Category Archives: Academic

Doing a PhD: Keeping it Simple

Given how many words I’ve already written in this series covering preparing at the outset, engaging with academia, reading and writing, organising yourself, and time, money, and location, it might seem a bit weird to finish by advising you to keep it simple. Doing a PhD is a complex matter, so the following points are about keeping it as simple as possible, rather than making it simple overall. There’s no need for additional complexity in an already complex endeavour:

 

When you encounter problems, look for simple solutions first

This is partly related to your confidence with the analytical technique that you’re using (see below). So, if you’re anything like me, then if (when) something goes wrong in an analysis that you’re relatively unfamiliar with your kneejerk reaction is to panic. This leads me to cast around for obscure solutions (the logic being that if it’s an obscure mode of analysis then the solution must be obscure too) when it’d be much better to start by looking at the most basic possible option (e.g. check the distributions of all the variables (you should have done this already, of course!)). Countless hours can be wasted looking for complex solutions and, if you didn’t try the easy things first, you’ll feel like a complete tool when you finally realise how simple it was to solve the problem.

 

Don’t use structural equation modelling (SEM), unless…

…you fulfil the following criteria (this is the most specific, and technical, piece of advice that I give):

  • You’re already confident with advanced statistics;
  • There’s a real benefit to using such a complicated approach;
  • Your models aren’t too complex.

Alas, I didn’t meet any of the above criteria. I finished studying maths (a subject in which I felt chronically underconfident) at GCSE and had barely looked at the subject for ten years. I had no A-levels in maths, advanced maths, or statistics, and knew little about any of those topics. As such, I did the basic quantitative module in my Masters (the advanced quantitative module is reserved for what I call ‘stats whiz kids’, and what others have referred to as ‘statsos’). Thus, from being brought up to speed in a relatively introductory (albeit very well taught) manner, I jumped in at the deep-end. Try reading an online SEM ‘help’ board some time; if you’re not au fait with statistics then you may as well try to get help from a website written in Latin (apologies to the classical scholars amongst you, who scoff at the idea that one wouldn’t know how to read Latin). Indeed, when one of my fellow PhD students who is much more confident and competent with statistics than me (one of those whiz kids I mentioned) heard that I was using SEM, they remarked on how difficult it is. This should have set off massive deafening alarm bells, but I just waltzed on by and carried on along the path of doom. And for what? I mean, really, what has structural equation modelling added to my analysis? Yeah, sure, I can wheel out arguments in favour:

  • It’s good that it allows for the simultaneous estimation of measurement factors and the structural relationships between them (crowd: ‘oooohhhh!’);
  • It has the helpful capacity to separately estimate residuals and measurement error, which allows for improved accuracy in models (crowd: ‘aaaaaahhhhh!’);
  • It’s neat that you can also estimate plausible alternative measurement and structural loadings (i.e. produce modification indices) and thus, perhaps, test competing causal propositions when running models (crowd: ‘wowwwww!’).

But really, even with all of the above acknowledged, what is structural equation modelling except a very complicated way to (still) not prove causality (even assuming that’s ever possible). Of course, I can be confident that some of the variables in my model are causally prior to others (e.g. it’s fair to say that age precedes political views), but that would also be the case if I’d used a run-of-the-mill multiple regression. By contrast, all of my variables of interest (e.g. levels of cultural capital and levels of political participation) can be plausibly argued to precede one another (or be mutually reinforcing). This point stands regardless of how complicated the analytical technique used to analyse their relationships is (and such techniques are no substitute for longitudinal or experimental data). Thus, having failed to meet the first criteria, I also fail to meet the second by not really being able to see the benefit of having poured days, weeks, and months into an analytical approach that is effectively just an over-the-top way of saying how clever you are with statistics (which I’m not). Finally, the failure to meet the first two criteria was confounded by the fact that I was trying to analyse overly-complex models (e.g. my final model included 106 indicators, estimating 34 latent factors), which the software that I was using (Mplus) really isn’t designed to do. In short, using SEM was a two-year nightmare that greatly undermined the other aspirations that I had for my research. So, my conclusion vis-à-vis SEM? Balls to that.

 

Don’t get waylaid by side analyses

Interim analyses (by which I mean using messing around with your data without a clear purpose), fiddling with interesting data, and working on analyses suggested by other people can all seem useful but, unless they have a concrete pay-off (e.g. for you publications), they should be deprioritised. This means that if you do decide to take them on then your main analyses should remain the priority (i.e. the first thing you spend time on each day), and there should be a limit on the time you spend on such side analyses. I spent months working on multiple regressions (the time consuming bit was processing and recoding data) that I thought might provide useful interim findings but ended up being of almost no use. Instead, I could have used that time to start getting my head around SEM (assuming I didn’t follow the above advice) and getting that analysis done in a timely manner. So, decide on your analytical approach and do it. Mistakes will happen, and time will be spent on results that get revised or dropped, but the focus should be on the process that will give you something to write about in your thesis or in publications.

 

Reserve the last three months

Whenever your final deadline is set for, make sure that the preceding three months are kept as clear as possible. This means opting out of conferences, extra-curricular responsibilities and (if you can afford it) teaching so that you can focus exclusively on finishing your thesis. Also, don’t be tempted to go to interesting looking events unless you’re confident that they’ll have a concrete pay-off. If this means that you feel like you have spare time then good, because you’ll need it. Also, it means that you can afford to take breaks, so that you’re less intellectually (plus emotionally and physically) exhausted as you sprint towards the finish.

 

Phew, that’s quite a list, but I’ve reached the end of everything I can think of for now. Of course, hindsight is a wonderful thing and all that, which is why I’ll keep this open to additions in future (comments, suggestions?). But, otherwise, and rather unceremoniously, rant ends.

 

 

 

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Doing a PhD: Time, Money, and Location

 

You might have a clear idea of your taste for engaging with academia, be a dab hand at reading and writing a shed load of stuff, and have excellent organisation skills for a PhD (all covered in previous posts in this series) but, alas, time and money are finite, and location can play a key role in your productivity too. So, you’re going to have to make decisions about all three of these things:

 

Be critical about the type of extra-curricular work that you engage in

It can be tempting to jump at every opportunity to gain work experience during a PhD, especially if it’s paid and you’re skint. You need to be more critical than this. During my research, I worked as a student reviewer of academic standards, an academic research assistant, an intern and then researcher at a polling company, as well as a teaching assistant in my department and at summer schools. In total, excluding the teaching assisting work (which is de rigueur if you want a career in academia), I spent over a year of my four-and-a-half years doing non-research-related work. Was it worth it? In the case of the polling company and teaching experience, yes. Otherwise, probably not. The money was nice but it would have been much more useful to have some of those months to focus on research (psychologically, I think it’s also important to retain a focus on the important work). I thought the experience would look good on my curriculum vitae in future, but I’m not convinced it does. No one cares if you were a student reviewer of academic standards if your thesis is lacking, or late, and you have no publications or impact. In fact, the contexts in which that experience is necessary are so few and far between that I’m bewildered I thought it would be useful. As for being an academic research assistant, it doesn’t look bad to have done it, but it also wouldn’t have been a great loss not to. So, before you apply for or accept extra-curricular work, make sure you think about how useful it will be in the long-run and whether it would be more useful to have the time instead. If there’s doubt in your mind about how worthwhile it is then don’t do it. Unless you need the money, in which case your hands are tied.

