Chapter 9: Data Analysis, Interpretation and Presentation

Chapter Introduction | Web Resources | In-Depth Activity Comments | Teaching Materials | Quickvote


Objectives

The main goals of this chapter are to accomplish the following:

  • 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 interpret and present your findings in a meaningful and appropriate manner.


Introduction

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 gathered. Broadly speaking, a qualitative analysis approach, a quantitative analysis approach, or a combination of qualitative and quantitative approaches may be taken. The last of these is very common, as it provides a more comprehensive account of the behavior being observed or the performance being measured.


Most analysis, whether it is quantitative or qualitative, begins with the initial reactions or observations from the data. This may involve identifying patterns or calculating simple numerical values such as ratios, averages, or percentages. For all data, but especially when dealing with large volumes of data (that is, Big Data), it is useful to look over the data to check for any anomalies that might be erroneous. For example, people who are 999 years old. This process is known as data cleansing, and there are often digital tools to help with the process. 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 any conclusions. A common mistake is for the investigator’s existing beliefs or biases to influence the interpretation of results. Imagine that an initial analysis of the data has revealed a pattern of responses to customer care questionnaires that indicates that inquiries from customers 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. For example, the customer care operatives in Sydney are less efficient, they provide more detailed responses, the technology supporting the inquiry process in Sydney needs to be updated, customers reaching the Sydney office demand a higher level of service, and so on. Which one is correct? To determine whether any of these potential interpretations is accurate, it would be appropriate to look at other data such as customer inquiry details and maybe to interview 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. Using words such as many or often or all when reporting conclusions needs to be carefully considered. An investigator needs to remain as impartial and objective as possible if the conclusions are to be trusted. Showing that the conclusions are supported by the results is an important skill to develop.


Finally, finding the best way to present findings is equally skilled, and it depends on the goals but also on the audience for whom the study was performed. For example, a formal notation may be used to report the results for the requirements activity, while a summary of problems found, supported by video clips of users experiencing those problems, may be better for presentation to the team of developers.


This chapter introduces a variety of methods, and it describes in more detail how to approach data analysis and presentation using some of the common approaches taken in interaction design.