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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 supports triangulation and provides flexibility.

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 develop.
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 problems.

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 design.

The main aims of this chapter are to:

Discuss the difference between qualitative and quantitative data and analysis.

Enable you to be able to analyze data gathered from questionnaires.

Enable you to be able to analyze data gathered
from interviews.

Enable you to be able to analyze data gathered from observation studies.

Make you aware of the kind 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 manner.