 

If you do other work then put time aside to keep working on the PhD

When I started one of my stints at the polling company I worked for, a member of academic staff advised me to carry on doing research work in my spare time (this links to not treating it as a nine-to-five). My immediate reaction was ‘seriously, you want me to work on my PhD in my spare time?!’ I should have listened. I had a total of nine months at the polling company and even if I’d only worked two evenings a week (and a weekend here and there) on my research it would have been beneficial. Beyond just providing more time to get the work done, it would have kept my research alive in my mind, and reminded me that, despite the hiatus, it was still a priority. As it was, I did very little and bore the costs at the end of my PhD, not least in the sense that I failed to do much of what I’d intended.

 

Save a proportion of your income every month

Despite my elaborate and detailed financial recording (as I said, Soviet  bureaucrat), I failed to do this most basic of helpful things during my PhD. My funding (granted through a collaborative deal between the ESRC and YouGov) paid my fees and awarded me a monthly stipend of just over £1,100 (tax free) for the first three years of my research. Had I saved just 5% of this, I could have had £2,000 in the bank when my funding ran out, which is to say a two month cushion in which I wouldn’t have to worry about income. If I’d stretched it to 10% then I could have had four months without worry. Or, an alternative approach would have been to live on my research funding and save any extra money I earned. Of course, there is another element to this, which is partially related to the below point: cost of living. I’d already worked for a few years so was used to having a (slightly) higher income and, crucially, wanted to live with my partner (now fiancée!) in London. This was the right decision (there are other things in life more important than research, despite the tenor of these posts) but it meant that I was essentially living beyond my means and rendered the extra work that I did necessary. Still, I could have saved more (i.e. any) money, and perhaps looked for a compromise on living arrangements; as it was, I ran out of money and ended up having to move nearer my institution to take on teaching work. So, you should accept that your funding is limited, adjust your living arrangements as necessary, and save as much money as you can for the (inevitable?) extra months you’ll need to work on your research at the end.

 

Live near your institution

This is partially about cost (i.e. if your university isn’t in London then try to avoid living there) but mostly about facilities, working environment, and social networks. For the last six months of my PhD I lived in the same town as my university, and it enabled me to work much more effectively. It meant that I had a desk in a shared office that I could use for work on campus, and therefore also had a clear physical divide between home and work. Thus, I could work late if necessary, safe in the knowledge that when went home the space would be entirely detached from my research. For me, this was hugely psychologically helpful because it enabled me to turn off entirely when I was away from my desk, even if only for the night. By contrast, when I lived in London my office was at home and I found it remarkably difficult to separate research from the rest of my life, which meant that I was never working effectively and yet, on some level, was always working. This partially prompted my decision to treat the work as a nine-to-five (see previous post) on the basis that if I couldn’t physically delineate my work and home life then I should do so with strict time limits instead. Alas, even despite this strict timekeeping, working from home enabled me to do lots of virtuous procrastination (e.g. washing up, hoovering, sorting out bills when they arrived in the post) that just wasn’t possible when I worked away from home in the final six months. Beyond having a separate physical space to work, living near campus also meant that I had access to a library whenever necessary (and one where I had full lending rights and computer access to boot), could get IT support quickly, and could arrange more ad hoc meetings with my supervisor (e.g. when problems with my analysis arose). Finally, it meant that I was embedded in an environment where I was surrounded by people who were going through the same experience as me. I could see them doing their work and progressing, which provided additional motivation on the basis that I could see it was possible, and because I wanted to emulate those who finished. I could also talk to people about the experience (whether moaning, joking, or expressing frustration) in the knowledge that they’d be able to empathise (as well as offer advice). Thus, for reasons relating to facilities, working environment, and social networks, I strongly advise finding a way to live near the institution that you’re studying at.

 

So, if you’ve figured out your goal, engaged with academia, read and written a tonne, organised the heck out of yourself, and sorted the contextual factors, then you might think you have it sussed. The kicker is that, in addition to balancing all those things, you also need to avoid over-complexity…

 

 

Doing a PhD: Organising Yourself

 

Of course, the demands of engaging with academia and doing plenty of reading and writing, which were covered in the previous posts in this series, require a certain degree of organisation. This is something that I was confident about but, it turns out, I wasn’t prepared for the task of getting a PhD done on time and to a satisfactory standard. This is a different kettle of fish to a nine-to-five job (or, at least the ones I’ve had) and should be treated as such:

 

Set realistic goals, but be prepared to knacker yourself to achieve them

It might seem like you only need to do one of these things but even realistic goals can be knackering to achieve. The point is that you need to get a balance; stretch yourself without trying to reach an unattainable point. I tried to do far too much in my PhD; mixed-methods research, gathering original quantitative and qualitative data from scratch, undertaking an overly-complex analysis of the former (see the point in the final post relating to structural equation modelling), and expecting to have massive impact with all of it. I could have opted to focus on either a quantitative or a qualitative approach (though I’m theoretically committed to mixed methods), chosen to use secondary data for either element, or conducted simpler quantitative analysis. Alternatively, I could have worked a lot harder than I did for much of my PhD (see not treating it as a nine-to-five, below); after all, this is a topic that I chose to research and that I’m passionate about. However, having knackered myself in the two jobs that I’d done before returning to academia (both of which I cared about deeply), I was adamant that I’d have a better work-life balance. Alas, I still didn’t get it right, and can attest that not achieving most of what I set out to do in my PhD is much worse than being tired and having less time for extra-curricular activities. So, I could have set lower expectations (which, as I’ve discussed with fellow research students, feels like an admission of defeat from the outset) or put in more hours. As it is, the time-consuming nature of my quantitative analysis, and my unwillingness to power through evenings and weekends (until the end neared), meant that I ended up with a thesis that doesn’t refer to my qualitative data, and certainly hasn’t had any significant impact. Needless to say, this is pretty disappointing.

 

Don’t treat it as a nine-to-five

As outlined above, I was under the misapprehension that my organisational skills meant that I could achieve all my research goals without putting in any extra hours. Thus, I approached my PhD like a nine-to-five, with a workplan and a timesheet, and I usually clocked off at the same time each day. It didn’t matter whether I’d achieved what I’d set out to do; there was always tomorrow. This was often pretty counter-productive; in the worst cases it meant that I stopped work without the satisfaction of achieving a particular task (which might have only taken an additional hour). By contrast, I’m now in the thick of a busy period in my new job whilst fitting in a bit of research work in the evenings and at weekends (there’s nothing like realising all the stuff you’d wish you’d done to provide motivation!), and I feel more content (despite being tired) than I did for much of my PhD. So, I think being flexible with your time is key; put in extra hours when necessary, and take breaks when needed. In the former case, this means that if you’re in the groove with a piece of work, or have a particular problem that’s motivating you, you can capitalise on it (the productive power of both of these situations should not be underestimated). In the latter case, an advantage of not really having a boss is that if you’re knackered (intellectually, emotionally, or physically; all are possible in a PhD) then you can take time to recuperate. I think this is at least as likely to be productive as my more rigid approach.

 

Don’t be a reincarnated Soviet bureaucrat

Someone once accused me of having an ‘administrative mind’ in a tone that was laden with intended offence (I just thought ‘fair point’). Indeed, my fiancée and I agree that I was probably a Soviet bureaucrat in my past life. It’s not that I love forms or anything, but I’m a badass at filling them out, and I’m scrupulously organised with all digital and physical filing (both personal and professional). This is an asset, up to a point. Unfortunately, it also has a tendency to take over, especially to the extent that it provides the opportunity for virtuous procrastination (‘I’ll just update my timesheet and then file these papers, then I’ll get onto the proper work’). Ultimately, to the extent that you’re administratively efficient, this has to play second fiddle to your main research work. It’s a good thing to have in addition to, rather than instead of, a focus on the actually important tasks. So, if you find yourself mindlessly filling time with tidying your desk, or ensuring perfect formatting in your latest supervision meeting minutes, then snap out of it and get on with the work that’ll actually be rewarding.

