quantitative information architecture
DESCRIPTION
Quantitative methods are applicable for IA thinking be it for hypothesis generation, instrumentation, data collection and analysis of information at scales never before possible with insights that are comparable over time, generalizable and extensible. Quantitative skills can allow IAs to interpret and analyze others’ designs and research more readily, as well as combine methods and models for meta-analysis to help IAs move from description to prediction in designing and developing future interfaces and architectures. This presentation will review why you should use quantitative methods and discuss both foundational and emerging ideas that are applicable for content analysis, behavioral modeling, social media usage, informetrics and other IA-related issues. http://donturn.com or http://twitter.com/donturnTRANSCRIPT
Quantitative Information Architecture Don Turnbull, Ph.D.
twitter.com/donturn
#quantia
http://donturn.com
whois?
• Software Developer
• M.S. @ Georgia Tech
• Software Design & Management
• Ph.D. @ U Toronto
• Principal @ Startup (Google)
• Faculty @ U Texas (Austin)
• Consultant & Entrepreneur
The fox knows many things, but the hedgehog knows one big thing. Archilochus
• Quantitative IA is one big thing: a specialty, a mindset • Designing appropriate experiments • Leveraging existing quantitative data • Conducting rigorous analysis
Ways of Knowing & Doing
What does the world look like to a Quantitative
Researcher?
• What’s Quantitative good for? • Understanding what users actually do
instead of what they said they do. • Making comparisons over time • Generalizable and extensible • Useful for interpreting and analyzing
others results
Quantitative Methods
• It is a discipline • Hypothesis-based • More applicable to (peer) review
• It requires a set of skills that have a (much) higher market value
• It examines characteristics that are constants • Behavior • Physical traits & abilities
Why you should go Quant
• Fight fire with Fire • Numbers speak the language of business
& technology (C-level execs)
• (Almost) infallible results • Qualitative decisions for Quantitative
measurement
The Power of Quant
Predict everything?
• Anyone can do qualitative research… • …and anyone does
• Hard to replicate, hard to validate, easier to do
• Domain of study (who) is main focus • Variability is often wide • Technique is critical
What about Qualitative?
Quantitative & Qualitative should complement each other
Because if they don’t
Things go wrong - fast
• Computational power & networked systems • We need new modeling techniques, even
new metaphors to examine the complex systems we interact with
• Finance, Psychology, Physics & Computer Science
• Verifiable or provable by means of observation or experiment: empirical laws.
Why Quant, Why Now?
The Network Effect(s)
• Understanding how people interact • Metcalfe • Milgram • Wellman, Watts & others
A (new) Era of Instrumentation
• We are undoubtedly in a new era of reasoning
• Scientific Engineering enabled the original Age of Reason
• Now to understand intent & interactions
Statistics
Statistics & every day life
• Weather • Stock Market
• Whole channels on TV devoted to both
• Sports…. All the time…. Everywhere • Your Net Worth, IQ, Postal Code, SAT
score, GPA...
Data Science
A New Kind of (Empirical) Science
What’s next ?
Analytics
HCIR (IRUX)
Atomic Information Architecture (a marketplace)
Pervasive, Emotive & Suggestive
Summary
New Age of (Empirical) Reason
Quant at scale = new Qual insights
Data Science (& its affect on IA/UX)
What’s next isn’t that far ahead…
