 Hello, I welcome you all once again to my channel Explore Education and I am Dr. Reshmi Singh, Assistant Professor Department of Education, SS Khanna Girls Trivi College, University of Alhava and today I am going to discuss a new topic under the theme of qualitative research methodology that is content analysis. Content analysis is mainly used in qualitative researches while it is also used in quantitative researches too. So let's discuss what is content analysis. The lecture will be in bilingual mode and will be very useful for various competitive teaching examinations as well as your MA and MET course and your research purposes too. Okay, so let's start. Content analysis. Vishay Vastuka Vishleshan. Content analysis, Shabd, we use it many times in general instead of knowing if it is actually this. So what is content analysis? From the name of content analysis, we understand that we have some content and we have to do analysis of it. So how does content analysis go? What needs to be done? What do we have to keep in mind to do content analysis? What are the ways of doing it? It is an approach used to quantify qualitative information by sorting data and comparing different pieces of information to summarize it into useful information. This is a method through which we have quantitative information. We have a lot of data and a lot of content. We can quantify it and give it an ink. By sorting data, we have to sort out the data, compare it and summarize it into useful information. All the information is not useful for us. The content can vary in content. It can be simple words, it can be Shabd, it can be text material, it can be pictures, it can be social media data, it can be a book, it can be a journal, it can be a website. Anything can become under content and we can analyze it for our research purposes. Content analysis is different from other research. It is different from other researches. How is it different? How is it different? As it does not collect data from people directly. We are not collecting data from people directly. Actually, we have data from a book, a journal, a website, a social media, we have to analyze it from a certain point. That is why we have to avoid the research bias. Instead, it is the study of data that is already recorded. We have recorded data from a certain point. We have to analyze it. Content analysis is a research tool used to determine the presence of certain words, themes and concepts within some given qualitative data. This is a research tool that we can know that we have content. How is there a word theme, a concept in it? How can we determine its presence? Using content analysis, researchers can quantify and analyze the presence, meanings and relationships of certain words, themes and concepts. What research can do while using content analysis is to find out what words and themes and concepts are in it, what is the concept, what is the earth, what is the interrelationship between them, what is the meaning of their presence. This research is about content analysis. I have not put the history part in this. I mean, what happened in the United States that we are in some data, in some newspaper. So, we just had to know how many times that particular word has come. While doing it, it has reached the form in which we are talking about content analysis. So, it is not just that. Generally, people believe it only that we have to look at the frequency of which word. We are doing research on Dalit. So, we want to know how many times it has come. So, it is not just about coming. We have to read the lines within the lines, the lines that have not been written. The content that we are searching, the themes and concepts that we are searching for, what is the meaning of it? What is the meaning of it? What is the definition of it? If you try to understand it through definition, then what is content analysis? Any technique, any such technique for making inferences, through which we can draw an inference by systematically and objectively identifying special characteristics of messages. We have a message with some special characteristics that we can systematically and objectively identify and get some inferences. That is the content analysis. Then, an interpretive naturalistic approach. It is both observational and narrative in nature and relies less on the experimental elements normally associated with scientific research. That is, scientific research or experimental elements have to be taken into account and this is the naturalistic and interpretive approach. And what is it? A research technique for the objective, systematic and quantitative description of the manifest content of communication. We have to give a quantitative, systematic and objective description of the content and how to define it as a method of studying and analyzing communication. That is, we do not have a study of communication. We do not have a method of communication in the form of content. We do not have to study it or visualize it in any way. It is systematic and objective and quantitative so that we can measure the variables. That is the content analysis. Types are the most important. There are mainly two types and you will get more if you search in other places. But this is more than sufficient for you. Conceptual analysis and relational analysis. We will move on to relational analysis. Conceptual analysis is that typically people think of conceptual analysis when they think of content analysis. That is, this is such a prominent part that if we think about content analysis, then conceptual analysis comes to our mind. We have to see the frequency in this. That particular theme, particular word, particular concept has come out many