 

Organising yourself, of course, doesn’t happen in a vacuum, and there are contextual factors that you need to account for, and which you will have make decisions about. Primary amongst these are time, money, and location

 

 

 

Doing a PhD: Reading and Writing

 

Whether you’ve engaged with academia or not (covered by the last post), you’re gonna’ have to do shed loads of reading and writing. That sounds simple (and it probably is, relatively speaking) but there’s still a load of pits that I managed to fall into. So, here’s what I’d advise:

 

Do a paper-based rather than a thesis-based PhD

This option may not be available to you but if it is then you should give it serious consideration, especially if you intend to go into academia. It will significantly increase the chance of having one or more articles published by the end of your research because it makes them, rather than a thesis, the focus of that research. Bizarrely, I also get the impression that it’s quicker to finish than the thesis-based route (though I’m biased by the fact that I’ve just spent quite some time doing a thesis). Indeed, a while ago I had a conversation with an academic in which I commented that I regretted not taking the paper-based route, having observed people who did so and went on to graduate well ahead of me (having started at the same time). If I’m not mistaken, their response implied that it‘s easier to finish on time if one opts for that route, which perplexed the heck out of me. ‘Isn’t the paper-based route for the go-getters?’ I asked, to which the answer is apparently ‘yes.’ So, it gives you more chance to get the publications (always with the publications) that you need to pursue a career in academia whilst also raising the likelihood of finishing on time. In that case, what’s the downside? And, more to the point, what’s the frickin’ upside of doing a thesis?! Answers on a postcard please.

 

Always be reading

This one does what it says on the tin. With my ridiculously rigid and linear approach to work (I am attempting to change this), I really thought I could just spend the first year doing the reading, then conduct the primary research, and then analyse and write up. Wrong. People will always be suggesting things for you to read, and this is good because it keeps you up-to-date with what’s going on in your field, introduces you to new ideas, and challenges the one’s you already have. Indeed, I’d go one step further and subscribe to a few key journals that are relevant to your research. If a journal that you’re interested in only has institutional (i.e. very expensive) subscriptions available then follow it on social media, or figure out their release schedule (and mark it in your calendar), so that you know when new issues are coming out and can get relevant articles via your institution’s library.

 

Be critical about your choice of reading

Of course, you could spend infinite lives reading everything that has been, and will be, published in your field so you also need to get good at discerning which things are worth spending time on. If you get one page, or three pages, into an article (or a chapter into a book) that you’ve dug up or been recommended and it doesn’t seem that relevant then you can usually trust your instinct. There’s really no point in scrupulously reading a source that won’t be either useful or interesting to you. Also, use the power of the contents and the index, and learn to skim-read (if you can’t already)! I’m conscientious to the point of self-destruction so, if I skip part of a book or an article then I spend hours worrying that I’ve missed the most important point, or the game-changing quote. If you’re like this then get over it; you’ll almost certainly lose more (i.e. in time and energy) from reading uninteresting or irrelevant sources than you gain. Of course, there are some things that you’ll want to spend plenty of time reading in depth, but it’s useful to learn how to distinguish these sources from those  than can be skimmed or skipped.

 

Always be writing

Again, I was thought there should be a nicely delineated ‘writing bit’ at the end of my PhD, and planned as such. This meant that I got less feedback, was rushed with the bulk of what I wrote, and couldn’t return to analysis and address issues that arose in the course of writing. Indeed, writing is the best way to formulate ideas and arguments, spot errors with your analysis or findings, and get that all-important feedback. It also provides materials that allow you to raise the profile of your research (if that’s what you want to do). So, in addition to writing articles or chapters, and papers for conferences, it’s worth writing for blogs and, if your topic becomes salient, established news sources. I’m guessing that every university has a public relations team and they’ll probably be glad to help you (try and) get your writing onto blogs and into news outlets (if you don’t already have relevant connections).

 

Set-up a website early, and blog every interesting finding

In the same vein as the above, and in addition to trying to publish academic articles, external blog posts, and news stories, it’s worth having your own website where you can write whatever the heck you want (well, within reason). Again, this can contribute to the development of your arguments and the refinement of your analysis, and it can also demonstrate (e.g. to potential employers) that you’re research active, showing what you can do analytically and that you can write both frequently and accessibly. More importantly, it can be invigorating to write for a wider academic, or even public, audience; even if your blog only gets a few visits, at least you have something to show for your efforts. By contrast, my approach (i.e. leaving all the writing to the end) meant that my thesis emerged from a stale, inward-looking process, and is the only substantial collection of writing that I have to show for my four-and-a-half years of work.

 

At this stage, it might seem like engaging with academia (conferences, papers, feedback, and teaching), plus the ongoing demands of lots of reading and writing could be a heavy burden. If that’s the case  then it might be helpful to organise yourself

 

 

Doing a PhD: Engaging with Academia

 

Continuing with the tips I’d give myself if I could go back to when I started my PhD, this post moves on from preparing for the journey ahead. It emphasises the importance of figuring out whether you want a career in academia, and offers some tentative ideas for how one might go about making that decision:

 

Decide whether you want to go into academia early

This is linked to the point about research as a means or an end in the previous post, and will shape what you do in your PhD; if you want to go into academia then you need to focus on that end goal from the outset. This means working on getting publications, building networks of fellow researchers, organising and attending events, gaining teaching experience, and flogging your research outside academia (i.e. impact). Evidence of these things will put you in good stead when it comes to applying for hyper-competitive academic and postdoctoral research posts. If you want to go into another sector then figure out what the requirements are for breaking into it early on, start going to sector-related events, network with relevant people (even if networking doesn’t come naturally to you), and tailor your research outputs to the requirements of that sector. Of course, it’s not necessarily easy to know whether you want to go into academia at the outset of your research, but there are a number of things you can do to help. The first is, again, to think about research as a means or an end, and the second is to follow next three pieces of advice.

 

Start going to conferences with paper deadlines early

This is an excellent way to set yourself external deadlines that have to be met (credible commitment, anyone?); once you’ve been accepted to present a paper at a conference it’s generally bad form to renege. All the better if the conference actually requires you to submit a written paper (rather than just turn up and present your research). This will make you write something and, in doing so, think about the flaws in your research, formulate your arguments, and consider how to present your findings. The earlier you do those things in your PhD, the better. Linking this to the previous point, it will also give you the chance to establish whether you like the academic conference experience. Travelling a long way and taking time out of busy schedule to present to a half-or-three-quarters-empty room can be disheartening but, at the same time, it means you get to meet ace people, many of whom will share your research interests, and you often get to do so in fantastic locations.

 

Seek and respond to criticism but don’t take it to heart

This is linked to the above; academic conferences are a key means by which to get external feedback on your research. Scary though that can be, it’s one of the best ways to ensure that your research is as good as it can be. Even if you’re not going to conferences, you should be writing things (see next post) for your supervisors to review and critique and, ideally, submitting articles for review. Reviewers (and supervisors) may not hold back in their criticisms, which can be demoralising, but again this is one of the best ways to improve your research. It’s also useful to seek feedback because if you can’t abide this sort of pointed criticism (and, potentially (though thankfully not in my experience), intellectual, methodological, and analytical prejudice or one-upmanship) then academia might not be the best sector for you. Of course, part of coping with such criticism is not taking it to heart, so remember that it’s rare for feedback to be based on unreasonable dismissal of the work or personal disdain for you.

