Mae'r next item of prison is topical questions. I go to question number one from Christina McKelvie. To ask the Scottish Government when powers over social security benefits will be devolved to the Scottish Parliament. Minister, Jeane Freeman. The Scottish Government has consistently been clear that we will have a Scottish social security agency delivering the 11 devolved benefits by the end of this parliamentary term. panfioediau rhai oedd honganol gynhyrchu, ac mae oedd unrhyw algu ddygu gweld penethau yn gweithio. The biggest transfer of power since the revolution began was not only because it was not a lift-and-shift transfer of a complete system—much is what we argued for. It is the transfer of responsibility for 15 per cent of the payments that were made by an integrated welfare system that has developed in a piecemeal fashion over more than 50 years and is again itself currently undergoing further reform and change. The scale and the complexity is clear and everyone sitting in a democratic parliament knows that safely delivering this transfer is not possible without the underpinning of both a legislative framework and a robust delivery infrastructure. Members of the social security committee will be well aware of the need to work closely with the DWP to make sure that the Scottish and the UK systems are aligned. We are determined to work closely with the UK Government to iron out issues and resolve the complexities, but we are also determined not to allow the important transfer of powers affecting 1.4 million people in Scotland to be used as a political football. Christine McElvie Can I thank the minister for that detail on the transition? Can the minister tell us how the Scottish Government would ensure that no one transferring to any of the benefits that will be delivered by the Scottish Government slips through the gaps between the two systems in that transition? Thank you, Presiding Officer. Ms McElvie has, of course, gone straight to the heart of what is important, and that is the safe and secure transfer of the devolved benefits. Making sure that every one of the 1.4 million people who rely on this support receive the money that they are entitled to on the day that it is due. Throughout the three-month consultation that we have just finished, no one asked us to do this quickly. Everyone asked us to do it safely. That is why the current experience groups with lived experience of the current benefit system and the other expertise that we will draw on are so important and why we will make sure that this Parliament has the time that it needs to fully scrutinise the primary and secondary legislation that will be required before we can deliver the benefits. That is, as it should be, in the interests of people across Scotland—people, Presiding Officer, who wake up every day in a cloud of worry and anxiety brought on by the impact of the UK benefit system. It is deeply disappointing that, in recent days, both Tory and Labour have tried to score political points on the basis of unfounded assertions with an utterly careless disregard for the impact their words have on those people that they claim to represent. The minister herself gets to the heart of the issue here. It is about safety and security, and that is what people seek. How will the minister reassure those who have been victims of the conscious cruelty of the current system, those with motor neurone disease, those with long-term conditions and those with life-threatening conditions? How will she reassure those people who have been affected by the conscious cruelty of the current system that any new system in Scotland will not just replicate those failures but address some of the failures that those people have experienced? I notice that Ms McKelvie uses the words of Paul Laffordie, the writer of I, Daniel Blake, to refer to the DWP's callous conditionality and benefit sanctions regime. There is a great deal, Presiding Officer, that we will learn from the experience of the DWP and the UK benefit system, not least a rush to give timescales and commitments that are persistently not met. If we take the example of universal credit, which was announced in 2010, delivered in their act in 2012 and is still not expected to be completed in its roll-out until 2022, that is why not only the approach that we have taken in the consultation of directly listening to those with lived experience of being on the benefit system and those with expertise working with them and those indeed working in delivering that benefit system is exactly the approach that we will continue in the weeks and months following the consultation through the experience panels and the other matters that I have referred to. Unfortunately, with 15 per cent, we cannot change all of the unfairness in the current system, but with the limited powers that we have, we are determined that we will take the time, we will listen, we will not be bullied into false timescales or deadline dates. Within the lifetime of this Parliament, we will deliver a social security system for Scotland that everyone in Scotland can be proud of. The Labour Party has supported you when you have talked about a social security system with fairness, dignity and respect at its heart. Why has the Government chosen to delay assuming the powers and continuing a system in which the Government's own backbenchers are talking about a system of conscious cruelty? Why did the delay in leaving the powers with the DWP? I have to say to the member that to call this delay is to completely misunderstand the process of how you go about building a new public service. First of all, you need the legislative competence in order to bring before this Parliament the legislative, the draft bill that you will be entitled to scrutinise, as will everyone else, which then forms a framework on which we can begin to deliver on executive competence. I am surprised to be frank that Labour has chosen to join the Tories in an attempt to use this situation for political posturing and to make political points. You have broken the consensus and the agreement that we had that we would work together on this matter. You should know, sir, full well that we need to take the time, not only to listen to those who are currently recipients of the benefit system, but also to put in place the significant infrastructure on which it will depend. We need to test that infrastructure because the last thing that I want—and I would imagine the last thing that you want—is that a single person, a single one of those £1.4 million, falls through a gap simply because members in this chamber are so concerned with the interests of their own political party and are little concerned with the interests of those that we are here to serve. If there is confusion about whether there is delay here, perhaps that confusion has been caused by the Scottish Government's refusal to explain to this Parliament in a timely manner what they are asking for in meetings with the joint ministerial working group on welfare. The most recent minutes of which disclose that it is Scottish ministers, not UK ministers, who have asked for something called split competence with regard to the transfer of welfare powers. Can the minister explain for the benefit of this Parliament for the first time what exactly split competence is? Can the minister clarify whether split competence will apply only to tranche 2 and not to tranche 1? Can the minister clarify what impact split competence will have on the timing of the transfer or commencement of powers under sections 22 and 23 of the Scotland Acton? Finally, can the minister explain why none of this—none of it at all—was explained to the Social Security Committee until after Damien Greene gave evidence to us on 3 November? What do the ministers here have to hide? That is quite astonishing. Mr Tomkins is a member of the Social Security Committee. Let me just read you a few words from the cabinet secretary's appearance before it on 30 June this year. That is a number of months away. It is unfortunate that Mr Tomkins did not have his listening ears on. It is important to distinguish between the commencement and the delivery of powers. The legal commencement of powers is the first stage in a process and is some distance away from the delivery of new or existing benefits. I think that that is clear. I repeated it myself when I was there on 29 September. It has been repeated in debates in this chamber, so it is unacceptable if not to say downright disingenuous for any member, particularly a member of the Social Security Committee, to claim that this is news to them, that this was unknown. It applies to tranche 2, and I have already explained the difference between legislative competence and executive competence. Legislative competence allows us to bring to this Parliament the draft bill, which will be the framework on which we will establish a social security system for Scotland. Executive competence will allow us to then deliver the benefits within that system and from that agency. The difference is clear, and I would have expected that someone of Mr Tomkins' learning would have understood it. The minister clearly has her brass neck on today, because, in 2014, the SNP told the Scottish people that they could establish all the mechanisms and all the institutions of a new state within 18 months. Now we find out that they cannot even administer 11 benefits within the next three years. Was the claim made in 2014 a mistake? Was it a typo in that great organ of truth, the white paper, or was it just a blatant attempt to mislead the voters? I have no brass neck on today, but I have to say that Mr Finlay should be ashamed of himself. If he goes and reads that white paper, he will see that we were very clear that there would be a period of transition. If Mr Finlay would stop for one moment and pause and think less about himself and less about his own party and think about what it is required—I appreciate it sometimes since you were in power—and you may have forgotten what it is required in order to bring into being a new public agency for the first time in Scotland. It requires that we take the time to get it right, unlike the UK Tory Government, and that we have first and last in our sights our attention to those £1.4 million, people who will rely on us to deliver those benefits on time in the right place to the right account. For us, they are our first and last focus. How unfortunate that, for neither Labour nor the Tories, that is not the case. To ask the Scottish Government whether powers will be removed from councils by its local government review, and, if