Authors: Preece, Rogers & Sharp
Introduction
Starters
Chapters
Case Studies
Interactivities
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2 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Chapter Index
Designing, Prototyping and Construction

Chapter Introduction | Web Resources | Assignment Comments | Teaching Materials


The assignment at the end of Chapter 8 depends on the work you completed for the assignment at the end of Chapter 7.

The chances are that you will have gathered some qualitative and some quantitative data, so look carefully to make sure that you�ve considered each aspect of the data. For example, any �comments� fields in a questionnaire will be qualitative data. Having scrutinised the data closely, you are then asked to decide whether your qualitative data can usefully be translated into quantitative data. You will find it helpful to re-read Box 8.1 in deciding whether or not to translate the qualitative data into quantitative measures.

The analysis parts of the assignment follow the analysis steps outlined in the chapter text. For example, first consider how to enter your data into a spreadsheet to make sure that you can draw out the appropriate points from your analysis. Then think about what, if any, graphical representations will help you to interpret the data. Reviewing Section 8.3 will help here.

To identify themes in the qualitative data, I suggest you start by writing down some initial suggestions, based on your reading of the data. These can then be confirmed or refuted by repeated reading of the data during which you search for confirming or disconfirming evidence. In this way the categories will be emergent.

The analysis of critical incidents requires you to focus on specific events and to unpack them and their consequences. Remember that a critical incident may be a positive or a negative event.

Finally, in preparing and presenting your findings, be careful not to overstate your conclusions. If you�ve collated data in support of your arguments as you�ve progressed through the analysis, then you�ll find this easier to do. It�s good practice to be rigorous about the data analysis so that your findings can be substantiated.