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How to... use ethnographic methods and participant observation

Options:     Print Version - How to... use ethnographic methods and participant observation, part 3 Print view

Article Sections

  1. Introduction to ethnographic methods
  2. Participant observation
  3. Analysing, theorizing and writing up

Analysing, theorizing and writing up

Analysis of unstructured data

What distinguishes the analysis of ethnographically generated data is that the research process is inductive and iterative. Unlike the neatly linear trajectory of some other research, when you construct an instrument to prove a theory, and do not analyse until you have collected all the data, in ethnographic research data collection and analysis may be simultaneous, while theories are formed on the basis of some data and then tested and refined against further data. This process is known as analytic induction.

Image: Analytic induction. Evidence -> Hypothesis -> Review evidence -> Refine hypothesis.

When you begin to collect data, you will find very soon that you get a lot. This is the time to begin an initial analysis. As you start the coding process, begin to look for groupings, based on frequency and patterns of and in the data. As you refine your coding structure, check your assumptions carefully: look out for data that don't fit the pattern, and for factors that might have weighted the evidence. Eventually, however, you will reach a point where you are relatively confident of your coding structure and you can begin to use it as a way of organizing your data.

There are a number of software packages – NVivo, QSR NUD.IST and The Ethnograph for example – that can help here, or you may prefer to use an ordinary office package such as Word or Excel. Some of the software packages also offer modelling facilities.

Whatever method you use, at this stage patterns will begin to emerge from which you will be able to build theory.

Analysis of structured data

The analysis of structured observation data is different in that the coding schedule is established before the start of data collection. In this case you either:

  • establish your own headings, which should be consistent with your research questions
  • follow an existing "off the shelf"coding schedule
  • use a combination of these approaches, modifying an existing schedule and perhaps putting in some of your own headings.

Note that in the examples given below, which relate mostly to unstructured data, the researchers also use the third approach.

Validity

The fact that data are situation specific and therefore not easy to replicate, together with the possibility of observer bias, are threats to validity with unstructured observation. These threats can be dealt with in a number of ways:

  • Checking the observations, and interpretations of them, with participants, as a form of triangulation.
  • Checking the coding structure, which can be by both the researcher him or herself checking against emerging theory, and other researchers coding the data to see if they come up with similar coding structures.
  • "Perspicacity" – the ability to abstract from the data general principles that can throw light on other similar situations.

Example

In "The (unlikely) trajectory of learning in a salmon hatchery" (Journal of Workplace Learning, Vol. 17 No. 4), Yew-Jin Lee and Wolff-Michael Roth
and colleagues maintain the validity of their research by constantly revising their hypotheses until they account for all known cases, progressive subjectivity (monitoring of the researchers' evolving constructs) as well as checking the constructs against the participants themselves.

Theory building

The literature review is commonly done at the beginning of the research process. But with ethnographic research, it often follows (at least some) data collection and analysis – because it is connected with theory building.

In ethnographic research, the researcher is often compared with a journalist researching a story, and looking for promising lines of enquiry. The difference, however, lies in the output: the researcher is looking for a theory, and not a story. As the data are being collected and patterns start to emerge, so may interesting lines of enquiry on which theories can be built.

The objective of the theory is not to predict, but to explain, to look for contextual structures and to provide a context for events, conversations and descriptions. You are providing an explanatory framework for the phenomena which you have been observing.

As indicated above, once you have formulated a theory you need to check it against the data, and check the data against itself – how valid is it?

The theory also needs to be situated in the relevant literature, and have its own theoretical context.

Writing up

For a dissertation, you should follow the guidelines of your own university and check out other dissertations which have used similar research techniques. A traditional approach, however, is introduction, literature review, philosophical approach and methodology, findings, analysis, discussion and conclusion.

For a journal article, you are best advised to look carefully at other examples of articles written for scholarly journals, particularly ones in which you are thinking of publishing. (See our companion " How to... find the right journal" in the Authors section).

Some examples

In "Ethnography of an American main street" (Susie Pryor and Sanford Grossbart, International Journal of Retail & Distribution Management, Vol. 33 No. 11), the researchers read through their interviews and field notes several times "to develop a coherent sense of the whole body of data and generate as many categories as possible". The next stage was to cluster emergent themes to look at shared meanings across actions and events. Finally, the emerging theoretical framework was related to the literature. A particular template was used but adapted to suit the framework as suggested by the data.

In "The human resource management practice of retail branding: an ethnography within Oxfam Trading Division" (Stéphane J.G. Girod, International Journal of Retail & Distribution Management, Vol. 33 No. 7), data was codified according to a literature based template and also the themes that emerged from the research, following which several main nodes were constructed – culture, paid staff, volunteers, customers, structure, reorganization and identity.

In "Participatory group observation – a tool to analyze strategic decision-making" (Qualitative Market Research: An International Journal, Vol. 5 No. 1), Christine Vallaster and Oliver Koll describe a research approach using a variety of data collection methods including interviews, observation and archival research. They then analysed the data according to a three-fold process of coding (i.e. labelling data); memoing (i.e. theorizing about codes and their relationships); and developing propositions.