 

Get teaching experience early

This was a game changer for me. I enjoy research (for research’s sake) and also hoped that my work would have impact outside academia (alas, as yet, it hasn’t got close to the levels I’d intended), which is all good and well. But, I love teaching. It’s essentially being employed to have interesting conversations (in my case about politics). You can help people understand things, be challenged by them (and change your own thinking), and see them develop. I found it deeply, deeply rewarding, but I didn’t try it, and thus find out, until the penultimate year of my research (out of four). You may or may not like teaching, but it is likely to be a large part of an academic career, so get experience of it early on and figure out whether you want to do it for much of your working life.

 

If you’re already engaged with academia, and on your way to knowing whether you want to stay in the field for the remainder of your career, congratulations. It took me until almost my final year to figure this stuff out. Crucially, whatever you’re preferred career is you’ll have to spend a great deal of time reading and writing to get your thesis done, as the next post makes clear…

 

 

 

Doing a PhD: At the Outset

 

I recently submitted my thesis and am spending an inordinate amount of time thinking about how I could have achieved more of the goals that I had at the beginning of my PhD. In that light, and in an effort to help those embarking on the postgraduate research journey avoid my mistakes, I thought I’d write down all the things I’ve learnt in the form of the advice that I’d give myself as I began my PhD. Think of this as ‘My PhD: A Warning from History’. Of course, this also has useful cathartic side-effects, as you’ll probably pick up in some of the points. Some of my thoughts will be of particular relevance to people doing quantitative social science research in the UK context, but other points will, hopefully, be more widely relevant. Some of the points are also blindingly obvious so, if you prefer, you can take this as an indicator of how bewilderingly oblivious I was during my research (which, I guess, may undermine my claim to offer any helpful advice). There are twenty-three points overall so, in an effort to make their consumption more manageable, I’ve split them up into sections that I will post separately. The theme of this post is ‘At the Outset’ whilst the next one will cover ‘Engaging with Academia’. Then there’ll be ‘Reading and Writing’, ‘Organising Yourself’, ‘Time, Money, and Location’, and finally ‘Keeping it Simple’. That’s about as far as the structure goes; I haven’t added the bells and whistles of lots of introductory or concluding text because the main focus is on the substantive points. I may add to points in various sections (or even add entire sections) as more things occur to me and, in the meantime, I’ll hawk this around my (many and varied) doctorate-holding friends so that they can contribute to, and riff off, it as they see fit. So, without further ado, here are the first three things that I’d tell myself if I could travel back to October 2012 (yes, it’s really that long since I started):

 

Think carefully about research as a means or end

Do you do research for the sake of research, or to achieve another goal? It’s good to know the answer to this question before you start a PhD, because it will influence your approach. In other words, do you get out of bed in the morning because you always have questions rattling around in your head that you want to think about more or try and find answers to? Or do you have a particular career, or type of impact, in mind that you’re hoping to achieve. Of course, this is a spectrum rather than a dichotomy but it’s useful to be cognisant of what drives you. Knowing this will certainly help when you enter the home straight and, whether you like it or not, the whole project becomes about meeting a deadline, submitting a document that conforms to particular requirements (i.e. hoop jumping) and, ultimately, getting a piece of paper that says you’re good (enough) at research. Also, your disposition will shape everything you do in your research so you may as well be explicit about it at the outset, and allow it to influence the particular questions that you focus on (e.g. of interest to more or less academic audiences), the methodology you adopt (e.g. something that appeals to experts in your field or to policy-makers), and the training that you do (e.g. courses that might be of use later down the line if you carry on doing research, or courses that will help you finish the PhD on time and move on to whatever’s next).

 

Don’t be complacent

Before my PhD, I worked for a year in campaigning, and then for two and a half years as an elections and representation coordinator at a students’ union. This sparked my interest in the topic that I ended up researching, and also gave me experience of project management, running training and events, and doing administration. Thus, I thought that my organisational experience would make undertaking a research project pretty easy. I was wrong. The requirements of academic research are totally different from those that existed in the other contexts that I’d worked in (and, I imagine, many other work contexts). There aren’t the same kind of external deadlines in academia, you can encounter deeply challenging intellectual, methodological, and analytical problems (which might take far longer to resolve that you could have anticipated), and you (probably) won’t have the same kind of line management and appraisal. Departments are increasingly putting hard deadlines in place so that pressure is brought to bear on students, but a PhD is essentially a self-motivated research project, and that’s no small undertaking (as the following points will hopefully demonstrate). So, don’t expect to walk it.

 

Have your end date lasered onto your eyeballs

OK, don’t go this extreme, but make sure you bloody well know the actual, concrete, end date of your PhD (by which I mean the end of your completion year or, if you’re mega-keen, the end of your three  years (funded, hopefully)). Print it out in bold, underlined, red letters, and have a copy above every desk you use. This is when you have (repeat, HAVE) to be done, and I think it’s good to be aware of this throughout. By contrast, I managed to lull myself into the misguided sense that it was a never-ending journey, and thus I didn’t work as hard as I needed to until the end was far too near.

 

So, with your motivation established, some humbleness adopted, and a clear end date emblazoned across every visible surface in your home and office, the next thing is to figure out whether you want to go into academia

 

 

 

Interim Survey Data Analysis: Conclusion

Sections: Introduction -> Survey Details -> Sample Information -> Survey Items -> Hypotheses and Analyses of Models -> Conclusion

 

The preceding analysis is interim in nature and leaves much room for improvement, not least in terms of specification of the variables included in the models. Still, if the variables used are accepted as rough indicators of the concepts that are at play then some initial observations can be made. First amongst these is that the Civic Voluntarism Model does a good job of accounting for the frequency of political activity, and performs better in this regard than the Bourdieusian and perceptual model. Those components do add a small amount of power to the Civic Voluntarism Model, when included in the full model, but they leave a lot to be desired from the perspective of parsimony.

Much of the power of the Civic Voluntarism Model stems from the inclusion of the group recruitment variable, which is by far the most powerful predictor of political activity in the full model. Questions remain around the proximity of the question underpinning this variable to the dependent variable in the original survey but, assuming that it is not compromised, it is clear that Verba, Schlozman, and Brady were right to identify it as a component of their model. In fact, the strength of its relationship with the dependent variable suggests that it may be worth running separate analyses in which recruitment is treated as the dependent variable. Further, if recruitment is such a strong predictor, it may be worth investigating whether there are other different processes at play across groups that are frequently or infrequently recruited, for instance by comparing separate analyses across those groups.

If the performance of the Civic Voluntarism Model, and recruitment in particular, is difficult to ignore this should not lead us to overlook the consistently active variables in all four elements of the full model. These robustly significant relationships indicate further interesting results that may need to be teased out through more complex analyses. Such analyses should focus first on specifying improved variables, based on the available survey questions, through confirmatory factor analysis. Such variables could then be utilised in structural equation modelling that has the capacity to accommodate a more complex structure of relationships between variables than does multiple regression. These are the next steps for the current research.

Until those analyses are undertaken, however, we can take away some interim observations about particular variables from the current analysis. First, identifying as non-heterosexual, considering oneself to be limited by a disability, and having health conditions are all associated with greater political activity. Second, exercising key civic skills at work, being interested in politics and discussing it, and being asked to get involved in voluntary groups are all positively related to political activity. Further, interest in politics and group recruitment are two of the strongest predictors of political activity. Third, having an array of cultural tastes is negatively associated with political activity but being culturally active is positively associated. By contrast, having a range of social acquaintances is positively associated with political activity. Fourth and finally, perceiving the role of privilege in structuring society is positively related to political activity. Thus, whilst Verba, Schlozman, and Brady’s Civic Voluntarism Model does an impressive job of explaining political activity, it seems that parts of the Bourdieusian and perceptual model also have a part to play.

