Step 8: Analysing & Interpreting Your Data

Your method of data analysis will depend upon the type of data you have collected (Step 7).

Qualitative Data Analysis 

Qualitative data analysis describes and summarises the mass of words generated by interviews or observational data.  You do this by breaking your data into manageable units, synthesizing the data, searching for patterns and determining what is important. You’ll use inductive techniques to search for patterns in your data and extrapolating from them to form a new understanding of your research topic. Your data analysis may help you expand upon the quantitative data of previous studies or produce a theory which could be developed and tested using advanced analytical techniques.

Although methods of analysis can vary greatly e.g. discourse analysis or grounded theory, the following steps are common in qualitative data analysis:

  • Familiarisation with the data through repeated reading or listening
  • Transcription of audiovisual material
  • Organisation and indexing of data for easy retrieval and identification, either by hand or using a dedicated piece of software e.g. NVivo
  • Anonymising of sensitive data
  • Coding data
  • Identification of themes
  • Development of provisional categories
  • Exploration of relationships between categories
  • Refinement of themes and categories
  • Development of theory and incorporation of pre-existing knowledge

Quantitative Data Analysis

Quantitative research techniques generate a mass of numbers that need to be summarised, described and analysed. Your quantitative data analysis will produce numerical representation such as graphs and charts to describe and explain the phenomena you’ve been researching. You use deductive techniques in your quantitative data analysis, meaning that you’ll use a framework to explore your data, from which you’ll either accept or reject your project hypothesis (Step 1).

Although methods of analysis can vary greatly, the following steps are common in quantitative data analysis:

  • Organisation and indexing of data for easy retrieval and identification, either by hand or using a dedicated piece of software e.g. SPSS or the open-source software JASP
  • Reviewing data for completeness
  • Conducting data entry
  • Coding data
  • Undertaking statistical analysis

Mixed Methods Data Analysis

Mixed method data analysis uses the most appropriate analysis technique for each data set you’ve collected. Your discussion will then combine your interpretation of all data sets into a single coherent narrative.

Suggested Reading

  • Bruce, N., Pope, D., & Stanistreet, D. (2018). Quantitative methods for health research: a practical interactive guide to epidemiology and statistics (2nd ed.). Oxford: Wiley. A practical introduction to quantitative research, data collection and data analysis.
  • Cresswell, J. W., & Clark, V. L. P. (2006). Collecting data in mixed methods research. In J. W. Cresswell & V. L. P. Clark (Eds.), Designing and conducting mixed methods research (pp. 110-127). London: Sage. Although a more recent version of this book is available, this link give you free access to the chapter on collecting data in mixed methods research projects from the first edition.
  • Denzin, N. K., & Lincoln, Y. S. (2017). The SAGE handbook of qualitative research (5th ed.). London: Sage. This is your go to handbook for all your queries regarding qualitative research.
  • Gorman, G. E., & Clayton, P. (2004). Qualitative research for the information professional: a practical handbook (2nd ed.). London: Facet. A comprehensive introduction to all aspects of qualitative research specifically written with the information working in mind.
  • IBEC Outcomes Toolkit: STEP 3. Analyzing Data. Focuses purely on qualitative data analysis.
  • Pickard, A. J. (2013). Research methods in information (2nd ed.). London: Facet Publishing.  A practical exploration of the whole research process.
  • Vaughan, L. (2001). Statistical methods for the information professional: a practical, painless approach to understanding, using and interpreting statistics. Maryland: American Society for Information Science and Technology : An accessible guide to how to analyse your quantitative data.

Next Steps

Now that you’ve completed your data analysis, you’re ready to write up and disseminate your findings (Step 9).