Speaker 1: In this video, we're going to dive into the topic of qualitative coding, which you'll need to understand if you plan to undertake qualitative analysis for any dissertation, thesis, or research project. We'll explain what exactly qualitative coding is, the different coding approaches and methods, and how to go about coding your data step by step. So go ahead, 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 intimidating world of academic research. My name's Emma, and today we're going to explore qualitative coding, an essential first step in qualitative analysis. If you'd like to learn more about qualitative analysis or research methodology in general, we've also got videos covering those topics, so be sure to check them out. I'll include the links below. If you're new to Grad Coach TV, hit that subscribe button for more videos covering all things research related. Also, if you're looking for hands-on help with your qualitative coding, check out our one-on-one coaching services, where we hold your hand through the coding process step by step. Alternatively, if you're looking to fast track your coding, we also offer a professional coding service, where our seasoned qualitative experts code your data for you, ensuring high-quality initial coding. If that sounds interesting to you, you can learn more and book a free consultation at gradcoach.com. All right, with that out of the way, let's get into it. To kick things off, let's start by understanding what a code is. At the simplest level, a code is a label that describes a piece of content. For example, in the sentence, pigeons attacked me and stole my sandwich, you could use pigeons as a code. This code would simply describe that the sentence involves pigeons. Of course, there are many ways you could code this, and this is just one approach. We'll explore the different ways in which you can code later in this video. So, qualitative coding is simply the process of creating and assigning codes to categorize data extracts. You'll then use these codes later down the road to derive themes and patterns for your actual qualitative analysis. For example, thematic analysis or content analysis. It's worth It's worth noting that coding and analysis can take place simultaneously. In fact, it's pretty much expected that you'll notice some themes emerge while you code. That said, it's important to note that coding does not necessarily involve identifying themes. Instead, it refers to the process of labeling and grouping similar types of data, which in turn will make generating themes and analyzing the data more manageable. You might be wondering then, why should I bother with coding at all? Why not just look for themes from the outset? Well, coding is a way of making sure your data is valid. In other words, it helps ensure that your analysis is undertaken systematically, and that other researchers can review it. In the world of research, we call this transparency. In other words, coding is the foundation of high quality analysis, which makes it an essential first step. Right, now that we've got a plain language definition of coding on the table, the next step is to understand what types of coding exist. Let's start with the two main approaches, deductive and inductive coding. With deductive coding, you as the researcher begin with a set of pre-established codes and apply them to your data set, for example, a set of interview transcripts. Inductive coding, on the other hand, works in reverse, as you start with a blank canvas and create your set of codes based on the data itself. In other words, the codes emerge from the data. Let's take a closer look at both of these approaches. With deductive coding, you'll make use of predetermined codes, also called a priori codes, which are developed before you interact with the present data. This usually involves drawing up a set of codes based on a research question or previous research from your literature review. You could also use an existing code set from the codebook of a previous study. For example, if you were studying the eating habits of college students, you might have a research question along the lines of, what foods do college students eat the most? As a result of this research question, you might develop a code set that includes codes such as sushi, pizza, and burgers. You'd then code your data set using only these codes, regardless of what you find in the data. On the upside, the deductive approach allows you to undertake your analysis with a very tightly focused lens and quickly identify relevant data, avoiding distractions and detours. The downside, of course, is that you could miss out on some very valuable insights as a result of this tight predetermined focus. Now let's look at the opposite approach, inductive coding. As I mentioned earlier, this type of coding involves jumping right into the data without predetermined codes and developing the codes based on what you find within the data. For example, if you were to analyze a set of open-ended interview question responses, you wouldn't necessarily know which direction the conversation would flow. If a conversation begins with a discussion of cats, it might go on to include other animals too. And so, you'd add these codes as you progress with your analysis. Simply put, with inductive coding, you go with the flow of the data. Inductive coding is great when you're researching something that isn't yet well understood because the coding derived from the data helps you explore the subject. Therefore, this approach to coding is usually adopted when researchers want to investigate new ideas or concepts or when they want to create new theories. So, as you can see, the inductive and deductive approaches represent two ends of a spectrum, but this doesn't mean that they're mutually exclusive. You can also take a hybrid approach where you utilize a mix of both. For example, if you've got a set of