Data analysis and synthesis
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This document provides an overview of methods for analyzing qualitative user research data, including sorting raw data into themes using affinity diagrams, modeling the data to create shared understanding using models of flow, culture, sequence, artifacts and the physical environment, and developing personas to represent archetypes of users. It describes the goals of synthesis as making sense of the data and moving towards insights, and discusses techniques like using a synthesis wall to explore relationships and patterns in the data.
Data analysis and synthesis
- 1. Data analysis & Synthesis Media Design course Autumn 2016
- 2. INDEX 1. Introduction 3. Methods for analyzing the data 3.1. Sorting and clustering 3.2. Data modeling 2.Analysis and synthesis
- 3. Results of user research 1. INTRODUCTION Recordings of the sessions (audio / visual). Need to transcribe the data. Observation notes Artifacts produced during the session
- 4. Data analysis - Art of finding patterns - Begins with open questions that progressively narrow - Checking reliability of assumptions - Integration of different types of data 2.ANALYSIS AND SYNTHESIS
- 5. Synthesis Synthesis goals: - Make sense of the data - Understand intent - Move towards insights 2.ANALYSIS AND SYNTHESIS “Synthesis is the process of making meaning through inference-based sensemaking” J. Kolko
- 6. The use of space helps to develop a strong mental model of the design space and escape from the mess of content that has been gathered. The synthesis wall At this stage the focus is to finding “good” relationships and patterns. Synthesis wall for iTEC project (Legroup). 2.ANALYSIS AND SYNTHESIS
- 7. Main methods for analysing data gathered during the contextual inquiry: • Sorting and clustering - Affinity diagrams • Modeling the data - Contextual design models - Personas 3. METHODS FOR ANALYSINGTHE DATA The analysis of each contextual inquiry results in a set of models. These needs to be consolidated into one view of “the work”. Data consolidation!
- 8. Process RAW DATA PATTERN RECOGNITION DESIGN CRITERIA Participants Opportunities … Attention areas Design ideas Design principles Archetypes 3.1 SORTING AND CLUSTERING TRANSCRIPTION CLUSTERING
- 9. Affinity diagram An affinity diagram is a collaborative analysis tool. It’s a hierarchical representation of the issues and insights that arise from the data collected. Key elements of affinity diagrams: • Organizes interpretation session notes into common structures and themes • Categories arise from the data • Diagram is built through induction 3.1 SORTING AND CLUSTERING
- 10. Affinity diagram Images published by Flickr user “.Sean munson”. 3.1 SORTING AND CLUSTERING
- 11. Video Card Game 3.1 SORTING AND CLUSTERING Images of the video card game created by Buur and Soendergaard (2000). Participatory design technique developed by Buur and Soendergaard (2000) in order to analyse video material part of the field studies with the design team.
- 12. A model provides: • a shared understanding of the user-data, • a shared language for the design team, • and an easily understandable deliverable for communication outside the design team. 3.2. MODELINGTHE DATA
- 13. Contextual design models FLOW: How work is divided among roles and coordinated, without regard for time CULTURAL: The influencers which define expectations, desires, values and the overall approach people take to their work SEQUENCE: The order of work tasks over time PHYSICAL: The physical environment in which work is accomplished ARTIFACT: The tangible items people create and use to help them get their work accomplished 3.2. MODELINGTHE DATA
- 14. Flow Model Flow model created by J. Kolko. 3.2. MODELINGTHE DATA Individuals Groups Responsibilities Communication Flow Artifacts Places Breakdowns in commu- nication
- 15. Cultural Model Cultural model created by J. Kolko. 3.2. MODELINGTHE DATA Influencers Extent of the influence Influence Breakdowns in cultural influence
- 16. Sequence Model Sequence model created by J. Kolko. 3.2. MODELINGTHE DATA
- 17. Physical Model Physical model created by J. Kolko. 3.2. MODELINGTHE DATA
- 18. Artifact Model Artifact model created by J. Kolko. 3.2. MODELINGTHE DATA
- 19. Steps for modeling the data 1. Ensuring everyone has transcript copy 2. General discussion about the interview 3. Assign roles (interviewer, modelers, recorder, participants, moderator) 4. Model creation 5. Record observations, insights, questions, design ideas and breakdowns 6. Summarize important insights (separate piece of paper) 3.2. MODELINGTHE DATA
- 20. Outputs of the data modeling session Models help to make-sense of the collected data. • The models created during the interpretation session show the main areas to focus on for redesign efforts: • Breakdowns • Tedious task flows, extraneous steps and inefficiencies 3.2. MODELINGTHE DATA
- 21. Personas 3.2. MODELINGTHE DATA • User models that are presented as specific, individual humans being. • Based on research • They present archetypes Personas help to: • Create empathic understanding of end-users • Determine what a product should do and how it should behave. • Communicate with stakeholders, developers and designers. • Measure design effectiveness
- 22. Personas 3.2. MODELINGTHE DATA Common information included in a persona: • Name, age, gender and a photo • Tag line describing what they do in “real life” • Experience level in the area of your product or service • Context for how they would interact with your product • Goals and concerns when they perform relevant tasks • Quotes to sum up the persona’s attitude
- 23. Development process 1. Identify behavioral variables (activities, attitudes, aptitudes, motivations, skills) 2. Map interview subjects to behavioral variables 3. Identify significant behavior patterns 4. Synthesize characteristics and relevant goals 5. Check for redundancy and completeness 6. Expand description of attributes and behaviors 7. Designate persona types PERSONAS
- 25. FURTHER READINGS This material uses Creative Commons License Recognition – Share alike. Beyer, H. Holtzblatt, K. (1998). Contextual Design: Defining Customer-Centered Systems. San Francisco: Morgan Kaufmann Buur, J., Soendergaard,A. (2000,April).Video card game: an augmented environment for user centred design discussions. In Proceedings of DARE 2000 on Designing augmented reality environments (pp. 63-69).ACM. Cooper,A., Reimann, R., Cronin, D. (2007). About Face 3:The Essentials of Interaction Design. Indianapolis, IN: Wiley Publishing, Inc. Cooper,A. (1999).The inmates are running the asylum:Why high-tech products drive us crazy and how to restore the sanity (Indianapolis, Indiana: SAMS). Holtzblatt, K.,Wendell, J.B., Wood, S. 2005. Rapid Contextual Design:A How-to guide to key techniques for user- centered design. San Francisco: Morgan-Kaufmann. Kolko, J. (2010).Abductive thinking and sensemaking:The drivers of design synthesis. Design Issues, 26(1), 15-28. Poldoja, H. (2010). Personas in interaction design. http://www.slideshare.net/hanspoldoja/personas-in- interaction-design Pruitt, J., Adlin,T. (2010). The persona lifecycle: keeping people in mind throughout product design. Morgan Kaufmann.