How to... analyse qualitative data

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Introductory considerations

"Researchers need to focus on ways in which the actors order their own world, and avoid counting everything." (Silverman, 2004, p. 181)

This sums up two ways in which the approach to data differs in qualitative research from that generated by quantitative investigation. The researcher is not dealing with numbers which can be crunched; neither is he or she dealing with an absolutely literal interpretation of the world. Instead, the researcher needs to use intuition, imagination and interpretation.

How qualitative and quantitative data differ

The process of quantitative research is linear: the researcher will start out with a theory, design a research process, collect data, analyse it and then review findings to see whether or not they support the hypothesis suggested by the theory.

In qualitative research, the process is much more iterative and inductive. The researcher will start out with a question or issue, collect data, analyse the data they have collected, start to formulate theory, go back and look at, or even collect, more data.

With quantitative research, the researcher will normally decide on the method of analysis, including statistical technique, before even data collection starts. In qualitative research, however, the process is a lot more messy, and it's common for the theory, design, collection and analysis phases to overlap.

"In qualitative research, sticking with the original research design can be a sign of inadequate data analysis, not consistency." (Silverman, 2004, p. 152)

Nor can everything be transformed to numbers, as with quantitative data. There is no common ground, and the researcher will amass large amounts of data in many different forms. Analysis therefore needs to begin with the data in its raw state, acknowledging that it may have come from various different methods of collection such as interviews, focus groups, documents, or images.

Each piece of data, then, needs to be approached in its own terms, and meaning extracted – which may need to be negotiated throught the lens of the cultural context in which the author is operating.

An ethnographic view of data: negotiation of meaning

In the scientific view, which is the dominant paradigm for quantitative research, reality exists independently and data can be collected to represent it. The researcher's task is to structure the data collection process so that the data represents the truth. For example, if the researcher wants to find out the most important factors sought in a washing powder, they need to formulate the questions in such a way that all the possibilities are catered for.

The collection and analysis of qualitative data, however, is dominated by the ethnographic paradigm. Ethnographers are concerned to interpret data according to the social world of their participants. Organizations, for example, have their own value systems which will be reflected in the language and the images used both by individuals and in collective statements. For this reason, it is not always possible to take data at face value.

Silverman (2001, p. 134) gives a couple of examples here:

"Notes on candidates for job interviews are grouped according to a number of headings – name, appearance, acceptability, confidence, effort, organization, motivation – omitting ability."

"Groupings of statistics often reflect a way of organizing information that in turn reflects cultural perceptions – for example, at some times, men are more likely to have their deaths regarded as unnatural than are women."

References

Silverman, D. (2001), Interpreting Qualitative Data: Methods for Analysing Talk, Text and Interaction, Sage Publications, Thousand Oaks, CA.

Silverman, D. (2004), Doing Qualitative Research, 2nd Edition, Sage Publications, Thousand Oaks, CA.