Research Methodology 101: Simple Explainer With Examples ( FREE Template)

in this video we're going to jump into the murky world of research methodology and answer some of the most common questions that we receive here at grad coach we're going to look at one what exactly is research methodology in simple language to what's meant by qualitative quantitative and mixed methods in terms of research methodologies three what exactly is and what are the options around sampling around data collection methods and around data analysis and four most importantly how do you as a student go about choosing the right research methodology for your project so grab a cup

of coffee grab a cup of tea and let's jump into it hey guys welcome to another episode of grad coach tv where we demystify and simplify the sometimes seemingly bizarre world of academia my name is derek and today i'm joined by one of our very own coaches karen warren karen is a seasoned researcher she's been published in various peer-reviewed journals she's authored textbook chapters she's got a phd msc bsc karen knows what she's talking about when it comes to research so today i'm going to be picking karen's brain about all things research methodology related we're

going to unpack those topics that you can understand it better and that you can make informed choices about your own research methodology so let's get into it all right karen so welcome back to grad coach tv thank you for sharing that brilliant mind of yours with us once again so we're going to talk about research methodology which is something that uh is a common pain point for the students that come through to us yeah grad coach so let's start with the basic question what is research methodology what does this mean in simple terms yeah that's

actually a really good question but uh the the simplest nastiest way of talking about research methodology is to just talk about it as the the doing chapter the how did you do it uh and and just to sort of set the scene to kind of get us all on the same page at this point you would have already probably undergone two chapters maybe a bit more depending on the nature of your thesis where you would have looked at your introduction and you would have discussed the previous literature throughout the literature review and so at this

point it would have been clear to the reader the examiner the reviewer what your research is about the general questions or objectives of your research and it would have also been a relatively clear hopefully to them that you understand the background of your research that you understand the the previous literature the uh the ways in which other people have undergone uh thinking about this problem researching this problem or similar problems and uh it will be very clear that you probably know what your research is and why it is important so that is that is essentially

just sort of setting the scene as to where you end up being by the time you get to your research methodology but at this point now you've got to say where you fit in right in terms of what you are going to be doing to help you answer that question that you've spent two or more chapters justifying and explaining so so that is why that is important because that means that this this chapter is very very uh essential at uh sort of placing where you are and what you're going to do and so it's the

how chapter so just to to jump in um to to to simplify this a little bit what you're saying is the introduction chapter is is about the what it's what are you going to be researching the and perhaps a little bit of the one and then the literature review chapter is assessing what other people have done exactly and and reinforcing your why reinforcing why your research is important why your your angle is important exactly and then the methodology chapter naturally comes after that and the research methodology is about how in other words how are you

actually going to undertake your own research is that a pharisee exactly exactly it's the it's the practical you would have probably spent the whole of the previous chapter going into all these theories and paradigms and bigger picture ideas and now you're going to be like well well what am i going to do about this how am i going to go about this absolutely um and so what you tend to really want at the end by the time when you start writing your methodology and to be honest you want to you want to know these things

as early on in your research as possible anyway so so you actually kind of want to know already probably by the time you've already started but definitely the reader and the examiner wants to know what data you you collected right who or where you got that data from and by and by data i mean those units of information that are going to help inform or help answer your question whether it is things like that you measured or questions that you asked people or the answers to the questions that you asked people that is all data

right and um and so it's the it's the what data is where it comes from or who it comes from the um how you collected it right was it through for instance uh surveys or interviews or but we'll talk about that um in a moment and the how you analyzed it so what did you do about that data to make sense of it and to help you answer that question right right and um if you've got those components then generally speaking you should be fine um in terms of of of having explicitly described what your

methodology is all right but by the time you've uh you've answered answered those sort of general ideas within the methodology remember that it should be very clear to whoever's reading your thesis what you did to the point that they should technically be able to repeat or mimic what you did right as to as high level as possible to as lower level as possible right right so you're basically providing to to really oversimplify this you're basically providing your recipe to to how you approach this this research so that others can go and go and reassess results

using a similar recipe or using the same recipe um yes i'm hesitant to use the word recipe because because a recipe is technically what you're wanting to it's the if you think about uh the method the methodology chapter is actually not uh is different from what you would see as a series of methods so it would be different from a of a recipe because a recipe isn't is just how you or the the different steps right and yes that is important it's important to know that method those series of steps but you actually want to

go a step further and to sort of say uh justify it right justify each of those steps so in in the case of a recipe uh you gotta whip the egg whites right but in the case of a methodology you're gonna say why you whipped the egg whites right it's in order to get some kind of volume into into your recipe or whatever the reason yeah so you also want to be able to justify justify those steps as well you don't always have to hop on about it but you do want to make it relatively

