Research Analysis – Making sense of data

Data modelling is used to help make sense of all the data gathered throughout the research phrases. Goodwin, K. (2009) suggests that models are excellent tools for doing this as all that information can be overwhelming and hard to use. We began this modelling of data by synthesising our initial findings as a team over Google chat. We briefly shared about our main finding from each research subject and to see if we could pick out some emerging themes at this early stage.

Emerging Themes

From our short 10 minute discussion, a common theme was that every user studied used the main search form, to carry out multiple tasks. A key finding from our initial briefing was that every user carried out their tasks ( booking trains, checking live times, checking prices & timetables).

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The process we followed for analysing the research data. Taken from Designing for a Digital Age, Kim Goodman 2014.

Qualitative analysis

Qualitative analysis is critical for design because it excels at explaining why and how, as well as what (Goodwin, K. 2009). We applied cross-case analysis, to analysis the data which involve looking at all the interview data at once while grouping and comparing the individual case to pull out trends and pattern behaviours.

Organising data through coding

We used a social science method of ‘coding’ for insights through our interview transcripts. We did this by first, transcribing all 6 of our interview recordings and then I went through each script and highlighted the various themes or ‘codes’ that began to emerge.

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An example of the coding through our interview scripts.

You can view the full scripts for all interviews here

The themes or ‘codes’ used to identify patterns were:

  • Frustrations
  • Behaviour
  • Demographics
  • Frequency
  • Goals

Affinity Diagramming

We used affinity diagramming to help synthesise both our open-questions from the survey & interview data into insights would then define the characteristics of our persona. Ideally, affinity diagramming is done with an entire design team, in a room and in silence using many colour sticky notes for each interview subject. Because we were unable to meet up to carry out the activity in person, this was done as an individual task.

Alan carried out the affinity diagramming of the open questions from our survey and I carried out an affinity diagram on interview transcripts.

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A digital affinity diagram of the open-questions of the survey results. View document here

 

Empathy Mapping

“Good design is grounded in a deep understanding of the person for whom you are designing”

taken from d.School Method Cards, Institute for Design at Stanford

Empathy mapping is a tool we utilised to help gain insights but synthesising our findings from observing and talking to our users.We carried out empathy mapping by creating a canvas that would be split into four quadrants.

  • Sees
  • Thinks + Feels
  • Says
  • Does

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An example of an empathy map that I carried out on based on a selection of our participants

Purpose of empathy mapping

There are two main purposes of empathy mapping: 1. Understand the needs of our user 2. Gain insights that can leverage better design solutions.  By carrying out this exercise we could get a better understanding of the main pain points that our typical user experiences along with opportunities to come up with solutions to get make the user’s life easier.

Emotion journey mapping

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Emotion journey mapping is a method we used to document the experience the user is having while observing that user in carry out a task in their natural context. We found emotion journey mapping usual as it acting as an immediate way of comparing research subjects and quickly identify key problem areas at a glance.

References

  1. d.School Method Cards, Institute for Design at Stanford https://dschool.stanford.edu/wp-content/themes/dschool/method-cards/empathy-map.pdf
  2. Goodwin, K. (2009). Designing for the digital age: how to create human-centered products and services. Wiley.

 

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