How to Analyze Mixed Survey Data in MAXQDA (Full Transcript)

[00:00:00] Speaker 1: Hello, everyone. I'm going to show you how to use MaskQDA to analyze your survey data. Imagine that you have closed and open-ended surveys. So let me show you what I have now. So you see here, I have my participant demographics, and also I have my demographic data here, and I have open-ended responses here, right? And also at the end, it's about quantitative data, which is measuring their cultural intelligence. So imagine that you have this kind of data, you have a survey data. How do you use MaskQDA to help you to make sense of your data? How do you analyze both the open-ended responses and also quantitative data that you have? I'm going to show you step-by-step how to do it. So in this demonstration, I'm going to show you how to prepare your data, right? You clean your data and make your survey data and make it ready for analysis. How to also create containers for your research questions, and also how to organize your code, develop themes, and also how to conduct statistical analysis for the quantitative data that you have in a survey format. And then you have to visualize your data, and we're going to go through all of these visualizations, and you don't have to do all of them. You just choose the one that you feel comfortable explaining, and that will add value to your presentation. So these are the things that I'm going to talk about. If you have any questions, you can put in a comment section. I'll be happy to address them for you. And also, don't forget to subscribe to my channel. And then, so let's move on. A little bit of background information about the data that I'm going to use. So this data is generated for demonstration purpose, right? And you can see here, this data is about mental health stigma. To explore mental health providers' perception about mental health stigma in terms of the cause and the contributing factors related to mental health stigma. And also, the second research question is mental health stigma solutions. What efforts are currently being made to address mental health stigma? And then for the quantitative portion, right, these are the two research questions that I want to address. The first one is, what is the effect of gender on cultural intelligence? The second one, what is the effect of education on cultural intelligence? These are the questions that I'm going to address using MasculD. So let's go straight to MasculD. But before we go to MasculD, you have to really make sure that your data is ready for you to upload. So I have my survey data. You see the first column should always be called document type. If you don't do that, the system might not be able to accept your document, right? So you can see here the document type, and I indicate that they are all surveys. You should create a first column, which is called document type, and then type survey, survey for all of them. So there are 100 participants, and I type survey. The second one is participant ID, and you provide participant ID. For me, I did it P1 to P100, right? And then I move on and, you know, I have my demographic information and also open-ended responses to questions that were asked. And lastly, the quantitative data about cultural intelligence. So this is how you have to format your Excel spreadsheet before you upload your information. The most important in terms of formatting is always the first column should be document type. So when you finish, you know, make sure that you save, and I'm going to go to MasculD. So this is what you see when you open MasculD. So we're going to start a new project. So we click on new here, and then it will ask you to name the project. I will just say Mental Health Sigma project. So then I click on save. So you have to look for a place to save this project, and then I click on save, right? Then this is what you're going to see. So the first step is to upload your survey data. How do you upload? You go to import, and you see here it says survey data. You click on that, and then you choose the one that is appropriate for you. So you can upload it. If you have an Excel spreadsheet, you can choose the first option. So you can upload it also from SurveyMonkey if you have it there. And also you can upload it if it's in SPSS format, right? Ours is an Excel spreadsheet, so I will click on this one, and then I look for the data. So this is the data. I select that, and click on open. And then this is the preview for you to make sure that everything has been uploaded, and you agree about the formatting. Now, so here you can see here is the Excel spreadsheet preview, and all of them. So if you are okay with that, you click on next. Here the system automatically indicates which one is the quantitative data, which one is the qualitative data. So you have to also review making sure that what the system has determined is right. So as you can see here, participant ID in terms of P1 is a qualitative data. Yes, I can choose that as a qualitative data. And then the survey type, yeah, I'll just leave it alone. For age is integer. So this one was chosen, and the level of education also integer is a quantitative data. So you just have to review and make sure that everything has been selected right, right? And here too, it's indicated decimal, because sometimes the number has a decimal point. So the system has selected decimal. So if you agree with everything, if you don't, you can unselect and choose the right one. So if you agree with everything the system has selected, the next step is to click on next. So here it says use case IDs from the columns. Here you select the ID that you have provided to them. So you can click on participant ID, and also the preview is here for you to see, right? And then you can go, in terms of the document type, I don't do anything here. I just go ahead and click on import. So as you can see here, we have the documents. This is the document name, which is data about mental health stigma. And then you see that when I click on this arrow, you can see