clear that you know why this these steps were crucial in terms of your understanding of what you're doing it's a great point and and something that that i've often mentioned to students that the the research methodology that you adopt and the research methodology chapter that you write needs to be more than just an account of of what you did and how you did it but it needs to explain why you made those choices we'll discuss some of those choices a little bit later in this video but there are many choices that you're going to have

to make in terms of of your research methodology and just as important as it is to choose the right one you need to justify why you chose those because ultimately what you're doing in explaining why you went left as opposed to going right what you're doing is you're showing that you understand research skills that you understand how to approach research in a in a systematic fashion in a formal fashion so it's really important for students to remember that it's not just about saying what you did or what you will do um but also saying why

you made those choices yes and and and also don't be don't be afraid of simple reasons um as well there are the higher level reasons i like these these really um important foundational reasons as to why you would maybe choose a specific method method um within your method your broader methodology but sometimes convenience is just a good one right convenience cost those are perfectly valid uh justifications for a step yeah so i don't be don't be shy of those justifications even though they might initially feel oh but is this the best possible method to help

answer this question actually sometimes the most convenient method is fine okay so just to recap the the research methodology this ominous thing called research methodology we can simplify that down to essentially being the how the practicalities of how you went and undertook your your specific research um the how in terms of of what data you collected perhaps um numerical perhaps textual and perhaps visual data that you collected um who you collected it from how you went about collecting it and then how you went about analyzing it so those are the the important components of um

the methodology does that uh is that a fair summary that's a pretty fair summary i think awesome so let's jump on to the next question all right so one of the the first pieces of terminology that students will stumble upon when undertaking their their research for the first time is the likes of qualitative quantitative and mixed methods so karen what what is this all about in simple terms yes um so i just want to sort of backtrack one one step and and think about who is most likely to have that problem uh because uh for

instance if you're in a site at the sciences or in engineering it's very likely that you only have one one of those problems but if you're doing uh business or social sciences then yes you do have to be very explicit about the kind of data that you have and um and a very sort of nasty general definition quantitative would mean that your your data is in the realm of numbers and is quantifiable is measurable is is numeric essentially many different ways of saying the same thing and uh qualitative is in the realm of ideas and

words and phrases and trying to make sense of those right so um that's sort of just as i said a general nasty overview of the two right a mixed methods means that you're coming at it from uh you're probably going to have a mix of both some of your data is going to be in the form of numbers and some of them are going to be in form of ideas or words or phrases in the sciences in engineering what tends to happen is is they'll think of qualitative data still as things that you can count

so because they're associated with words like uh there are so many um green uh green peas versus purple peas there's no green peas or purple peas white flowers or purple flowers on pea plants for instance and they'll think of those as qualitative because they're they're worded or they're they're categorical but um in almost every other discipline that will still be considered as quantitative because you're going to be counting those you're going to be using using those words in order to create a proportion or a percentage of uh of uh the the what you're looking at

so um so that's sort of like the the sort of general nasty sort of definitions of of those but it's also important to know why it's important to be explicit about that the reason it's important to know whether your data is quantitative qualitative or mixed is because that essentially tells you a little bit about your philosophy and once again in business studies and in um and in social sciences it's very common for you to be explicit about the philosophy and by philosophy i'm just meaning the angle at which you're going to be looking at your

research question at your research problem right uh the two extremes of the philosophies that people typically end up coming to us with is that they'll either be doing something in the realm of the positivist paradigm the positivist philosophy which is that they have a theory or a hypothesis or an inference and they're going to use the data to test it or they're going to have an interpretivist paradigm of philosophy and and that is the approach they're going to look at their data at and in that realm they're going to actually do it the other way

around they're not going to come at their data with any assumptions or with minimal assumptions they're just going to use their data to make an inference or a hypothesis or a theory right so it's almost like the reverse the data drives the theory whereas for positivist the theory or the hypothesis would will be um tested by the data right so if that makes any sense it's sort of the angle and the direction um of of which your data plays a role in answering your question just to to to try recap and simplify that so uh

if we talk about research philosophies um at at a simplified level we can say the two the two broad approaches are positivist um and interpretivist and positivist is is very much about testing hypotheses confirming um confirming theories about measuring and cutting and so it might lean towards the the quant side um whereas interpretivist is is very much about sort of starting from scratch and letting um the knowledge emerge letting the theories emerge which then at a later stage might then sort of circle back and be tested with the positivist approaches is that fair to say

