Lecture 10 - Introduction to Qualitative Research Methods Flashcards
What is quantitative research?
Quantitative research uses numbers as data, which are described and analysed using statistical techniques
What does quantitative research aim to test?
Causal relationships between variables, whilst controlling for the influence of others (experimental)
What is qualitative data?
uses words as data, collected and analysed in a wide variety of ways
What are examples of methods of qualitative data collection?
- Interviews – unstructured/semi structured/structured
- Open questions in questionnaires
- Focus groups
- Online chat groups/forums
- Diaries/videos
- Texts
- Story completion
In what areas of psychology is qualitative research used?
- Clinical
- Social
- Health
- Developmental
- Environmental
- Cognitive
- Animal behaviour
What are research paradigms?
- Positivism
- Realist ontology
- Deduction
What is positivism?
- Knowledge = Science (scientific method)
- Goal of research is to produce ‘objective’ knowledge (knowledge as impartial and unbiased, based on a view from “the outside”)
What is realist ontology?
Qualitative research can be conducted in line with a more quantitative philosophy – this is known as small q – qualitative research - you may view the researcher as more external and separate in this situation
What is deduction?
- “Top down”
- Research questions derived from pre-existing theoretical frameworks
- Data tests theory
What is the inductive approach?
- Moving from data to theory.
- Understanding the meanings humans attach to events.
- Close understanding of the research context.
- Collection of qualitative data.
- Flexible structure to permit changes of research emphasis as the research progresses.
- Realisation that the researcher is part of the research process.
- Less concern with the need to generalise.
What is thematic analysis?
- Umbrella term to describe a wide range of approaches used for making sense of qualitative data
- These approaches differ in philosophy and procedure
- At a minimum, TA describes and organises patterned responses across the dataset
- At a maximum, TA interprets aspects of the phenomenon
What academic disciplines is thematic analysis used?
- psychology to business - in applied and clinical
- research and in policy contexts
What does reflexivity mean in reflexive thematic analysis?
- Being aware of your role in the research process
- Active not passive process
- Themes generated not discovered or emerged
- Recognising your beliefs/stance/positionality from personal and professional experiences and knowledge as part of the research
- Thoughtful engagement with the data
- The role of a paper trail and memos
- Having honest discussions with your supervisor/research team
- Bringing in theories
What is a theme?
Captures something important about the data in relation to research question
What ‘size’ does a theme need to be?
- More instances (prevalence) doesn’t necessarily mean more crucial
- Nor the amount of time spent on it in each data item
- Key is ‘significance’ or meaningfulness of the theme in relation to the research question
Who proposed the phases of thematic analysis?
Braun & Clarke, 2006
What are the phases of thematic analysis?
- Familiarising yourself with the data
- Generating initial codes
- Generating themes
- Reviewing themes
- Defining the naming themes
- Producing the report
1-6. Reflexivity
What does it mean by familiarising yourself with the data?
- Transcribing data (if necessary) – checking of accuracy of automated transcriptions
- Reading and re-reading the data (immersion!)
- Jotting down initial ideas
What does it mean by generating initial codes?
- Coding interesting features of the data systematically across whole data set
- Codes identify feature of data that appears interesting to the analyst
- ‘The most basic segment, or element, of the raw data…that can be assessed in a meaningful way regarding the phenomenon (under investigation)’ (Boyatzis, 1998: 63)
- You are not coding for ‘themes’ at this stage rather, anything which is meaningful and related to the research question
- Collate all data that fit under each code (cut-n-pasting or N-vivo)
- Begin to think about the relationships between different codes
- Process can be data-driven or theory-driven
What does it mean by generating themes?
- Sort different codes into potential themes
- Codes as building blocks of themes
- Analysing codes, how do different codes combine to form overarching themes
- Zooming out to look at bigger picture!
What does it mean by reviewing themes?
- Some potential themes will prove to not really be themes (not enough data to support them)
- Some themes may collapse together into one theme
- Some themes may need to be split into two separate themes
- Review all coded extracts in each theme (do they fit?)
- Review your entire data set in relation to your identified themes (does it represent the data? Did you miss anything?)
What does it mean by defining and naming themes?
- Need to identify the ‘essence’ of what each theme is about and what aspects of your data capture that theme
- Begin to write a ‘story’ about your data, using your data extracts to support your ‘story’
- Don’t just paraphrase the content of the extracts
- Identify what’s interesting about them, and WHY
- Provide a detailed analysis of each theme, and how the themes fit together to form an overall ‘story’
- Begin to think about what ‘names’ you will give your themes in the final analysis
What does it mean by producing the report?
- Need to tell the story of your data in a way that convinces the reader of the merit and validity of your analysis
- Should be clear, coherent and interesting (non-repetitive)
- Need to provide evidence for your themes (in form of suitable data extracts and analysis of them)
- Need to go beyond just describing your data
- Must make an argument in relation to research question
What does it mean by reflexivity?
- Keeping your paper trail
- Notes or memos about your active role in the research process
- How you felt reading the first interview
- Your initial impressions and reflections
- How you are staying close to the participants viewpoints
- How theory is coming into your analysis and process