so, which? Ministerial colleagues and I are currently engaging with COSLA on a wide range of public service reform issues. We believe that local control by, rather than on behalf of communities, is key to delivering better public services and improving outcomes for all in Scotland. Scottish ministers are currently engaging with COSLA on a wide range of public service reform issues. Our discussions will include consideration of the scope and timing of a review of local government functions. That review will be an opportunity to build an emerging good practice to energise local democracy and to increase community empowerment. The Times reported on Friday that ministers are planning a major assault on town halls, with measures such as forcing councils to merge services, devolving services down to local areas, and stripping councils of some areas of responsibility such as roads. The idea of a national road service with no democratic accountability fills me with horror. The article further said that the plans were meant to be kept secret until after next year's council elections, and all this on top of raiding council coffers to pay for a national priority. Can I ask the minister for a straight answer to this? Was the article correct in any of its claims and, if not, can he guarantee that none of these things will happen? First of all, we do not have town halls in Scotland, and we should start from the very basic knowledge of how we do business here. Local government is essential to the health, wellbeing and prosperity of every community in Scotland, and we hugely value the work that local authorities do. Scottish Government and local government also share the same ambitions for stronger communities, a fairer society and a thriving economy. Local government is and always will be an essential and equal partner in creating a fairer and more prosperous Scotland, and that includes being a key partner in our work on community empowerment. Let me read you this, and I quote, we understand how difficult it is to throw off the shackles of the current way of looking at democracy. However, the reality is that, if we are serious about making Scotland fairer, wealthier and healthier, then we need to start putting local communities in control of what matters to them. That was Councillor David O'Neill after publication of the commission on strengthening local democracy and local government, and I agree with Councillor O'Neill in that regard. Graham Simpson. Well, thank you. The minister has not actually answered the question that I asked. I asked him for a straight answer, so I'll try again, shall we? The article said that ministers, presumably him, are planning to force councils to merge services and strip councils of areas of responsibility such as roads. Is any of that incorrect? Can we just have a straight answer to that? He mentions COSLA. Let's look at COSLA. COSLA is so engaged with this Government that it now doesn't even want to talk to you about the educational attainment fund because you do not want to engage with the whole issue of democratic accountability. You've stripped that away from local government. Presiding Officer, like Mr Simpson's knowledge of local government, I would say that much of the article in The Times lacks knowledge, too. As I have said already, we are engaging with COSLA at this moment in time. That, for me, is an opportunity to build on emerging good practice and energise local democracy. What I entered politics for was to ensure that people had a real say in the public services that are delivered, not public services that shouldn't be delivered to them, but public services that they have a real say in shaping. That is what we intend to do. That has been the way that this Government progressed during the course of the last parliamentary session with the community empowerment bill. We intend to go much further and we intend to do that in partnership with local government colleagues. Does the minister acknowledge that many local authority services are buckling at the seams under the financial pressure that they have been under for some time now? Over 27,000 jobs have gone on local government since 2009. The demand on those services and the pressure on those services, however, have not reduced so that you can imagine the pressure that is on the staff that are absolutely left. Public service reform is one thing, but it is set against a backdrop of increasing cuts in services. Finally, did the minister read the report by Spice, the University of Glasgow, and heard it walk university into the impact of the cuts in local government in last year's budget, which clearly showed that there was a disproportionate impact on the most poorest and most vulnerable in our communities? Will he, this year, in looking at the settlement for local government, ensure that there is a proper impact assessment on how and who it will impact most? The Scottish Government has treated local government very fairly. Despite the massive cuts to the Scottish budget from the UK Government—facts, absolute facts—it would be more apt for you to point the finger at them for continuing to cut our budget here in Scotland. Instead of carping from the sidelines, you should be joining us in fighting Tory austerity. Instead, your Westminster colleagues marched through the lobbies to sign up to George Osborne's austerity compact. You should be ashamed of that. To ask the Scottish Government what consideration it has given to research carried out by Herriot-Watt University regarding ways to reduce income and other inequalities. The Scottish Government pays close attention to research on reducing inequalities and welcomes the research that supports the rationale behind one of the Government's key priorities of extending the provision of free early learning and childcare. That includes increased provision for all three and four-year-olds to 600 hours per year, extended provision to include over a quarter of two-year-olds, and we are committed to nearly doubling free early learning and childcare entitlement to 1,140 hours per year by 2020. That will save families over £3,000 per child per year. In addition, our fairer Scotland action plan outlines 50 concrete actions that we are taking to tackle inequality and to create a fairer Scotland. John Mason. I am grateful that the minister talks about helping women into work through childcare and other such ways. Can she also confirm that the Government will be tackling the gender pay gap, which is mentioned in the report? Yes, absolutely. As Mr Mason rightly recognises the importance of childcare in alleviating pressure on family households and the cost of living with entitlement free at the point of need and also in terms of freeing women in particular up to enter the labour market. However, he raises an important point in and around the pay gap. The research that he mentions along with investment in early learning and childcare also identifies the importance and policies that close the gender pay gap. I am pleased to say that the evidence published by ONS a few weeks ago shows that the gender pay gap in Scotland is continuing to fall. We outperform the UK, but the fact that we still have a pay gap means that we need to continue with our work on occupational segregation. We need to continue the work in terms of encouraging public and private sector employers to publish information on the gender pay gap. We need to of course do the long-term work to encourage more women to pursue and remain in STEM-related careers. John Mason, I thank the minister for that response as well. I think that we all believe that we should grow the economy, but up until now the gap between those who have a high income and a low income, who are richest and poorest, has been unacceptably large. Can the Government confirm that, as well as growing the economy, we are committed to reducing these gaps between the richest and the poorest? We know that inequality has a very negative effect on economic growth and, according to the OECD, rising income inequality between 1990 and 2010 reduced the UK economic performance by nine percentage points. That is why inclusive growth is central to this Government's economic strategy and to our approach to a fairer Scotland, making sure that we tackle inequality so that everyone can benefit from a more prosperous economy. That obviously chimes with the research published by Herriot University, who, as well as talking about investment in early learning childcare and endeavours to close the gender pay gap, also talks about the importance of regionally balanced economic growth. That is reflected both in our economic strategy and the labour market strategy and also in terms of some of the work within the fairer Scotland action plan, which is very pragmatic and contains 50 concrete actions that are about tackling poverty and inequality in all its forms. I rise to make a point of order regarding an inaccuracy of factual information provided to Parliament on Wednesday of last week. As I understand the guidance, members, including ministers, have a personal responsibility to be accurate and truthful in their contributions during parliamentary proceedings. Parliamentary proceedings include answers to oral questions. The Minister for Transport and the Islands, in an answer to an oral question, told Parliament that his plan for a bill on railway transport policing was an SNP manifesto promise. He said, and I quote, that we were elected on a manifesto promise to do what we are doing. Following that answer, I checked the entire contents of the SNP manifesto upon which the minister was elected. Nowhere did any such promise appear. Can I therefore call Nade Demand, the minister, to come to this Parliament at 5 o'clock tonight to set the record straight at decision time this afternoon? I thank the member for his point of order and thank for the advance notice, too. I would first of all make a number of comments. If the member believes that any member has not been fully accurate in their comments, they are able to make a number of interventions. They can intervene on the member when speaking and ask them to correct themselves. They could put the matter in writing to the member and ask them to correct it that way. They could, if relevant, lodge a motion or a question for debate. Of course, it is up to all members if they wish to raise matters in the media. In terms of procedure, if any member realises that they have given incorrect information in the contribution of the chamber, they can request that information that is addressed in the official report. I hope that addresses...