exactly and what you said was important as well which is that positivist tends to lean towards quantitative data right because quantitative data can be measured and you can get you could do some statistics on it throw some statistics on it and then you know spew out a yes no this uh this hypothesis is uh uh proven or disproven or uh or at least supported and that's very much in line with the positivist approach and an interpretivist approach is a little bit more exploratory it's a little bit more like oh well let's actually kind of see

let's kind of generate a theory from the data yeah so the data doesn't prove a theory it actually almost generates the theory if you will and um and and there's neither is right or wrong it's just it depends really on the kind of information that you find valuable and that will help best answer your question and as you said you could almost do a little bit of an iteration between the two right you can have the sense of well we think it might be um that this is the hypothesis but we don't just want to

test the hypothesis we want to take it a step further maybe you want to sort of ask people how they're feeling about something right maybe a product on the market you want to ask people how they feel about it and you could get a very easy quantifiable answer you know maybe they're very happy with it or very sad with it and you can sort of quantify how where where they fit in in that in that uh space yeah and then you can take it a step further and say well what will make you happier right

in which case you're kind of giving them an option to give you a series of ideas words phrases of which you can generate a new theory so that's sort of just a general nasty uh overview of of of why it's important to understand quantitative or qualitative or mixed methods because it really tells us the angle at which you're going to be answering that question and the kind of data that you're going to be using to answer that question right right i i think i think let me just give you another dirty example uh one one

thing that sort of just spread to mind is this idea of let's say your research question is uh what is the value of youtube for helping our graduate students understand their research methodology then yes in which case you could have several ways of answering that question right maybe we're coming at it with this idea of we just want to know the value right and we're just wanting to uh analyze the value in these ways and we're just going to give people sort of a very valuable not very valuable way of answering the question in which

case we could just send out a whole bunch of surveys get that uh that scaled data back that numbered data back and then we just quantify it yes yes uh so many people think of youtube as a very valuable resource right in the graduate school programs but there's also that qualitative interpretivist way of looking at it which is we don't just want to just so sort of know a number we actually want to sort of explore that a little bit more in which case we're probably going to want to interview people for instance maybe interview

graduate students about their broader use of youtube in there under in trying to understand research methodology right in which case you'll have that qualitative data associated with it right right all right so so to recap um qual quant and mixed methods qualitative is essentially for at least for most areas of study qualitative data is about words it's about images it's about data which is not numeric whereas quantitative data is all about the numbers and then mix methods is sort of mixing the two together um and then if we if we extend that through to uh

how that links to to research philosophies there's a link between the qual and quant and positivist and interpretivist and it's that fair to say pretty much yeah cool awesome so let's jump on to the next question okay so one of the the the important parts of research methodology as we we discussed earlier is understanding or choosing um what data you're going to be looking at in other words what what what portion of data one is going to be looking at and that is the field of sampling and this can get really really complicated and really

really intimidating for a lot of students so can you give us a a basic overview of what is sampling all about and what are the main decisions that a student needs to make in this space yes um and that's a very important question because um ultimately it that is it's going to determine the um the sort of accuracy if you will of your data and so um understanding what a sample is is crucial um but i think in order to understand sampling you you un want to understand population the population is every possible person who

could be of interest to your research so if my research is on south africans uh then the population is 50 million people right but everyone for me that will be ridiculous there's no way i'm going to be able to connect to everyone so what i'm wanting so what you want and what you will get for research is only a proportion and that proportion that that small proportion of of south africans in the case of my potential research will be the sample and so that's sort of a just a general nasty once again a nasty overview

of uh of of who of that and the assumption is that that proportion that sample is generally speaking representative um and that really that really is uh is dependent and that will determine the kind of sampling you do the likelihood that you will have a fully representative sample right so so before we before we jump into to more detail there um i guess to to simplify this down into an analogy um we spoke about the population being essentially all the people that of interest to your research and the sample being the the the group of

people that you actually have access to or you choose to to connect with exactly and to create an analogy here um if we had a a big chocolate cake and we wanted to taste what the chocolate cake tastes like we would cut a slice out of that and we would taste that slice and so the analogy would be that that the cake itself is the population we're interested in what their population tastes like but we certainly don't need to eat the whole thing it would be impractical so a sample is essentially a slice of that

cake and then to to your other statement about generalizability depending on what part of that slice we ate that would give us either a view that that gives us a view of what the whole chocolate cake tastes like or if we just ate the icing for example um we we wouldn't have an accurate view of what the whole cake tastes like is that is that a decent analogy oh that's nice i'm just imagining somebody going around eating the icing no matter how much you want to um and exactly exactly the the people no matter how

