Cómo aplicar la estrategia colectiva de IPA en MAXQDA (Full Transcript)

Guía paso a paso para importar transcriptos, codificar temas emergentes y agruparlos en temas superordinados usando MAXQDA.
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[00:00:00] Speaker 1: Hello, everyone. In my previous presentation, I showed you how to use MassQDA to analyze your qualitative data if you are using IPA, which is Interpretative Phenomenological Analysis, right? I focus on individualized strategy, right? So, as I indicated, you go through your data and then create containers for not only your research question, also individual participants, and then develop themes based on what you see from the data. So, if you want to learn more about the individualized strategy, you can click on the link above, and you'll be able to watch the previous video. But for this one, I'm going to show you how to analyze your data using the collective strategy. The collective strategy focuses on going through the data and identifying information that are significant, and develop themes, and then categorize the themes to develop the overarching themes or the subordinate themes. So, the first step is always the same compared to the individualized. For the first step, you have to read and reread the transcript. But before you read and reread the transcript, the first step that you have to do is to upload all the five transcripts, right? So, how do you do that? So, let me quickly go to MassQDA and show you. So, you open MassQDA, you click on new to start a new project, and then you give it a name that you want. I will say burnouts or BO, and then underscore collective, because I'm using the collective approach. You can give it any name that you want and look for a place to save. I'm going to save it on my desktop and click on okay. Then the next step is to upload all the transcripts. How do you do that? You go to import transcripts, and then you choose a second option, which is upload without timestamp. You click on that, and then you look for the transcript. So, I have my five transcripts here, and I click on open, and I have all these transcripts here. You can also bring your demographic information if you want to, or demographic data. How do you do that? You can go to variables, and then you can click on data editor for document. If you want to put the demographic information manually, you first have to go to list of document variables, and then what you do here is you click here and then state the variables. So, let's say you have one variable called age. It's an integer. You click on create, and then you click on the second one, maybe level of education, and also years of experience. Then you see here, you click on this one, data editor, and then the variables that you click on will be in each of the columns. So, you see the age here. If I've created another variable to be here, then you can go ahead and enter the information. You can just click on it and enter. So, let's say if this person is 37, I put it there. If this person is 45, I put that there. That's the manual way of entering that information. If you want to get a complete understanding about bringing your demographic information, I have another video that talks about that. I think I can put it here for you so that you can click on it. Alternatively, you can just upload your demographic information, and if you look at my previous video about the individualized strategy, I also gave steps that you can do to upload. It's going to be easy. The first step that if you want to upload, let me try to delete this one. I can go here and here to delete. Let's say you want to bring the variables in Excel spreadsheets. So, let me show you an Excel spreadsheet. So, make sure that you follow this formatting. If you don't do that, the system will not be able to accept that. The two columns should be in line with this one. When you go to data editor here, you see that column, the first column is document group, and the second column is document name. Look at mine here. The first column is document group. There's nothing here. It shouldn't put anything here. The second one is the participant ID, but it's also called document name. So, P1 to P5, and then the next column will be age, gender, and years of experience. This is not here, as you can see. You just have to focus on the first two columns should be in line with what is here so that the system can accept your demographic information. So, after putting these two, then you can enter all the demographic information, the variables, and also the values. When you're ready, you go to import, and then you look for that information. I think I have it here, and then you can import demographic information. So, you see how I've imported the demographic information here. As I said, it's not required to bring demographics. If you want to further analyze the database on the demographic information, then you should bring that information here, but if you're not planning to do that, you just want to develop themes addressing the research question. You don't have to bring demographics. After that, what you have to do is to create a container for the research question. So, similar to what we did for the individualized one, you click here, and then in parentheses, you can type, okay, I have only one research question. If you have two research questions or three research questions, you have to create more than one container for the research question. So, each one will have one container. So, I have only one research question, so I'm going to create one container for that. So, this is my research question label. So, it's about how they make sense of the experience. I made here RK1 experience of burnout, and then here you can put a full research question there if you want to. So, this is my research question here. I'm focused on how they make sense of the experience. After you create a container for the research question, you go to each participant's transcript. So, the same kind of skill that you use to analyze the data if you are using thematic analysis is quite similar. You double