all the P1, P2, until P100, right? There's 400 participants. If you want to see each of them, participant response also demographic information, you can double click. So you see that when I double click participant P3, all the participant information here, their demographics and open-ended responses are also here. And what is not here is the quantitative data. And I'm going to show you where you can find the quantitative data. So this one, so if you want to start the coding process, you just double click on the case, which is each of the participants, and start the coding process. For here, the system has grouped participant responses into various containers. All participant response related to their thought on stigma is here. So when you double click on the thought of stigma, and then you can see participants, these are their responses, right? And you can even code the data here if you want to. So you can click on, let's say you want to really code only their responses related to their thought about mental health stigma, you can just double click on it and click on here, and then start the coding. So if you want to code, you can click on this one, this icon, code retrieved segment with a new code, and then click on that. And then it can indicate the new code that you want to use. And if you want to use an existing code, you can click on this one, and then look for the existing code that you want. And you can also do auto-coding, but this is, it's not part of this presentation, right? So as you can see here, participants' responses have been grouped based on the questions that you asked them. So you asked them questions about the causes of stigma, and when you double click on this one, you can also review their responses here, right? And you can code it directly using this icon, just with the code, if you want to code it into existing code, or if you want to code it with a new code, then you can click on this second one, right? So this is another way of you, if you want to just focus on a specific responses, participant responses to a specific question, you can code it from here. But I'm interested in coding it from above here. So I just click on participant one, and I look through the response and started coding, and after that, I go to the participant two and do the coding. So now let's see what we have done now. We have finished preparing the data, right? So we finished with this one. Perfect. So the next one that we have to go is to create containers for the research question. Remember that we have two qualitative research questions, right? So these are the two qualitative research questions. And then, so we're going to create containers for only the two qualitative research questions. For the quantitative one, we will address that later. So you first will have to have a label for your research question. Why? Because you want to create a container in MathsQDA and then code the data and arrange codes based on the research question that you have. So the first step is to label your research questions so that it can help you to create containers that will help you to also organize your code that you're going to use. So the first one, the label is mental health stigma causes and contributing factors, right? And the second one label is mental health stigma solutions. So what we have to do is that you go to codes and click on the plus icon here. And then what you're going to do is I'm going to copy my research question label and bring it here, right? So, and then you can give it any color that you want. So let's give it this color, red. And then under code memo, you can also bring your research question, just to remind you that you are addressing this research question and then click on OK. So you see that I've created a container for research question one. I'm going to do the same thing for research question two. So I bring the cursor here and then plus and then bring the second research question. So this is also the second research question. I'm going to also bring the research question itself. So I have it here and then I click on OK. So I have two by two research question, research question one and two, right? Then let's see. The next step is to start the coding process. So you can see that we finished creating containers for the research question done. The next step is to organize the codes, right? So see coding as going through participant transcript or participant data, identify information that are significant and then develop a label and connect the significant information to the label and then bring that label under its respective research question. That label will be a code. So a code is just a phrase that represents a significant information that you have identified. So let's focus on research question one and go through. You can do both at the same time if you go through each of them. So we're going to start with participant one, right? And then we're just going to go through and see, identify information that are significant. So looking at this, you can see the media portrayals, jokes and stereotypes might be one of the causes of burnout. So this information is significant. Then you decide what label should I use to represent the significant information. So the label could be negative media portrayals, right? So when you decide on a label, what you have to do is that you go to, you decide which research question is this one addressed. It is addressing the first research question. You bring the case ID and click on a plus sign. And then you bring the label, which is the code here. And then you can also give a definition or a description of the code. It's not required, but if you want to describe, you can say that this code represents participant expression of one of the causes of mental health stigma, which is the negative portrayal of media. So something that will help you to define what this information represents. It doesn't have to be perfect or you don't have to write anything, especially if you know what this represents, you don't have to. And then when you