Sections: Introduction -> Survey Details -> Sample Information -> Survey Items -> Hypotheses and Analyses of Models -> Conclusion

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Interim Survey Data Analysis: Hypotheses and Analysis of Models

Sections: Introduction -> Survey Details -> Sample Information -> Survey Items -> Hypotheses and Analyses of Models -> Conclusion

 

As the overarching title of this post suggests, the analysis presented here should be treated as interim. Many of the variables outlined above are approximations of concepts and require considerable refinement, particularly in terms of subjecting their components to confirmatory factor analyses. The use of multiple regression is also a starting point, with the aim being to move onto structural equation modelling in the hope that it can accommodate the complexity of the theoretical models. Still, the results provide some food for thought in the sense that they identify variables in all four elements of the model that are robustly important in accounting for levels of political activity.

The main purpose of this analysis, before identifying consistently significant independent variables, is to consider how well the four elements of the full model account for political activity. This is achieved, first, by cumulatively adding those elements to the model and observing their impact on the Adjusted R-Square (more conservative than its non-adjusted counterpart), or proportion of the variation in the dependent variable accounted for by the independent variables. As such the hypotheses that are being tested in the interim analyses are as follows:

 

Hypothesis 1 – Adding the variables representing the forms of capital will improve the Civic Voluntarism Model in terms of the amount of variation in the dependent variable accounted for;

 

Hypothesis 2 – Adding the variables representing perception of privilege will improve the Civic Voluntarism Model in terms of the amount of variation in the dependent variable accounted for;

 

Hypothesis 3 – The Bourdieusian capital and perception of privilege model of political participation will perform as well as the Civic Voluntarism Model in terms of the amount of variation in the dependent variable accounted for.

 

The results of the cumulative analyses are shown in Table 9, which presents the variables relating to background characteristics as the baseline model. The components of the Civic Voluntarism Model were then added in the second model, followed by the additional Bourdieusian economic, cultural, and social capital variables in the third model. Finally, the variables relating to perception of privilege were added to create the full model.

Despite including variables that are statistically significant (highlighted in bold and italics) at the 10% level or above in terms of their impact on political activity, the baseline model (Model 1) does not perform well in terms of Adjusted R Square (0.083) and accounts for less than 10% of the variation in the dependent variable. Things get more interesting when the variables from the Civic Voluntarism Model are added (Model 2). Again, some of the variables are significant at the 10% level or above but the real action is in the impact on Adjusted R Square, which shoots up to 0.460. This means that, statistically speaking, the components of the Civic Voluntarism Model account for a full 37% more of the variation in political activity than do background characteristics alone.

Less impressive is the effect of adding the Bourdieusian elements of economic, cultural, and social capital to the model (Model 3). Some of those variables are significant at the 10% level but, combined, they only increase the Adjusted R Square to 0.486. The 13 variables in the Bourdieusian element of the model, then, only account for 2.6% more of the variation in the dependent variable than do background characteristics and the Civic Voluntarism Model alone. Finally, the addition of the three variables relating to perception of privilege (Model 4) raises the Adjusted R Square only a tiny amount (to 0.489). These results are summarised in Chart 3, in which each iteration of the model includes an indication of the number of variables in it (in parentheses), and the full model is labelled as ‘Civic Voluntarism, Bourdieusian, and Perception’.

 

Table 9 – Cumulative Addition of Elements to Construct

Full Model Accounting for Political Activity

(please click on the table for a full-size version)

2015-08-13 Corrected Interim Survey Data Analysis Table 09

 Chart 3 – Adjusted R Squares of the Cumulative Models

2014-10-14 Interim Survey Data Analysis Chart 03

 

The fifth model in Table 9, labelled as ‘Parsimonious Full’ in Chart 3, includes only the significant (at the 10% level) variables from the full model. As can be seen this model includes fewer variables than all of the others except the baseline model but it performs just as well as the full model in terms of Adjusted R Square (0.493). This means that with just 13 variables we can account for over 49% of the variation in the dependent variable. Still, the parsimonious model performs only slightly better than the Civic Voluntarism Model, which seems to account for most of the impressively high Adjusted R Square. However, from the cumulative models we cannot observe how well the elements of the full model perform on their own and, therefore, in comparison to each other.

Of particular interest when treating the elements of the full model as separate models in their own right is the performance of the Civic Voluntarism Model compared to that of the Bourdieusian and perceptual model. This is because the former can be considered the established model whereas the latter is a new one drawing on the theories outlined in the literature review. Each model is tested in various forms but the variables from the baseline model are included in all instances. This is because they are widely used control variables and because they represent important background characteristics in their own right. The results of these comparisons appear in Table 10.

The focus again is on amount of variation in the dependent variable, which remains the frequency of political activity, that is accounted for by the independent variables in each model. The first model (Model 2a) is the Civic Voluntarism Model with the group recruitment variable removed. This has been done because the power of that variable (Beta = 0.445) may indicate problems with it. In particular, the question that it is based on is only separated from the question that underpins the dependent variable by three other questions. Further, those three questions are themselves about levels of political or voluntary activity, and requests to engage in such behaviour. As such there is a risk that respondents, having just answered questions on their levels of political and voluntary activity, will have subconsciously altered their answers to proximate questions on recruitment so that they reflected those levels of activity more closely. The power of the group recruitment variable can be seen in the reduction of Adjusted R Square (from 0.460 to 0.301) when it is removed from the Civic Voluntarism Model (compare Model 2 with Model 2a), and it is for this reason that the subsequent models are tested both with and without it included.

The parsimonious Civic Voluntarism Model (Model 2b), including the significant variables from the full equivalent, is tested next. Given that it only includes eight variables it has a remarkably high Adjusted R Square (0.463), though a large proportion of that comes from the inclusion of the group recruitment variable. Still, this is by far the best performing model if the number of variable included is considered alongside the Adjusted R Square. By contrast, the model including the variables representing Bourdieu’s economic, cultural, and social capital as well as the perception of privilege variables (Model 4b) accounts for 20% less of the variation in the dependent variable (Adjusted R Square = 0.264) than does the parsimonious Civic Voluntarism Model. Even if the recruitment variable is included alongside the Bourdieusian and perception variables (Model 4c) they do not perform as well as the Civic Voluntarism Model (Adjusted R Square = 0.434). The parsimonious versions of the Bourdieusian and perceptual models excluding recruitment (Model 4d) and including it (Model 4e) perform only marginally better (Adjusted R Squares of 0.273 and 0. 438 respectively).

 

 Table 10 – Comparison of Elements of the Full Model

as Separate Models Accounting for Political Activity

(please click on the table for a full-size version)

2014-10-14 Interim Survey Data Analysis Table 10

 

The results of the model comparison are summarised in Chart 4, which again indicates the number of independent variables in each model (in parentheses), and includes the full Civic Voluntarism Model (from Table 9) for reference. The Bourdieusian and perception models perform consistently worse than the various iterations of the Civic Voluntarism Model. If a conservative approach is taken and the group volunteering variable is excluded (because of the question’s proximity, in the survey, to the question underpinning the dependent variable) then the gap is narrowed to a very great degree. Indeed, it accounts for between 2.8% and 3.5% less variation (respectively, in its parsimonious (Model 4d) and non-parsimonious (Model 4b) forms) in the dependent variable than does the Civic Voluntarism Model excluding the group recruitment variable. This gap is sustained if the variable is included in the parsimonious versions of both (Model 2b and Model 4e). However, if a strict reading of the texts that inform models is adhered to then the group recruitment variable should only be included in the Civic Voluntarism Model, making it much stronger than its Bourdieusian and perceptual counterpart.

