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Qualitative Data Analysis:
 Planning your analytic
         strategy

       Martyn Hammersley
             CREET/CHDL
Faculty of Education and Language Studies
Preparing for probationary
            review
• Framing research questions, outlining the
  rationale for them, planning methods.
• Carrying out literature searches and
  reviewing relevant literature.
• Doing pilot research: collecting data or
  acquiring secondary data for analysis;
  producing an initial analysis.
• Outlining a schedule of future work leading
  to completion of the thesis.
The key role of data analysis
All aspects of the research process lead to, and
   from, analysis.
It is a machine that must be built, fuelled, and
   provided with material, so that it can generate
   the structure and content of the thesis.
But this is too mechanical an analogy!
It’s a way of thinking and working, and one that
   must change over time, so as better to deal
   with the data, and answer the developing
   research questions.
Intended products: answers to
     research questions
• Descriptions: of people and their attitudes,
  dispositions, habitual patterns of
  behaviour etc; of places and the patterns
  of activity that occur there, etc.
• Explanations for the patterns described,
  along with evidence showing the
  presence of the causal factors in the
  contexts studied, and/or their effects.
Some methodological
philosophies that have influenced
       qualitative research
 •   Positivism
 •   Pragmatism
 •   Realism
 •   Phenomenology
 •   Hermeneutics
 •   Marxism, ‘Critical’ Theory, Feminism
 •   Post-Structuralism and Postmodernism
Qualitative research: key features
•    A relatively open-ended, exploratory research
     design.
•    The collection of relatively unstructured forms of
     data:
•    interviews in which informants talk in their own
     terms about matters relevant to the research.
•    observations recorded in fieldnote descriptions.
•    audio- or video-recordings and transcriptions.
•    written documents of various kinds.
•    photographs, drawings, etc.
•    electronic data from virtual interactions.
Research design
• A flexible process – rather than one that
  begins from fixed hypotheses and is
  focused entirely on testing these.
• Plans for data collection and analysis,
  and even research questions
  themselves, may be changed during the
  course of inquiry.
The production of data
• Data are not simply ‘collected’.
  To one degree or another, in one way or
  another, they are produced.
• At the very least they have to be ‘worked’
  into a form that allows analysis. In particular:
  - Fieldnotes ‘jotted’ in the field must be
  written up in full later.
  - Audio- or video-recordings must usually be
  transcribed.
These are time-consuming activities.
What is analysis?
• The development of interpretations of
  data that contribute towards answering
  research questions; but may also serve
  to clarify, improve or reformulate these
  questions.
• Checking the reliability of assumptions,
  interpretations, and conclusions.
Forms of qualitative analysis

• Analysis of text: a distinction between
  theme analysis and discourse analysis.
• Analysis of images: semiotic and
  compositional analysis.
• Multi-modal analysis: combining aural
  with visual data, and that from other
  senses.
Theme analysis
• Seeking to answer research questions
  about why particular patterns of action, or
  particular outcomes, occur.
• May integrate data of multiple kinds (from
  observations, interviews, photographs, etc).
• The aim is to develop conceptual categories
  that relate to particular people and/or
  places, or types of these, operating across
  the different kinds of data used.
Discourse analysis
• Tends to focus on one particular type or
  source of data: documents, recordings
  of naturally occurring talk, or interviews.
• More detailed attention to specific
  textual features, with a view to
  understanding their mutual relations,
  functions, etc.
• Tends to use a much smaller amount of
  data.
Types of discourse analysis

•   Narrative analysis
•   Conversation analysis
•   Discursive psychology
•   Linguistic discourse analysis
•   Critical discourse analysis
•   Bakhtinian analysis
•   Post-structuralist discourse analysis
Stages of theme analysis
1. ‘Coding the data’: generating categories ‘from’
   the data. Initially, involves backgrounding
   research questions and trying to find what is
   ‘in’ the data, particularly as regards the
   perspectives of participants, distinctive
   features of the settings, etc.
2. Constant comparative method: comparing
   data placed in the same conceptual category,
   in order to clarify and develop ideas about
   each category and its interrelations with
   others.
CAQDAS
• Computer assisted qualitative data
  analysis.
• Does not do the analysis, but facilitates
  the coding, storage, and retrieval of
  data for analysis.
• Is it worth it? Yes if dealing with a large
  amount of data and using theme
  analysis.
• Which program?
Conclusion
• Try to be as clear and realistic as you
  can about what you are doing.
• Engage in recurrent assessments of
  what you have achieved and what you
  are aiming at.
• Learn to live with uncertainty!
References
• Ashmore, M. (1989) The Reflexive Thesis, University
  of Chicago Press.
• Bignell, J. (1997) Media Semiotics, Manchester,
  Manchester University Press.
• Coffey, A. and Atkinson, P. (1996) Making Sense of
  Qualitative Data: complementary research strategies,
  Thousand Oaks CA, Sage.
• Glaser, B. and Strauss, A. (1967) The Discovery of
  Grounded Theory, Chicago, Aldine.
• Hammersley, M. and Atkinson, P. (2007) Ethnography:
  principles in practice, London, Routledge.
• Miles, M. and Huberman, M. (1994) Qualitative Data
  Analysis, Thousand Oaks CA, Sage.