are done, you click on, you can also choose different colors that you want. If you want it to be a little bit colorful, you can choose, maybe I choose this color, which is violet and then click on. Okay. Now I've created a container representing the significant information under the first research question. The next step is to drag and drop that information into the code. So I can, you see when I drag and drop that information here, the tag will be or come here representing the significant information. So that's how you're going to do it. You're going to go through each of the participant transcript and then identify information that are significant and then try to match as much as possible to come up with a label that best represent the significant information and then create a code representing the significant information. So let's go through again. I can see you here, education, culture and finance. It can be one of the contributing factors of mental health stigma and the label could be cultural beliefs and misconception. So when you are okay with the label, you go to the respective research question, which is research question one. You click on the plus sign here and then you bring the label. The code doesn't have to be really perfect. You can always go back and make the changes that you will have to make, but just make sure that the code is in between two to five words fairly representing the significant information and then you click on okay. Right. If you change your mind about a code, you can right click on the code and go to properties and then you can change the name here if you want to. You can always change the name if you think about the best label that you want to give to represent the significant information. Then, so when you are done, you click on save and here I'm going to drag and drop the significant information into cultural beliefs and misconceptions. So when you drag, you see here that there's one information here. If you want to see what is in this container, you can double click on it and then you can click here and to show what is in the container. This is only one quotation. So that's how you can, you do the, so the coding process is going to identify information that are significant and develop codes. Right. Sometimes as you are going through the data, you can identify significant information that you can just drop it into existing code. You can do that. Right. So you don't have to create a code all the time. When you see significant information, you always sometimes have to review what you have created and see whether you can connect that significant information to the existing code. If you cannot connect to any of the existing code, then you can, you can create a new code that represented that significant information. So that's how the coding process is. Right. So then let's see what the next one. So I found another one, which is there. Okay. So when you finish with this one, you go to the second participant. So participant you, so you can double click on participant two, and then this is what you're going to see. And then start the coding process again. So you look for information, you look for information that is significant. So I can see education and lack of awareness. Education or lack of awareness is a significant information. Then you can decide what label should you use to represent it. It can be education as a structural factor. So let's assume that the label will be education as a structural factor. You click on a plus sign because it's addressing research question one. You bring the code here, click on okay, and then you can drag and drop it into that. Right. So that's how you have to do it. And let's assume that this one is significant. Right. And then, okay, so let me see. So I see one that is also called ignorance and lack of empathy or understanding. Right. So this one is a significant information that can be the causes of mental health stigma. And then I have a label called ignorance and lack of lack of understanding. So I go here, plus sign, bring that information here, and click on okay, and then drag and drop. So that's how you're going to do. When you finish, go to participant three, do the same thing. Let's assume that this information is significant and is related to ignorance and lack of understanding. You can drop it into this container. All right. Let's say this one is also maybe significant and related to education as a structural factor. You drag and drop. So that's how you're going to do. You go through and drag and drop it into existing information data. So this principle here is that whenever you see a significant information, you have to, you know, and you have existing code, you have to think about which of the code can you connect to this significant information. If you cannot find any code that you can use to connect to this significant information, then you create a new code. So that's how you're going to do. Because of time, I want to show you my final product, how it looks like, so that you can see. As you can see here, I've coded the data addressing the first research question, and all these are the codes under the first research question. You can see here that for cultural beliefs and misconception have 16 significant information. If you want to see all the 16, you can double click on it. And then this is what you're going to see. And you can click on it and can see all the quotations that was extracted. So now, you know, we have to do the same thing for research question two, right? So now, when you finish coding all the data and you have your codes, the next step that you have to think about is categorizing the code to develop themes, right? And you can categorize it within MaxQDA. And let me show you how to do the categorization. You can go to codes, click on creative coding, you can use this space to group the codes and then give it a label or develop themes. And then after that, it will reflect on your left side, right here. So to start a process, you have to bring all the themes, all the codes to the