much they want to there's a lot of barriers to eating the entire cake there's a lot of barriers to assessing the entire population and those are generally speaking in the realm of convenience annoying the people who you wanted to get information from there's a lot of reasons to to not do that but having said that uh you very rarely are in a position where you actually need to uh to look at everyone in a population it's way too it's not always very um it's not only not essential but it's actually just quite often a waste

of money a waste of resources a waste of time so you only need a small proportion of that population right but that proportion as you mentioned uh is important um yeah or or at least if it's if uh or at least you need to be explicit about where that proportion comes from so where that sample comes from and that is why the sampling section of a methodology is very important what you tend to get are two different kinds of sampling methods two broad kinds of sampling methods you get a probability sampling and a non-probability sampling

in the context of a probability sampling it means that the your sampling method of choice has been designed in such a way that the people who you sampled are representative of the broader population right so if i were for instance uh interest as you mentioned earlier you need to be able to have dissected that entire cake made sure that you got a little piece of every like section in order to understand that in order to be able to generalize the the cake and that is uh it's very tempting to think of that as the most

the most important or the most appropriate way of doing research but there is another kind of sampling which is uh the non-probability sampling which is that you actually are using a sample that is convenient to you that is that is targeted that is um that is perhaps well thought out for multiple other reasons be it convenience and time in fact convenience sampling is is an example of non-probability sampling just because those people are available to you and while you cannot generalize non-probability sampling to the same extent as you can probability sampling that doesn't mean that

it's the wrong way of going about it sometimes a convenience sample means that you're not annoying the people that you are interviewing or surveying it could mean that you are that you are saving resources and you're creating and you're allowing yourself to explore a certain topic a little bit further right before maybe doing something a little bit more random random something probability sampling uh has therefore also has its inconveniences and one of them is that it's actually not very easy to to get everybody as representative within a population if i think about um once again

i think i use that analogy of south africa as an example it's going to be very difficult to be able to draw from at uh the different socio-economic racial gender all of these different think ways in which people can be categorized in a convenient appropriate way that can therefore make it representative of south africa but if i am thoughtful about my my sample and am explicit about the sample that i used then i can be uh that i could still make a valid uh research uh level academic level statement based on the data that i

get from that sample i i guess it's it's very much about being well aware of what the limitations of your sampling approach are so that exactly can you can draw insights out but you are careful about um the extent to to which you generalize them so to to drag us back to the cake example you can see what i have on my mind yeah um drag us back to the cake example let's just say we did cut um the bottom half or let's just say we took a slice off the bottom that had a little

bit of cake and a little bit of icing and a little bit of the underside of the cake that would that would not be a probability sample because it's only a cut it's it's not a it's not a clean slice of full slice of the entire cake um but if we acknowledge that and we say okay well we know that we we are looking at this portion of ear then we can be careful about what we say and the conclusions we draw and we know that okay what whatever our findings are they apply to that

portion of the cake as opposed to the whole cake it doesn't mean the the findings are useless it just means that you need to appreciate what their limitations are precisely and um and especially in a time like uh like like now we've got coronavirus and all those things so a lot of research is being done online that means that uh you're not going to be able to maybe delve into the population or the populations of interest that you would have normally done yeah that doesn't mean that that research is invalid it just means that you're

using what is available um now and and that's fine that's completely justifiable as long as you're explicit okay so to recap sampling is about deciding which slice of the of the pile of the the cake one is going to take a slice of you're making me so hungry when undertaking research you're interested in a population whether that's a population a large population such as the population of a country or perhaps a group of managers within an industry or a group of whatever you will always have a population that you're interested in but as a researcher

it's always going to be highly unlikely that you can reach that entire population and it's also just unnecessary to reach the entire population so you'll take a sample thereof and to recap what we discussed uh the two main approaches are that you can take a probability sampling approach where you get a truly random random sort of cross-section a slice of that that population so there might be a perfect full cut slice of the cake um or you could potentially go the non-probability route where it's just about convenience or your your access might be limited in

some other way in which case the data is not useless you just need to know what the limitations of the data are and that you can't you can't um treat that as representative of the entire population as representative of the whole cake is that a fair summary yes that is a very fair summary and acknowledging those limitations are important examiners and reviewers are very happy when they see that you know what's going on and and that those limitations awesome so let's jump onto our next question all right so the next important decision that that students

need to make in terms of research methodology is around data collection methods in other words how they go about getting the information how they get about how they go about getting the data so let's uh let's talk a little bit about that what are the what are the most common sort of data collection methods yeah uh well there's there's a lot i mean basically basically this uh if you think of the world as data thinking about collecting those methods there's just an infinite uh number of possibilities but i think generally speaking the kinds of data