 

 Chart 4 – Adjusted R Squares of the Comparative Models

2014-10-14 Interim Survey Data Analysis Chart 04

 

In terms of the three hypotheses, the first two are supported to a very small degree whilst there is no evidence emerging to support the third. That is to say that adding the Bourdieusian and perception of privilege variables do improve the Civic Voluntarism Model, marginally, in terms of the amount of variation in the dependent variable that is accounted for. However, the number of variables added to gain such a small increase in Adjusted R Square suggests that the Civic Voluntarism Model alone is preferable. This is further supported by the fact that none of the analyses indicate that the Bourdieusian and perceptual model alone performs better than the Civic Voluntarism Model.

Although the preceding seems pretty conclusive there are hints of other paths that may be worth following. The fact that the Bourdieusian and perception of privilege models perform only slightly worse than the Civic Voluntarism Model suggests that they may be variables of interest included in the former, which is confirmed if Table 9 is reviewed in more detail. This reveals that four of the variables in the baseline (background characteristics) model remain significant at the 10% level or above regardless of the other variables that are added to the model. Taking the results of the parsimonious full model are it appears, first, that respondents who did not identify as heterosexual were more likely to be politically active than those who identified as heterosexual (Beta for the heterosexual binary variable was -0.067). Although the Bs are difficult to interpret because the dependent variable is the sum of eleven different political activities, it can still be observed that heterosexual respondents had an average score that was over two points lower than their counterparts who did not identify as heterosexual (B = -2.276).

Still focusing on the parsimonious full model, having some level of limitation due to a disability was also related to a greater level of political participation (Betas for the binary variables indicating a little limitation and a lot of limitation were, respectively, 0.051 and 0.065). Respondents with a little limitation, on average, had a dependent variable score that was more than one point higher than respondents with no limitation stemming from a disability (B = 1.130) whilst those respondents with a lot of limitation had a score that was a approaching two points higher (B = 1.849). Similarly, having a larger number of health conditions was positively related to political activity (Beta = -0.065). The negative sign preceding the numbers relating to this variable appear because it has been inverted so that a higher score equates to being healthier (i.e. having less health conditions). Thus, amongst the respondents to the survey, having an additional health condition was related to an increase of almost three quarters of a point on the dependent variable scale (B = -0.665, again the negative sign is present because the independent variable is inverted). These findings may appear counterintuitive and any interpretation at this stage is speculative, however all of the groups that have higher levels of political activity either have a history of being socially marginalised, or have practical issues in their day-to-day lives that may prompt them to be more active in pursuing influence.

Moving on to the active components of the Civic Voluntarism Model we can see that work based civic skills are positively related to political activity, though the effect is small (Beta = 0.062) and a one point rise on the civic skill scales is associated with a rise of less than a tenth of a point (B = 0.080) on the dependent variable. By contrast, political interest has a much stronger positive relationship with political activity (Beta = 0.272) and, in fact, is the second strongest predictor in the parsimonious full model. On average a one point rise on the combined political interest scale is associated with approaching a half point (B = 0.457) rise in the rough measure of political activity used here. Only group recruitment has a stronger positive relationship with political activity (Beta = 0.411) than political interest. In fact, as mentioned, group recruitment is by far the strongest predictor of political activity, and a one point rise on the group recruitment scale is associated with over a half a point rise in the dependent variable (B = 0.547). The three active components of the Civic Voluntarism Model, then, all have the anticipated positive relationships with the dependent variable, and political interest and group recruitment are the two variables in the parsimonious full model with the strongest relationships with political activity.

As can be discerned from the comparisons of the various models above, the active components of the Bourdieusian model are less powerful than those in the Civic Voluntarism Model. Nevertheless, there are some interesting relationships at play. The count of cultural tastes variable has a negative relationship with political activity (Beta = -0.116), which is to say that having more encompassing cultural tastes is associated with being less politically active. This is not an especially strong relationship as it is a non-integer variable running from zero to three, so a full one point change on the scale is a great deal of movement. Still, such a change is associated with something approaching a one and a half point lower score on the dependent variable (B = -1.399). Slightly stronger is the impact of the frequency of cultural activities (Beta = 0.161), which is positively related to political activity. This is a summary variable with a long scale and a one point change on it is associated with only a very small change on the dependent variable (B = 0.077). Together, the impact of these measures of cultural capital suggests that those people who are more frequently culturally active are, perhaps unsurprisingly, also likely to be more politically active. However, the idea that cultural omnivores, with wide ranging tastes, are also more likely to get involved in politics is not supported by this analysis of the survey data.

Moving on to social capital, the count of the number of categories of people, based on job status, that respondents know is positively associated with political activity (Beta = 0.077). A one-point change on this variable, which is to say being acquainted with someone from one more category of job status, is associated with around a fifth of a point rise in political activity (B = 0.181). Lastly in the Bourdieusian components of the parsimonious full model, the diversity of respondents’ acquaintances is positively related to political activity (Beta = -0.066, again the variable is inverted so a negative sign indicates a positive relationship). This variable has a short constructed non-integer scale meaning that a full one point change indicates a large difference. Such a change is associated with a two-thirds of a point change on the scale indicating political activity (B = -0.693). Both of these relationships support the idea that being acquainted with more groups of people increases political activity. Crucially, there is no evidence emerging from the current analysis to suggest that knowing people with particular statuses is important, rather it seems to be having a wide set of acquaintances that plays a role.

Finally, and reassuringly in light of the focus of the research, higher perception of the role of privilege in structuring social hierarchies is associated with greater political activity (Beta = 0.060), though not especially strongly. A one point rise on the measure of perception of the importance of privilege in society is associated with approximately a quarter of a point rise in the dependent variable (B = 0.242). This is an interesting finding and suggests that those who see a more important role for privilege in structuring society are apt to undertake more political activity. Again, interpretation at this stage is speculative but the results suggest that perceiving privilege in society motivates a desire to influence politics. Whether this is tendency is stronger amongst those with higher or lower levels of privilege is a question for further analysis but those possibilities suggest quite different motivations.

Sections: Introduction -> Survey Details -> Sample Information -> Survey Items -> Hypotheses and Analyses of Models -> Conclusion

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Interim Survey Data Analysis: Survey Items

Sections: Introduction -> Survey Details -> Sample Information -> Survey Items -> Hypotheses and Analyses of Models -> Conclusion

 

The first wave of the survey contained 72 questions, and the second wave contained 100 questions. These were distributed between the three sections in each wave as shown in Table 8, and many of them were filtered questions. This means that they were only asked if particular answers to previous questions had been chosen by respondents. In some cases this meant that no question was asked and in others it meant that only one version of multiple alternative questions was asked. Further, some of the filtered questions were only asked of respondents if their answers had not already been recorded in previous surveys. As such, it would be time consuming to establish how many questions respondents received on average but it is the case that no respondent would have received all 172. It is also the case that the 1501 respondents in the sample provided answers to all of the questions that they received because progression through the survey was conditional on providing valid answers. This means that the combined first and second wave answers provide a rich picture of the respondents.

 

Table 8 – Number of Questions and Filtered Questions

in the Sections of Each Wave of the Survey

2014-10-14 Interim Survey Data Analysis Table 08

 

As outlined in the introduction there are four elements of the full model analysed below. These are background characteristics, the components of the Civic Voluntarism Model, further elements of economic, cultural, and social capital, and perceptions of privilege. Before the analysis of those elements is presented it is useful to outline the variables that constitute each of them. As will become clear, some of the variables are simply single survey items whilst others are based on multiple items. A number of these variables are likely to be respecified in the course of further analysis. As such, the below variables reflect the interim nature of this analysis.