codes you've derived from a literature review or a previous study, in other words, a deductive approach, but you still don't have a rich enough code set to capture the depth of your qualitative data, you can combine deductive and inductive approaches, which we call a hybrid approach. To adopt a hybrid approach, you'll begin your analysis with a set of a priori codes, in other words, a deductive approach, and then add new codes, in other words, an inductive approach, as you work your way through the data. Essentially, the hybrid coding approach provides the best of both worlds, which is why it's pretty common to see this in research. All right, now that we've covered what qualitative coding is and the overarching approaches, let's dive into the actual coding process and look at how to undertake the coding. So, let's take a look at the actual coding process step by step. Whether you adopt an inductive or deductive approach, your coding will consist of two stages, initial coding and line-by-line coding. In the initial coding stage, the objective is to get a general overview of the data by reading through and understanding it. If you're using an inductive approach, this is also where you'll develop an initial set of codes. Then in the second stage, line-by-line coding, you'll delve deeper into the data and organize it into a formalized set of codes. Let's take a look at these stages of qualitative coding in more detail. Stage one, initial coding. The first step of the coding process is to identify the essence of the text and code it accordingly. While there are many qualitative analysis software options available, you can just as easily code text-based data using Microsoft Word's comments feature. In fact, if it's your first time coding, it's oftentimes best to just stick with Word as this eliminates the additional need to learn new software. Importantly, you should avoid the temptation of any sort of automated coding software or service. No matter what promises they make, automated software simply cannot compare to human-based coding as it can't understand the subtleties of language and context. Don't waste your time with this. In all likelihood, you'll just end up having to recode everything yourself anyway. Okay, so let's take a look at a practical example of the coding process. Assume you had the following interview data from two interviewees. In the initial stage of coding, you could assign the code of pets or animals. These are just initial fairly broad codes that you can and will develop and refine later. In the initial stage, broad rough codes are fine. They're just a starting point which you will build onto later when you undertake line-by-line coding. So, at this stage, you're probably wondering how to decide what codes to use, especially when there are so many ways to read and interpret any given sentence. Well, there are a few different coding methods you can adopt and the right method will depend on your research aims and research questions. In other words, the way you code will depend on what you're trying to achieve with your research. Five common methods utilized in the initial coding stage include in vivo coding, process coding, descriptive coding, structural coding, and value coding. These are not the only methods available, but they're a useful starting point. Let's take a look at each of them to understand how and when each method could be useful. Method number one, in vivo coding. When you use in vivo coding, you make use of a participant's own words rather than your interpretation of the data. In other words, you use direct quotes from participants as your codes. By doing this, you'll avoid trying to infer meaning by staying as close to the original phrases and words as possible. In vivo coding is particularly useful when your data are derived from participants who speak different languages or come from different cultures. In cases like these, it's often difficult to accurately infer meaning thanks to linguistic and or cultural differences. For example, English speakers typically view the future as in front of them and the past as behind them. However, this isn't the same in all cultures. Speakers of Aymara view the past as in front of them and the future as behind them. Why? Because the future is unknown. It must be out of sight or behind them. They know what happened in the past so their perspective is that it's positioned in front of them where they can see it. In a scenario like this one, it's not possible to derive the reason for viewing the past as in front and the future as behind without knowing the Aymara culture's perception of time. Therefore, in vivo coding is particularly useful as it avoids interpretation errors. While this case is a unique one, it illustrates the point that different languages and cultures can view the same things very differently, which would have major impacts on your data. Method number two, process coding. Next up, there's process coding, which makes use of action-based codes. Action-based codes are codes that indicate a movement or procedure. These actions are often indicated by gerunds, that is words ending in ing. For example, running, jumping, or singing. Process coding is useful as it allows you to code parts of data that aren't necessarily spoken but that are still important to understand the meaning of the text. For example, you may have action codes such as describing a panda, singing a song, or arguing with a relative. Another example would be if a participant were to say something like, I have no idea where she is. A sentence like this could be interpreted in many different ways depending on the context and movements of the participant. The participant could, for example, shrug their shoulders, which would indicate that they genuinely don't know where the girl is. Alternatively, they could wink, suggesting that they do actually know where the girl is. Simply put, process coding