Data analysis - Data

Qualitative data

Quantitative data

Qualitative research gather information that is not in numerical form

E.g., diary accounts, open ended questionnaires, unstructured interviews + unstructured observations

Qualitative data is typically descriptive data and as such is harder to analyse than quantitative data

Attitudes, beliefs and behaviours

Evaluation of qualitative data

  • More detail than quantitative data
  • Greater external validity - more meaningful insight
  • Difficult to analyse - patterns can be hard to find
  • Conclusions can be open to researcher bias
  • Quantitative research gathers data in numerical form which can be put into categories, or in rank order, or measured in units of measurement
  • This type of data can be used to construct graphs + tables of raw data
  • Often a narrow view of behaviour
  • Looking at averages + differences between different groups

Evaluation of quantitative data

  • Simple to analyse
  • Comparisons between groups is easier
  • May not represent 'real life'
  • Numerical data is less open to bias

Primary data

  • Field research
  • Original data collected
  • Arrives from the participants themselves
  • Questionnaire, interview, observation

Strengths: Can design the questionnaire to suit what you want to find out

Weaknesses: Time, effort, money

Secondary data

  • Data collected by someone other than the person conducting the research
  • Often already been subject to statistical testing and significance is known
  • E.g. Data in journal articles or website or population records held by the government

Meta-analysis (a form of secondary data)

Strength: Cheap, little effort

Weakness of meta-analysis: researcher may leave out studies with negative or non-significant results. It also depends on the quality of the studies/data being used

Meta-analysis: data from a large number of studies involving the same methods of research. Perform a statistical analysis of the combined data.