right side, right? Including the label for the research question. So how do you do that? You select everything here in terms of the research question one, and then drag and drop it here, this space. So let's start the categorization process, right? So as you can see here, your role here is to group them based on similarities. So you have to examine each of the codes and see whether they have any connection with other codes, right? And if they have, you can always categorize them, right? So if they have a relationship, then you can group them. And then after that, you can label the group and that label will be a theme. So let me show you how it's done. So let's assume that, so ignorance and lack of understanding. And then you look for any code that has some relationship with ignorance and lack of understanding. And I can see that lack of awareness has something to do with ignorance. And limited, let me see, limited mental health education can also be some relationship with the two codes that I have here. And the next one is unwillingness to learn. And then education as a structural factor, education as a structural factors. So I think these five codes has something in common, right? They all talk about, you know, education, they all talk about lack of awareness, and lack of awareness also goes well with education. And they have a relationship with ignorance, right? So the principle here is that think about what each of the codes represent and find out whether there's a relationship between them, right? Based on that, you can group them. And then after grouping, you can decide, okay, for this group, what label should I give, right? Or you can finish grouping all of them and then start the label, right? So let's try the next group, right? The next group that we can think, so where is the fear, something related to fear and misunderstanding. So I can put it here and see, and then I can also group cultural beliefs can be here. I don't know whether they, I think there's, there might be some connection, but for now we'll separate them. Then societal norms and expectations can also be part of the cultural beliefs and misconceptions. And then we have historical biases too, can also be part of this one. And education and cultural background can be here. And then poverty, something about media can also go here, right? This is about media, lack of mental health understanding. This one is socioeconomic constraint. So where is socioeconomic constraint, socioeconomic constraint. Okay. So poverty assess can be here. So you can always review and make some changes to it. If you think any code doesn't belong to the need, the initially developed group, you can always move them around. So for the first group, we can call it knowledge deficit and educational gap. And then what you're going to do is that you see here, you click on new code, and then you can bring the name of the code here. You have to make sure that you put the theme in a parenthesis so that you'll be separate from the other ones. And then you can give it a different color that you want. Click on. Okay. So I have this one connected to these. So what are you going to do is that you click on this connector. It's called link, right? Define a SAP code. So you click on that, and then you can first link this one to the code, right? So when you link it to the code, it's disconnect from the research question that you have here. So, and then you can link this one to this code, this is one. So link to them. And as you are linking to you also have to review and make sure that everything is right. And then I will link this one to the theme. So when you finish, you can click a link again so that you could be able to move stuff, right? So looking at this one, which is called knowledge deficit and educational gap, is there anything that can join this one? I think this one is along. So the next one, this one is, which is along can be called psychological and emotional barriers. So this one can be labeled as psychological emotional barriers, and then you can click on new code, and then you can bring the theme here, type theme, and then click on okay. So after doing that, you have to click on link and then connect this one to the code and then connect this one to the theme, right? And click the link back, and then you can move it, right? So that's how you're going to do it. You just go to group them, develop codes, and then connect, right? So the next one is cultural and societal conditions, and I think it's linked to this one, right? So I click on new code, bring the theme here, and then I can indicate this one, can give it a different color, click on okay, and what you're going to do is to connect. So what I'm going to do here is to click on the link, connect this to, so I finished connecting, and then I'll connect the research question to the theme, right? When you finish, you click on that, and this is what you're going to do. You see, so that's what you're going to do for all of them. Because of time, I don't want to, I will not be able to do this, but you do it for all of them, give it the names, the labels, and then when you finish, you click on finish creative coding, and then it gives you this information, and you can click on yes, and then when you do that, what will happen is that everything will come here, so you see the theme and also the codes that are connected to the theme. It is the same thing, right? So let me show you the final product so that you see how everything looks like. Let me go to home and open the final one. So I think this is one of the themes. So as you can see there for research question one, I have all the themes here, and then codes under them, right? So that's what you're going to do. You just go through the process using the creative coding, go to codes and use creative coding platform or function that will help you to group your codes, right? So you're going to do the same thing for research question two, the same process. Let's go back here and see where we are. So let me see. Oh, okay. So we have done organizing