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Data analysis methods are the tools and techniques used to evaluate, explore, interpret, transform and/or model primary or secondary data. They can be used to analyse quantitative data (e.g. descriptive statistics, inferential statistics or data analytics) and to analyse qualitative data (e.g. content analysis, thematic analysis or comparative analysis).

Your choice of data analysis methods depends on a number of factors, including your epistemology, ontology, theoretical perspective, research topic, research question and methodology. It also depends on the data collection methods you choose (see the module Data collection methods).

Click on the images in the graphic to find out more about qualitative, quantitative and mixed data analysis methods.

Continue on to find out more about qualitative, quantitative and mixed data analysis methods.

Research descriptions (Sameer and Nyah) based on Törnberg & Törnberg (2016) and Fry et al. (2017)

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This screen raises a number of issues that you may need to explore further. The following resources will help you to do this.

Corpus linguistics:

  • O'Keeffe, A. & McCarthy, M. (eds.) (2012) The Routledge Handbook of Corpus Linguistics. Abingdon, Oxon: Routledge.
  • Weisser, M. (2016) Practical Corpus Linguistics: An Introduction to Corpus-Based Language Analysis. Chichester: John Wiley & Sons Ltd.

Data analysis software:

  • Comprehensive information and advice about choosing qualitative data analysis software can be obtained from the CAQDAS Networking Project, University of Surrey, UK.
  • A useful primer from Loughborough University, UK, on choosing qualitative data analysis software can be obtained from the ReStore repository.
  • Friese, S. (2019) Qualitative Data Analysis with ATLAS.ti, 3rd edition. London: Sage.
  • Schmuller, J. (2017) Statistical Analysis with R for Dummies. Hoboken, NJ: John Wiley & Sons, Inc.

Critical discourse analysis:

  • Wodak, R. & Meyer, M. (eds.) (2016) Methods of Critical Discourse Studies, 3rd edition. London: Sage.
  • Machin, D. & Mayr, A. (2012) How to Do Critical Discourse Analysis: A Multimodal Introduction. London: Sage.

Statistics:

  • Dancey, C. & Reidy, J. (2020) Statistics without Maths for Psychology, 8th edition. Harlow: Pearson.
  • Graham, A. (2013) Statistics: A Complete Introduction, reprint edition. London: Hodder and Stoughton.
  • Rowntree, D. (2018) Statistics without Tears: An Introduction for Non-Mathematicians, 2nd edition. London: Penguin.
  • A straightforward and useful glossary of statistical terms can be accessed from the STEPS website.

Participatory visual methods:

  • Gubrium, A. & Harper, K. (2013) Participatory Visual and Digital Methods. Walnut Creek, CA: Left Coast Press, Inc.
  • Gubrium, A., Harper, K. & Otañez, M. (eds.) (2016) Participatory Visual and Digital Research in Action. Abingdon, Oxon: Routledge.
  • More information about participatory methods can be obtained from the Institute of Development Studies in the UK.

Some researchers struggle to choose appropriate data analysis methods. However, there are a number of factors that you can consider that will enable you to narrow down the possibilities.

Consider the following conversation between Maya, Sameer and Nyah. Then proceed to the question, select an answer and consider the feedback.

Consider the following conversation between Maya, Sameer and Nyah. Then proceed to the question, select an answer and consider the feedback.

Researchers Maya, Sameer and Nyah are sitting talking in a café.

Sameer: How will you choose your data analysis methods?

Maya: I'm collecting qualitative data so this will influence my choice. Maybe some analysis by hand, some using software.

Nyah: I'm the opposite. I'll collect quantitative data so I'll need to look at statistical software.

Researchers Maya, Sameer and Nyah are sitting talking in a café.

Sameer: I'll collect both types of data in a mixed approach.

Maya: Will both fit with your methodology?

Nyah: And help you to answer your research question?

Sameer: Good points. I'll have to think about those.

Researchers Maya, Sameer and Nyah are sitting talking in a café.

Nyah: So what software will you use, Maya?

Maya: ATLAS.ti is available at my university. I'll be able to work through documents and images, coding and creating categories. This will help me to raise analytical questions.

Researchers Maya, Sameer and Nyah are sitting talking in a café.

Sameer: Do you know how to use the software?

Maya: My university runs training courses and there is a free trial on the software website. It'll take time, but it'll be worth it because it'll save on mechanical analysis tasks.

Researchers Maya, Sameer and Nyah are sitting talking in a café.

Nyah: I am looking at statistical packages. I've narrowed my options to SPSS, Stata and R. I'm thinking R might be best because it's open source and runs on a variety of platforms.

Maya: Yes, there are also free online tutorials available to help you and you could enrol on a statistics course here at the university.

Sameer: It's daunting, but it's worth taking time to choose the right software and learn how to use it properly!

What is the best way for the researchers to ensure that their data analysis methods are appropriate to their research approach, methodology and research question?

Adopt the most commonly used data analysis methods within their field of study Choose the most user-friendly and available qualitative or quantitative data analysis software Avoid complex analysis techniques or software packages that require additional education and training Read around the subject, increase knowledge and understanding, and discuss options with their supervisor or supervisory team

Maya, Sameer and Nyah should read around the subject to increase their knowledge and understanding. They might find it useful to read books on epistemology, methodology and data analysis techniques, as well as the methodology and data analysis sections of Ph.D. theses, for example. Choices can be discussed and refined through conversations with their supervisor or supervisory team.

If they decide to use data analysis software, they need to consider what software is most suitable for their type of project and methodology, and for the data that they intend to collect. They should weigh up different software, using demonstration or free versions. This will enable them to consider features and functions such as handling and storing data, and searching, querying and visualising data. They will also be able to see how approaches and tasks differ, and assess which software is most suited to the way they like to work.

When considering your options for data analysis methods, think very carefully how they relate to your data collection methods and your methodology. More information about this can be obtained from the following modules in Principles of Research Methods: Knowing about methodology, Knowing about data collection methods, Knowing about sampling methods and Knowing about data analysis methods. Remember that for some research projects data collection and data analysis take place simultaneously.

Ensure that the analysis methods you choose will enable you to answer your research question and are appropriate for the type of research you intend to carry out.