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Qualitative data analysis - Martyn Hammersley

  • 1. Qualitative Data Analysis: Planning your analytic strategy Martyn Hammersley CREET/CHDL Faculty of Education and Language Studies
  • 2. Preparing for probationary review • Framing research questions, outlining the rationale for them, planning methods. • Carrying out literature searches and reviewing relevant literature. • Doing pilot research: collecting data or acquiring secondary data for analysis; producing an initial analysis. • Outlining a schedule of future work leading to completion of the thesis.
  • 3. The key role of data analysis All aspects of the research process lead to, and from, analysis. It is a machine that must be built, fuelled, and provided with material, so that it can generate the structure and content of the thesis. But this is too mechanical an analogy! It’s a way of thinking and working, and one that must change over time, so as better to deal with the data, and answer the developing research questions.
  • 4. Intended products: answers to research questions • Descriptions: of people and their attitudes, dispositions, habitual patterns of behaviour etc; of places and the patterns of activity that occur there, etc. • Explanations for the patterns described, along with evidence showing the presence of the causal factors in the contexts studied, and/or their effects.
  • 5. Some methodological philosophies that have influenced qualitative research • Positivism • Pragmatism • Realism • Phenomenology • Hermeneutics • Marxism, ‘Critical’ Theory, Feminism • Post-Structuralism and Postmodernism
  • 6. Qualitative research: key features • A relatively open-ended, exploratory research design. • The collection of relatively unstructured forms of data: • interviews in which informants talk in their own terms about matters relevant to the research. • observations recorded in fieldnote descriptions. • audio- or video-recordings and transcriptions. • written documents of various kinds. • photographs, drawings, etc. • electronic data from virtual interactions.
  • 7. Research design • A flexible process – rather than one that begins from fixed hypotheses and is focused entirely on testing these. • Plans for data collection and analysis, and even research questions themselves, may be changed during the course of inquiry.
  • 8. The production of data • Data are not simply ‘collected’. To one degree or another, in one way or another, they are produced. • At the very least they have to be ‘worked’ into a form that allows analysis. In particular: - Fieldnotes ‘jotted’ in the field must be written up in full later. - Audio- or video-recordings must usually be transcribed. These are time-consuming activities.
  • 9. What is analysis? • The development of interpretations of data that contribute towards answering research questions; but may also serve to clarify, improve or reformulate these questions. • Checking the reliability of assumptions, interpretations, and conclusions.
  • 10. Forms of qualitative analysis • Analysis of text: a distinction between theme analysis and discourse analysis. • Analysis of images: semiotic and compositional analysis. • Multi-modal analysis: combining aural with visual data, and that from other senses.
  • 11. Theme analysis • Seeking to answer research questions about why particular patterns of action, or particular outcomes, occur. • May integrate data of multiple kinds (from observations, interviews, photographs, etc). • The aim is to develop conceptual categories that relate to particular people and/or places, or types of these, operating across the different kinds of data used.
  • 12. Discourse analysis • Tends to focus on one particular type or source of data: documents, recordings of naturally occurring talk, or interviews. • More detailed attention to specific textual features, with a view to understanding their mutual relations, functions, etc. • Tends to use a much smaller amount of data.
  • 13. Types of discourse analysis • Narrative analysis • Conversation analysis • Discursive psychology • Linguistic discourse analysis • Critical discourse analysis • Bakhtinian analysis • Post-structuralist discourse analysis
  • 14. Stages of theme analysis 1. ‘Coding the data’: generating categories ‘from’ the data. Initially, involves backgrounding research questions and trying to find what is ‘in’ the data, particularly as regards the perspectives of participants, distinctive features of the settings, etc. 2. Constant comparative method: comparing data placed in the same conceptual category, in order to clarify and develop ideas about each category and its interrelations with others.
  • 15. CAQDAS • Computer assisted qualitative data analysis. • Does not do the analysis, but facilitates the coding, storage, and retrieval of data for analysis. • Is it worth it? Yes if dealing with a large amount of data and using theme analysis. • Which program?
  • 16. Conclusion • Try to be as clear and realistic as you can about what you are doing. • Engage in recurrent assessments of what you have achieved and what you are aiming at. • Learn to live with uncertainty!
  • 17. References • Ashmore, M. (1989) The Reflexive Thesis, University of Chicago Press. • Bignell, J. (1997) Media Semiotics, Manchester, Manchester University Press. • Coffey, A. and Atkinson, P. (1996) Making Sense of Qualitative Data: complementary research strategies, Thousand Oaks CA, Sage. • Glaser, B. and Strauss, A. (1967) The Discovery of Grounded Theory, Chicago, Aldine. • Hammersley, M. and Atkinson, P. (2007) Ethnography: principles in practice, London, Routledge. • Miles, M. and Huberman, M. (1994) Qualitative Data Analysis, Thousand Oaks CA, Sage.