I think social media is incredible. It's also horrible. It's like, it's lowering the bar. There's a reason why on average, it takes about seven to 10 years. Did I hear you correctly when you said it takes seven years to become an artist or discover who you are as an artist? It takes about average, and it might be a little longer now because the burden is solely on the artist and because of social media. But the average is about seven to 10 years, I'm gonna put in quotes to find your voice. Right? You think of a painter and you can see like their painting series and like their pathway and then they kind of hit something. You go, oh, that's when they hit. Like that's when they hit their stride and everything after that is in that realm. And they'll have different series but like they found their expression. And that's about a seven to 10 year journey on average. So is it seven to 10 years from the moment they decide to start, I guess, creating an art or the moment they start deciding to seriously pursue being an artist? That's a good question. Probably from the time that they're just creating art, but I think there has to be a little bit of like, the word might not be seriousness, but clarity to it, right? Like at some point someone might be like messing around and then they go, oh, wait, I wanna like, I wanna get behind this. Like I wanna pursue this. And it gets a little like, seriousness isn't the word. It's more dedicated or yeah, like I'm on this path. It's probably from that, you know, that's probably more of where you kick in, but it takes about a hundred live shows to be at zero. Like I've done a hundred live performances. Okay, I got something now. Like everything that could have gone wrong has gone wrong. So when it happens again, I'm not freaked out. I've been through this before. I'm understanding how to engage with my audience. Like I'm feeling it. I'm not trying to please them anymore. I'm just showing up being me. Like there's so much that has to happen. Live audience touring. Like there are things I cannot teach an artist. It will only happen on tour.  So I'm not blowing it on this night. Like I cannot teach it to you. You can only learn it in the experience of touring. I can prep you for it. I can give you the tools to use when you're in it, but the lesson won't happen like in my space here. It'll happen night after night with a different audience in every city. You know, and you're looking at like, for example, when people play LA, New York and Nashville as a rule, a lot of bands don't enjoy those gigs because they're full of industry people or Nashville is full of a lot of musicians. And people are like, ah, no, sorry, Nashville, I'm not trying to be bad at you.  So they're often not being their fan. They're being, they're like going, oh, wow, that chord change. Oh my gosh. They're like, they're studying, right? Or if it's an industry thing, everyone's kind of going, hmm, the hell is this band gonna sell? Like they're not showing up like a music fan going, you know? And so when people, when you play those audiences, you gotta learn like, okay, how do I still deliver? How do I do my job when I'm not getting the kind of feedback I get from, you know, Atlanta and Ohio where people are just mad for the music? And only on tour do you figure that out. Do you experience it? Now, do you see social media as a challenge for artists when we're in an era where an artist can record and we see this especially, especially in hip hop, but it's now in a lot of other genres these days too, where it could be a young teenager on TikTok, they record their first song or a few songs in for fun, they blow up and they haven't had that seven years that you just discovered. How do you look at that? And maybe have you even like worked or spoken with some of those people who have had troubles in that experience personally? I totally have. There's so much, I mean, like anything, they're pros and cons to anything. I think social media is incredible. It's also horrible. It's like, it's lowering the bar, you know? There's a reason why it take, you know, if it takes seven to 10 years, I don't think anyone should wait.   You need to do 10 drafts before you release it. Like you gotta, you gotta see your tendencies here. All right, so I wanna give a reminder that being independent is not just about not being signed to a label. It's actually making money without being signed to a label, being able to have a sustainable career. And for those of y'all who actually want to be able to make money from your fan base, you're serious about figuring out how to monetize. I have a free video that you can check out. I don't need your email. I don't need your phone number. I don't need any information. All you have to do is go to www.nolabelsnecessary.com slash monetize. And I'm gonna show you the lies that artists have been told that have been keeping them, probably you too, from monetizing your fan base and how shifting that perspective has allowed one artist we're working with to be on track to make over $500,000 this year. This is a different era. Don't fall for that trap saying artists can't make money. Artists do not have to be broke. So if you want to escape that trap, go to www.nolabelsnecessary.com slash monetize. You do have to make sure you put the www in the beginning when you type it in your URL and watch this free video again. You're not gonna be asked to put in your email. You're not gonna be asked for your phone number, but it won't be up forever. Check it out. Because the fact that anyone can release anything, so the really good side of it is we're hearing more voices, right? We're getting information that we've, we're having conversations that we have never had. It's incredible, right? People are learning things that they didn't know were existing because you can, you know, you're hearing things from around the world. But also you can put anything out at any time. So people are putting out their first songs, which can be awesome, but also can be not ready, right? Like you gotta develop yourself a little. You gotta get yourself somewhere to a certain kind of standard before you go public, I think. At the same time, people love to take a journey with you.  Please don't try to be. That woman has decades of experience and releases and teams and teams of people.  So, you know, that's why like nothing I say is like 100% right for everyone.  So I can bring myself to that center. Oh, I'm a procrastinator. Okay, let me do, do, do, let me release, release. Oh, I just go, go, go. Maybe I gotta slow down a little and put some thought into things. So with what you're talking about is, yeah, it's a problem because they have no leg, like they have no groundwork. They have no foundation underneath them to build upon. So I see a lot of burnout where they're like, okay, like fire, I went viral, let's go, go, go. But they don't know what they wanna actually put out next or there's a lot of, they just really get burned out. Like, okay, this is what my audience is responding to. So I'm gonna just keep doing more and more of that, but it's missing like layers and qualities of who they are. So they're not being seen for who they are. They're just kind of highlighting one thing really a lot. As opposed to like Alec Chambers, who's an artist that I've worked with since he was a teenager. And he put out music, original music already. He's done a little touring. He's definitely done a lot of performing. He's a multi-instrumentalist. When he started going viral on TikTok, he already had years behind him of like dedication and practice and being in the business. And then so then when he went viral, he was able to ride that wave. It wasn't all easy. I mean, he has his challenges for sure, but he just, he had a lot underneath his belt already that he's like, I'm building on something rather than like, whoa, I just, I feel like I'm like on a surfboard all the time, right? Nothing stable. So I think it's important to get like team around you that can support you and also sometimes like, take your time to like take steps forward. You know, in the world we live in, like content is on demand. Capitalism is like, you know, put it out there, make people like monetize it, you know? And you've got, not everything is for consumption. Like you've got to write a bunch of songs to find the two that are really good. You got to write the same song 10 times. So you go, that's the one. That's the best version of that story. That's the one I'm putting on the EP. And meanwhile, make other content, like show other things that maybe some songs are just like, you share them, but they don't record and release them. Maybe some things are like, this is my process. Like you have to decide what you're sharing with the world. But I think that, you know, I used to go to, well, I can't say I don't, but the gig economy has made everything commercialized and not your whole life shouldn't be commercialized. You know, like one of the artists I work with, she brought this into play and I was like, that's a really smart way of thinking about it. Child actors historically don't fare well in their lives. Like if you think of people who were child actors when they were young, they didn't really mess with their lives. But they went to a set, they acted, they went home, they had a family, but now because of the social media, like everything, you're just on all the time.  