 Hello, I welcome you all once again to my channel Explore Education. I am Dr. Lashmi Singh, Assistant Professor, Department of Education and this is Kanna Girls Rudy College, University of Allahabad. This is my email ID and the lecture will be in bilingual mode and nowadays we are discussing various modes of sampling and qualitative results and today I am going to discuss theoretical sampling. Okay? And the lecture will be useful for various competitive teaching examinations as well as your MA and MIT course and UGC-Net Education too. Okay? So, let's start. First of all, sampling in qualitative research. When we talk about good atma, for example, the sampling in it is different from quantitative research. We have talked about this. But we have talked about positive sampling. So overall, sorry, overall the sampling is in qualitative research. The qualitative research in qualitative research sampling is not about reaching numerical significance. That is, when we are taking good atma, we do not have the intention of reaching an arm's length. Not does the sample size underpin what researchers interpret as significant. That is, we do not have a significant sample size or numerical significance. What are the qualities of the sampling in qualitative research? First of all, second, generating data in qualitative research is about strategically selecting participants whose views and experiences can add meaning to it. That is, when we do some research on good atma, we generate data. We generate data. Look, here we do not collect data. We generate data. This is a difference. So in that, we choose those participants strategically. We choose those participants who use views and experiences, whose viewpoints or whose experiences we can give meaning to. Illuminate. Illumination of some knowledge. And in some cases, help explain the phenomena of the study. And in some cases, the event we are focusing on, the event we are focusing on, the explanation of that incident will help me. These are the characteristics of qualitative researchers. So we select them strategically. Participants are also important. Then, sampling that enables researchers to capture similarity, difference and or anomalies in the pursuit of new and meaningful data are a poor feature of any qualitative research. They are saying that the researcher, sampling that enables researchers, such as sampling that enables researchers to capture and capture similarity, similarity, or anomalies in the pursuit of new and meaningful data in the pursuit of new and meaningful data. This is the main feature of a poor child. And sampling in qualitative research is usually conceptualized as focusing on the characteristics of the cases or instances identified as pertinent sources of data for the inquiry. That means, the qualitative researchers, in sampling, can see that focusing on the characteristics of the cases, that is, the events, the cases, their characteristic feature focuses on that data. Not that we are going to the bank, not that we are going to the sample size, but that we are going to see that these are all the basic differences in quantitative and qualitative research. In qualitative research, we have chosen people strategically who can give meaning, illuminate, or explain through their views or experience. This is a basic, special qualitative and quantitative research. Then, sorry, let's talk about the grounded theory. We have discussed the grounded theory earlier. So, what is sampling in the grounded theory? However, grounded theory introduced another premise for sampling concerned with generating data, specifically related to conceptual categories that emerge as of interest to the inquiry. I mean, we were just talking about the sampling of qualitative research. I mean, how it will be done, how it will be sampled there. Then, when grounded theory, which is a type of qualitative research, the sampling in it is specifically different from other qualitative research. This means, it is different from a percussive sampling and the rest of the sampling is different. How is it different? The sampling here is grounded theory. When we use the methodology of research, the main purpose of sampling is that the generation of the theory of data comes from data to theoretical sampling. This refers to theoretical sampling. The theoretical sampling is the form of sampling in qualitative research that is not bounded by the limits of a priori selection. They are saying that the theoretical sampling in qualitative research is not bound by the limits of a priori selection. You cannot decide from the beginning how many samples you will take or how many interviews you will do. Rather, what happens is that the critical sampling entails pointfully collecting and analyzing data to decide what data to collect next and where to find them to develop theory. This means that you generate theory from theoretical sampling. Secondly, you cannot decide from the beginning how many samples you will take or how many times you will talk to them. Rather, what happens is that you will talk to them and then you will analyze them. You will decide what data to collect next and when to collect the next data and from where you will get it. It involves identifying emerging concepts in data being generated which are then used to guide where, how and from whom more data should be collected and with what focus. This means that the first time you talked to a few people, your sample was done and after talking to them, you analyzed it and it will give you some concept identification and some concepts will emerge. Where, from where, how, how and from whom from whom you can get more data and what your focus will be. Theoretical sampling is an innovation of the grounded theory method. Theoretical sampling is an innovation of the grounded theory method. There is a systematic research approach that builds concepts and theory from data. The concept of the theory comes from the data. The main aim of the grounded theory is the generation of theory from data. This is the main