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Dissertations 5: Findings, Analysis and Discussion: Analysis

Quantitative Data Analysis

Quantitative analysis techniques 

Raw numerical data need to be processed and analysed to make them meaningful. Quantitative analysis techniques include tables, graphs and statistics (Saunders, Lewis and Thornhill 2015, p496).

Establish patterns and relationships

The way you present your data will help identify patterns and relationships in your research. These can be (depending on the field/subject) (Cottrell, 2014, p173):

  • Trends and developmental patterns over time (are there any patterns in the data? Do the data rise, fall, plateau? Where/when? How - gently or sharply?)
  • Correlations and relationships between sets of data (do they sets of data move in a similar way? Or do they move in an opposite way? Or do they have no relation at all?)
  • Relationships between events
  • Cause and effect (can you spot any causality?)

Qualitative Data Analysis

In qualitative research, meanings are derived from words and images - not numbers, as in quantitative research. Words and images can have multiple meanings, and need to be interpreted with care (Saunders, Lewis and Thornhill 2015, p568). For more information about qualitative data see the section on Methodology

How to undertake qualitative data analysis:

  • Group the data in themes to make sense of them (summarise, condense, code the data).
  • Link these themes and categories in a way that can help you answer your research question.
  • Reflect on whether the data support your original argument. If yes, make sure that when you present your data you emphasise how the data support your argument. If not, you should revise your original argument!

Approaches to analysing qualitative data 

Qualitative data analysis can take place using specific methods such as (there are many more, depending on your field!) thematic analysis, content analysis, grounded theory, narrative analysis, discourse analysis (see link below).

The most generic approach to qualitative data analysis is thematic analysis, whose purpose is to identify patterns in qualitative data (interviews, observations, documents etc.) (Saunders, Lewis and Thornhill 2015, p579). Thematic analysis can be inductive, deductive or hybrid:

  • Inductive - a data-driven process of "coding the data without trying to fit it into a pre-existing  coding  frame,  or  the  researcher’s analytic preconceptions" (Braun and Clarke, 2006, p. 83)
  • Deductive - deriving from an existing theoretical framework
  • Hybrid - using both a data-driven inductive approach and a deductive a priori template of codes approach (Fereday and Muir-Cochrane, 2006)

Braun, V. and Clarke, V. (2006). Using thematic analysis in psychology, Qualitative Research in Psychology, 3(2), 77-101, DOI: 10.1191/1478088706qp063oa

Fereday, J., & Muir-Cochrane, E. (2006). Demonstrating Rigor Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development. International Journal of Qualitative Methods5(1), 80–92.