What is qualitative analysis?

How do you actually analyse qualitative data? How do you turn the results from your research into findings that can answer your research questions?

Analysing qualitative data requires drawing meaning from it, and getting to some higher level of interpretation than reading the data at face value. This is the process that can seem difficult for newcomers to qualitative techniques, or those used to quantitative methods where the interpretation seems more obvious. However, in statistical analysis of data, the result is often a single figure which still needs to be interpreted in context, by looking at things like the sample size, population and normal distribution.

Qualitative analysis also requires this final stage of understanding the results in context, but before then it is often necessary to digest the large amounts of data in some way. While words and meanings can’t be averaged in the same way as numerical values can across different participants or cases, multiple readings of qualitative data may give you a personal perception of the important themes in the data.

This is probably the most important part of your analysis, reading and re-reading your data to become familiar with it and understand what is interesting and surprising. However, you will need to present this insight to others in some digestible way. That’s why I generally describe qualitative analysis as being a cycle of three stages:

Interrogate, summarise, connect

First, ask questions of the data to interrogate it. What do people say about this? What words to people use to describe that? When you are reading through the transcripts of your data create a series of specific lines of inquiry that map onto your research questions. Through this process you can start creating a mental or thematic summary of the data that answers your research question for each of the sources or each of the important topics in your data.

The next step is to draw connections from your summaries, so that you can find common themes across the different sources of your data. So perhaps a particular opinion is shared by lots of respondents, or only some of them. You will also want to look for connections between your themes, to see which parts of your research questions are interrelated, such as if people are often talking negatively about a particular service. One way to do this is to break down data to smaller themes that are common across all sources, then building up to a deeper level of analysis and understanding by looking at these common themes in the wider context of the data. Codes and coding can help you through this, we’ll look at this later.

Although the above gives a general approach to qualitative analysis, but there are a large number of different approaches to how you read and interpret your data. Some of the most common are below, but each of these is really deserving of their own blog post, or chapter in a textbook, so make sure you read about the different options before you settle on one.

• Thematic or content analysis
• Discourse analysis
• Narrative analysis
• Semiotic analysis
• Interpretative Phenomenological Analysis

Which ever approach you have to reading and interpreting the data, there are two general approaches to dealing with the themes from the data. The first is generally called ‘grounded theory’, emergent coding or inductive (data driven) analysis. Here, the researcher lets the data suggest the themes and codes that will be used to summarise and explore the data. It is assumed that there is no pre-conception of what will be interesting in the data, and the coding framework will grow organically with multiple readings.

The alternative is framework or structured analysis where the coding and analysis are driven by pre-existing theory. In this approach the topics used to explore the data are defined before reading the data, based on the research questions and existing literature or research. Usually the next stages will involve coding and categorising the sources to make sense of the volume and depth in the data in these stages:

Develop a ‘framework’ – a list of topics or nodes
 (except in grounded theory where this is inductive)
 ‘Code’ sections of data to one or more topics
 ‘Retrieve’ data at topics
 Explore connections between the data

If you were to illustrate these stages with examples, it might look like this:

Research Question:
 “What is seen as a healthy breakfast?”

Break down into themes:
 Healthy, sugar, portions, marketing

Find quotes that fit these themes:
 “Muesli is very sweet, but feels healthy”

Draw conclusions across the data:
 Most people thought Muesli was healthy

While CAQDAS or QDA software like Quirkos can help you with each of these steps of the coding process, this is only the middle stage of the process after your reading. You still need to examine the results of the coding and make the jump to proper analysis and interpretation of the data – and that’s the next post to read!

Once you are ready to experiment with coding and qualitative analysis, give Quirkos a try with the one month free trial. It’s the most straightforward and intuitive qualitative analysis software around, with licences that don’t expire, free updates and true Windows, Mac and Linux compatibility. We do qualitative software in all the right ways, so you can focus on your data, not battling software.