Chapter 8: Data Analysis, Interpretation and Presentation
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The main aims of this chapter are to:
- Discuss the difference between qualitative and quantitative data and analysis.
- Enable you to analyze data gathered from questionnaires.
- Enable you to analyze data gathered from interviews.
- Enable you to analyze data gathered from observation studies.
- Make you aware of software packages that are available to help your analysis.
- Identify some of the common pitfalls in data analysis, interpretation, and presentation.
- Enable you to be able to interpret and present your findings in a meaningful and appropriate
The kind of analysis that can be performed on a set of data will be influenced by the goals
identified at the outset, and the data actually gathered. Broadly speaking, you may take a
qualitative analysis approach or a quantitative analysis approach, or a combination of qualitative
and quantitative. The last of these is very common as it provides a more comprehensive
account of the behavior being observed or performance being measured.
Most analysis, whether it is quantitative or qualitative, begins with initial reactions or
observations from the data. This might involve identifying patterns or calculating simple
numerical values such as ratios, averages, or percentages. This initial analysis is followed by
more detailed work using structured frameworks or theories to support the investigation.
Interpretation of the findings often proceeds in parallel with analysis, but there are
different ways to interpret results and it is important to make sure that the data supports
your conclusions. A common mistake is for the investigator's existing beliefs or biases to
influence the interpretation of results. Imagine that through initial analysis of your data
you have discovered a pattern of responses to customer care questionnaires which indicates
that inquiries from customers that are routed through the Sydney office of an organization
take longer to process than those routed through the Moscow office. This result can
be interpreted in many different ways. Which do you choose? You may conclude that the
customer care operatives in Sydney are less efficient, or you may conclude that the customer
care operatives in Sydney provide more detailed responses, or you may conclude that the
technology supporting the processing of inquiries needs to be updated in Sydney, or you
may conclude that customers reaching the Sydney office demand a higher level of service,
and so on. In order to determine which of these potential interpretations is more accurate,
it would be appropriate to look at other data such as customer inquiry details, and maybe
interviews with staff.
Another common mistake is to make claims that go beyond what the data can support.
This is a matter of interpretation and of presentation. The words 'many' or 'often'
or indeed 'all' need to be used very carefully when reporting conclusions. An investigator
should remain as impartial and objective as possible if the conclusions are to be believed,
and showing that your conclusions are supported by your results is an important skill to
Finally, finding the best way to present your findings is equally skilled, and depends on
your goals but also on the audience for whom the results were produced. For example, in
the requirements activity you might choose to present your findings using a formal notation,
while reporting the results of an evaluation to the team of developers might involve
a summary of problems found, supported by video clips of users experiencing those
In this chapter we will introduce a variety of methods and describe in more detail
how to approach data analysis using some of the common approaches taken in interaction