Like it's just constant on. Like you need downtime, you need quiet time. You need time in nature. You need time to marinate and be inspired. Like input days, you need these that are for input. You know, I think I have a thing called practice with that. Make a list of all the ways that you can practice when you go into your space. There's vocal practicing, there's songwriting, there's making beats, there's getting up and like, and performing like you're on stage. You gotta rehearse that.  I'm gonna put a, my computer over here, my iPad over here, my phone there, I'm gonna hit record on three cameras and I'm gonna perform. And then I'm gonna watch it later and see like, what works, what doesn't? What's weird? Oh, that's cool. That's a good weird. Like, so all the ways you can practice so that when you go into your space, you have a blend of dedication. I'm dedicated, I go in regularly and room for inspiration. Like, oh, I'm inspired to do this today. I haven't done that in a while. Oh, I've been avoiding finishing that song. I gotta finish that song. Okay, I'm gonna practice live. And then you gotta have input days. Input days are, I'm gonna go to a museum and look at art outside of music. Input days are, I'm gonna watch music videos of iconic artists and just watch live performances. What are they doing that works? What's, I wanna learn from them. I'm gonna listen to an album top to bottom. So every day is not an outward production day. You need inspiring days and you need rest time. And in our capitalistic society, like you are not encouraged to do that. But artists and creatives, you gotta have downtime and you gotta have, gotta live too, right? You gotta live and get your heart broken and take risks and be wild and you gotta live to have something to say. Appreciate you for watching. 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Today's topic is about Confrontal Factor Analysis, and I was asked to give a topic, pick myself, and I was thinking, okay, so what do people talk about, CFA, that is not common knowledge? Like, there's a lot of point-and-click tutorials how to do this, so what would be something new for people who have actually used this before, and I decided to do a talk about diagnostics, but then again, sorry, a little louder, yes, for sure, I can, yeah, please remind me to do so, and then I also need to go to the basics, so this is kind of like we start very basics, like you go to a swimming hall, you go to a pool that's knee-deep, and then we jump to the deepest pool ever at half way of the presentation, and I'll try to keep it understandable. So, if you want to learn more about factor analysis, my slides for this presentation come from my Advanced Course on Quantity and Research Methods. There is a unit for measurement, the measurement validation, and then there is a unit for structural equation models. Structural equation models, I talk about how to identify the variables of models and things like that, and then this is about how do we actually apply. Most of my stuff on these slides come from here. If you take a photo of this QR code, it will take you to this screen on YouTube, and you can learn more. There's like 40 some videos on this specific topic, like around it. So, let's take a look at what is the basic idea of factor analysis. Why would we ever want to do factor analysis? And my application area here is focusing on factor analysis in measurement validation. And the basic idea of what factor analysis builds on is pretty simple. So, if we have two measures, we measure A and measure B, and they measure the same thing, we expect them to be correlated. If we have a bathroom scale, and we measure a person's weight with that, and we ask a person to self-report, those are two measures of the same thing, they should be correlated pretty highly, if we have enough variance in the sample. And then, if we have measure C that measures something different, for example, we have a person's self-reported height, we should expect that measure to be correlated less with A and B than what A and B correlate to get. So, the idea of what factor analysis does is that it tests this kind of hypothesis. To give an example, this is from the PISA study. So, this is the PISA study about attitude towards school and all kinds of school-related things. And this is a five-item scale about enjoyment of science, and all these questions that are positioned in different parts of the survey form are supposed to measure how much the students enjoy science, and they should be expected to be highly correlated because if a person is indicating positively that they enjoy acquired new knowledge in science, they should also indicate positively that they are interested in learning more about science. So, do these items correlate highly enough for us to say that they measure the same thing? And here is another scale, self-concept, how much a person thinks that they know science roughly, and, for example, school science topics are easy for me. We should expect school science topics are easy to be, for me, to be correlated with, I enjoy, acquiring new knowledge in science, but correlate less than this item, because these are supposed to measure the same thing, and this is supposed to measure something else. What factor analysis does, it allows us to test this kind of hypothesis. So, do the pattern of correlations in my data support the idea that the variables that I have in one set measure one thing, and the variables that I have in another set measure, another thing that is distinct from the thing that the first set measures. Factor analysis, converter exploratory, answer this kind of questions. There are variations, more technical, more specifically what factor analysis does, it tries to discover or confirm the existence of certain dimensionality in the data. So, if we have two measurement scales that are measured, supposed to, that are intended to measure two different things, we should have two dimensions in the data, the two things that we want to measure. And factor analysis answers the question of what the measures have in common. Comes in two variants, exploratory factor analysis, where you give computer data, and then you tell computer that these data, I think there are three factors in the data, find me the factors. And in converter factor analysis, you tell that these three items, these three variables belong to this factor, these other three belong to this other factor, and is that true? So, it's more like you ask a question, and you expect a true or false answer, and in exploratory factor analysis, you expect an answer of the various of how these items vary together. Exploratory factor analysis, by far easier to apply. I typically tell my students to always start with the exploratory factor analysis, because it's not easier to mess up a converter analysis than an exploratory one. And the hard part about converter factor analysis is that you might not have realized that you have actually messed it up, but you end up reporting statistics that are not trustworthy. Much harder to misuse exploratory factor analysis than converter factor one.  