is useful as it allows you to, in a concise manner, identify occurrences in a set of data that are not necessarily spoken and to provide a dynamic account of events. Method number three, descriptive coding. Descriptive coding is a popular coding method that aims to summarize extracts by using a single word that encapsulates the general idea of the data. These words will typically describe the data in a highly condensed manner, which allows you as the researcher to quickly refer to the content. For example, a descriptive code could be food, when coding a video clip that involves a group of people discussing what they ate throughout the day, or cooking, when coding an image showing the steps of a recipe. Descriptive coding is very useful when dealing with data that appear in forms other than text. For example, video clips, sound recordings, or images. It's also particularly useful when you want to organize a large data set by topic area. This makes descriptive coding a popular choice for many research projects. Method number four, structural coding. True to its name, structural coding involves labeling and describing specific structural attributes of the data. Generally, it includes coding according to answers of the questions of who, what, where, and how, rather than the actual topics expressed in the data. For example, if you were coding a collection of dissertations, which would be quite a large data set, structural coding might be useful as you could code according to different sections within each of these documents. Coding what centric labels, such as hypotheses, literature review, and methodology, would help you to efficiently refer to sections and navigate without having to work through sections of data all over again. So, structural coding is useful when you want to access segments of data quickly, and it can help tremendously when you're dealing with large data sets. Structural coding can also be useful for data from open-ended survey questions. This data may initially be difficult to code as they lack the set structure of other forms of data, such as an interview with a strict closed set of questions to be answered. In this case, it would be useful to code sections of data that answer certain questions, such as who, what, where, and how. Method number five, values coding. Last but not least, values-based coding involves coding excerpts that relate to the participant's worldviews. Typically, this type of coding focuses on excerpts that provide insight regarding the values, attitudes, and beliefs of the participants. In practical terms, this means you'd be looking for instances where your participants say things like, I feel, I think that, I need, and it's important that, as these sorts of statements often provide insight into their values, attitudes, and beliefs. Values coding is therefore very useful when your research aims and research questions seek to explore cultural values and interpersonal experiences and actions, or when you're looking to learn about the human experience. All right, so we've looked at five popular methods that can be used in the initial coding stage. As I mentioned, this is not a comprehensive list, so if none of these sound relevant to your project, be sure to look up alternative coding methods to find the right fit for your research aims. The five methods we've discussed allow you to arrange your data so that it's easier to navigate during the next stage, line-by-line coding. While these methods can all be used individually, it's important to know that it's possible, and quite often beneficial, to combine them. For example, when conducting initial coding with interview data, you could begin by using structural coding to indicate who speaks when. Then, as a next step, you could apply descriptive coding so that you can navigate to and between conversation topics easily. As with all design choices, the right method or combination of methods depends on your research aims and research questions, so think carefully about what you're trying to achieve with your research. Then, select the method or methods that make sense in light of that. So, to recap, the aim of initial coding is to understand and familiarize yourself with your data, to develop an initial code set, if you're taking an inductive approach, and to take the first shot at coding your data. Once that's done, you can move on to the next stage, line-by-line coding. Let's do it. Line-by-line coding is pretty much exactly what it sounds like, reviewing your data line-by-line, digging deeper, refining your codes, and assigning additional codes to each line. With line-by-line coding, the objective is to pay close attention to your data, to refine and expand upon your coding, especially when it comes to adopting an inductive approach. For example, if you have a discussion of beverages and you previously just coded this as beverages, you could now go deeper and code more specifically, such as coffee, tea, and orange juice. The aim here is to scratch below the surface. This is the time to get detailed and specific so that you can capture as much richness from the data as possible. In the line-by-line coding process, it's useful to code as much data as possible, even if you don't think you're going to use it. As you go through this process, your coding will become more thorough and detailed, and you'll have a much better understanding of your data as a result of this. This will be incredibly valuable in the analysis phase, so don't cut corners here. Take your time to work through your data line-by-line and apply your mind to see how you refine your coding as much as possible. Keep in mind that coding is an iterative process, which means that you'll move back and forth between interviews or documents to apply the codes consistently throughout your data set. Be careful to clearly define each code and update previously coded excerpts if you adjust or update the