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Types of Data Analysis

Learn more about the different types of data analysis.

In this step, we’ll look briefly at the next stage of the research process after you have collected your data: analysis.

Analysis is often a challenging part of the research process because it involves the researcher interpreting and making judgements about their data to answer their research questions or test their hypotheses.

This is why sometimes it is tempting to just present the raw data – for example, simply write out what a participant said in their interview or list the number of responses from a survey. Doing so represents the old saying that, ‘the data speaks for itself’ – all anyone needs to do is read this data and they will understand its significance.

This is not true and one of the biggest mistakes a researcher can make. Data cannot speak for itself, it needs a researcher to speak for it and to show why it is important and how it answers a specific question.

This means that the data has to be analysed. It needs to be organised and categorised and it needs to be interpreted to reveal its significance.

Let’s say that a child has been playing with Lego and has tipped a box of 1000 pieces of different sizes and shapes all over the floor. To avoid stepping on a piece, you walk around the room picking up all of the Lego and putting it in a single box – we can say that this is your process of data collection.

But you know that when the child wants to play with the Lego again they may want to choose specific pieces or build a particular shape. This means that you need to organise the Lego – or analyse your data. So you decide that you will first separate it into different colours – all of the red pieces together and the green and so on. When you have done this, you see that there are twice as many red pieces as there are yellow and you wonder if that is significant.

You don’t think that it is, so you decide to start again – all of the Lego goes back in the box and then you organise it by the size of a brick. When you look at this, you find that the non-standard bricks appear unused and you decide that this is because the child is a Lego novice who makes simple shapes. Based on this analysis, you resolve to help them in the future by sending them to Lego school.

What does this extended metaphor tell us? It shows that data analysis is critical because it is how we come to make sense of the data we have collected and use it for a specific purpose. It is in the analysis where the research questions are answered or the hypotheses tested and where your knowledge of the research problem will come forward so that you can make a judgement about which aspects of the data are important and why.

There are many different options or technologies that can you can use to support your analysis. These techniques tend to map onto whether the data is quantitative or qualitative.

Quantitative data analysis involves subjecting the data to statistical analysis in an attempt to find the relationships between different variables and how representative these are of wider populations. You can use analytical tools such as SPSS to do this in order to increase the rigour in your approach.

Qualitative data analysis is more abstract and often involves the researcher matching similar perceptions from their participants to see where the themes emerge. For example, thematic analysis is a technique which seeks to provide analysts with a framework they can use to draw out key themes from their data. Tools such as NVivo can help with this because they allow you to quickly search for keywords and similar phrases through your qualitative data.

It is essential that, as a researcher, you dedicate time and energy to data analysis. This is where you build confidence in what you have found in order to communicate the ‘so what’ issues we talked about in Step 1.11.

Your Task

Look at these pictures of different crowd scenes and identify the different variables you could use to analyse them.
Here are two to get you started:
  • Gender and ethnicity – can you measure the diversity of the crowds in each picture. What does this tell us about the type of event pictured?
  • Behaviour – are there different behaviours on display? What do these tell us about how people behave in crowds?

Image of a crowd of protesters sitting on the steps leading to a classical building. They appear to be chanting and recording themselves. They are of all ages, though generally younger and appear to be evenly split between men and women. © Getty Images News/Anthony Devlin/Getty Images

Image of Chelsea fans at the stadium. Some are wearing team shirts, some are looking pensive whilst others are smiling. There is a mix of genders, ethnicities and ages. © E+/Tomazl/Getty Images

Image of an audience at a large indoor stadium. They are sitting and most are concentrating on the stage, though some are also looking at their phones. They all look to be of white ethnicity and generally middle-aged. There is an equal gender split. © NurPhoto/NurPhoto/Getty Images

© Coventry University. CC BY-NC 4.0
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Academic Research Methodology for Master’s Students

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