Dissertation Suite: Introduction to Quantitative Research

Join the University of Chester's Academic Skills Team for The Skills Pod. In this episode of our Dissertation Suite, Maths and Statistics Advisers, Mikayla, Shirley, and Matt, discuss quantitative research methods. They chat about the importance of planning your research, how visualising your data early on in your analysis will help you to spot patterns and potential issues, and how not finding patterns in the data might be more interesting than finding a pattern. 

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Transcript

What Quantitative Analysis Means

Mikayla 0:04

Welcome to the Skills Pod episode. And today we are starting to think a little bit around quantitative analysis. I'm Michaela, I'm one of the senior academic skills advisors for maths and stats, and I'm here with a couple of my colleagues.

Shirley 0:17

So hi, I'm Shirley John, and I'm a senior academic skills advisor for maths and statistics.

Matt 0:24

And I'm Matt, and I am an academic skills advisor for maths and statistics.

Mikayla 0:29

Perhaps we're uh we've got the full complement of uh maths and stats advisors with us here today as part of this episode. Um so when we're thinking about quantitative analysis, what we're starting to think about, what is it that that we mean by that?

Matt 0:43

Well, fundamentally, we're dealing with numbers. Um so that that could be that you you know you've measured something or you've counted something, um, or you put things into different groups. Um essentially, if it can be described using numbers, that's quantitative.

Shirley 1:03

I think I might say it's perhaps things you could analyze with statistical methods, because I think the thing about putting things into groups is you might be recording something like what course students are studying, which isn't necessarily a number as such, that you can actually use statistical methods with that kind of information.

Mikayla 1:26

Yeah, absolutely. And I think you know, a lot of the questions that students come to us is around, you know, oh, um I've wants thoughts and opinions, so I can't really do that with numbers. But you know, when we think about um, you know, some of the different scales that we use to measure some of those um different elements, actually, we can start to gather some of that opinion. Um, you know, we can do agreement scale, so um, like it scales from strongly disagree to strongly agree with things. So I think I yeah, I absolutely agree with the both of you, which is around that kind of statistical analysis, kind of converting things into kind of numeric form to then be able to draw some of those broader conclusions from that data. Um is yeah, it's is kind of that key thing that we're that we're looking at with quants.

Variables And Data Types Clarified

Matt 2:11

Yeah, I suppose it's it's you can you can say there's a difference between you know two groups, you know, group A and group B. The statistics allow us to make a conclusion about whether that really matters, whether it's a real effect or if it's just some random thing that's happened, it just happens to look that way. So that that allows us to speak about our results with a bit more certainty.

Mikayla 2:35

Yeah, for sure. And and I think you kind of, I think when we're thinking about this, we kind of hit the nail on the head here, which is what I think is really important when we're starting to think about quantitative analysis in terms of what is it that we've actually collected? What type of data have we got? And therefore, how do we interpret that? So, you know, when we're thinking about the type of information that we've collected, we're thinking about things that we tend to call variables. And there's a couple of different ways that we can that we can approach that.

Shirley 3:05

Yeah, absolutely. And I think that um what we might call a variable, I guess, is anything we can count, measure, or record. And I uh think that understanding what data you've got is really key to understanding what you might be able to do with it, because I think if you understand that your data, you're halfway to understanding what might be appropriate. Because I think one of the things that's really perhaps a bit scary for people with quantitative methods is there's quite a lot of different methods, and it's kind of puzzling over which ones might be useful. But I think understanding your data really helps with that.

Matt 3:43

Yeah, and as well as understanding what it is that you want to get from your data. So having a hypothesis in mind, you know, what do you reckon is actually happening in terms of interactions between the data? Should a value be higher for one group or lower for another group? Having an idea about what you're aiming towards can really help inform the correct method to analyze the data.

Mikayla 4:09

Yeah, definitely. And it's I know we've all spoken about this before when we're kind of talking to students about kind of extended research projects, particularly is what is that angle? What research question have you got? What aims, objectives, and hypotheses is it that you're trying to understand and unpick that one helps you to understand the type of data that you need to collect. Um, and then, as Shirley mentioned, also therefore what potential test is it that you could run because all of these things are so closely linked together. And it's the first thing whenever I'm doing any research of my own is writing down and being really clear about what is it that you've got, how has it been measured, how has it been collected, and what type of variable is that. And we typically have two main branches of types of variables. Um fundamentally it's kind of categorical data. Um, so I know that you mentioned before, you know, Matt, in terms of you know, groups or something like that, that might be something that's very categorical in nature, um, as well as things that are more slightly numeric scale, kind of continuous measures as well. Um, because there is there's so many different ways that we can kind of collect that information.