collection methods that are relatively common that we get from multiple different disciplines is in the realm of interviews or focus groups or surveys or observations those are probably the most common that we get here um right and once again that links back to um a previous section where we're talking about quantitative and qualitative data you will probably find situations where the collection methods for qualitative data are typically things like interviews where you have uh something like this like you're sort of if you were interviewing me about my knowledge of research methodologies um it would be

something along those lines a sort of a back and forth between uh the researcher and the uh and and and the participant or of interest yeah and um and this is useful if you're wanting to say get a an idea or an explore the ideas within a person's mind right around a specific topic so interviews are one approach another another approach to the data collection for qualitative data is focus groups focus groups if you're interested in still exploring people's minds are useful but they're useful for a different reason focus groups almost allow people to feed

off of each other right if you will so in an interview uh what's valuable about an interview is that you're sort of getting a glimpse into a single person's mind without them being uh influenced by external forces other than you as a researcher and in an interview that's valuable because then you just have to observe and watch and listen to this one person but in a focus group people are sort of feeding off of each other they're getting energy and ideas and thoughts from each other and building on them and that creates a different kind

of a value in the sense that maybe uh people forget about a certain idea and then somebody could sort of stimulate uh stimulate that idea further or take it a step further in which case focus group could be incredibly valuable and rich for that reason right right but then the participants are influencing each other of course there's a i catch me two of those sorts of things but those are the two sort of very common qualitative approaches you also get things like document analyses uh analyzing texts maybe in terms of understanding history or historical records

that is a very important uh way of doing things you get ethnographic or observation data collection methods where uh the researchers sort of stepping outside of a particular situation and maybe just making observations making uh recordings or jotting down notes about what's happening in a particular setting so there's many different ways of collecting that kind of data but those are probably the primary or fundamental ways of collecting qualitative data right in the context of quantitative data relatively straightforward ways of collecting qualitative data include surveys uh surveys can collect qualitative data so i don't want to

completely put it into the uh the quantitative realm but you can sort of just ask people open-ended questions within the context of a survey that will still be qualitative in nature but you can also allow people to sort of uh answer categorical data such as maybe things like their race or or their their understanding about something in which case you can give them a scale of one to five and they can say yes uh i feel very strongly about a certain statement uh which will also be pretty quantitative so that's one way of collecting a

lot of quantitative information from from people who are participating in your surveys um you could do measures measurements right measurements are very much in the quantitative realm and they can be done using a number of different instruments that are available from a simple ruler through to a ct scanner and so there are many different ways of collecting quantitative data um but it really just depends on once again your research question right so to just to recap on on the data collection methods i think it's important for for students to understand uh we're going back now

to to the broader the the broader thing of philosophy what is it that you're you're trying to understand what is your research question and how are you going to approach it and that will heavily influence what your data collection methods are is that fair to say totally yes and never forget never at any point in your writing of your methodology forget that this all has to answer your research question right um so as as and once again you can justify whatever um collection you used convenience is always an appropriate appropriate way of justifying your data

collection methods now we see a lot of interviews being done on zoom not always done that way it's it's convenient but it also allows you to get access to people who might you might never have had access to it previously because they're too far away as you mentioned exactly exactly is now um it's a little bit easier but also there are limitations there too um obviously seeing people's ways of answering questions can sometimes give the research a little bit of clue as to whether a topic is uncomfortable for instance um and and that's a little

bit less easy to see on um on an online call but having said that interviews and surveys are definitely uh of choice uh right now um i think for for obvious reasons as you said um and yeah i think i think though that's important but once again never forget that at the end of the day your data collection method is still feeding into you answering your question yeah and it needs to be appropriate for that awesome awesome so i think the the the key takeaway yeah or a really important takeaway is that you can't you

can't go about your research starting with your data collection method we see that sometimes students come to us and they say i i like surveymonkey and i want to under undertake a survey for for my dissertation and that's really really the wrong starting point as you said the research topic and the research questions those are the things that dictate what what approach you're going to take you're going to those are the things that dictate the methodology and therefore the the data collection methods yes exactly and um i think i think what you what you said

is an important uh i is an important thought right is that um is that you you cannot have the the how before before you know what what it is you're looking at um and quite often um i think you actually mentioned earlier that an interview itself comes comes with you having a good understanding about your research question it's very rare and unless in very specific kinds of approaches that you will come into an interview kind of just having a chat you really want to be at least somewhat prepared and and this this could generally be

in the realm of you coming in with a set of questions that need to be answered a very structured approach a semi-structured approach where you have a general set of questions that you allow allow the uh participant to go into a certain series of directions with but you'll sort of reign them in or a very unstructured approach where you kind of will just generally have a chat and just generally heard see where it goes person in a certain direction yeah but uh exactly so kind of see where it goes um and so you do want

