Data

• Data Types

  Qualitative:

  • Descriptive
  • Difficult to statistic analyse and generalise
  • Detail (high valid)
  • Comes directly participant eco valid;
  • Can be made to quantitative data; thematic analysis

  Quantitative:

  • Numerical
  • Shows behaviour frequency
  • Reduces to numbers, less valid
  • Reliable
  • Replicable
  • Objective
  • Generalisable
  • Comparable
  • Type: nominal (categories), ordinal (ranking), interval (ranked, know gap size) and ratio (interval, true zero)

  Self Report:

  • Easy statistic analyse
  • Eco valid
  • Honest, articulate participant
  • Social desirable
  • Demand characteristic
• Question Types


  Closed

  • Fixed response
  • Select applicable answer
  • Quantitative
  • Reliable
  • Replicable
  • Easy statistic analyse
  • Objective
  • Well-controlled
  • Low validity
  • Demand characteristic (social desirable)
  • Superficial
  • Fatigue (long questions)
  • Frustration (reductionist answer range, lower engagement)

  Open

  • No restriction
  • Qualitative
  • Allows explanation
  • May generate unexpected content, new inquiry lines
  • Detailed
  • More valid
  • Flexibility
  • Good if unknown answer range
  • Low reliability
  • Subjective
  • Difficult to analyse
  • Time-consuming
  • Less replicable
  • Generate less data
• Thematic Analysis


  • Identify patterns, trends + themes
  • Develop codes to label data, with ideas, attitudes + behaviour (themes)
  • Evidence for themes may be direct quotes or inferences
  • E.g. PA for positive attitudes
  • Transcript may run many pages, codes useful to see how often themes emerge
  • Deductive: Search through data for pre-existing themes
  • Inductive: Analyse data to make themes
  • Similar themes categorised together, patterns/trends
  • Themes change/emerge after more analysis, constant adjustment
  • Allows qualitative analysis, meaningful + detailed
  • Subjective
  • Researcher bias
• Primary or Secondary

  Primary Data:

  • Gathered by researcher themselves
  • Quantitative or qualitative
  • Specific sample (more representative) and procedure (more valid)
  • Smaller samples
  • Expensive
  • Ensure operationalisation, standardisation and control
  • Time-Consuming

  Secondary Data:

  • From other pre-published sources
  • Quantitative or qualitative
  • Meta-analysis
  • Larger database (more representative)
  • Cheaper
  • Unsure of control in studies, unreliable methods
  • Use studies not for this aim/purpose
  • May be out-of-state
  • Fast
• Grounded Theory

  • Way to analyse qualitative data without using existing theory
  • Focus on theory ‘grounded’ in data
  • Similar to thematic analysis, always inductive
  • Aim to generate useful theory from data
  • Researcher looks from common themes + ideas
  • Group data into categories using themes
  • Strengths
  • Allow qualitative data analysis, keep data meaningful + detail
  • Results a useful theory, may indicate future research ideas
  • Weaknesses
  • Considered unscientific, themes dependent on subjectivity
  • Theory may lack generalisability to individuals out initial sample data was drawn