Shirley 5:29

Yeah, and I think the terminology can be really confusing as well, can't it? Because we can talk about categorical. Some software talks about nominal names of things, other, you know, sometimes it's scale or continuous. So yeah, it's basically understanding that you've got things in categories or you've got things in numbers that have been measured, isn't it? And I think understanding that difference is is quite key.

Independent Vs Dependent: Inputs And Outcomes

Matt 5:54

Yeah, and also you your variables can be things that you change as an experimenter and things that you measure as a response to that change. So this is where we kind of lead on to the idea of dependent and independent variables, which are unfortunately very similarly named. So independent variables are things that we as experimenters can change. So we can put people into groups, or we can give somebody a dose of a particular drug. That's our decision that we have made, and then we measure the response on the dependent variable. So that might be heart rate or income level or something like that. We're looking at the response on the dependent variable.

Mikayla 6:37

Yeah, absolutely. Did you ever used to have those like little robots that you used to? I typically did them in primary school where it's like something goes in, something happens, and then something comes out. It's the same kind of process or concept, isn't it? Where you know your independent variable is the one that kind of goes in, and then what happens on the other side of that robot, what comes out the other side? Um, so kind of yeah, does this thing affect this thing effectively? Yeah.

Shirley 7:06

Yeah, I think I think that's much more helpful to think about it, isn't it? I wish we didn't use dependent and independent, because I think input, output, or um response and explanatory variable. In other words, you get a response, as Matt has said, an outcome, or you have a variable that explain tries to explain that because you've given a certain dose or put it in a certain group. Um yeah, unfortunately, I think it's one of those things about statistics, isn't it? There are lots of different people have different ways of describing the same thing, which doesn't help when you're trying to get your head round it in the first place.

Mikayla 7:43

Absolutely. It's um it can be a really challenge. I think with mathematics and statistics, uh, you know, we use a lot of either the same word for different things or different word for the same things, um, which can, particularly when you're you know, kind of reading around the topic, it's almost like a jigsaw pause and you're like, oh, is that the same thing as I'm thinking about in this book where it says explanatory or is that different?

Matt 8:07

Yeah, and unfortunately, some of these things um they mean completely different things if they've got two letters different. So dependent and independent, or paired and unpaired. So it and then each of those things could have another way of describing them as well. So it's yeah, it it can be confusing. Um and unfortunately, you just have to pay attention to what you read um and and just try and get a consensus. And that's of course something that we ask can help with is understanding the meaning of the words if if needed.

Mikayla 8:44

Yeah, definitely. Um I'd say it it it can be a real challenge, um, particularly when you're starting out on that research process. Um, how does all of this fit together? And something else I know that a lot of students come to us and and talk a little bit around is that age-old question of how many how many people do I need or how many pieces of data do I need to make it good?

Shirley 9:08

Yeah, and of course, of course, the answer is as many as you can, but uh you might be limited, certainly in research, you might be limited by funding or something like that. The students are often limited by time, aren't they? But um yes, you're always aiming for as much as possible within your constraints, aren't you? Really, because the bigger your sample, the better your data, your statistics is likely to be.

Sample Size, Power, And Ethics

Matt 9:35

Although I'd say in certain cases, there could be um too many, in the sense that, especially in medical trials, if if you're giving a placebo or an experimental drug to a number of people, you don't want to accidentally cause harm more than is necessary in order to achieve the result. So there is a balance between not having enough in order to not show what you think is happening, and also too many that a your own effort is essentially not needed to go that far, and that potentially you could even do harm to your participants. So there is a balance to be struck between those two things.

Mikayla 10:11

Yeah, and how do how do we kind of make that decision? What's the best approach when we are thinking about sample sizes?

Matt 10:20

So so we we we have some software that we can use that can help us estimate what is an appropriate sample size given some assumptions. Because we hadn't we don't necessarily have the data yet, but perhaps we can look at what similar studies in the past have done, and that can help inform you know what's a what's a reasonable outcome for our experiment, and we can use that to estimate using the software what is an appropriate sample size. You know, it's only an estimate though, you know, it it gives an indication do we only need 15 participants or do we need 15,000 participants? And so it just gives an idea about what we can expect from our study.