definition of any code, or if you split any code into narrower codes. Line-by-line coding takes time, so don't rush it. Be patient and work through your data meticulously to ensure you develop a high-quality code set. Stage three, moving from coding to analysis. Once you've completed your initial and line-by-line coding, the next step is to start your actual qualitative analysis. Of course, the coding process itself will get you in analysis mode, and you'll probably already have some insights and ideas as a result of it, so you should always keep notes of your thoughts as you work through the coding process. When it comes to qualitative data analysis, there are many different methods you can use, including content analysis, thematic analysis, and discourse analysis. The analysis method you adopt will depend heavily on your research aims and research questions. We cover qualitative analysis methods on the Grad Coach blog, so we're not going to go down that rabbit hole here, but we'll discuss the important first steps that build the bridge from qualitative coding to qualitative analysis. So, how do you get started with your analysis? Well, each analysis will be different, but it's useful to ask yourself the following more general questions to get the wheels turning. What actions and interactions are shown in the data? What are the aims of these interactions and excerpts? How do participants interpret what is happening, and how do they speak about it? What does their language reveal? What are the assumptions made by the participants? What are the participants doing? Why do I want to learn about this? What am I trying to find out? As with initial coding and line-by-line coding, your qualitative analysis can follow certain steps. The first two steps will typically be code categorization and theme identification. Let's look at these two steps. Code categorization, which is the first step, is simply the process of reviewing everything you've coded and then creating categories that can be used to guide your future analysis. In other words, it's about bundling similar or related codes into categories to help organize your data effectively. Let's look at a practical example. If you were discussing different types of animals, your codes may include dogs, llamas, and lions. In the process of code categorization, you could label, in other words, categorize these three animals as mammals, whereas you could categorize flies, crickets, and beetles as insects. By creating these code categories, you will be making your data more organized, as well as enriching it so that you can see new connections between different groups of codes. Once you've categorized your codes, you can move on to the next step, which is to identify the themes in your data. Let's look at the theme identification step. From the coding and categorization processes, you'll naturally start noticing themes. Therefore, the next logical step is to identify and clearly articulate the themes in your data set. When you determine themes, you'll take what you've learned from the coding and categorization stages and synthesize it to develop themes. This is the part of the analysis process where you'll begin to draw meaning from your data and produce a narrative. The nature of this narrative will, of course, depend on your research aims, your research questions, and the analysis method you've chosen. For example, content analysis or thematic analysis. So, keep these factors front of mind as you scan for themes, as they'll help you stay aligned with the big picture. All right, now that we've covered both the what and the how of qualitative coding, I want to quickly share some general tips and suggestions to help you optimize your coding process. Let's rapid fire. One, before you begin coding, plan out the steps you'll take and the coding approach and method or methods you'll follow to avoid inconsistencies. Two, when adopting a deductive approach, it's best to use a codebook with detailed descriptions of each code right from the start of the coding process. This will ensure that you apply codes consistently based on their descriptions and will help you keep your work organized. Three, whether you adopt an inductive or deductive approach, keep track of the meanings of your codes and remember to revisit these as you go along. Four, while coding, keep your research aims, research questions, coding methods, and analysis method front of mind. This will help you to avoid directional drift, which happens when coding is not kept consistent. Five, if you're working in a research team with multiple coders, make sure that everyone has been trained and clearly understands how codes need to be assigned. If multiple coders are pulling in even slightly different directions, you will end up with a mess that needs to be redone. You don't want that. So keep these five tips in mind and you'll be on the fast track to coding success. And there you have it, qualitative coding in a nutshell. Remember, as with every design choice in your dissertation, thesis, or research project, your research aims and research questions will have a major influence on how you approach the coding. So keep these two elements front of mind every step of the way and make sure your coding approach and methods align well. If you enjoyed the video, hit the like button and leave a comment if you have any questions. Also, be sure to subscribe to the channel for more research-related content. If you need a helping hand with your qualitative coding or any part of your research project, remember to check out our private coaching service where we work with you on a one-on-one basis, chapter by chapter, to help you craft a winning piece of research. If that sounds interesting to you, book a free consultation with a friendly coach at gradcoach.com. As always, I'll include a link below. That's all for this episode of Grad Coach TV. Until next time, good luck.