So, when I do factor analysis of a scale, I pretty much always apply both, and I will talk about the different roles in this. The converter analysis is my main analysis tool, but quite often, the converter factor analysis answers no to my question. My question is, do these items work really, really well in measuring two or three or how many different things, and then the factor analysis gives me no as an answer. Then the next question, of course, is to understand why they don't work that well, and that's where the exploratory analysis comes into play. That's one tool in my toolbox for doing diagnostics about this kind of models. So, let's take a look at a concept or example of what a converter factor analysis does, and this is a set of slides that I stole from Todd Little, so he was the guy who actually got me to understand what factor analysis is. He showed me this slide of data as variance components. So, let's assume that we are measuring two things, let's call them A and B, and we have indicators A1, A2 and A3 that measure A, and indicators B1, B2 and B3 that measure B. So, we can see here that each of these circles represents a source of variance. So, the item A1 varies because there's some variation in A. For example, if this is a measure of height, height measures vary because people vary in how tall they are. Intelligence measures vary because people vary in how intelligent they are. So, there's variation due to the construct that we want to measure. There are also other sources of variation. There is random noise. If we do an IQ test, if we have a bad day, we might not do that well. On a good day, we might do better than average. There's random noise, and then there might be some, we call it item specific variance. I use these colored letters. For example, if there is in an IQ test, there is a pattern recognition task, and then there is a verbal task. There's something distinct in the pattern recognition task that differs, that is reliable, but that differs from the verbal task. What Confederate Factory Analysis does is that it presents a hypothesis to the data. So, we have a hypothesis that these variables A1, A2 and A3 have something in common that we might be interested in. These have something in common that we might be interested in, and then there is some variation in these items. We call it measurement error that we are not interested in. Conceptually, when you estimate the Confederate Factory Analysis, it separates the variation in the items, into the factor variance, and into these error variances. So, it kind of like takes the variation. It answers the question, why do my items vary? Why do they correlate? And it estimates a model that is supposed to explain variation and correlations in the data. One key advantage of Confederate Factory Analysis is that because it splits the error variances out from the indicators, conceptually not technically, it allows us to estimate how much these constructs A and B correlate. Without that correlation being biased because we are measurement error in the data. So, this is the basic case. It asks how much of these various and in these items is explained by A, how much various of these items are explained by B, how much is error, and how much A and B correlate. We of course want these error variances to be small. Typically, we would like the error variance to be less than half of the total variation of the indicators. In that case, these lambdas that we call standard as loadings would be 0.7 or greater. But that is not everything that you could do. You can also model imperfections in the data. So, rarely, your data work really, really well that it's just exactly how you estimate it to be. So, you can do correlated errors. For example, if we have a variance component here R, that is belongs to both that affects A3. Let's talk in a minute what A might be. So, we have this sort of variance and this sort of variance affecting two items that will throw off the factor analysis because now we have variance in A that doesn't belong there. We have variance in B that doesn't belong there. So, factor analysis would not be correct. We cannot relax assumptions. So, we can specify in a factor analysis that, by the way, these two errors of A3 and B1 can actually be correlated. And that allows this correlated measurement error to escape in the data terms and we get a nice factor analysis. Of course, adding correlated errors must be theoretically justified. So, when would B1 like to do this kind of trick to make the model fit better into our data? For example, this is that I use on a management course. If our measures are, we have innovativeness measures of furnace innovative. We have many patents. Our personnel are innovative. And then we have productivity measures. Our personnel is productive. Our furnace is more with less of furnace productive. Why might these two items be more correlated than, for example, why might these items be correlated? Well, they both have this person dimension. So, they also, they are not only measure innovativeness and productivity, but they measure something about the quality of the personnel in this hypothetical company. And converter factor analysis allows us to model this kind of imperfections in our data. And this is one of the key advantages in converter analysis. Of course, if we want to be a bit more rigorous, we could, instead of specifying that these can be correlated, we could add an additional factor. Let's call it C and call that the quality of personnel. We would call, this would be kind of like what we call a by factor model because some of these items load on more than one factor. But yeah, depending on how you define by factor, that might or might not be. But by factor model is something that, that if you hear that term, it refers to something that is related to this kind of configuration. So, adding these correlated errors or additional factors is, must be theoretically justified. I'll show you an example in a moment. Yeah. So, this would be personnel demands. So, what is hard about converter factor analysis? This is a difficult technique to apply. Correctly. So, there are things that are easy and things that are hard. It's fairly easy to do this kind of model. If you use data or if you use M plus, you can point and click and draw a path diagram like I just had and click estimate. AMOS only allows you to specify the model that way. If you are using, let's say R, then you have to specify the model. You have to write it as code. But that's not, that's not terribly complicated. And things that are hard, convergence.  So, what now? I have a, a set of 10 videos or so talking about convergence. If you want to know about it, it's over there. And then, other thing that is hard is, is diagnostics. So, you have to understand that when our, our software tells us that these, this model is not right for this data. There's something wrong with the model, something wrong with the data. Understanding what that wrong thing is, is, is difficult. It requires practice and it requires expertise. A lot of research, I, I seen a lot of bad practice. Like, let's say that, that you decide that, that converter factor analysis must have a CFI statistic more than 0.95 for the model to be any good. And then your CFI statistics 0.93. So, what do you do about it? Well, you'll find an article that recommends that 0.90 should be the right benchmark and ignore the problem. That is bad research practice. So, when the model doesn't fit, you need to understand what to do. And that is the role of diagnostics. And, independent isn't the results beyond rule of thumb. So, it's fairly easy to check if, a set of statistics like CFI is more than 0.95 or whether a factor loading is more than 0.7, but going beyond that, then it gets really difficult to understand what the numbers actually mean. So, that's the first part of the very basics. And then the second part, I'll explain a bit of my workflow and how I do diagnostics using real data. And this is my integral example. I'll be using the PISA dataset. I have three things that are measured in this data. And enjoyment of study science, self-concept about studying science, and carrier aspirations. These data have been used in a number of meteorological articles to demonstrate certain features of structural regression models. And the idea is that we will test the hypothesis of, we will test two competing hypothesis. It's either person's self-concept or the enjoyment that determines their aspirations in carrier science. These are students in like high school and middle school, if I understand correctly. I use this as an example on my course. If you want to work through the example yourself, then you can scan that QR code and you can access to the data. I also use this as an example in a paper that we are now working on for psychological methods. It was being used by Kelaava in the same journal before. And the data are from OACD. It's freely available. It's a bit hard to find. So, perhaps the QR code that I gave is where to get the data. Here is, if you want to see worked examples, here's the QR code for some analysis of this data using M plus and LaVanne in R that we use in the psychological methods paper that we are under review. So, the piece of data measures things related to the school accomplishments and attitudes towards school. We are interested in the attitude in our measures here. So, our measures are enjoyment, five questions about where the student enjoys science. We have a self-concept, six measures, six questions about how much the person thinks that they are good at science. And then we have four questions about career aspirations. Would the kid want to work with science when they grow up? And we fit this kind of converter factor on a small. We test the hypothesis that the items that measure enjoyment, they go to one factor, career aspiration, go to another factor, and self-concept goes to a third factor. All right. And now, we just specify, so that would be the R syntax for the model. You can point and click, but the hard part comes now. You get the results. What