click on participant P1, and then the first step here is to read and reread so that you can get a deeper understanding of what participant is telling you and what your interpretation is. As you are going through, you can take notes. Let's say you see that if this one is significant, if this one is indirectly or directly addressing your research question or it has something to do with the research question that you have, and you ask yourself, what is participant telling me and what does this information mean? Based on that, if you come up with an idea about the meaning or your interpretation, you can right-click on it and go to paraphrase selection and paste or type in your interpretation of what participant is saying and then click on OK. I will not be able to go through each of them, but I just want to show you what exactly you have to do so that when you have your data, you know what to do. So, you select if this one is significant. You don't have to write notes for each of the things that you see. You just have to write if you want to document your understanding of what participant is telling you. So, let's assume that you have an understanding of what participant is telling you here. Imagine that you ask participant this question. Do you experience burnout or stress at workplace? And the participant said yes. And many physicians do as well, right? What do you think a person is communicating to you? Person is telling you that it's not only me, it's affecting a lot of people. It's prevalent. You document your interpretation. You select, you right-click, you go to paraphrase selection and then put your interpretation here and click on OK. So, that's what you're going to do for all of them. As you can see here, I've done it for all of them here, right? And as I said, you don't have to do it for all of the significant information that you see. You decide which information do you want to put here so that as you go through, you start a coding process, you'll be able to review your notes and that will inform the kind of theme that you want to come up with, right? So, imagine that you've gone through everything and then you've written your notes. The next step is to start a coding process. There are some terms that you have to be familiar with in interpretative phenomenological analysis. Some of the terms that are given to themes are emergent theme or themes. You can see it as initial codes or initial themes, right? And then we have superordinate themes. This is after you have your initial themes and you want to categorise them to develop, you categorise them to develop themes. The theme that comes out of the emergent theme is called superordinate theme. Sometimes you can even call it master themes if you want to, right? But the most important is that whether it's called master theme, overarching theme, or superordinate theme, it comes after you have gone through all the transcript and developed emergent themes, right? So, the first step is to develop emergent themes and it's very straightforward. You start with this one, emergent themes. So, you start with the first participant and then you go to if this one is important, if this information is important, let me see. So, if you think that this information is important, so if you think that this information is important addressing the research question, what you're going to do is to click on the plus sign here, decide on the label which is your interpretation of this information and you put it in two to five words representing your interpretation of this significant information. You can also define what this code is, right? You can say this code represents participant's expression of how she became interested in medicine by observing what the parent was doing or taking care, how the parent was taking care of elderly patients, right? So, you can provide that information here if you want to. It's not required, you can leave that place alone. The most important is the theme that you have. You click on okay, and then now you drag and drop it here, right? So, that's what you're going to do. If this one is important, you ask yourself what is the meaning of this one and how does it really help you to, how does it address the research question on what is my interpretation of what this one is telling me. So, based on that, you'll be able to come up with a label. So, as I said, it's quite similar to thematic analysis. You go through, you make sure that you understand what this one is telling you, and then you develop a theme which is called emergent theme. So, you go here, close to the research question, click here, and then put that information here, and then define it, and you can now drag and drop. As you can see here, under research question one, I have all the themes based on not only participant P1, but also participant P2, participant P3. So, when you go to participant P2, the same thing you go to if you select information that is significant, you look here and see whether you can drop that information into the existing code or existing theme. If not, you create a new container, right? So, you see that I have a lot of themes here, and then you now move to the next stage where you can categorize the themes based on the similarities, and then you come up with the master theme or super ordinary theme. So, that's what you're going to do. So, what is the process here? First, create containers. So, let's say you finish going through all the transcripts. You have all the themes here. Now, we move on to categorizing the theme. So, what you're going to do is you right click here, you go to new code, right? Or you can also click here, plus sign, and go to the new code, and then you can say ST1, right? And then go here, plus sign, ST. You go here, ST. You close it, go here. You can create about five for now. You can create more than that, but at the beginning, just create about five. If you need more, you can create more as you are grouping the themes. Now, what you're going to do is that you're going to start a sorting process, grouping them based on similarities, right? So, before we go on, you see