times. In conceptual analysis, the concept is chosen for examination and the analysis involves quantifying and counting its presence. What we have to do in this? We have chosen any concept and we have to see how many times its presence has to occur. The main goal is to examine the occurrence of selective terms in the data. We have to see how many times it has come in the data. There can be explicit or implicit in terms. There can be explicit and implicit. Explicit means that it is easy to identify. But the coding of implicit terms is more complicated. You need to decide the level of implication and base judgments on subjectivity. The subjectivity will come in between the lines and within the lines. That is what it means. Therefore, the coding of implicit terms involves using a dictionary and contextual translation rules. Explicit means that it is easy to identify. It is clear that we have come so many times that we have to count its frequency, how many times it has come in the data. This will be conceptual analysis. How do we do these steps? General steps for conducting conceptual analysis decide the level of analysis. We have to decide at what level we have to do the analysis. Words, sense, phrase, sentence, themes, whose counting we have to do. Then decide how many concepts to code for. We have to see how many concepts we have to code. Develop a pre-defined and interactive set of categories and concepts. We will first decide whether these are the words that we have to search for. Or when we are looking for them, we will get more in that curve. Decide whether to code for the existence or frequency of a concept. We will have to decide whether we have to see how many times it has come in the data or its existence. Decide on how you will distinguish the concepts. Develop rules for coding your text. Decide what to do with irrelevant information. Decide what to do with irrelevant information. You will have a lot of irrelevant information. What will you have to do with that? You have to decide first. Code the text. After that we will coding our text and then analyze the results. It seems simple to look at that. But it is complex. When it seems that we have to check frequency, reap the rain, that we have to check the words, words, sense, phrase, sentence and themes. I have not come out of this concept. Relational analysis. This conceptual analysis is one step ahead. It starts from there. I mean, you have to see how many times I have come out. But it begins like conceptual analysis. Conceptual analysis is like this. But what we will do after the concept is chosen here? How does the analysis involves exploring the relationships between concepts? Just a little bit that we have to keep it a secret. Yes, this word is 1000 times and our research will be completed. No. We have to see what is the relationship between concepts. What is the relationship between concepts? Individual concepts are viewed as having no inherent meaning. And rather, the meaning is a product of the relationships among concepts. It will not have any meaning in itself. In fact, why will it have a meaning? It will come out after a long time. We will see the relationship between the concepts. So, there are three techniques of relational analysis. There are a lot of matters on this. But a lot of matters cannot be put into PPT. Effect extraction, proximity analysis and cognitive mapping. These are the three techniques of relational analysis. Effect extraction is an emotional evaluation of concepts explicitly in a text. In this, we do emotional evaluation of concepts. What is the relationship between them? Then, what is the relationship between proximity analysis and an evaluation of the co-occurrence of explicit concepts? We have chosen the concept. How is it coming along? How is it affecting each other? And cognitive mapping. What is the aim of your mind? Visualization technique for either effect extraction or proximity analysis. What do we have to do? In what direction do we have to take our research to analysis? So, cognitive mapping is going to be a signatory. So, we have to first effect extraction. Then, we have to see proximity analysis. And then, the first relational analysis of cognitive mapping will be done. So, what are its general steps? General steps for conducting relational analysis. First, we have to determine the type of analysis. First, you have to decide which analysis you want to do. Then, reduce the text to categories and code for words or patterns. We have to reduce our text to categories. Which is one type of category? Where is the word? Which pattern is following your text? Then, explore the relationship between concepts. Then, you have to see the relationship between the concepts we have chosen. How will we see? Strength of relationship. What is the strength of the relationship between two words or two concepts? What is the sign of relationship? Is it good or bad? What is the direction of relationship? We are going together to oppose each other. Then, we will code the relationship. Then, we will code the relationship. How? A difference between conceptual and relational analysis is that these statements and relationships between concepts are coded. That is, we have to see the difference between conceptual and relational analysis after the frequency. Perform statistical analysis. After that, we will go through the steps of the statement. And lastly, we will go through the map out representation such as decision mapping and mental models. Then, relational analysis is completed. So, how can we check the content analysis? We don't have to work in the research. Because of the human nature of researchers, coding errors can never be eliminated. You can