Introduction to qualitative research

• Introduction to Qualitative research

  What is qualitative data?

  Rich, contextualised and deep analysis of data

  Use of words to infer human behaviour

  It’s growing in popularity

  Suitable for variations within populations

  Examples inc. case studies, interviews, focus groups
• Common myths

  biased and subjective

  lacks rigour

  not empirical
• Glassner & Hertz (1999)

  qualitative findings usually invisible unless close, psychologically informed attention is paid
• Advantages of qualitative research

  participant led = high ecological validity

  appreciation of context

  design, data collection and analysis is flexible & open to change

  exploratory and leads to theory formation

  systematic & transparent

  high impact value e.g. useful in changing policies at health practices

  smaller sample size

  reuse & recycle (can combine data from different sites to improve representations)

  ability to find ‘golden thread’ in large amounts of data
• Epistemology

  Philosophical study of nature, origin, & limits of human knowledge
• Positivism - Auguste Comte (1798-1857)

  this research paradigm was preferred in 19th cent. quantitative research recognises only that which can be scientifically verified and falsified.

  prior to this positivism only applied to natural sciences.

  has 3 principle attributes
• 3 principle attributes of positivism

  REALIST PERSPECTIVE - focuses on predictive power & control (reducing human behaviour)

  CAUSAL KNOWLEDGE - understands there is one objective reality, through observation of one things affect on another

  DEDUCTIVE REASONING - can explain individual behaviour through general theories we have
• Criticisms of quantitative data

  Bias in experimentation & data analysis

  Replicability crisis

  Low ecological validity

  Reductionist & determinism

  Data ‘doesn’t speak for itself’ = why certain things affect some people but not others
• Post-positivism (1960-)

  moves away from need to be seen as a hard science

  inductive reasoning = experiences of a few can explain the many
• Qualitative methodological frameworks

  Three methodologies

  
• Phenomenology

  Captures individual interpretation of a particular phenomenon

  Concerned with the interpretation rather than what happened

  +interpretive approach remains close to raw data

  -can miss important precursors, consequences & factors associated with the phenomenon itself
• Ethnography

  Can understand behaviour and interactions of a group by walking through their footsteps. Examines particular cultural groups characteristics and how they ascribe meaning to everyday life

  +captures wider perspective

  -misses dynamic interactions between individuals within a group
• Grounded theory

  Generates inductive theory that is fundamentally grounded in the data

  +bridges principles of qualitative and quantitative methods

  -fails to acknowledge researchers role in constructing and interpreting data
• Donnellan et al. (2017)


  association between family support and wellbeing in older carers

  q. = ‘How often do you see any of your family?’

  quant. -> what does ‘often‘ mean to individual. no insight to the nature of the relationship

  qual. -> not as many assumptions. subjective expectation over objective measurements
• Rubin & Rubin (1995)

  designing a study is like planning a holiday

  can choose either a deductive (many can explain a few) or inductive (few can explain the many) approach
• Mixed methods

  Chamberlain et al. (2011) = The use of multiple methods to better examine the different dimensions of a given domain

  use of qualitative and quantitative

  Happ (2009) = challenge is appropriate integration
• 4 basic levels of integration (Creswell & Plano-Clark, 2007)

  PARALLEL - quant and qual data collected separately. Finding compared and contrasted for integration.

  SEQUENTIAL - big qual or quant component. First study guides the next one.

  INTERPRETIVE MIXED - both types analysed together with aim of interpreting and understanding the context and meaning behind results. Integration is simultaneous

  TRANSFORMATION - one form of analysis converted into another