 

Background Characteristics:

Age (years) – As the name suggests, this is a simple interval variable indicating respondent age in years.

Female (binary) – Again, as the name suggests, this is a binary recoding of the sex variable with ‘Female’ coded as 1 and ‘Male’ as 0.

White British ethnicity (binary) – This is a recoded version of the ethnicity variable included in the data with ‘White British’ coded as 1 and all other ethnic groups as 0. The latter includes the ‘White Other’ group because it is considered that such respondents are likely to be European migrants who, as such, should be considered distinct from the majority population.

Religious (binary) – This is a recoded version of the religious denomination variable included in the data with all of the religious options combined into the 1 category and the remaining non-religious categories combined into the 0 category.

Heterosexual (binary) – This is a recoded version of the sexuality variable included in the data with ‘Heterosexual’ recoded as 1 and all other sexualities as 0. The ‘Prefer not to say’ answers were recoded into 9 and excluded from the analysis.

Disability limitations (three categories) – This is an inverted version of the original three-category variable in the data so that 2 represents significant limitations resulting from disability, 1 equates to some limitations, and 0 indicates no limitations. The categories have been split into binary variables in the model with the no disability limitation category acting as the reference.

Health Conditions (count) – This is a summary variable inverting the count of positive (with a value of 1) selections of each category of health condition, meaning that a higher value in this (with a maximum possible of 19) represents having fewer of the listed health conditions.

 

Components of the Civic Voluntarism Model:

Educational qualification (highest) – This is an ordinal variable included in the data, with higher numerical values (there are 18 categories) associated with higher educational qualifications. The categories have been split into binary variables (with the ‘No qualifications’ acting as the reference category) and the ‘Don’t know’ and ‘Prefer not to say’ answers are excluded from the analysis.

Gross household income (fifteen categories) – This is an ordinal variable included in the data, with higher values (as the name suggests, there are 15 categories) indicating a higher income bracket. The categories have been recoded into binaries for the analysis (with the lowest income acting as the reference category).

Free time (hours per weekday) – This is an interval variable included in the data indicating the average number of free hours per weekday reported by the respondent (after sleep and a range of responsibilities such as work and childcare are accounted for).

Work-based civic skills – This is a calculated summative variable indicating the frequency of undertaking four key skills in current or past work. The highest possible value (28) indicates that all four of the skills (writing a formal email or letter, taking part in a decision-making meeting, chairing or planning a meeting, and giving a presentation) are used every day. The original answers were inverted before summing so that the ‘Daily’ answer had the highest value.

Political interest – This is a calculated summative variable indicating the level of attention paid to local and national politics, and the frequency with which each is discussed. The component variables were inverted so that high interest and frequent discussion had the highest values before they were summed. This means that the highest possible value in the summative variable (24) indicates ‘A great deal’ of interest in both local and national politics, and that both topics are discussed ‘Every day or almost every day’.

Political efficacy (internal and external) – This is a calculated summative variable indicating both the belief that citizens have influence over the political system, and the respondent’s self-assessed level of influence relative to others, as well as their ability to understand politics. The latter variable was inverted before summing so the maximum score (36) on the summative variable indicates that respondent strongly disagrees that it is often difficult to understand what is going on in government and politics, that they believe members of the public have ‘A great deal’ of influence at local, regional, and national level, and that they believe they have ‘Much more’ influence than most people at all three of those levels.

Political knowledge – This is a calculated summative variable indicating locally relevant political knowledge and national government knowledge. The variable is coded so that the maximum value (9) indicates correct identification of the local MP (from a list), correct answering of a question relating to whether MPs have to answer letters, correct identification of five senior politicians (of varying renown), and certainty of identifying a local group meeting space.

Identifies with a party (binary) – This is a recoding of the nominal party identification variable included in the data so that identification with any party is assigned a value of 1 whilst lack of identification is coded as 0.

Group recruitment – This is a calculated summative variable indicating the frequency with which invitations to get involved in voluntary groups are received via a range of means. Each of the constituent variables was inverted before summing, meaning that the maximum possible score (44) indicates requests being received ‘Once a month or more often’ from each of eleven possible sources (mass communication, family member, friend, neighbour, colleague, member of a religious congregation, political party member (if respondent is a member of a party), trade union or professional association member (again, if respondent is a member themselves), campaigning organisation member (if respondent is a member), charity member (if respondent is a member), or campaigner from an organisation that the respondent is not a member of).

 

Bourdieusian Elements of Economic, Cultural, and Social Capital:

Count of cultural activities – This is a calculated summative variable totalling coded binary variables indicating whether a number of cultural activities are undertaken outside the home, in the home, and when on holiday. The variable is itself a sum of three summative variables relating to each of those areas. Because the three constituent scales are of different lengths (there are more external activities to choose from than there are home-based activities or holiday activities) they are divided by the maximum score possible in each case so that none of the three scales can contribute more than 1 to the overall summative scale, which therefore has a (non-integer) numerical value of between 0 and 3.

Count of cultural tastes – This is a calculated summative variable totalling a sequence of binary variables indicating tastes in different areas. These are the venues visited when eating out, the cuisines consumed when eating out, the radio stations listened to, the types of magazines read, the television channels watched, the music genres selected, and the film genres selected. As with the previous variable, the overall summative variable in this case is a sum of five constituent summative variables, each of which has been divided by the maximum score possible in each case so that they contribute equally. As such, the overall summative variable has a (non-integer) score of between 0 and 5.

Frequency of cultural activities – This is a calculated summative variable totalling the frequency of a range of cultural activities taking place outside the home (from playing bingo to attending the opera), inside the home, and of holidays. The constituent variables were inverted so that frequent attendance was coded with a higher score. This is a simple overall frequency variable so greater frequency of any cultural activity will contribute to a higher overall score. As such, the maximum score (160) on the summative scale indicates that all seventeen external activities are done ‘A couple of times a month or more often’, all eight home-based activities are done ‘Daily’, and holidays are taken ‘Twice a year or more often’ with all four possible groups of people.

Legitimate status of activities – This is a calculated summative average variable. The frequency of each of the external, internal, and holiday cultural activity was multiplied by a score of between 1 and 3 (e.g. bingo and opera, respectively, in the case of external activities), those scores were summed and then divided by the count of cultural activities participated in to give an average legitimate status of activities taking into account their frequency.

Legitimate status of tastes – As above, this is a calculated summative average variable. The eat out venues, radio stations listened to, magazine types read, television stations watched, music genres selected, and film genres selected were assigned a score of between 1 and 4 (e.g. fast food outlet and fine dining restaurant, respectively, in the case of eating out venue), those scores were summed and divided by the count of those selected to give an average legitimate status of tastes.

Friends and acquaintances – This is a calculated summative variable totalling the numbers of friends seen daily, weekly, and monthly with the number of close relatives living nearby and further away, and the number of neighbours known. The maximum score (24) indicates that the highest option was opted for in each case, with no inversion of the original variables needed to achieve this.

Intensity of Relationships – This is a calculated summative variable totalling the frequency of seeing friends and acquaintances as well as the level of support received from various groups. The option selected for number of friends seen daily was multiplied by three (allowing a score of up to 12), for number of friends seen weekly by two (allowing a score of up to 8), and monthly by one (meaning a maximum score of 4). To these variables was added the inverted frequency of talking to neighbours (maximum score of 4), the inverted frequency of going out with colleagues (maximum score of 7), and the number of thirteen different types of help received from five groups (giving a maximum score of 65). This meant that of the total maximum summative score (100) considerably more than half could come from the help received by respondents. This was an intentional weighting in the summative variable towards the measurement of what can be relied on in relationships rather than just the frequency of engaging with acquaintances.