Mikayla 10:59

Yeah, absolutely. And I know that that is absolutely something that we can support students with. Um, you know, when you are starting to think about particularly your ethics form, I know Mike, you mentioned that a little bit before in terms of is it ethical, particularly in medical trials, to give or not give treatment to particular people. It is something that typically is expected as part of that ethics process.

Shirley 11:19

Yeah, and it does depend also, doesn't it, on how big an effect you think there might be. So how big a difference there might be between the groups, because obviously the smaller the difference you want to be able to identify, the more um, the bigger the sample size you need to detect that. If it's a very big difference, then you might only need quite a small sample size to be able to say, yes, that's definitely a genuine effect, it's not just randomness. So it depends on a number of different things that will be unique to each context, won't it? So yeah, definitely something we can talk to students about and help understand what's right for um different types of data.

Choosing Tests From Questions And Data

Mikayla 12:01

And part of that is also thinking or the differences in terms of sample size is also linked to the statistical test that you're aiming to use to look for differences, look for relationships and things like that. Um, so it is really important to, like I say, not only think about your types of variables and your questions, but also use that to inform the type of analysis that you're then going to go on to do. So, what type of statistical test is it that you're going to use? And from my perspective, you know, I always think of it as, you know, the the kind of base is your descriptive statistics. They give you like that gut feeling for this is what my kind of data, this is what it's kind of looking like. Um you can start to draw some conclusions from this, look at kind of measures of average, um, so your kind of mean, median, or you know, measures of dispersion. So your standard deviation give you, like I say, I always call it my gut feeling. Um, it's like, oh, this is kind of what this is is looking like. Um, this is what maybe my demographics look like, this is what my participants um are kind of saying within my data. And then we go to that next step, don't we, which is around um your inferential statistics, so drawing some of those broader conclusions from the data that you've got. And yeah, I know Shirley you mentioned early on in the episode there's so many different tests that are um that are potentially available. And you know, it really does depend on the type of data that you've got and the questions that you're trying to answer.

Shirley 13:39

Yeah, and I think the other thing you didn't mention in terms of exploring the data, which I mean, I'm a really visual person, so I love a I love a good graph or a plot. Yeah. And I think that's a really good way of just getting a handle on what does my data actually look like? Is there a funny value in there that might be just an error? I've written it down wrong, or is it actually an unusual value that might actually then start to affect my analysis? So yeah, I think it's always great to have a nice graph or a plot of something, get a handle on what your data actually looks like, as well as some of those descriptive statistics that give you an idea of what's typically going on, what does it sort of look like? And I think that gives you a really good idea, doesn't it, where you might go next. Yeah.

Matt 14:26

Yeah, I mean, graphs, graphs are brilliant. I mean, I I say to almost every dissertation student, plot some graphs of your data, even if it doesn't end up in the in your final uh product, it just really helps you understand the just the shape of the data. You know, what what do you have? And and as Shirley said, you can you can easily spot really, really erroneous points. You know, maybe you forgot to put a decimal point in somewhere, and now your value is 10 times higher than what it should have been. Also, you can just spot the patterns because if you have a table of numbers, probably 99.999% of humans cannot look at that and understand what relationship there is between the numbers. But as soon as you put that into a visual pattern, humans are amazing at spotting these patterns. So why so we should just use this innate ability that we all have to be able to identify patterns visually, and it can really help you understand what's actually going on with your data without really looking at any numbers at all or any statistical tests.

Descriptives, Visuals, And Data Cleaning

Mikayla 15:23

Yeah, absolutely. I'm I'm with you both. I love a good plot if I'm being honest. Um, even sometimes if I'm trying to understand what plot I want to create, I'll just like sketch it on a piece of paper to be like, oh, this this might be something interesting to look into. And then I will kind of map that out. I'm a big, I'm a bit of an over planner, if I'm honest, um, and like to write every minute detail in a giant notebook or giant piece of paper to help me kind of plan that process through and whilst using or then moving into kind of different softwares and things like that to actually look at my data, see what, see what that process looks like. Um, and I know that between all of us, we kind of we typically support with a range of different softwares. Um, there are different software types available, some maybe more specific to your discipline.

