Qualitative Data Analysis: Planning your analytic

strategy

Martyn HammersleyCREET/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

1. A relatively open-ended, exploratory research design.

2. 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 analysis1. ‘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.

3. Checking interpretations and conclusions.

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.