aim of the grounded theory. So what is theoretical sampling? Theoretical sampling is theoretically oriented. Why is it theoretically oriented? Why? Because it is directed towards the development of theory. It is directed towards the development of concepts and the generation of theory is generated from it. And the focus is data collection on building and refining theory. What is the focus of the data collection? What is the focus of the data collection? What is the demand of the theory? The theory can be refined. Difference with the purpose of sampling. You have seen how it is different from the purpose of sampling. Because the main qualitative research is purpose of sampling. But the theoretical sampling is in the grounded theory. So grounded theory studies differ from other qualitative research approaches. And one of these is in the way that sampling occurs. The way it is interpreted is how sampling happens and how it is done. Just as with purposeful or purposeful qualitative sampling, theoretical sampling involves selecting participants with non-specific characteristics. It means that the beginning is right. The purposeful sampling also starts from there that we choose a sample from there that has a specific goal, which we observe. Critical is also started from here. But the difference between the two lines in this stage at which participants are selected. So in normal, the difference is in a certain stage where participants are selected. In the purposeful sampling, researchers select the participant sampling criteria prior to conducting research. In the purposeful sampling, you select your sample before you start the research. Whereas in grounded theory studies theoretical sampling occurs as the data collection progresses. In the grounded theory, After the researcher identifies his research topic and question, they identify a small handful of people to interview based on a set of criteria much like in purposeful sampling. When they interview those people, this is where theoretical and purposeful sampling diverges. What does it mean? Where is the specific criteria? It is in the primary selection. Purposeful sampling is in purposeful sampling because you know what is your purpose and what can be selected by the individual. But in theoretical sampling, there are many different types of phones. So the first time we have talked to them, the first thing that will be discussed with them will be that next time we will talk to them. Then we will talk to them, then we will talk to them, then we will talk to them. In this way, the purposeful sampling is released. But the first time we have talked to them, we will choose them and then we can solve them. Then if we look at the definitions and characteristics of this, then theoretical sampling in ground data is defined by laser and straws. Is it a way of collecting data and deciding what data to collect based on the theory and categories that emerge from your data? How to define laser and straws? The laser and straws are the work of ground data theory. And the critical sampling is the part of the ground data theory. That is why they have defined it. This is a way of collecting data that decides which data will be collected based on the theory and categories that emerge from your data. The data that is coming out of the category, the evidence that is being shown to us by the ground data theory tells us where to collect the data next. There is no preset notion. It will be a very special thing that you can't do it from the beginning. Who to recruit or any trade redefine groups of people to compare, how many groups will be there, how many people will be there. We will get to know this in the process. Instead, you start somewhere in the data collection. You can start from somewhere. You can analyze the data, analyze it and then determine from where to collect the data next. And after that, we will analyze the data, analyze the data, analyze the data next. When utilizing theoretical sampling, the process of collecting data, coding it and analyzing it happens simultaneously and recursively. And not as discrete steps that need to be followed. Generally, what happens in the quantity of data is that we go to the field and collect the data from there and analyze it at home. But the quality of the data is not like that in all together. In theoretical sampling, it is not possible at all. You will go there, talk to them, take out the nishkosh, then you will go there, talk to them, take out the nishkosh. This is the basis of what is going on. Everything goes side by side. And theoretical sampling is part of ramgrid theory. And the theoretical sampling is part of ramgrid theory. And the procedure is a little difficult because it has no proper guideline. Begin data collection by starting somewhere. This is what we have to do. For the first set of data, you collect based on existing domain knowledge and partial framework. Even if you don't know yet whether or not these constructs will be relevant to your theory. You will have to start from somewhere. You will have to collect the knowledge and logic that you have learned from somewhere. It is not necessary that it is your work. Because after that, what will come out of it, it will take out our path. What we have to do is, be theoretically sensitive and have an open mind. Overall, you will have to go to open minds in qualitative researches. As you collect data, you should have an open mind about various theories and categories that can emerge from your data. Because we have to see what is coming out of the data, that it is coding. We have to make different categories. Otherwise, theoretically, you should constantly be welcome to discover relationships between categories you derive from the data. And what are the relationships that are coming out of the categories? Because it is a very intense work. Do not plan your data collection in advance. You cannot plan your data collection in advance in theoretical sampling. In theoretical sampling, instead, you take the initial set of data you collect