Matt 16:15

Yeah, so so often people will have their data and they'll stick it straight into Excel. Um, because A, because most people are pretty familiar with it, at least at a basic level. But Excel is probably a jack of all trains. It can do a little bit of everything, but it's not really specialized in stats or plotting um or sort of data analysis. Um it can do these things, but it's not the best. Um so we have some alternatives, and some of these are used preferentially by different different departments within the university. Um so should we perhaps should we go through the sort of three main ones that we that we look at?

Shirley 16:55

Yeah, so I um a lot of students will use SPSS, and that's something the university does provide on a license. You can download it for free whilst you're a student. Um, we also I think really encourage students if you're not got a particular type of software that your department recommends to look at Jamovi, which is quite a nice friendly one, isn't it? It's easy, it's it's open source, you don't have to pay to download it. Um, it does pretty much the same thing as SPSS in probably a bit of a friendlier way.

Matt 17:28

Yeah, it for for most students that we see, Jamovi can do pretty much everything that they're going to need to do. Um, it can do more advanced stuff if you set it up in that way, but you don't need to do that for the most part. But for most students, um it's nice and friendly, it's visually appealing, easy to get to grips with, and the same as it's free. So you if you want to, you can continue to use it after you graduate at no cost to yourself. Whereas an individual license for SPSS is um it's about a thousand pounds a year. So it's quite a commitment.

Mikayla 18:03

Absolutely. And in my head, I'd like to think that everyone gets super enthusiastic about their data that they absolutely want to continue to look at it um even after they've left us and kind of just keep keep on top of some of their kind of skills. I think data analysis is is a really important skill, you know, when we're thinking about skills for future careers. For me, I think that is is a key thing. And I think that once you've got those skills, I think you should shout about it, you know. So if you are thinking about writing your CV, make sure that some of those analysis skills are in there, whether or not you're going into a research job or not, it still shows that you're learning new skills, that you're able to understand and interpret data, which I think is yeah is key.

Software Tour: Excel, SPSS, Jamovi, R

Shirley 18:48

Yeah, I think it's a pretty I think it's a pretty key skill, isn't it, today? That um, you know, whatever job you go into, there'll be data um that you need to understand to do your job effectively. And um yeah, if you've been willing to put a bit of effort in to get your head around the quantitative methods, then I it will probably really pay off later.

Matt 19:11

Yeah, even if you're not personally producing statistics, being able to understand them out in the real world is is really useful. If you think about the number of sort of articles in the news that have statistics, being able to critically evaluate these things and understand, well, actually, does this make sense? Uh is it telling me the full picture? That's a really useful thing to be able to know, not just in your professional life, but in your personal life as well, uh, as you navigate the world for the next 30, 40, 50 years.

Mikayla 19:40

Yeah, absolutely. And it's interesting that you kind of touched on that because I know that, you know, when we're thinking about presenting information, you know, there's different ways that we can do that, whether or not that's for different audiences, or in one kind of report where we start to think about actually, do I want to break this as a sentence? I always say, you know, sometimes it feels a little bit like a shopping list if you've got the same analysis that you're doing time and time again. We don't really want to send your reader to sleep. We want to, you know, hit them with a bit of pizzazz with your results. So there are a couple of different ways, aren't there, um, to report results. Um, like I say, I've just kind of mentioned that you can write it in text, you can write it in your main body where you kind of write it as sentences. Um, but there's a couple of alternative options available to you as well.

Data Skills For Careers And Life

Matt 20:29

Yeah, so you can say you can present things as text, um, you could summarize your results into a table. Um, and there are also ways that you can present your results um as as visual uh representations. Um so one of one of the other packages that we we didn't mention before is R Studio, which is uh again a free way of analyzing data. Uh it's got quite a steep learning curve at first, but once you've got beyond that, it it's it's kind of nice because the commands are pretty simple. But one of the things that you can do in there is create really nice visualizations of your data after you've done your analysis, and well, depending on what the analysis is, and you can really just sort of dive in and summarize things really nicely in a way that's easy for someone to look at. Um, something that I I read some time ago is that any graph in any paper should be understandable on its own within about 30 seconds. So you don't so you don't necessarily need to read all the text around it to understand the overall pattern in it. You know, is this thing increasing as this thing increases? Or, you know, is there no pattern whatsoever? Because that in of itself is a result, and that's something that I think we see quite a lot is that we have students that come in and they've they've done their experiment and you know they've done everything right and they've done their analysis correctly, and they find that there's nothing there. That's still a result because you've shown that there is no connection between these things within the parameters of your experiment. So it's you know, it's something that we sometimes see where students get a bit panicked. It's actually good to find and it gives you more to talk about in your discussion, actually.