to make sure that you're clear about uh linking that collection to your question because the question is what's important all right so now that we have spoken about data collection the next question is naturally about data analysis so let's jump into that okay so let's talk data analysis or data analysis yes what are what are the sort of main approaches there and what are students um in my experience by the time i get a methodology chapter this is usually the smallest section in everyone's methodology chapter and for for good reason it's it's very uh difficult

to be sure about what you're doing right and and i think that analysis is the part that scares people the most so what you tend to find um as you said earlier is that people know very much that they're wanting to do a survey or that they're wanting to do an interview and then when it comes to the analysis they're actually we all actually especially the first time we've done an uh a data analysis of this kind don't really know how we are meant to describe it or what we're meant to say or what is

appropriate um and i think once again just to always keep in mind this idea of every every piece of this research needs to be able to answer that research question your research uh and and be justified and justifiable and and this includes the the analysis um right right and in the case of qualitative data you at this point have had your interviews done your focus groups maybe aren't get gotten some on open-ended answers within your surveys and now you need to make sense of it all but you need to make sense of it in light

of your research question and so what you tend to have have are a number of ways of approaching your data and looking at the information looking at the words and the ideas um once again i'm keeping into the qualitative for the moment uh the words and ideas and trying to sort of group them into a sort of um and understand a series of understandings if you're all a series of themes and um that is the that is the the typical way of going about qualitative data is to essentially read listen and read and listen over

and over and over again until you're so sick of hearing those people's voices for reading those sentences but you are so sure about what they're saying and how that fits into your research question so in the in the case of qualitative data we have a series of approaches that you can take on how to think while you're listening to your interviews over and over again or reading the responses over and over again and a couple of examples of this is to do a content analysis where you are just listening to what they're saying and theming

them according to what the participants in your interviews for instance were saying right so if they were for instance talking about if you were doing research on the value of youtube for research methodologies and people went uh on a little bit of a tangent about supervisors for instance then that could be a theme yeah right and and the way that people speak about their supervision at university level might vary from interview to interview but you can always sort of say well actually this is how they've uh they've discussed how we've discussed the value and supervisors

tend to come up in multiple different ways but they all end up talking about supervisors so that's one of the ways in which you could sort of for instance theme uh or approach uh the the the analysis is in theming it uh by the content that naturally comes up during the interview or the or the focus group process you could actually be coming at your data with a very specific lens such as in understanding the discourse between two people for instance in a discourse analysis so for instance if you're observing two people having a discussion

or in the focus group observing people uh participating in your focus group and you see that some people are a little more shy and some people seem to be a lot more authoritative that might be because of varying power dynamics within that space and that could also be of interest to you and then of course you've got an another way of approaching your data which is to look at the narrative style of the data how are people talking about their experiences um i see narrative uh narrative approaches are being used a lot more in in

among nurses now because uh because people are interested in hearing about how nurses feel about their um about the situations in which they've been placed in to help other people and whether they're coming at it uh from a very hopeful direction or whether they're coming at it from a very uncertain direction is very important and uh and the way they tell that story is important so these are various ways of sort of analyzing qualitative data if you will that that's and there's a very nasty examples but uh i think hopefully they give you a broad

overview so just to to to recap that on the on the qualitative content and others aside the the three common approaches that that you've mentioned or one content analysis where it's really about picking up on themes and ideas that emerge from um review of of the uh the data whether that's interviews or transcripts whatever the case may be there's discourse analysis which is about understanding how people speak to each other and what that reveals about the the environment and then there's narrative analysis which is about just understanding the stories that are being shared and and

the meaning of those is that is that a fair sort of summary of um the three common approaches we see exactly exactly i think you did it better than i did so on to the quants what's quant all about well i mean i i love quant so i'm always going to be valuable uh or not valuable i'm always going to be excited about this this kind of stuff so you probably have to rein me in a bit but a quick and nasty way of thinking about quantitative analyses is to think about whether what you're doing

is describing your data or almost sort of comparing your data making inferences with your data and um in the case of describing your data you know you've taken a whole bunch of measurements for instance about on something or people have answered a whole bunch of questions then you can also then you can use descriptive statistics to describe what people how people answered or what you measured and descriptive statistics include things like taking the average or the mean right and are saying well the average person um feels very uh happy about uh using youtube as a

medium to understand their research methodology you know and or the average person um is 1.6 meters tall in my mind everyone's really short because i'm really short so that's a perfect average for me and um and and that's just a general quick and nasty way of looking and showcasing your data but that's nice i love descriptive statistics because it tells me that you know what your data looks like and a lot of people tend to go i tend to jump a whole bunch of steps and go right through to inferential statistics and comparing various different

