8 DATA ANALYSIS, INTERPRETATION, AND PRESENTATION

8 DATA ANALYSIS, INTERPRETATION, AND PRESENTATION

1. 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

2. The First Steps in Analyzing Data

   • Interviews
     • The notes need to be written up and expanded as soon as possible after the interview has taken place so that the interviewer's memory is clear and fresh. The audio recording may be used to help in this process, or it may be transcribed for more detailed analysis. Transcription takes significant effort, as people talk more quickly than most people can type (or write), and the recording is not always clear. It is therefore worth considering whether or not to transcribe the whole interview, or just sections of it that are relevant to your investigation
       • Transcription of recordings. Expansion of notes
   • Questionnaires
     • Clean up data. Filter into different data sets
   • Observation
     • Expansion of notes. Transcription of recordings. Synchronization between data recordings

3. Simple Quantitative Analysis

   • For simple collation and analysis, spreadsheet software such as Excel is often used as it is commonly available, is well understood, and offers a variety of numerical manipulations and graphical representations. Initial analysis might involve finding out averages, and identifying any outliers, i.e. values that are significantly different from the others. Producing a graphical representation of the data helps to get an overall view of the data and any patterns it contains

4. Simple Qualitative Analysis

   • As with quantitative analysis, the first step in qualitative analysis is to gain an overall impression of the data and to start looking for patterns. Some patterns will have emerged during the data gathering itself, and so you may already have some idea of the kinds of pattern to look for, but it is important to confirm and re-confirm findings to make sure that initial impressions are not biasing analysis. For observation data, the guiding framework will have given some structure to the data
   • Identifying Recurring Patterns or Themes
     • Note that patterns and themes in your data may relate to a variety of aspects: to behavior, to your user group, to places or situations where certain events happen, and so on. Each of these kinds of theme may be relevant to your goals. For example, descriptions of typical users (personas) may be an outcome of data analysis that focuses on patterns of participant characteristics. Although we include thematic analysis under qualitative analysis, patterns and themes may also emerge from quantitative data.
   • Categorizing Data
     • Transcripts of meetings, interviews, or think-aloud protocols can be analyzed at a high level of detail, such as identifying stories or themes, or at a fine level of detail in which each word, phrase, utterance, or gesture is analyzed. Either way , elements identified in the data are usually categorized first using a categorization scheme. The categorization scheme may arise from the data itself, if the investigation is exploratory , as it might be in the requirements activity , or it might originate elsewhere in a well-recognized categorization scheme, or a combination of these two approaches may be used. The principle here is that the data is divided up into elements and each element is then categorized