Count of acquaintances status categories – This is a simple calculated summative variable totalling the number of binary selections indicating acquaintance with people holding a range of job statuses. The maximum possible score is 14.

Diversity of acquaintances – This is a calculated summative variable reflecting the proportion of friends from the same gender, ethnicity, and religion as the respondent. In the case of gender, fifty was set as the most diverse point on the percentage scale, meaning that a new variable was calculated with fifty set as zero (with zero becoming minus fifty and one hundred becoming fifty) so that the numbers could be squared to give a curve with its lowest point at zero (reset from fifty). Thus, a score higher than zero indicated that more of one gender was known than of the other. This score was then divided by two thousand to give a maximum possible value of 1.25, which was then added to the proportion of friends from the same ethnicity and from the same religion, each divided by eighty (again giving a maximum possible value of 1.25 in each case). The variables relating to ethnicity and religion, each of which (unlike gender) has notably more than two possible groups, were not recoded (except for being divided by eighty) because it is considered that a lower proportion of friends from the same ethnicity or religion increases the likelihood of a diverse friendship group. This is a flawed assumption (the diversity is likely to depend on whether the respondent is in the majority group in society) but is considered acceptable for the interim analysis. As such, the maximum possible score on the summative variable is 3.75. This value was opted for as the maximum so that, if combined with the previous variable it would contribute less of the overall variation (because of the flawed assumptions involved in calculating this variable).

Average status of acquaintances – The fourteen different categories of acquaintances’ jobs were recombined as necessary to give the nine categories reflecting the NS-SEC and then assigned a weight on the basis of their status. The weighted values were then summed and divided by the count of categories selected by respondents to give an average of the status of acquaintances, with the maximum possible score being 8.

Property ownership – The five binary variables indicating ownership of a range of forms of property were weighted between 1 and 3 (depending on the asset type (e.g. ownership of commercial premises was weighted with a 3 whereas holding a mortgage on a property to live in was weighted with a 1)) and the scores summed to give this variable with a maximum score of 10.

Assets (fourteen categories) – This is an ordinal variable included in the data, with higher values (as the name suggests, there are 14 categories) indicating a higher income bracket. The categories were recoded into binaries for the analysis (with the lowest asset value acting as the reference category).

State benefits received – This is a simple constructed summative variable totalling the binary variables indicating receipt of a range of state benefits. There are twelve binary variables so the summary variable has a maximum possible score of 12 (indicating receipt of all listed state benefits).

 

Perception of Privilege:

Perceived role of privilege in society – This is a calculated summative variable based on the binary selection and inverted rank score of privilege-orientated explanations of status differences in society. One point was assigned if either ‘Because of their backgrounds’ or ‘ Because of inequality based on things like sex, race, and religion’ was selected from a list of possible explanations for status differences in society. A further three points were assigned if either was subsequently ranked as the top explanation, two points if ranked second, or one point if ranked third. This allows a maximum possible score on the summative variable of 7 (both privilege-orientated explanation selected, one ranked first, and the other second).

Perceived role of privilege in own life – This is a summative variable that was calculated in a similar manner to the previous variable. However, because there was no initial multiple-choice question in this case the summary score is based only on the ranking of the background or structural inequality explanations as they apply to the respondent’s life. In addition, a subsequent filter question went to those who did not rank either option asking them whether they thought background played any part in their status. If they answered yes to this prompt then they were assigned half a point. As such, the maximum possible score on the summative variable is 5 (ranking background first and structural inequality second, or vice versa, as explanations for own status, in which case the subsequent prompt question would be skipped).

Perceived status in society and own group – This is a simple calculated summative variable totalling the respondent’s self-placement on two ten-point hierarchical scales (respectively relating to their status in society at large and within the group of people they know). The two component variables were inverted so that high values reflect high self-perceived status, and the summative variable has a maximum score of 20.

 

Dependent Variable:

Frequency of political acts – This is a summary variable of the frequency of undertaking eleven different political acts (from displaying materials to going on a protest). The original answer categories have been inverted so that higher frequency has a higher value. As such, the maximum value of 55 indicates that all eleven activities are done ‘Once a month or more often’.

Sections: Introduction -> Survey Details -> Sample Information -> Survey Items -> Hypotheses and Analyses of Models -> Conclusion

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Interim Survey Data Analysis: Sample Information

Sections: Introduction -> Survey Details -> Sample Information -> Survey Items -> Hypotheses and Analyses of Models -> Conclusion

 

The YouGov panel is comprised of more than 360,000 respondents who have opted-in to answer surveys for the company. Such respondents are recruited through the YouGov website as well as through advertising on other websites, and the company makes extensive efforts to recruit underrepresented groups to the panel. Nevertheless the sample, though drawn at random from the YouGov panel, is not a random sample of the United Kingdom population. Comparing the sample to the 2011 Census data we can see that the sample is broadly representative of the British population in terms of sex and region of residence. However, the sample is less representative in terms of age, ethnicity, and religion.

YouGov does not recruit panellists who are below the age of 18 which explains the low proportion of respondents in the 15-19 cage category defined by the Census. Putting this aside it remains the case that younger people (below the age of 30), older people (over the age of 75) and, less dramatically, people in their forties are underrepresented. By contrast respondents in their thirties and especially in their fifties and sixties are overrepresented in the sample. In terms of ethnicity, White British people are overrepresented in the sample whilst almost every other ethnic group is underrepresented. The story is a similar one in relation to religion, with respondents with no religion overrepresented and all other religious groups underrepresented in the sample.

 

Table 3 – Comparison of

Sample and Population Sex

2014-10-14 Interim Survey Data Analysis Table 03

 

           Table 4 – Comparison of Sample                                Table 5 – Comparison of Sample

       and Population Region of Residence                            and Population Age Categories

2014-10-14 Interim Survey Data Analysis Tables 04 and 05

           Table 6 – Comparison of Sample                                Table 7 – Comparison of Sample

              and Population Ethnic Group                                   and Population Religious Group

2014-10-14 Interim Survey Data Analysis Tables 06 and 07

 

The fact that the sample is not representative of the British population may have implications for any findings emerging from the subsequent analysis, especially if the goal is to generalise those findings to the population. However, representativeness of samples is more important when presenting descriptive statistics than it is when examining causal relationships. This has been demonstrated in an article by David Sanders, Harold D. Clarke, Marianne C. Stewart, and Paul Whiteley.[1] They conducted a survey experiment in which a face-to-face survey was fielded to a clustered probability sample whilst a self-completion survey containing the same questions was fielded online to a sample of YouGov panellists. Analysis of the resulting data revealed significant but minimal differences in the levels of party support indicated by the two samples. By contrast, the significant variables emerging in models of voting behaviour were almost all the same. Based on those findings, and given that the goal of the current research is to test models of political behaviour rather than to describe distributions in the population, it is reasonable to utilise the survey data gathered despite its lack of representativeness.

Sections: Introduction -> Survey Details -> Sample Information -> Survey Items -> Hypotheses and Analyses of Models -> Conclusion

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[1][1] David Sanders, Harold D. Clarke, Marianne C. Stewart, and Paul Whiteley, ‘Does Mode Matter for Modeling Political Choice? Evidence from the 2005 British Election Study’, Political Analysis, Vol. 15, No. 3 (Summer, 2007), pp. 257-285.