Speaker 1: In this video we're gonna jump into the often confusing world of qualitative analysis methods. We're gonna explore these six most popular methods one at a time so that you can make the best choice for your qualitative data analysis. We'll also cover some useful tips and tricks as well as some common pitfalls to avoid when you're undertaking qualitative analysis. So grab a cup of coffee, grab a cup of tea, whatever works for you and let's jump into it. Hey welcome to Grad Coach TV where we demystify and simplify the oftentimes seemingly bizarre world of academic research. My name is Emma and today we're gonna unwrap the sometimes daunting field of qualitative data analysis methods. That is quite a mouthful. We will unpack the most popular analysis methods one at a time so that you can approach your analysis with confidence and competence. Whether that's for a dissertation, a thesis or really any kind of research project. If you're new here be sure to hit the subscribe button for more videos covering all things research related. Also if you're looking for hands-on help check out our one-on-one coaching services where we help you through your dissertation thesis or research project step by step. It's basically like having a professor in your pocket whenever you need it. Now if that sounds interesting to you you can learn more and book a free consultation with a friendly coach at www.gradcoach.com. Alright with that out of the way let's get into it. To understand qualitative data analysis we need to understand qualitative data. So let's take a step back and ask the question what exactly is qualitative data? Well qualitative data refers to pretty much any data that's not numbers. In other words it's not stuff that you measure using a fixed scale or complex statistics or mathematics. So if it's not numbers what is it? Words you guessed? Well sometimes yes. Qualitative data can and often does take the form of interview transcripts, documents and open-ended survey responses. But it can also involve the interpretation of images and videos. In other words qualitative data isn't just limited to text-based data. So how's that different from quantitative data? Well simply put qualitative research focuses on words, descriptions, concepts or ideas. While quantitative research focuses on numbers and statistics. Qualitative research investigates the softer side of things to explore and describe. While quantitative research focuses on the hard numbers to measure differences between variables and the relationships between them. If you're keen to learn more about the differences between qual and quant we've got a detailed post over on the grad coach blog. I'll include a link below. Now you might be thinking qualitative is probably easier than quantitative right? Well not quite. In many ways qualitative data can be incredibly challenging and time-consuming to analyze and interpret. At the end of your data collection phase, which takes a lot of time in and of itself, you'll likely have pages and pages of text-based data or hours upon hours of audio to work through. You might have subtle nuances of interactions or discussions that have danced around in your mind or that you've scribbled down in messy field notes. Making sense of all of this is no small task and you shouldn't underestimate it. So long story short, qualitative analysis can be a lot of work. Don't stress though, in this video we'll explore qualitative data analysis, QDA for short, by looking at these six most popular analysis methods. These QDA methods can be used on primary data, data you've collected yourself, or secondary data, data that's already been published by someone else. So without further delay, let's get into it. Right, let's start by outlining the analysis methods and then we'll dive into the details for each one. The six most popular QDA methods, or at least the ones we see at Grad Coach, are number one, qualitative content analysis. Number two, narrative analysis. Number three, discourse analysis. Number four, thematic analysis. Number five, grounded theory. And number six, IPA. If that all sounds like gibberish, don't worry. We will explore each of them in this video. So let's do it. First up is a QDA method called qualitative content analysis, or just content analysis for short. Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content. For example, words, phrases, or images, or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches. With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about. Like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations. For instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country. Because content analysis can be used in such a wide variety of ways, it's important to go into your analysis with a very specific question and goal, or you'll get lost in the fog. With content analysis, you'll group large amounts of text into codes, summarize these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method. Naturally, while content analysis is widely useful, it's not without drawbacks. One of the main issues with content analysis is that it can be very time-consuming, as it requires lots of reading and rereading of the text. Also, because of its multi-dimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication. Content analysis also tends to concentrate on a very specific timeline and doesn't take into account what happened before or after that timeline. This isn't necessarily a bad thing though, just something to be aware of. So keep these factors in mind if you're considering content analysis. Every analysis method has its drawbacks, so don't be put off by these, just be aware of them. Right, let's take a look at the next QDA method, narrative analysis. Okay, next in line we have a powerful qualitative analysis method called narrative analysis. As the name suggests, narrative analysis is all about listening to people telling stories and analyzing what that means. Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analyzing their stories and the ways they're told. You could, for example, use narrative analysis to explore whether how something being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analyzing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives. In other words, narrative analysis is about paying attention to the stories that people tell and, more importantly, the way they tell them. Of course, the narrative approach has its weaknesses just like all analysis methods. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it's difficult to test the findings of some of this research. Similarly, research bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases that you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative method. Just keep these limitations in mind and be careful not to draw broad conclusions. Alright, let's take a look at the next QDA method, discourse analysis. Number three on the list is discourse analysis. Discourse is simply a fancy word for written or spoken language or debate. So, discourse analysis is all about analyzing language within its social context. In other words, analyzing language, such as a conversation, a speech, etc., within the culture and society it takes place in. For example, you could analyze how a janitor speaks to a CEO or how politicians speak about terrorism. To truly understand these conversations or speeches, the culture and history of those involved in the communication is important. For example, a janitor might speak more casually with the CEO in a company that emphasizes equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country. So, as you can see, by using discourse analysis, you can identify how culture, history, or power dynamics, to name a few, have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method. Because there are many social influences in how we speak to each other, the potential use of discourse analysis is vast. Of course, this also means it's important to have a very specific research question or questions in mind when analyzing your data and looking for patterns and themes, or you might end up going down a winding rabbit hole. Discourse analysis can also be very time-consuming, as you need to sample the data to the point of saturation. In other words, until no new information and insights emerge. But, this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Right, so far we've covered content analysis, narrative analysis, which analyzes stories, and discourse analysis, which analyzes conversations and interactions. Next up, we've got thematic analysis, which focuses on themes and patterns. Let's jump into that. Thematic analysis looks at patterns of meaning in a data set. For example, a set of interviews or focus group transcripts. But, what exactly does that mean? Well, a thematic analysis takes bodies of data, which are often quite large, and groups them according to similarities. In other words, themes. These themes help us make sense of the context and derive meaning from it. Let's take a look at an example. With thematic analysis, you could analyze 100 reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data. For example, fresh ingredients or friendly waitstaff. So, as you can see, thematic analysis can be pretty useful for finding out about people's experiences, views, and opinions. Therefore, if your research aims and objectives involve understanding people's experience or view of something, thematic analysis can be a great choice. Since thematic analysis is a bit of an exploratory process, it's not unusual for your research questions to develop or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage, as it means that the data needs to be re-reviewed each time a research question is adjusted. So, basically, thematic analysis can be quite time-consuming, but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments. Right, let's hop on to the next QDA method of choice, grounded theory. All right, it's time to get grounded. Well, kinda. Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory or theories using the data at hand through a series of tests and revisions. For example, you could try to develop a theory about what factors influence students to watch a YouTube video about qualitative analysis. The important thing with grounded theory is that you go into the analysis with an open mind and let the data speak for itself, rather than dragging in existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up, hence the name. In grounded theory, you start with a general overarching question about a given population, for example, graduate students. Then you begin to analyze a small sample, like five graduate students in a department at a university. Ideally, this sample should be reasonably representative of the broader population. You'd then interview these students to identify what factors led them to watch the video. After analyzing the interview data, a general hypothesis or pattern could emerge. You might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods. From here, you'll look for another small sample, maybe five more graduate students in a different department, and see whether this pattern or this hypothesis holds true for them. If not, you'll look for more commonalities and adapt your theory accordingly. As this process continues, the theory develops. What's important with grounded theory is that the theory develops from the data, not from some preconceived idea. You need to let the data speak for itself. So what are the drawbacks of grounded theory? Well, some do argue that there's