do you do about it? The results tell us that the P value for a model is essentially zero. So, the model is not correct for the data. Now, a lot of people, when they face this, would take a look at these alternative fit indices. There are rules of thumb that, for example, CFI and TLI must be more than 95. And this is based on who and Bentley's work. The model is not correct for the data. We know for sure because the P value for the model is very small. But is it adequate? That is kind of like a question. One way to answer this question is to look at these fit indices that the software produces. So, CFI, TLI looks okay. And then RMSI must be less than 0.05, and SMRR must be less than 0.05 again. So, it looks good on these statistics. But this is actually something this indicates good fit, but then the model test rejects. So, now, what do we do? One statistic rejects the model. Others say that this is good. Do we ignore the statistic that says that this is not the right model and just focus on the statistics that say that this is the right model? A lot of researchers go that road because the other road called diagnostics is very hard. So, we need to understand in which way the data does not fit. And when I have three factors, and I'm estimating a full model, a computer tells me that the model is not right for the data. What would be an obvious next step to do? If you have a car that doesn't run, what do you do?  But you take a look at, like you have a car that doesn't run, then you start setting individual parts. Do I have gas? Is the power in the battery? Is the engine ignition working? So, you take a look at different parts of the bigger problem. And this is what I do when I do diagnostics. So, if I have a model with three factors, it doesn't work. If I just take, estimate the same model, one factor at a time, would it work that way? And if not, is there something that I can do about it? So, what I would do is that I would then do diagnostics. So, I would start by looking at this model as, first, either as whole or as smaller sub-models. If you look at this as whole, you might take a look at something that is called residuals, that all of your statistical software that does digital models can report. And this is just a pattern of correlations. And the higher the correlation here, the less the model explains that correlation. But looking at this kind of, like, 11 by, sorry, 13 by 13 correlation matrix and trying to find patterns, that's pretty hard with plain eye. So, what I would do instead is focus on one factor. So, these, or do an exploratory factor analysis. So, if a confirmatory factor analysis indicates that the model doesn't, is not exactly right for this data, then I would run an exploratory analysis next. And that exploratory analysis tells me, okay, so if my model is not exactly right, what kind of model would the computer recommend? And this exploratory factor analysis indicates that there is a, there are high loadings here. And importantly, these, some of these item, most of these items don't load on any other factor. So, we are, when we do diagnostics, we are not looking at which factor an item belongs to. But we are looking at, is there some evidence that this, for example, Y4 actually measures something other than what it's supposed to measure? We know that Y4 is a measure of carrier aspirations, which is the third factor here, but does it measure these other factors? So, we, these are pretty small numbers, but we still could do better to understand what is going on. The next step would be that we do a single factor. So, we might do a single factor. Let's do one factor and let's do just the, the enjoyment. So, these factor loadings should be more than 0.7. That's a rule of thumb. And this is a bit low. So, we would like to understand why this is a bit low factor loading. Also, the model is, is not correct for these data. So, when you are looking at the single factor that doesn't really, it's not, these data are not fully explained by a single factor. Then, we can take a look at the, okay, so what are the questions? So, if we have these questions, are there some dimensionality? So, the correlations are here. First and second item correlate a bit more strongly with one another than the other items. And fourth and fifth item correlate more strongly with one another than the other items. So, we started reading why would a person who, who rates number four highly be more likely to rate number five highly than to rate number one highly? So, is there something else going on in the data? And what might number four and number five have, have in common and what might number one and number two have in common? So, well, number, number three is specifically about doing. So, there's this action part. If we take a look at number, item number one, that is just about learning generally. The number two is about reading and then number three is more like active things. So, you like doing science problems. Some person might read like reading math, other person might like doing math problems. So, these are two different things that you might, they are highly correlated but they are different things. So, we might identify based on looking at these correlations that these are items one and two have something in common and these items three, four and five might have something in common and we might decided, okay, items number one and number two are about reading about science and nine items number three, four and five are more, more active. They're more about doing. So, reading and doing. That's not like an exact split but maybe that helps us to understand why there's some dimensionality in the data. Then, what we do is that we can add a second factor. We do an exporter factor analysis. So, the second factor indicates that there is indeed some dimensionality. So, the second there's, there are, this is not one factor. It's one main factor and two minor factors. And if we add the minor factor to the model, we add a second factor reading for the first two items. We still have the main factor and now we are getting closer to a model that is not rejected by the Kaiser-Greuer. So, we see that the big model doesn't fit. We take a look at smaller part. Then, we try to understand why the smaller part doesn't fit. We add modifications and then we re-estimate. So, rejected, we can see also that the items don't perform as well. But when we add another factor, yeah, another factor doing, then the model fits well. So, we have one main, one major factor that measures how much these students like science, enjoy science and then students vary whether the enjoyment is more focused on doing versus reading. And this is the minor factor. This would be called a by-factor model because there's one major factor and a minor factor. So, you start thinking about this data. When the data don't fit, there's typically dimensionality. The model doesn't fit the data. There's some dimensionality to the data that the model doesn't really fully explain the data. What is the dimensionality that is typically discovered by looking at what are my data? If we do two bathroom measurements of person's weight and then we ask that person to self-report, the two bathroom measures will be more highly correlated bathroom scale measures than self-report because they are measured using bathroom scales that are that in common in addition to having it in common that they measure the same person's weight. Yeah, so this is not rejected. We would be happy with this model, with this modification. So, what do we do of diagnostics? We do first like the full model. We almost always doesn't fit exactly. Then we try to understand why does it not fit? My workflow includes running an exporter analysis to see if it gives me a same kind of pattern that a converter factor analysis does, then looking at that one factor at the time to see if I can identify some sources of misfit. Then diagnostics one factor at a time. I do this on a video for all the other factors. And yeah, so I discussed this reading and doing factors, not the other alternative, and then proceed with the next factors. Then finally you test the full model. So a final outcome of this kind of process might be that we have our original confrontationalist model with the full data. And then we have this kind of like series of different models in which we add certain modifications based on what we think makes sense. And then we compare, does adding these modifications make a difference? If we are trying to measure enjoyment self-concept correlation, our original estimate was 0.529, and then my estimate of doing like everything that I possibly can is 4.76, there is about a 10% difference. So, doing a model that has been modified to fit better gives us a different result than the original model. And now the question is that if we have a simple model with no modifications, and we have models where we add modifications, this is from a bigger example, which one do we pick? So there is a 10% difference between the most modified model and original model. 