You could, for example, use narrative analysis to explore whether how something is being said is important. Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told. Every analysis method has its drawbacks, so don’t be put off by these – just be aware of them!Īs the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means. So, keep these factors in mind if you’re considering content analysis. This isn’t necessarily a bad thing though – just something to be aware of. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.Ĭontent analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline.

One of the main issues with content analysis is that it can be very time consuming, as it requires lots of reading and re-reading of the texts. Naturally, while content analysis is widely useful, it’s not without its drawbacks. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method. With content analysis, you’ll group large amounts of text into codes, summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.īecause content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. For example, a collection of newspaper articles or political speeches. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. QDA Method #1: Qualitative Content AnalysisĬontent analysis is possibly the most common and straightforward QDA method. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here.

Qualitative research investigates the “softer side” of things to explore and describe, while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics. So, how’s that different from quantitative data, you ask? In other words, qualitative isn’t just limited to text-based data. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. Words, you guessed? Well… sometimes, yes. In other words, it’s not the stuff you measure using a fixed scale or complex statistics or mathematics. Well, qualitative data refers to pretty much any data that’s “not numbers”. To understand qualitative data analysis, we need to first understand qualitative data – so let’s take a step back and ask the question, “what exactly is qualitative data?”. What (exactly) is qualitative data analysis?

Tools for data analysis in research