‘What is data analysis? In fact, what is data?’
‘Statistics are just plain scary!’
‘Can't I get someone else to do this bit?’
What is data analysis?
Data analysis is a vital step in the research process. Whatever method has been applied to data collection, at some point it will be necessary to translate raw data into usable information. Analysis is usually undertaken after all the data have been gathered, but it may also be undertaken during the collection period or as part of a pilot study to ensure the most appropriate method of collection is selected. Whichever approach is undertaken, it is important to remember that without data, no analysis can take place, and that the quality of your analysis will depend, to a great extent, on the quality of your data. As Robson puts it: ‘No data – no project’ (Robson, 2002, 385).
What comes before?
Before data analysis can commence it will be necessary to go through a number of stages. These have been discussed in earlier chapters, but in summary, before the analysis stage, you should have undertaken the following activities:
You should now be ready to start analysing your data.
Types of analysis: quantitative and qualitative
There are a variety of approaches to conducting research (see Chapter 1) but in general research tends to use an inductive or deductive approach.
An inductive approach seeks to build up a theory that is adequately grounded in a number of relevant cases and involves comparing concepts or categories emerging from one stage of the data analysis with concepts emerging from the next stage. These comparisons form the basis of the emerging theory. The researcher continues with this process of comparison until no new significant categories or concepts can be identified (see Lacey and Duff, 2001, 7; Robson, 2002; Strauss and Corbin, 1998).