codes. We have done with developing themes. We are going to do the statistical analysis addressing the two research questions. So let me show you the two research questions again. So what is the effect of gender on cultural intelligence, and what's the effect of education on cultural intelligence? So how do you do that? You go to stats tab, and then click on this one, stats with all documents. You click on that, and this is what you're going to see. It's quite similar to the Excel spreadsheet, right, that we have. So we have all the participant information. And here, what we are interested in is this cultural intelligence, and we're also interested in the gender and education. So for gender, because we have only two groups, we can do t-test. So what are you going to do? You can also do descriptive statistics. You can do descriptive statistics for the demographics. You click on descriptive statistics, and you click on frequencies, right? Normally, you just focus on the categorical variables, so it gives you the number of participants connected to each of the groups under the variables. So I'm choosing gender and education. I click on OK, and then this is what you're going to see. You see gender. We have 51 females and 49 males, and then we have their percentage. If you want to see education, you go here, change to education, and see the number of participants for associate degree, bachelor's degree, doctorate degree, high school, master's degree, and the numbers are there, and their percentages, right? You can also click on this one to show visual representation. This is giving you the percentage. You can change the percentage to numbers, right? You can change it to numbers, right? And so you click on it to change it to percentage or numbers if you want to. If you want both, you just click on it again, both. So, and then, so this is what you're going to have. You can also change it to pie charts, as you can see here. You can, you know, always make some changes that you want. You can even change the colors by going here and changing the colors, right? And you can export it if you want to export, right? That's what I have for you for demographics in terms of descriptive statistics. We can also go to descriptive statistics again and go to descriptive statistics here on that. And then this time we are focusing on, because we want to see the mean and the standard deviation, we want to focus on the continuous variables like age, right? And maybe cultural intelligence, right? And then click on, okay. So this is what you're going to see, the age, this number of participants, there are a hundred. And also these are the mean and standard deviation for the age and also cultural intelligence call. And there's no missing data. And this one too, you can click here. This one is giving you like a boss plot. If you are not familiar with boss plot, that's okay. You can learn more when you go to maybe chart GPT. You can put this information there in the GPT. We can also give you a description of what this one is all about. If you want to change it to cultural intelligence, you can change it to cultural intelligence now. But if you want both, you click on this one and this one is showing you for age, boss plot for age and boss plot for cultural intelligence. One basic information that you can see here is that there's no outlier. So there's no score or information that is out of the ordinary, right? You cannot see any plot or everything is within the box, right? So most of the participants are within the box. And then this is also showing the average, which is about 40 for age. And this one is cultural intelligence, the average to maybe in the middle here, right? So as I said, if you are not clear, you can always use AI tool to help you to make sense of this. You can screenshot and send it to chart GPT or cloud AI or GEP9 for you to provide more information. You can also click on this one. This one is like histogram for cultural intelligence. You can change it to, right? So it looks like cultural intelligence is close to normally distributed. It's normal distribution. I'm not all that sure about that, but just looking at it, it looks more like it's normally distributed. But when you look at this one, yes, it's normally distributed because this score is more than 0.05. So I'm assuming that this one shows whether it's normally distributed or not, right? But you can also check and see, right? So this is all about the descriptive statistics. Let's go to our main purpose, which is to address the research question, right? So what is the effect of gender on cultural intelligence? How are you going to do it? I think the first thing that you have to think about is that you have two groups, right? You have male and females, right? You have the two groups here. So, and then you have the cultural intelligence will be the dependent variable and also gender will be the independent variable. And under independent dependent variable, we have two groups. So t-test, you are comparing two groups. So you have to use t-test. So if you can click on compare groups and click on t-test for independent samples, and then you can bring, so you see here groups, you bring the gender here, you select gender and bring it here and then indicating group one is female, group two is male, and then they click on cultural intelligence scores and bring it here as a dependent variable. When you finish, you click on okay. So if you don't have a solid background of understanding or you don't have a good background of statistics, I think that's okay. I think what you could do is, as I said, you can screenshot and send it to AI2 for it to explain to you. But just let me give you a basic understanding of what you see here. So you see here variance homogeneity, right? Think about, you know, for you to use independent t-test. First, there are some statistical assumptions that has to be met. One of the assumptions is that the variance should be homogeneous, right? So this number is just showing that there is homogeneous, right? So whenever