Mikayla 22:05

Yeah, definitely. And and you know, sometimes we actually want there to be no difference. Um, so the example I typically think of when we think about that is things like the gender pay gap, is we actually want there to be no difference in pay between males and females. And in that case, that is an important result in its own right. Um, so yeah, absolutely. Like from my perspective, um, try not to get disillusioned if you don't have results that say that there is a difference or that there is a relationship. And I think that's really, really key result, Matt.

Shirley 22:38

Yeah, I think that can be important in other contexts as well, can't it? That you know, if you've got two treatments, for example, and one of them's really expensive and one of them's much cheaper or easier to um use, then actually being able to show there's actually not much difference between them says maybe you can use the cheaper one, and that's going to be just as good as the fancy expensive one. So um, whichever way your results go, I think um, certainly for the purposes of a dissertation, there's always something you can write in your discussion, isn't there, whether you get a nice significant result or whether you show that actually there's no pattern there at all.

Matt 23:15

Yeah, I mean you're not going to be marked on whether the theory works. You'll be assessed on how you dealt with the data you actually have. I mean, I uh there's two things I say to students a lot, which is a null result is still a result, and the data you have is the data you have. You know, you can't change it ethically. So uh you have to deal with what you have and using the tools of statistics, sift through that and figure out what is happening. Even if even if that what is happening is actually there is nothing happening, that's still a result.

Reporting Results: Text, Tables, Visuals

Mikayla 23:48

Yeah, or it could lead to more questions. You know, research doesn't stop once you kind of hand in a dissertation, extended research, or kind of you get to the end of a res a piece of research that you're doing. That for me is a springboard. It's the kind of it's the start of what happens behind you. Um, you know, it will always lead to more questions, or good research typically leads to more questions, um, that allows you then to think, okay, well, it's not this, could it be due to this, or it is this? Actually, what specifically within this is is is the reason. Um so yeah, like I say, for me, research is just really exciting because I'm quite uh inquisitive person. I always think, oh, well, why? What could that be because of how does that work? What does what's going on here? Um and yeah, statistics helps to to unpick that a little bit.

Shirley 24:44

Yeah, so what do we think the common mistakes are that students make with quantitative research? So I think um I was thinking back to the the planning stage, um, and I think that's really underestimated, isn't it? And um, I don't know how often we've seen students, I mean it's so easy, isn't it, to rush off and go and you know, kind of set lots of questions on your questionnaire and rush off and get all this data. But if you haven't really sat down and thought about, as we mentioned earlier, actually, what is it you're hoping to achieve? I think it's very easy, isn't it, to collect loads of data and then actually maybe you didn't collect the right kind of data. So I uh that's where I'm at, I think, is always plan, plan, plan and more plan, get your planning right, and then you will um have some really good data to uh use with the analyses.

Matt 25:39

Absolutely. I mean, what one of the things I've said to sort of students is make sure the questions you ask your participants actually answer the question you want the answer to. Um, because I I've seen occasionally students will ask their participants lots and lots of questions, which in of themselves could be very interesting, but none of them actually answer the research question. So just have a think what you know, what information do you need from your participants, and therefore what questions do you need to ask of them or what measurements do you need to take from them in order to get the information you need from them. And that also leads into the planning of what tests are you going to do, uh, you know, what analysis are you going to do. So when you know these things, everything else will slot into place once you you actually have the data. So again, yeah, I agree, I agree totally, planning is uh a huge thing that people often underestimate.