and analyze it. Determine what some emergent categories are and then decide from these categories and take note of new questions that emerge. Use these gaps and new questions to guide your criteria for recruitment in the next round of data collection. That is, if you have done a round of data collection, you will get new insights from it. You can also ask this, this, this, this. You will also find out that if you are not able to tell this, who will be able to tell you. So, you will go and talk to them. What is your data collection? And in this way, you can open your mind. What are the categories that are coming out of it? What are the relationships that are coming out of them? How will you do coding? Will you do a similar set together? Will you do a different one? This is a very intense, complicated, time-taking and hard work. It is not that easy. Then there is a theoretical saturation. Because it comes up that when will we collect data? We are talking to them, then we talk to them, then we talk to them. When will we collect data? Because if you do not do it, then you will have to end it. So, when doing theoretical sampling, you need some way to determine whether you have collected enough data. Who will make sure that we have collected data from the environment. You don't determine the amount of data that you collect ahead of time. Neither can we determine from the beginning that yes, we will feel that we have done it. Instead, you collect data until you reach theoretical saturation. We are saying that you have to collect data until you reach theoretical saturation. So, what is theoretical saturation? In rounded theory, the goal is always to reach theoretical saturation data. When you use the rounded theory research methodology, then your goal is always to reach theoretical saturation data. How will it be? It will be distinctly different from saturation signaled simply by the absence of new data from deeper. This does not mean that if you have to come up with a new data, then you have to understand that your sample has been collected and reached saturation. What does this mean? Rather, critical saturation refers to the point when no significant new insights from data can better describe, dimensionalize or contextualize the categories that have emerged from data. But the new data that has been collected is a different matter. What should we do? We should not be able to understand the data. We are not getting any new category, we are not getting any relationship, we are not getting any dimension, we are not getting any insight. Then we will say that now our data has reached saturation. If the new data has been collected, then we will not take it. All the insights should end up at the point of saturation. Then we can say that yes, now our data has reached a critical saturation. In classical ground rate theory, it is considered to be the point when comparisons of incidents in data yield additional properties of patterns. There is no sample, no pattern, no additional property, there is no new category, there is no merger. Even in previously described categories, there is no relationship. Then, theoretical saturation can only ever be pursued through theoretical sampling because the focus of ground rate theory is to pursue saturation of a theoretical construct emerging from data to develop a theoretical outcome posited as the best fit of a concept. Whatever concept comes out of it, whatever theory comes out of it, the whole saturation of theoretical construct should be there. If there is no other category emerging, then your data collection can go through theoretical sampling. What are the advantages? The possibility to strengthen the rigor of this study, this study attempts to generate a theory in this area. If we are going to generate a theory in this area, then this is a very good sampling method which strengthens our study. The application of theoretical sampling method can provide a certain structure to data collection and data analysis processes that addressing one of the main disadvantages of qualitative methods is related to lack of structure. It is said that qualitative researchers have a hack hazard, they do not have any structure, they are not organized. So, you can do this with theoretical sampling as we are systematically discussing the previous answer which is the question of our next interview. This type of sampling usually integrates both inductive and deductive characteristics, thus increasing the comprehensiveness of the study. You are also using inductive methods, you are also analyzing the data so this is a comprehensive method of the study. These are the advantages of this study. What are the advantages? The application of theoretical sampling method may require more resources, it requires a lot of resources, it takes a lot of time, it takes a lot of money, you need a lot of other research, you need so many online tools, you have all the tools, you have a lot of material, a lot of data, how to get the category, how to make comparisons, you will have a lot of time taking and hard work. There are no clear processes or guidance related to the application of theoretical sampling, in practice, you will be able to read it in theory, theoretical saturation is like this, but there is no particular guideline, there is no particular process that this particular school is equal to that, so it is very difficult to do it practically. Overall, theoretical sampling is most complicated than other sampling methods, and if you look at the terms of complication, then you can say that the sampling method is complicated, but it is not. So this theoretical sampling is very interesting, it will be used only in the grounded theory, your purpose is also different, there is no a priori set of a collection of your sample and it is very complicated, you have to yield theoretical saturation and it is very interesting, it is very important. So it is all about theoretical sampling, so thank you and don't forget to like and subscribe my channel, explore it, done from my side.