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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.
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.