the p-value is more than 0.05, using this test, we call a Levin test, right? So Levin test is used to find out whether the variance is homogeneous or not. So if it's more than 0.05, it means that it's homogeneous, right? If it's homogeneous, then you read here, right? You read this row, right? If it's not homogeneous, you read the second row. So you see here that it's homogeneous. So we're going to read the first row here. So you can see here that t-value is negative 0.726. And what we are interested in is the p-value is 0.7653. Because the p-value is more than 0.05, it's just showing that there is no significant differences between the two groups, right? So you see there's no significant difference between the two groups. So you can see here that the mean score for the cultural intelligence for the female is 3.72 and the male is 3.93. The difference is not much and that might be the reason why the difference is not significant, right? So this one is just showing that there's no significant difference between the two groups that you have. So at the end of the day, the conclusion here is that there is no effect of gender on cultural intelligence because the p-value is more than 0.05. Because this is not statistics, I'm not teaching statistics, I will not be able to go into detail. But as I said, if you want to learn more, you can always screenshot and send it to GPT or any AI tool and you'll be able, the system will be able to explain, break things down to you. But the number here is showing that there's no significant difference between the two groups. Therefore, there's no significant effect of gender on cultural intelligence, right? Cultural intelligence doesn't depend on the score. Participant's score of cultural intelligence cannot be explained by their gender. So that's what this one shows, right? So we finished with gender. Let's do education, right? So education, we're not going to do t-test for education because we have more than two groups, right? We have about five groups under education. So what do you have to do is we have to do ANOVA, right? Whenever you have more than two groups, you have to use ANOVA, right? So we click on ANOVA, which is analysis of variance. And it's one way because we are focusing on one independent variable, which is education. So we bring the factor here, and then we can also bring the score, which is the cultural intelligence score here. When you are done, you click on OK, and let's see the results. Here, there's a lot going on, but don't worry about this one. Let's focus on this part first, right? So what you are interested in right now is the p-value. You can see here that the p-value is 0.3536. It's more than 0.05. So it's just showing that there's no significant difference between the groups with respect to the cultural intelligence. Or you can say that there's no significant effect of education on participant cultural intelligence, right? If there's no significant effect, there's no need for you to go and review this one, because if there is an effect, then you can see, you can find out where is that effect. Is it in between associate degrees, participants, and bachelor degrees, or is it in between high school and doctoral degree? But because overall effect is not significant, there's no need for you to review this information. Again, if you don't understand, you can screenshot, and then you can give it to Chatship AT or any AI tool and it will explain it to you. So the second research question, what is the effect of education on cultural intelligence? There's no significant effect, right? And even this one too, if you give it to an AI tool, it will also help you to give you how to write the findings, right? Based on what you have here. So we are done with the statistics. Let me know whether you have any questions. I'll be happy to address them for you. If you want me to go into detail too, let me know. I can do a video only focusing on this part. So we finished the statistics. Now we're going to do visualization. Okay, so we're going to do visualization. So let's first visualization is that if you want to create a table that will show all the themes and also the number of segments or the number of significant information connected to the theme, then you can use, you can go to the code tab and go to see the code statistics, click on that, and then choose the second option, subcode statistics. And then what you're going to do is that you'll bring the research question, right, to display. The reason why we have only one research question is that I didn't code for the second research question, right? If you code for all the research questions, it will be here. So you bring the one that is appropriate here and focus on only research question one, and then you leave everything alone. You don't do anything here. You make sure that coded segments is checked and then you click on okay. And this is what you're going to see. So this one, it gives you all the themes, right? And also the number of segments. So the first theme has 80, the second one has 58, and the next one, 21, 17, and 16, right? And also the percentage. What you could do is that you can visualize it by clicking on view chart. And this is what you're going to see here. You can change the color. If you add the color here, you click here to change the color to the one that you like. You can also turn it around. If you make it vertical, if you want to, you can also add the numbers and also the percentage if you want that. So there are many things that you can do and explore. You can make it pie chart if you want to. You can bring the percentage if you want to do that. And you can export. Whenever you are done, you can export this one. This one is going to be helpful if you want to show how many significant information or segment that are connected to a specific theme, right? So we are done with this one. Let's go to the second one. The same place, not the same place. So the next one is you are trying to compare quotes, right? So you want to show a table that will show you the