that these are the themes, and also you see these numbers? This number is showing the number of significant information that was extracted from the data and connected to the themes, right? So, you see here that the first one, autonomy over work schedule, there are three quotations that was extracted from the data. If you want to see the three quotations, you can always double-click, and then to open that, you see here, and you see that it's from participant P3. So, these are the three quotations, right? And so, now we're going to go ahead and then sort them. So, let's start with the agency and autonomy. Okay. So, we have, I think we have autonomy. So, I think this one, I can put it here, autonomy over work schedule. Okay. So, let's look for the next one, inadequate as burnout driver. I think inadequate. So, this is what we have here. You can bring it up into ST1, and the next one is knowing burnout as coping. The next one is knowledge building as coping. So, you see the knowledge building as coping is here. You can put it there, here. So, you see what I'm doing? I'm trying as much as possible to see whether I can combine or group the themes based on similarities, right? So, that's what I'm doing now. So, you go here, and then you just, if they don't belong to, or they have no connection with the S1, you can put them into S2, right? Let's assume that I've grouped all of them, right? The next step is to label the themes, all the superordinate themes. So, based on the content here, what label do you want to give to it? When you decide on the label, what do you have to do is you can right-click on ST1 and go to properties, and then you can put a label here, because this one is about agency and autonomy as burnout protection, right? So, you see here that you see I've changed it. Now, you're going to do the same thing to the other one. At this stage, you have to be flexible because you can move things around. If you can change your mind, okay, you can say, oh, I think this one doesn't belong here. Let me put it in ST3. Oh, let me change the name of ST1. It doesn't really fit or represent the information under. You can always change that. So, the approach here is that you categorize them as well. You can also name them, or you can first categorize them and then review the content under them and then name them. When you go to the product, this is how the final product is going to be. So, you see that I have ST, ST, ST. I've removed the numbers there because now I've labeled them, right? So, you see that the ST, agency and autonomy as burnout protection, and I have the second one here, burnout as externally driven and systemic. And then you have the next one. So, all of them, you see that I've grouped them under their theme, right? So, at the end, we have one, two, three, four, five superordinate themes addressing the research question. This is what I have for you. As you can see, now you have your research question and all the themes, the main themes that are connected to the research question. And then now you can tell overall stories based on these themes. I hope this one was helpful. Let me know whether you have any questions. I'll be happy to address them for you. And also, don't forget to subscribe to my channel. Thank you for your time.

ai AI Insights
Arow Summary
El ponente explica cómo analizar datos cualitativos en MAXQDA usando la estrategia colectiva dentro del IPA (Interpretative Phenomenological Analysis). Describe el flujo de trabajo: crear un proyecto, importar varios transcriptos (con o sin datos demográficos), crear un contenedor para la(s) pregunta(s) de investigación, leer y releer, tomar notas mediante “paráfrasis” para documentar interpretaciones, y luego codificar generando “temas emergentes” (códigos iniciales) de 2–5 palabras. Posteriormente muestra cómo agrupar esos temas emergentes por similitud para formar “temas superordinados” (overarching/master themes), crear contenedores ST, mover códigos dentro, revisar citas asociadas y renombrar los ST según el contenido, manteniendo flexibilidad para reubicar y ajustar nombres. El resultado final es un conjunto de 4–5 temas superordinados que responden a la pregunta de investigación y permiten construir la narrativa de hallazgos.
Arow Title
MAXQDA e IPA: estrategia colectiva para desarrollar temas
Arow Keywords
MAXQDA Remove
IPA Remove
Interpretative Phenomenological Analysis Remove
estrategia colectiva Remove
análisis cualitativo Remove
transcriptos Remove
importación de datos Remove
variables demográficas Remove
paráfrasis Remove
codificación Remove
temas emergentes Remove
temas superordinados Remove
master themes Remove
pregunta de investigación Remove
agrupación por similitud Remove
Arow Key Takeaways
  • Crea un proyecto nuevo e importa todos los transcriptos antes de empezar el análisis.
  • Los datos demográficos son opcionales; si se importan, deben seguir el formato de columnas (document group y document name) para que MAXQDA los acepte.
  • Crea un contenedor por cada pregunta de investigación para organizar el sistema de códigos.
  • Lee y relee; usa ‘paráfrasis’ para registrar interpretaciones clave sin necesidad de anotar todo.
  • Genera temas emergentes (códigos iniciales) con etiquetas breves (2–5 palabras) y, si quieres, añade definiciones.
  • Reutiliza códigos existentes cuando corresponda; crea nuevos cuando aparezcan significados distintos.
  • Agrupa los temas emergentes por similitud en contenedores ST para formar temas superordinados (overarching/master).
  • Usa el recuento de citas por código y la inspección de quotations para verificar coherencia del agrupamiento.
  • Renombra y reestructura los temas superordinados de forma iterativa hasta que representen bien el contenido.
  • El producto final son varios temas superordinados conectados a la pregunta de investigación, base para la narrativa de resultados.
Arow Sentiments
Neutral: Tono instructivo y didáctico; se centra en pasos y demostración del software, sin carga emocional marcada más allá de invitaciones a ver otros videos y suscribirse.
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