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 East Midlands RDS.

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 East Midlands RDS
    • 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