kinds of data right or using using things like regressions or correlations or even sort of anovas and t tests and chi squared tests which are just a lot of fancy ways of saying how certain kinds of data sets compare with each other or the kinds of trends that they make together and um and that's important and valuable to know because quite often the magic or the way in which you answer a question actually lies in how you compare different kinds of data but that cannot be done unless you know what your data looks like or

it shouldn't be done unless you know what your data looks like so descriptive statistics should always be considered and then the ways in which you infer or make sense of your data further are other ways in which you are the ways that you can link the data to answering your questions right if that's sort of a general once again a big nasty overview of quantitative statistics yeah so to to sort of recap the uh the the two sort of pods or not parts the the two areas um that one would delve into with with quantitative

analysis is first descriptive statistics understanding just the nature of the data understanding the nature of the sample looking at what the averages are who's involved and and and really getting a feel for what the data is all about and then the inferential statistics such as regressions and so forth that's really about understanding the relationships between different variables understanding how things compare to each other um and and that might be sort of at the core of what your research question was about for example understanding um uh the relationship between uh watching grad coach youtube videos and

uh uh the end mark on a dissertation when one can collect that data and see okay what is the relationship that will be inferential right yes exactly exactly so and that's usually where um a lot of the very uh foundational sort of quick summaries of how what what you ended up getting out of your research ends up giving you it's from the inferential statistics but you shouldn't do inferential statistics unless you know what your data looks like and so even if it doesn't end up making it into your thesis make sure that you know what

your data looks like i cannot stress this enough how many people do certain kinds of very exciting methodologies but they actually fundamentally don't know what their data says and then they're very confused by the outcome because it's like it makes no sense so it's like but but you don't but it's because you actually didn't look at your data to say okay well where were the nuances in terms of how people might have answered each specific question where is the um what are the proportions in which people uh looked at uh specific or the measurements for

instance or or the proportions of counts and that's actually really important information to just get a sense of okay well this is where i'm standing uh and this is uh and therefore i can use these kinds of analyses and another important thing is that a lot of those inferential statistics cannot be done on data that looks a certain way [Laughter] one of the things that we find especially in survey questions is that you tend to see a lot of people suddenly answer a certain kind of question in a very specific way um and you'll find

for instance in a like it based uh question where you've got a scale of one to five uh the threes tend to like go way above the root right especially like this yeah because they don't actually but it's not because they actually feel that way it's because they just don't know how to answer that question or because it's not applicable to them so they just clicked three and uh that is very important information to know before you start comparing your data to each other because you want to know well uh why why is everyone suddenly

answering three for question five and and another another important function of of that descriptive data especially the demographic data of age and gender and ethnic group and so forth an important way to use that is to sort of use that as a sense check for for whether or not the data that you've collected is representative of the of the population so if you know if you know some details about your population and you know that the population should be 50 percent female and fifty percent male then you can check your descriptive statistics to see okay

well does does my data actually line up with this or do i have a bit of a skew towards one side or the other so you can use you that descriptive data that basic data that a lot of students just kind of float over you can use it to check okay am i representative if i'm trying to create data that's representative do i actually have that yeah exactly um i don't think i've ever been in a situation where people have answered the survey 50 male 1 especially with online services it was always like at least

a 60 female sort of lean and understanding that is important because that really allows you to sort of take that step further in the sense of maybe you actually don't want to compare the different variables to each other for the entire sample maybe you want to actually break it up into males and females just to see what's happening at a little bit of a further level but you don't usually know that you want to do that until you've had an exploration of your different data using what is otherwise very simple statistics yes yeah all right

so so to recap on the data analysis methods obviously as we've discussed the the methods will will be heavily heavily influenced or completely influenced by whether or not you're going the qual or the quant route you can't go and analyze quantitative data in a qualitative fashion so on the qualitative side um content analysis is sort of the simplest level then we spoke about discourse analysis and narrative analysis and then on the quant side descriptive statistics don't overlook them they are the the foundation of your data and a really important way of making sure you understand

your data and and then of course the exciting stuff is the inferential statistics to try and understand the relationships and the differences between different variables yes and to be honest there's actually a little bit more if you were to be very very very fancy um and you're wanting to do sort of um supervised or unsupervised machine learning uh and that sort of thing with your quantitative data that is all possible but at a very basic level you probably don't need a lot more than descriptive and inferential statistics right right all right so let's jump onto

our final question all right so on to our final question for this video and and possibly the most important one the big question is how does a student that is fresh into research that just starting out how do they go about choosing their research methodology we spoke about all these different choices today and all these different directions that one could go how do they decide or at least how do they start thinking about um what research methodology to to adopt yeah um i actually have a very particular way of going about with new clients on