10% is a really big difference. But if we take a look at the one where I just did diagnostics adding a couple of minor factors, then we only have a 4% difference. We would go for one model, decide that this, after this point of modifications, there is no longer any differences in estimating the correlation that is our primary interest. So we're interested in understanding whether enjoyment and self-concept, for example, how much they're correlated, if we decide that some of these modifications based on diagnostics no longer make a difference, then we would not do them based on the parsimmonic principle. And this is how it might look reported in the paper. This is from a real paper I'm working on, and we would say that the original model doesn't fit the data. Then we did diagnostics looking at what the items were, what the questions actually said. We look at exploratory factor analysis, we look at the correlations with the items. Then we do additional factors. We dropped some items later on. I realized that there are some questions that don't work that well in that context. I decided not to use them. I had five items. Three is the typically minimum for a factor analysis. I could easily drop one of them, and then I estimate the full model, and then I take a look at the diagnostics for the full model, and then finally, does it make a difference? And then I report it. So this is kind of like state-of-the-art way of doing and exploratory factor analysis. First, you run the full model. It typically doesn't fit. Then you do exploratory factor analysis. That typically discovers that the data has roughly the correct dimensions. Then you are pretty much a winner already, and then you go one factor at a time to see and understand if there is any like big problems, and then you return again to the full model, and then you compare across different modified models to pick the one that you want to report as the final model. Yeah, this is the sensitivity then, I would say. So that is, yeah. In a nutshell, what is the point of this? The point of this is that if you rely on the rule of thumb that CFI must be pointed greater than 0.95, there's a lot of research showing that, including my study that I'm now working on, that important mis-specifications go undetected with that rule. So you might have, for example, if you have 11 items in a fairly good data, you might switch to items that are in completely different incorrect scales, and the CFI greater than 0.95 rule would not detect that as mis-specific. This is the reason why we do diagnostics. And getting a non-significant statistic for the model fit is not that important. Some reviewers insist, then you don't need to go and see what it would take to get a significant chi-square, non-significant, but I wouldn't bother reporting those. And then you finally take a look at if it makes sense. So diagnostics is super important because the conventional rules that CFI is 0.95 don't detect all possible mis-specifications. The model test, the chi-square statistic and the p-value will tell you that the model is somehow incorrect for the data. It doesn't tell you in which way. And to understand which way it is correct, that's where the diagnostics come from, conveniently. Okay, so we had a bit of shallow, like basics, and then we jumped into the deep end. Thank you. I guess we have now time for a couple of quick questions. There is some. Yeah, Pili? Yeah, I have a short question about the role of theory or the, like, ideas behind these questions, or what do you think about how important it is to do statistical things because they can be so small differences. Do you think it's more important to think it, like, theoretical, for example, the interest and all that, like, moving and meaning should be, like, actually the same, same concepts or different concepts? I have, in papers that I've worked on and in projects where I've been consulting or helping, I don't take money from friends, but I've made decisions against the empirics. Like, if there's a good conceptual reason to believe that I'm interested in learning about science is more about enjoyment than it's about career aspirations, then it should be included in the measurement, measure of enjoyment instead of career aspirations, if it doesn't belong their concepts or if you have plenty of items like you have here, you realize that one doesn't really behave well and you have a reason for it, then you can take it out. But yes, the conceptual reasons are the most important, like, we take a look at these items for reading, for self-concept, then we don't group these items one and two because the computer tells us, we group them because the computer tells that you should consider and then we as researchers make the call that, yeah, this is about reading, more like passive things and this is more about doing, I guess, and that would be our rational. So theory always, empiric success what you should do and theory justifies doing so. So that's my take on it. There's a lot longer version of this talk on my channel. Please, I could actually still ask something, because you are recommending to use the FA because it's kind of safer, but what is your take on? Because the good side of the CFA is that at least you have theory and you are testing a theory, but when you take EFA instead, then you are constructing a whole new measure which is not based on theory anymore. So what do you think about this? It depends on how you consider the EFA result. So if we have a hypothesis that these data measure self-concept and enjoyment and carry aspiration, three different things, and our hypothesis is that these why items measure the aspirations, why would we not consider this as a test? So it indicates that these have belong to one factor and one factor only. So you can do EFA in a converter manner. So you can have a prior hypothesis that these patterns would emerge from my data.  Then that's a converter way of applying EFA. Similarly, you can apply CFA in a purely exploratory manner. So you can just add things based on modification indices, for example. And you can do diagnostics, add factors without much thinking. That is also possible. That's called specification search. So it's not like exploratory must be used for exploratory purposes. A converter analysis must be used for converter purposes, but they can be both used for both purposes. Yeah, it's kind of a misunderstanding that exploratory architecture analysis is only for exploration, for new measures. So why do you think it's a misunderstanding? Yeah, because you can use it for confirming. Confirming in the sense that we have a pattern that matches our theoretical expectation. We don't have a p-value for that, but why would we need one? For instance, when you use EFA, you just find what is common for them. So you can, for instance, take away the method. No, that's right. You can't model these more complex structures like method variance or secondary factors. You're absolutely right about it. So that's why I'm saying about the importance of theory in there. So at least in CFA, you have the strength of the theory behind but when you are modeling with exploratory factor analysis, then you are kind of just finding some common factor in there. But you cannot say kind of what's kind of the core of the factor. Yeah, but that is about the factor interpretation problem. How do you give meaning to the factors? But this using EFA to confirm a pattern applies when each indicator loads on one indicator, one factor only. If each indicator loads on one factor, you can run an EFA using CFA software and you can run a CFA using EFA software. And this is actually something that I teach my students. So I tell them to use status EFA command to run a converter factor analysis and then compare that against the converter factor analysis result and then do the same the other way around. So you can run a converter factor analysis with EFA an expert analysis with CFA. That is also possible. Just allow all items to load on every factor except one and then you get an exploratory analysis using CFA software. Yes, thank you. Yeah, there's a lot of examples on the course that I gave. Just a quick question. Go ahead. So here, if you go to this QR code, there's an assignment for students. I don't know if it's this one or they are the previous unit where I tell students to use EFA and EFA with CFA. So what about the paper in psychological methods? Our paper. Yeah. It's about latent interactions. That's still under review. So that's second round now. Yeah, but I can send you the paper if you want. It's about latent interactions and how we test them. It's more advanced than this stuff. But we use this example there.