quotes, one or two or more quotes, and also the significant information that are connected to each of the quotes so that you can compare the evidence, right? So if you want to compare all the quotations based on the quotes that they are connected to, you can use this one. So we go to code comparison and let's say I choose these two codes, right, to do a comparison. I click on OK. This is what you're going to see. So you see that I have all my participants, right, checked and then all the quotations related to this code, which is media influence, and then another and all the quotations for negative media portrayals, right? So these are the two you can compare. If you want to, you can export if you want to export. And if you want to limit it to a specific participant, you can also select those participants that you want to see the significant information that are linked to, right? So you have a choice to do explore further and find out some connection between the evidence that you have and explore further, right? So this is what I have for you concerning code comparison. As I said earlier, not all of the visual representation is useful. You just have to choose the one that first you understand and then secondly will help you to present your findings in a meaningful way. So the next one is to compare cases with the cases at each of the participants, right? You are comparing cases based on the codes that have been developed and also significant information that you have, right? You can go to analyze and click on compare cases. But before you do that, you can even activate. If you want to bring all the cases, you can click on here, this one to activate all the cases. And then if you want to bring all the themes under research question one, you can also activate this one. You see when I click on research question one, it selects all the codes and team under research question one. When you are done, you go to analysis and go to compare cases and groups and choose the first option which is qualitative. And then if there's something here, you have to clear them and you see here you can click on insert activated documents, right? If you haven't activated any document, you want to bring individual document, you just have to select and drag and drop this one. We want to bring all of them. So we're going to click insert activated document and we want to bring all of the one that we have activated or selected. So we click on inserted activated codes. And then when you are done, you click on OK. So this is what you're going to see, right? And if you want to see all the themes connected to a specific participant, you can just collapse the theme, the codes under the themes here and then you can click on them. So when I click on this one, 21 statement, I think they are shown in some of the places, right? So you see here 50 participant, five, zero. You can see this quotation and I go here, I can see all the quotation. When I click on the second theme, move around, I can see there are a lot, there are 100 participants. So you have to move around and see, right? So this one will help you to know for the specific theme, who are connected to the team and what did they say, right? And you can export this one if you want to, you can export it and you show all the quotations connected to a specific theme, right? So this is what I have for you. So let me move on to the next one. The same place again, but this time we're going to select this one, quantitative code frequency, right? So let's see what we're going to get. I click on quantitative code frequencies and then I click on insert activated document, insert activated code. So I bring all the codes and also all the all the documents and also all the codes here and then including the themes and I click on okay. And this is what you're going to see, right? So you have all the themes, you can collapse it and also you can also expand it if you want to. Whenever you expand it, there'll be nothing there corresponding to the theme, but when you collapse it, you can see that it comes together, it aggregates in terms of the number of codes under each of the themes, right? So this is what you're going to see. You can always change the display. You can bring percentage if you want, if it makes sense. You can bring numbers if it makes sense. You can also export if you want to export this information. So it looks like there's going to be a big table, right? But this one can help you to explore further who are connected to a specific theme or codes, right? So one means that one significant information is connected to this theme, right? Okay. So let's talk about the next one. So the next one is you are using the mixed method function and you're going to segment metrics. So the segment metrics, think about segment metrics is like comparing demographic variables based on the themes that you got and also seeing the evidence that you have, right, connected to the theme and also connected to the variables. So if you are not sure about this, let's do it together and see what we're going to get. So you go to mixed methods, you click on segment metrics and click on the segment metrics again. And this is what you're going to see. I think I've already brought this one here. Let me clear this place. So let's say we want to look into gender, right? So we select gender and bring it here. We have to choose the groups that we have. We want to choose the females and then double click on gender again and choose the male so that both of them will be here, right? And then I click on okay. So when you click on okay, this is what you're going to see, right? You can see here that I have all my themes. Let me collapse. I have all my themes and when I click on it, I'll be able to see significant information connected to the females and also connected to this theme, right? Structural and economic barriers, right? And then we have significant information connected to females, right? When I click on the second one, which is about the media, you have all the significant information from