Speaker 1: In this video, we're going to unpack the oftentimes misunderstood topic of qualitative content analysis. We'll explain what it is, discuss its strengths and weaknesses, and look at when to use this analysis method. By the end of this video, you'll have a solid big picture view of content analysis so that you can make a well-informed decision for your project. By the way, if you're currently working on a dissertation, thesis, or research project, be sure to grab our free dissertation templates to help fast-track your write-up. These tried and tested templates provide a detailed roadmap to guide you through each chapter, section by section. If that sounds helpful, you can find the link in the description below. So, what exactly is content analysis? Well, at the simplest level, content analysis is a qualitative analysis method that focuses on analyzing recorded communication taken from artifacts. For example, extracts from books, newspaper articles, interviews, audio and video recordings, or even blogs. Importantly, content analysis can be used on both primary and secondary data. In other words, data you collected yourself or data that was already in existence. This makes it quite a flexible qualitative analysis method as you have more choices in terms of data sources. By the way, if you're new to qualitative analysis, be sure to check out our primer video up here. Now, let's go a little deeper. At the highest level, there are two types of content analysis, or rather, two ways to perform content analysis. These are conceptual content analysis and relational content analysis. These two approaches are quite different in terms of how they work, so let's take a look at each of them. First up is conceptual content analysis. In this case, the analysis is focused on the explicit data. You look at the actual appearance of particular words and phrases without offering interpretation of them. So, the main concern here is the frequency of words and phrases. In other words, the number of times they appear in the selected data set. For example, you might be researching changes in attitudes to women's rights issues. In this case, you could look at the frequency of words like equal pay, gender equality, and patriarchy in popular culture over a certain period of time. So, in short, conceptual content analysis is explicit, non-interpretational, and surface-level focused. It's also somewhat quantitative in nature. In other words, it considers some numbers, even though it's still a qualitative analysis method at its core. Next up is relational content analysis. Here, the focus is on the meaning and the use of words and phrases. This meaning is derived from looking at the relationships between the various words and phrases to those around them. Contrary to conceptual analysis, the one we just looked at, the focus here is on the implicit data, or the information interpreted from looking at how certain words are used in relation to others. For example, if a research project is aimed to analyze polling data around a particular political candidate to understand general sentiment, relational content analysis could be used to look at the words used around mentions of that candidate, for example, ethical, trustworthy, dubious, etc. to determine patterns and themes of meaning that could indicate popularity and sentiment. So, in short, relational content analysis is implicit, interpretational, and is focused on meaning. It's also worth mentioning that there are multiple approaches to relational analysis, but we won't dig into them in this introductory video. If you're interested, you can check out our detailed blog post covering all of that. The link is in the description. To recap then, content analysis can be undertaken using either a conceptual approach, where you're interested in the frequency of concepts, or a relational approach, where you're interested in the meaning of language based on the connections, or relationships, between words and phrases. Now that we have a clearer picture of what content analysis is, it's important to discuss the strengths and weaknesses so that you can make the right choice in terms of analysis methods for your research project. One of the main strengths of content analysis is its flexibility, as it can be used on a wide range of data types, including written records, interview recordings, and speech transcripts, as well as non-text-based data. This means you have more choices in terms of the data sources you can draw on, allowing you to develop a rich dataset. Additionally, content analysis tends to be very unobtrusive, since quite often, the analysis can be performed on data that already exists. This means that there are fewer ethical issues to consider, and it's easier to access the data you need. All that said, as with any analysis method, content analysis has its drawbacks. First, there's the problem of reliability. After all, drawing conclusions from the frequency of words and phrases, or their relationship to each other, can be a subjective process, and not quite scientifically rigorous enough. This is especially true if more than one researcher is working on the dataset. Content analysis can also sometimes be considered as rather reductive. In other words, the focus on particular words and phrases can of course result in you missing context, nuance, and culture-specific meanings. Lastly, the results from a content analysis can't usually be easily generalized. Since content analysis is often time-intensive, it can be difficult to analyze a dataset large enough to draw broad conclusions about the research topic. Of course, this can be said for many qualitative methods, but it's worth keeping in mind if you're considering using content analysis. Hey, if you're enjoying this video so far, please help us out by hitting that like button. You can also subscribe for loads of plain language, actionable advice. If you're new to research, check out our free dissertation writing course, which covers everything you need to get started on your research project. As always, links in the description. Now that we've got a clearer picture of what content analysis is, the logical next question is, when should you use it? As a qualitative method focused on recorded communication, content analysis is often most appropriate for research topics focused on changes and patterns in communication around social, economic, or political issues. For example, a research project that involves analyzing government policy regarding healthcare in the UK might look at the use of phrases like healthcare, the NHS, and hospitals in political commentary. An analysis could then be done on the frequency of these phrases and or their relationship to other associated words and phrases. On the other hand, research that's focused on the use of language in context might not be the best fit for content analysis. In other words, if your research is about the particular impact of language in specific social contexts, then content analysis could potentially be too narrowly focused. For example, if you're wanting to assess how political speech is used in impoverished environments to impact beliefs and opinions, a more context-oriented analysis method, such as discourse analysis, could be more appropriate. Simply put, make sure that you always consider the nature of your research aims when you're deciding on an analysis method. Okay, that was a lot, so let's do a quick recap. Content analysis is a qualitative analysis method that draws findings from analysis of recorded communication, which can include both primary and secondary data. As we discussed, content analysis can be approached in two ways. Conceptual analysis, where the focus is on the frequency of concepts, and relational analysis, where the focus is on the meaning of and relationship between concepts. As with any analysis method, content analysis has its own set of strengths and weaknesses. As a result, content analysis is generally most appropriate for research focused on changes and patterns in recorded communication. If you got value from this video, please hit that like button to help more students find this content. For more videos like this, check out the Grad Coach channel, and subscribe for plain language, actionable research tips and advice every week. 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