Hi, everyone. I'm Nicole. I'm a research fellow at the University of Oric, and I recently defended my PhD thesis. For my thesis, I collected data on the offline meetings between German-language Wikipedia, and in today's talk, I want to showcase that data set. My thesis was concerned with Wikipedia, particularly with the people who write it. And I didn't only care about the online component, but I was especially interested in the people meeting offline in the real world. Such meetings come in different shapes and sizes, from the casual sharing a beer and a pub, to more organized tours, for example, having a guided church excursion, or even booking a pilot to take area pictures. In my thesis, I was asking how offline meetups influence online behavior, and the first step to answer this question is getting the data. So the goal of my data collection was to collect all offline meetings taking place in the German-language Wikipedia, organized since its launch in 2001, up to 2020, when it came to a halt due to the outbreak of the coronavirus pandemic. Most meetings are clearly organized on Wikipedia. There's also a few over a few pages which list them. On the right, you see all currently active regional entities which regularly organize meetups. So the organization of a meeting then looks as follows. What you see here is the screenshot and the translation of the very first meeting of the right Hessian regional entity. And they've met for the first time in 2008. And what you can see is that they have a list of attendees, of people who have signed up, a list of apologies, of people that would have liked to come, but can't make it, and they also have a recording, a sort of result section recording what has happened. During my data collection, I aim to include all information which you can see here and which is relevant. So I try to collect who, met where, and when, and also the apologies sent and the minutes recorded if available. There are multiple pages which I checked for meetings because I try to collect all offline meetings organized. So I started with over a few pages which include all meetings or which include all editathons and all open editing events. And there's also an overview list of other events. I also checked all Viki projects and all task forces to see whether they have organized any meetings. Throughout the scraping of all these pages, I use the snowballing approach. So if any page linked to any other one which was relevant and included a meeting, I also scraped those. There are a few pages and meetings which I excluded from the data. First of all, I skipped all virtual meetings. Second, I didn't check any portals, whether they included meetings because portals are more, they provide landing pages and are directed more towards readers than offers of Wikipedia. I also didn't check meetings which were organized anywhere else but the German language version of Wikipedia. So I didn't collect meetings organized on meta or comments. And I also needed to skip very regular meetings taking place in community spaces. Community spaces are interesting but there are places of extremely high Viki media activity and in most of them, meetings take place several times a week and people stopped having recording their attendance. Because of this, it was not possible to reliably give a list of attendees in community spaces. I ended up with a list of over 4,400 meetings and information on the place, when you, where they took place and to corresponding coordinates, the date, the type of meetings, so whether it was more social or more work oriented like an editathon, and a list of attendees collected from the minutes recorded after the meeting if available, if not then from the list of attendees. I also collected the apologies of absences as well as the minutes available. So what we have is all attendees from 2001 up to 2020. The first two meetings out of those 4,400 took place in 2003 in Munich and they have increased over time up to around 2008. Since then, we've observed around 300 meetings per year. The dataset looks like this. So what you see here is the first 5 lines of the dataset and the most important variables. You see 5 example entries of meetings which were organized in Switzerland. Now the offline data is interesting but I think it really shines if you combine it to online data, for example to the datadump which provides detailed information on all online activities undertaken. We need to merge the users on the basis of their user names and this isn't that straightforward because user names can change. Users can also sign up with new names and scraping data and information from different sources comes with issues regarding and coding and capitalization. So to be able to merge online and offline data, my goal was to create a name to ID lookup table. So the goal was for each actual person, I want to have one unique identifier and connect all user names this person uses to that one unique identifier. To do that, I collected all redirection links to a user. I collected all renames which were locked in the renaming logbook and I also collected any explicit mention where users said that they are changing their user names or that they have a new one. This then allows me to combine online and offline data and it opens up the door to many new research opportunities. So I have already used the data to assess the meetups causal effect on the number of contributions of editors. For all sociologists in here, I also tested Coleman's mechanism of norm enforcement and I explored how meetups matter in Request for Artmanship. But there's so much more. So there's a spatial component in the data, there's a temporal component. We also have networks. There is lots to do. And if you have ideas now and you want to get the data, then you can do that in the open science framework. The data and documentation about all variables is there. I shared the data with a creative comment, attribution, share like for license. So feel free to use it and also improve it and check the preprint on archive. The extended app, the preprint is currently under review and the extended abstract is a very short version of this preprint. With this, I say have fun with the data and many thanks for listening.


The House of Representatives Committee on Information Communication Technology has a promise to introduce a legislation that will drive the country into ICT compliance. This is part of the promise made by the Chairman House Committee on Information Communication Technology Honourable Stanley or Lajide during the inaugural meeting of the committee at the National Assembly Complex in Abuja on Wednesday. The Chairman says that his committee, amongst others, will cooperate with the Communication Ministry with the aim of coming out with a comprehensive national policy on ICT as well as liars with other House committees to bring them in conformity with global best practices. All matters concerning information, technology development and utilization in Nigeria will be our focus. Needless to say that all matters concerning information, technology development and utilization in Nigeria will be closely looked at. And finally all matters related to technology control is one to develop technology and the other one is to control it. And I extend my hand of fellowship to each and every one of you, urging us all to seize this opportunity to drive the information technology sector to the forefront of our national development. Our collective efforts will be geared towards ensuring that all sectors align with the information technology mandate. Members of the committee in their responses assured of 100 percent support for the success of the committee with the agreement that a country is at a critical stage of its development especially in the area of ICT. The federal government, especially the IT sector, has gone light years ahead of the rest of the executive council with regards to setting an agenda. The technology helps the ICT's entrepreneurial schemes. I want to crave the endodians of the chairman who is very capable and well able to see how we can establish the necessary frameworks to be able to make sure that even with the speed they are still operating within the frameworks that are set by law. Chairman, I want to assure you of one thing. Myself and other colleagues, they will give you all the required assistance to succeed. But I just want to advise us that we must ensure that the executive and legislative leadership is kept intact because we cannot achieve much if there is no leadership, if there is no adequate leadership between the executive and this committee. You get notified about fresh news updates.


should be grateful yes and that and then there's a couple other typos but those those are the things I was concerned about where are the typos you want the other typos I picked up or want to let them slide well we have it open why don't we just do it quick where are they all right page four first full paragraph third one for the bottom the wetlands page four regarding yeah the wetlands are gravel okay and then then second paragraph from the bottom mr gill that addressed the traffic planning he stressed that there have been no either there has been no reduction or there have been no reductions but you got to make them agree yeah and and then last page five last paragraph is that page five well they've moved around now that we've added some text so it might be the start of page six after mr mr kofman questioned whether it is acceptable okay and then page six you're missing it is a first full paragraph fifth line okay things got moved around so it's the paragraph that begins with the applicant mr gill said it may be harder you see that one two three and the fourth line mr gill said it may be harder for public works to plow but it is what they were asked to do got it okay good that's all I have thank you careful reading frank thank you very much I oppose these changes when you have a little bird to read your shit around although no just always good to have a grammarian and a lawyer looking at these so thank you any other comments about the dance making noise down at the end he's not behaving no I'll let it slide because he might vote to approve the minutes all right um do we have a motion to approve the minutes do we have a motion to approve I'll make a motion to approve thank you as revised second any discussion all in favor of approving the motions say aye aye I oppose great we are done for the evening thank you everyone we will see you back here in september oh have a good break