females and the significant information from males. And you can export to this one if you want to by clicking on this one, right? So this place, again, you can also explore your data or your findings using crosstab, right? So click on the crosstab and go to crosstab. And here, if let's say you are still interested in gender or this time, let's say we are interested in education, double click on education and select the groups that we want. So we select the first one, double click, select the next one. So it depends on the group that you want. If you want to bring all the groups, you have to do it one by one, double click, select the next and click on okay. And then what will happen is that we show all the themes, right? As you can see here and then the groups that you have and a number of significant information. So you can see here that for bachelor's degree, we have five significant information connected to this theme. We have seven significant information connected to this theme from the high school groups. You can also export it if you want to export. And then this one's all giving the total. So you can see here that in total, 28 statements were connected to associate degree participants, right? So this is what you can have. You can also explore the visualization, whether you want to indicate a percentage or just the numbers, right? And you can export it whenever you are ready. You can also send it as an Excel spreadsheet. So the next one is we can explore visual tools and what we are really interested in is code metrics browser, right? So let's click on that and see what you're going to get. So this one, it gives you the option to choose groups, document group or the document. We just want to focus on the document this time and then click on okay. And this is what you're going to have. It's quite similar to what we have previously done, but this time you have individual cases. Individual participants are here, right? So you can see here also the number of significant information connected to each of the theme also are here. And this one too, you can change it to percentage. You can change it to numbers and you can export it if you want to, right? So this one, as I said, is quite similar to the cross tab, but this one is for individual participants, right? So if you want this one too, you'll be able to create that. So the last one, okay. The last one is creating word cloud. So you can create a word cloud by, you see here saying visual tools and click on word cloud and then you drag and drop the list of documents to this place, right? You can easily drag and drop. We can do individual cases and drag and drop them, right? But this one, we are not interested in individual cases. So I can click on clear list to clear this place. I just want to select all the activated documents or cases. So I click on that and I bring all of the participant information here. So you can do it for individual participant or you can do it for all of them, right? And click on, okay. So when you click on, okay, this is focused on everything participants said, right? The open-ended responses. And then you can go to formats. You can change the colors if you want to, you can change it to any one that you want. And then after that, you can always go to start here and then export, right? So that you can be able to use that. So this one shows that the bigger the font, the higher the number of times they make reference to it. So they talk more about health. They talk about mental. They talk about year. They talk about support. And you see that it shows the number of hits too. This one is like 53 hits. This one is 138. You can also do the same thing for code, right? So you can click on code cloud. And then this time you're going to select the document. I'm going to bring all the documents here, all the cases. And this time I'm just going to focus on OLLI. Let me see, two codes and let me focus on all the codes, right? In this case, I've activated all the codes for research question one. So I can click on insert activated codes and click on okay. So here you see that it's for all the codes under research question one. And you see here that it shows you the dominant code. This one is the dominant code under research question one. It has 53 stack segments. And the next one could be this one, which is about 23, right? And go to format. You can change the colors if you want to, and you can export, right? So this one is just showing the codes and the bigger the font, the higher the number of significant information connected to that code. So another one that you can do for where cloud is simply clear this one. So let's say you want to focus on only the significant segments, right? The one that you have selected. And I want to really focus on maybe all participants. So I select all participants, all hundred participants. But this time I want to focus on all the codes under this theme. So I select all the codes under this theme. So when you do that, all the quotation will be here, right? So it looks like we have 79 quotations, right? And from 61 document, maybe it's showing 61 participants. Now you can click here, right? Where cloud, and then it will show you visual representation for only the codes that I have selected, right? Only the codes I've selected under this theme, right? And you can always change the colors. You can export it if you want to. You want to click on also the specific code. You want to click on a specific word to show you where you can find the words, right? Within participant responses, right? So these are the things that I have for you. I hope this one was helpful. Let me know whether you have any questions. I'll be happy to address them for you. I did not touch on using AI or do a presentation focused on only that, using AI to make sense of your data. But with this one, just focus on if you have a survey data with open-ended responses and also quantitative data, you can use Maskudio to analyze it. Yes, you can do that, and I've shown you the process, right? Thank you so much for your time.