going about doing this and that is right at the beginning you know they've just gotten they've just freshly gotten their new research topic or decided on their new research topic and they kind of have a vague idea as to where they're going but they don't know too much else um in which case what i'll do is we'll sit together and we will plot uh these four things being the title and the question our five things really the aims and objectives yeah and the methodology and the reason i do that is and it really is just

a page a page of work right it's just a what a single page uh where we make sure that the title and the question go well uh go hand in hand and that the question and the methodology go hand in hand and um and that the aims and objectives sort of fill that intermediate space of uh repeating the question in an actionable way and making sure that that action is linked to the methodology so the reason this is important is because we allow each other to interrogate what they're going to do if you have a

title and a research question in mind whatever methodology you use needs to be able to answer that question and by iterating between these different components we're making sure that we are using the best possible methodology to answer that question and that those that question is therefore tied into those aims and objectives right if that makes any sense right okay so so students i mean we we understand this broader principle of um the the research question the research objectives etc etc these things dictate and at least heavily influence um how one designs their methodology but what's

the starting point and what what are the first sort of things that a student should think about in terms of the nature of their research and what might that mean for the direction they take and that's actually really important it really sort of filters back into that sort of idea of research approach um are you going to be having an exploratory or interpretivist approach to your research in which case you're wanting to sort of figure things out in which case you are probably heading into that qualitative um interview focus group realm right and then it

just becomes about practicality who do you have access to what's e what's what's straightforward and easy and appropriate to do maybe given uh the people that you're you're trying to figure out information from and um and in which case uh that that ends up just being the big dictator and it is important to just think about those details right from the beginning because you do want to know well if i'm doing interviews do i have access to those people to interview do i um do i have the way do i have the space to do

the interview should i be recording the interview these are little practicalities that become really important at some point for you because you have to set up the interviews right um so that's one way another direction is once again into that sort of um confirming hypothesis testing a positivist realm where you are going to probably be using quantitative data yeah in which case once again you need to make sure that you have the uh instruments to do the measurements that you need to do the measure that that you need to measure or you need to be

understand that you might need to do a survey of sorts right and where to set up that survey so those are the kinds of things that you need to think about is the direction exploration theory building or theory testing um and and confirming direction and then the practicality what what is the most reasonable way of doing this considering my budget and my uh and the the instruments that are available to me right right okay so so just to sort of recap that and and emphasize the important bits is the the starting point for someone who

is is just on to think about their research and has no idea with in terms of methodology would be to to first understand the thing that you're researching is is the nature of that exploratory are you are you sort of starting from a blank canvas and you're wanting to sort of uncover and try build a theory from from from nothing or at least from a very um sort of nascent um start or do you already have something a theory that you're wanting to go and test something that you're wanting to confirm in a specific context

exactly exploratory versus confirmatory and if you are on the exploratory side chances are you're probably going to err down the road of qualitative data and interviews and potentially focus groups and if you're on the um confirmatory side you're probably going to take a more quantitative approach and whichever side you're on as you say give give some thought to to the the practical co the practicalities and and how you're going to actually collect data and what your limitations are as a researcher is that a fair summary absolutely awesome well i think that pretty much recaps this

video of research methodology we've uh we've thrown a lot at our poor viewers today hopefully some of that sticks um karen thank you so much for your time and for dropping the knowledge bombs on us again and we'll see you in the next video bye derek well that wraps up this episode of grad coach tv hopefully you're feeling a little bit more confident hopefully you're feeling a little bit more informed and you can now tackle the research methodology of whatever research project you're working on remember that karen is one of our grad coaches that helps

students like you every day with their research so if you're interested in getting a helping hand with your research visit gradcoach.com and you can book a free consultation with one of our friendly grad coaches just like karen so there you have it in this video we have unpacked research methodology just a little bit we've looked at one what is research methodology two what exactly are qualitative quantitative and mixed methods approaches three what are the main choices around sampling design around data collection and around data analysis and for the most important one how do you go

about choosing a research methodology for your research if you have any questions about anything we've discussed in this video today please do leave a comment below we'll do our best to reply and you might also want to check out the grad coach blog where you'll find loads of other free resources free information covering research methodology covering pretty much everything research related from literature review through to analysis through to writing up so check out the grad coach blog at greatcoach.com forward slash blog if you enjoyed the video please do give us a thumbs up and you

might also want to subscribe to the grad coach youtube channel because we are doing our best to produce more content like this so if you're writing a dissertation thesis or any sort of research project you'll probably find our other content to be quite useful so for me derek thanks for watching this is grad coach signing out you