a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population. This helps you reduce the amount of bias in your interpretation. However, in many circumstances, it's also thought to be very unwise to approach a research question without knowledge of the current literature. So basically, it's a bit of a chicken or the egg situation. Regardless, grounded theory remains a popular and a powerful option. It can be a very useful method when you're researching a topic that is completely new or has very little existing research about it. It allows you to start from scratch and work your way from the ground up. Right, time for us to move on to the final qualitative analysis method, IPA. Let's jump into it. Interpretive Phenomenological Analysis, IPA. Okay, no, let's just stick with IPA, okay? IPA is designed to help you understand the personal experiences of a subject, for example, a person or a group of people, concerning a major life event, an experience, or a situation. This event or experience is the phenomenon or phenomena that makes up the P in IPA. These phenomena may range from relatively common experiences, such as motherhood or being involved in a car accident, to those which are extremely rare, for example, someone's personal experience in a refugee camp. So, IPA is a great choice if your research involves analyzing people's personal experiences of something that happened to them. It's important to remember that IPA is subject-centered. It's focused on the experiencer. This means that while you'll likely use a coding system to identify commonalities, it is important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often will not be able to draw broad conclusions about the generalizability of your findings, but that's okay as long as it aligns with your research aims and objectives. Now, another thing to be aware of with IPA is personal bias. While researcher bias can creep into all forms of research, self-awareness is critically important with IPA as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way that he interprets the experience of someone who was kidnapped. So, if you're going to undertake IPA, you need to be very self-aware or you could muddy the analysis. Keep these limitations and pitfalls in mind and you will have a powerful analysis tool in your arsenal. All right, so there we have it. The six most popular qualitative data analysis methods that we work with here at Grad Coach. So, at this point, you're probably asking yourself the question, how do I choose the right one? Well, selecting the right qualitative analysis method largely depends on your research aims, objectives, and questions. In other words, the best tool for the job depends on what you're trying to build. For example, perhaps your research aims to analyze the use of words and what they reveal about the intention of the storyteller and the cultural context of the time. Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event. Or perhaps your research aims to develop insight regarding the influence of a certain culture on its members. As you can see, all of these research aims are distinctly different and, therefore, different analysis methods would be suitable for each one. Also, remember that each method has its own strengths, weaknesses, and general limitations. No single analysis method is perfect, so it often makes sense to adopt more than one method. This is called triangulation, but this is also quite time-consuming. As we've seen, these approaches all make use of coding and theme-generating techniques, but the intent and approach of each analysis method differs quite substantially. So, it is really important to come into your research with a clear intention before you even start thinking about which analysis method or methods to use. Start by reviewing your research aims, objectives, and research questions to assess what exactly you're trying to find out. Then, select a method that fits. Never pick a method just because you like it or have experience using it. Your analysis method or analysis methods must align with your broader research aims and objectives. Okay, so let's quickly recap on the six methods. Firstly, we looked at content analysis, a straightforward method that blends a little bit of quant into a primarily qualitative analysis. Then, we looked at narrative analysis, which is about analyzing how stories are told. Next up was discourse analysis, which is about analyzing conversations and interactions. Then, we moved on to thematic analysis, which is about identifying themes and patterns. From there, we went south with grounded theory, which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question. And finally, we looked at IPA, which is about understanding people's unique experiences of a phenomenon. Now, of course, these aren't the only approaches to qualitative data analysis, but they are a great starting point if you're just dipping your toes into the waters of qualitative research for the very first time. If you do want to learn about other qualitative data analysis methods, drop us a comment below. If you enjoyed the video, please hit the like button and leave a comment if you have any questions. If you are in the process of writing your dissertation, thesis, or any other research-based project, be sure to subscribe to the Grad Coach channel for more research-related content. And lastly, if you need a helping hand with your research, check out our private coaching service. This is where we work with you on a one-on-one basis, chapter by chapter, to help you craft a winning dissertation, thesis, or research project. If that sounds interesting to you, book a free consultation with a friendly coach at www.gradcoach.com. As always, I'll include a link below. And that's all for this episode of Grad Coach TV. Until next time, good luck.