8. Analyse the data and interpret findings

Quantitative Data Analysis

• Quantitative research techniques generate a mass of numbers that need to be summarised, described and analysed.
• Characteristics of the data may be described and explored by drawing graphs and charts, doing cross tabulations and calculating means and standard deviations.
• Further analysis will build on these initial findings, seeking patterns and relationships in the data by comparing means, exploring correlations, performing multiple regressions, or analyses of variance.
• Advanced modelling techniques may eventually be used to build sophisticated explanations of how the data addresses the original question.
• Although methods used can vary greatly, the following steps are common in quantitative data analysis:
  • Identifying a data entry and analysis manager (e.g., SPSS)
  • Reviewing data (e.g., surveys, questionnaires etc) for completeness
  • Coding data
  • Conducting Data Entry
  • Analysing Data (e.g., sample descriptives, other statistical tests).

Qualitative Data Analysis

• Qualitative data analysis describes and summarises the mass of words generated by interviews or observational data.
• It allows researchers to seek relationships between various themes that have been identified or relate behaviour or ideas to biographical characteristics of respondents.
• Implications for policy or practice may be derived from the data, or interpretation sought of puzzling findings from previous studies.
• Ultimately theory could be developed and tested using advanced analytical techniques.
• Although methods of analysis can vary greatly (e.g., Grounded Theory, Discourse Analysis ) the following steps are typical for qualitative data analysis:
  • Familiarisation with the data through repeated reading, listening etc.
  • Transcription of interview etc. material.
  • Organisation and indexing of data for easy retrieval and identification (e.g. by hand or computerized programmes such as Nvivo -formally NUD*IST)
  • Anonymising of sensitive data.
  • Coding (may be called indexing).
  • 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.
• For more information see 'Qualitative Research' from Trent RDSU.

Interpreting Data

• Visit RDDirect for a list of websites containing relevant information on statistics
• The last step of data analysis consists of interpreting the findings to see whether they support your initial study hypotheses, theory or research questions.
• Data interpretation methods vary greatly depending on the theoretical focus (i.e., Qualitative or Quantitative research) and methods (e.g., Multiple Regression, Grounded Theory).
• You should seek further advice for this step from:
  • Your supervisor/Other experts within your organization
  • Computer Package Manuals (e.g., SPSS, Nvivo) and methodology books
  • Statistics in Research from Trent RDSU
  • The material in Section 3 of this flowchart on statistics and sampling issues
  • The panel of advisors at RDDirect tel. 0113 295 11 22 (e-mail).

Suggested Reading

• Books on data analysis and interpretation from the reading list from the University of Leeds’ School of Medicine’s Health Research course MEDR 5120 Module 5: Analytic Research