never eliminate it. Because how are we coding? How are you coding? How is it possible? But only minimize it. But we can reduce it. So, they have chosen three criteria. If you work according to them, then it can be said that this is reliable. How? Stability. The tendency for coders to consistently recode the same late and same way over a period of time. That is, if you have coded, then if we have coded it from another order, then if we recode it accordingly, then this means that this is reliable. Stability is there. Reproducibility. The tendency for a group of coders to classify categories is membership in the same way. That is, if we reproduce it again and give the same result, that is, another group of coders also classifies the category in the same way. This means that this is reproducibility and this is reliable. Accuracy. Accuracy means the extent to which the classification of text corresponds to a standard or non-statistically. That is, if we had to do statistical analysis later, then the classification of text corresponds to a standard or non-statistically. That means it is accurate. If we do this much, then it can be said that there is reliability in content analysis. If it follows stability, reproducibility and accuracy. Then how will we know that it is not made the same way, which means the same concept is not made. So there are three criteria in this. Closeness of categories, conclusions and generalizability of the results to a category. What will happen in closeness of categories? This can be achieved by utilizing multiple classifiers to arrive at an agreed upon definition of each specific category. That is, we have made a category in our text. If we bring in many classifiers, then it is the same if it is decided on a definition that this category is right. This means that this is valid. Conclusion. What level of implication is allowable? Do conclusions correctly follow the data? Are results explainable by other phenomena? If we go to the same conclusion, we can say that this is valid. That is why the analysis of a computer is difficult in content analysis. Because it does not teach the lines within the computer. It is very high-tech. Then generalizability of the results to a theory. How can we generalize the results in the form of a theory? It depends on the clear definitions of concept categories. We have made a definition of these things in this category. These things in this category. If they are clear in this way, that is, these different coders are also taking them out. How they are determined, how reliable they are at measuring the idea of what it is seeking to measure. We will have to do this much. There are many advantages. You can directly examine the communication text. It can be used in both qualitative and quantitative. It can be used in complex model of human thought or language. We can get insight in use. It is generally understood. It is a powerful tool. It is an unobtrusive data collection. As I told you earlier, you do not have to talk directly to the subject. That is why your bias will not affect it. We are not going to influence the data. Participants are not going to influence it. Anyone who is recording the data should not create a problem. It is the most important thing. This is an extremely time consuming. You can understand that what is the intensity of the word. What is the strength of the word. What is its sign of the relationship. The more you increase the mental work and fatigue, the more mistakes will occur. Often devoid of a theoretical base, this is not a theoretical basis. It is inherently reductive. And in the end, the data that comes into a few words that this word has come out so many times, then it is overly reductive. And it tends too often to simply consist of word counts. It is said that it is possible, if the researcher is not so skilled, he will just take it out and say that this word has come out so many times. What inference will you draw from it? And it can be difficult to automate or computerized. That is why its automation and computerization is difficult. Because a lot of words are used in what meaning? It can be put into different words many times. But computer will only count and tell you. So all these disadvantages are with it. But despite this, this is a very good popular method of research. Developing the category system to classify the body of the text is the heart of content analysis. That is, if we want to do a good content analysis, then we have to make the category right. Bursalen rightly points out that content analysis stands for false whites category. It can only fall on the thumb of the category. That is, we have to make the categories right. Particular studies have been productive to the extent that categories were clearly formulated and well adapted to the problem in the content. That is, if the categories have been clearly made, then it is good for the problem. According to Chadwick et al., categories must be mutually exclusive so that a word, a paragraph or a theme belongs in one and only one category. That is, we have to make a mutually exclusive category. It is not like a word is going in every category. So how will we get it out? At times, miscellaneous and residual categories are added for units that occur rarely and are uncodable for other reasons. That is, sometimes we have to make a residual category from miscellaneous. That is, the word that will not go in any category will of course be added. So the point is that if we want to do a category formation in content analysis, then this can be a very good method. Okay, this one also done. Okay, thank you and don't forget to like and subscribe. 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