Null Results Have Value

Mikayla 26:37

Yeah, absolutely. It's it's right up there in terms of exactly what I was gonna say is taking time to plan means that your analysis will be so much quicker. You know, in a couple of days, you could have your results section, your data analysed, your results tentatively draft form written up, because you will know exactly what it is that you're doing, what test it is that you're gonna run. You can have practice whilst you're waiting for your put for your data to come in. So if you know that you're gonna run a t-test, for example, you could potentially have a practice on some practice data so that you know what it is that you're looking for in terms of actually running it, how do you actually run it? Um, where does your data come out? What does it all mean? And then when you get your data, it will make it much more kind of a quicker, easier process. Um the other thing kind of linked to that as well, that I always think about is particularly if it's uh survey questionnaire based, is that pilot study. So even if you only ask one person to go through it, that is absolutely going to help in terms of what kind of I'm gonna call them weird and wonderful answers are people gonna give. Do people understand your question? Um, so having somebody sit down and say, okay, I've answered like this because this is how I interpreted the question, you go, oh no, that isn't quite what I meant by that question, or I've taken it from a validated questionnaire and actually it's not quite measuring what I thought it was going to measure by your interpretation. That can be really key before you've sent it out, you've got a hundred participants, you've got all of your data, and you realize actually that people have answered them the questions very differently because of their interpretation of the question.

Common Mistakes And Better Planning

Shirley 28:26

Yeah, I think that's um it's so easy, isn't it? When you're caught up in your research project and your dissertation work, you're very close to it, you know exactly what you want to find out, and it's very easy to um, you know, make the questions up and assume that everybody's going to read them in the same way that you are thinking about them. So yeah, pilot study, friends and family, that's where they come in useful, isn't it? Um, and and also obviously um the Academic Skills Maths team um can uh we can actually help you with wording and stuff like that as well, can't we? Um because we're coming in with fresh eyes, we're not so invested in the research, we can kind of perhaps see it from a different perspective and identify perhaps a wording that's perhaps not as clear as it could be before you've sent that out. And um, yeah, perhaps people have responded in a different way. I know I'm I'm I'm an awful person filling in a questionnaire because I'm always looking at the questions and and going, is that what you really mean? Is that and you know, actually there's not an answer on there, there's not an option for me to pick that actually is is what I want to answer. So, but yeah, pilot study is great because it it can perhaps smooth out some of those issues, can't it, before you've gone live with your actual questionnaire.

Matt 29:45

Yeah, I think I think actually a lot of these uh sort of common mistakes that we're seeing here are almost summarized in the idea of you need to walk before you run. So, in terms of you know, obviously asking a small number of people before you ask a large number of people, or uh making sure that you've got the fundamentals of your study well established before you go and do the study. Um again with the analysis. Don't start off doing the really, really fancy, complicated stuff. Start off looking at just the descriptives, look at your visual stuff, just to get an idea to walk before you run.

Mikayla 30:19

Perfect.

Matt 30:21

Okay then, so I think that's kind of rounded off our discussion for today. So hopefully everyone's enjoyed what we've gone through here and got some useful points to think about. Uh, and of course, if anyone has any further follow-ups that they'd like to talk to any any of us about, you can of course book in an appointment with us. Um, and hopefully we'll be seeing you soon.

Mikayla 30:43

Thanks everybody. Bye. Thank you.

Shirley 30:46

Bye.

Emma 30:46

Bye. Hi there. If you're a University of Chester student, here are the ways you can access support from your academic skills team.

Pilot Studies And Clear Questions

Anthony 30:54

On our Moodle pages, we've got lots of interactive resources for you to use. On our literacies Moodle page, you'll find help with a range of skills from academic rating to revision. On our Maps and Statistics Moodle pages, you'll find help with different statistical tests, calculations, and formulas.

Emma 31:11

You can also use our Feed Forward email assistance service. You can send 750 words, which is around three paragraphs, of your work, to ask at chester.ace.uk and we'll respond within three working days with generic and developmental advice on aspects such as paragraph structure, criticality and referencing.

Anthony 31:31

You can also book a one-to-one with the Academic Skills Advisor via on Moodle pages. These appointments typically last 30 minutes and are available online and in person. You're able to see the campuses we're at by looking at our booking scheduler. You can send across an extract of your work for us to look at in preparation for the one-to-one, or you can book a one-to-one to discuss a generic skill such as referencing or critical thinking.

Emma 31:55

If you and a group of your course mates are struggling with the same academic skill, you can book an Ask Together session by emailing ask at chester.ac.uk with details of your availability, how many people are in your group, what skills you want to cover, and where you'd like the session to take place.

Anthony 32:13

You can follow us on Instagram and Facebook using the handle AkETSkillsUAC, where we post practical tips on a range of academic skills, and it's also a great way to see what the team are up to.

Emma 32:24

And of course, you've got the skills pod. If you have a topic that you'd like us to cover or you'd like to be involved with our podcast, please email ask at chester.ac.

Anthony 32:36

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