they ticked the box for ethnicity, ticked any other ethnic group as the category that they identified with. If they were in that category in the tick box, then half of them actually when articulating ethnicity themselves were using complex ideas. Ideas not captured in standard data collection. And overall 20% or one in five ethnic minorities and even use a complex articulation of ethnicity. So in one way this tells us that most people are using standard articulations. So the care for work of developing these ethnic group categories that has gone on for several decades now in statistical agencies has really paid off. Most people feel pretty happy with what is being collected. But there's this minority is 20% that actually the way they think about talk about articulate their ethnicity is not being captured in the current way that ethnic groups are categorized. How did people describe their ethnicity? These are some examples from the writing responses that really indicate this kind of complexity. And here I've picked up ones that have a theme of place and a theme of cultural cultural affiliation as well. I'm born in Kenya, great great grandparents from India, brown skin, but of African origin. However, I consider myself British, a Londoner through and through. So local place is important. Cornish, not British, not English. Hungarian from Transylvania, Romani. This is an ethnic minority in Romania. I'm Turkish, but my mother is of Tartar descent and my father immigrated to Turkey from Greece, where he was part of a Turkish-speaking Muslim ethnic minority. So this person who when they do a tick box, they say I'm an other ethnicity, they have Turkish nationality, their mother has a different descent, they have immigration history from Greece via Turkey, and they're part of a religious minority. So we get here at some of the complexities of the meanings and self-identifications of ethnicity. I would describe my ethnic background as Latino. Usually I don't see any option that I feel described my ethnic background when I asked to record my ethnicity. It seems like they forget of the people from the American continent. So some really rich and interesting information there about ethnicity, ethnic identification, and ethnic measurement. And I want to end the snapshot of results from evens with one on racism, which is one of the more unique aspects of the dataset, which of course can be used in combination with health outcomes, with other outcomes of social cohesion of socioeconomics, for example. This is from work that's in preparation. So please for this on the previous slides, don't cite these for the time being they're working in progress. 80% of evens participants from minoritised ethnic groups have experienced some form of racial discrimination at some point in their life. And we have that proportion displayed here across the ethnic groups. And I think this in itself really points to the need to both collect data about experiences of racism and to better understand what this means to people and to their everyday lives. Here's some examples of research areas that we have going on within the evens and code team. And I'd be really delighted to hear about what you are doing or think you might do with the evens data. We're looking at life course experiences of racism across ethnic groups, connections between racism, ethnicity and loneliness, protective effects of religion for loneliness and social contact during the pandemic, social connectedness, migration and loneliness, prevalence of common mental disorders during the COVID-19 pandemic across ethnic groups, ethnicity and local neighbourhood belonging. What does ethnicity have to do with local belonging? How is ethnic identity articulated and what can we learn from this for official ethnic group categorisations? Political trust and how this connects with COVID-19 compliance? Did you get a vaccine? Did you stay at home? And methodologically, how do we produce robust non-prolability survey data? So reflections on our methods and the success of our methods. We're going to show you now how to access the evens data. Then we should have a bit of time at the end for any final Q&A response. But Joan McKayla and I can continue to try and type away in the Q&A as Jen takes over and tells you how you can get the evens data via the UK data service. Yeah that's right. So I'm going to talk through how you might access the evens data from the UK data service. So the data is freely available from the UK data service. I've got the full catalogue record here with a unique study number as well. The data is available under it's safeguarded data and it's available under what we call the end user license, which essentially means you need to register to access the data. For those who are very new to the UK data service, to sort of access this data, you need to register with an email address. For UK higher education, you use your institutional username and password. For others, you may need to request a username when you go about accessing the data, but you'll be guided through this process when you do. So the first part in trying to access it is to find the data. We're going to put a link into the chat and I'll also show you how to find it in the catalogue as well. And the process of accessing it is so once you've found it in the data catalogue, you will go through to access the data. And then you sort of step in terms of accessing it is you set up a project where you just add a few words to describe how you might be using the data, you allocate the data to a project and you can then download it. So if I just move over to the UK data service catalogue, so this is a home page and you can search for data and luckily as even has such a nice name, it's very easy to find it in the data catalogue. So here it is at the top and see number 9116 Evidence for Equality. If I click on this link, it will take me through to the catalogue page. And the catalogue page consists of a number of tabs where you can find all the information that you need. So if you tell a tab provides a consistent version of all the information, you might need to know about the study. And the documentation, you can access more information about the data. So this is where we have to use the guide. We have the technical report that Lisa mentioned earlier that goes into all of the details around the data collection and processing. And we have a code book as well that you can use to explore variables in the data set. And what's important to know is that you need to register to access the data, but this documentation is freely available on the catalogue records. You can spend time looking through this and finding out more about the study before registering. The resources tab here includes some useful things. So under publications and reports, you can find a link through to the book that Lisa has shown some findings from and also a link and other to the even project websites. You can find out more about the project in general. And then if you go to access data, you find the steps to follow to go through. So here it's prompting me that I need to log into my account in order to access this data. If you're registered already, you just log in. If you're not already registered and you click this is when it will stop prompting you to go through that registration process. So that's essentially everything that's involved in accessing the data. One final comment is if you do use the data, we do encourage people to cite the data. And you can find all the citation details for the data on the catalogue record and sort of citing data in this way provides a way to sort of credit the work of Lisa and her team and also helps them see how the data is being used. And finally, the final slide to give you the contact details for me, for the centre on the dynamics of ethnicity and for the even survey. If you're on social media, please do follow us. Please do sign up for the code newsletter. Very glad to hear feedback. Thank you very much for your interest and engagement this morning.