When we do IA, we inadvertently reveal patterns within psychology and behavior. When we introspect, we hope to reveal patterns within our psychology and behavior. When we methodologically combine the two, something beautiful happens.
The Quantified Self movement is all about the process of measuring behavior as a means for self-knowledge. While many self-documenters rely on automation to both gather and analyze their data, there are massive amounts of unstructured information that remain unexplored, despite their capacity for self-awareness. This is where IA comes in. Applying thoughtful categorization to virtually any source of personal data (emails, phone logs, journals, spreadsheets, more) provides an opportunity for people to understand their behavior in a more complete manner.
To demonstrate the power of analyzing personal data, Robin will share a taxonomy she created for text-based apologies exchanged between her and a former romantic partner over a ten month period. What kinds of things did they apologize for? Who was more or less likely to apologize for which reasons? Whose apologies were more sincere? A few statistics based on the IA reveal complicated facets of their personalities and their relationship.
Robin Weis has been recording, analyzing, and visualizing the details of her personal life for more than ten years. After earning a degree in psychology and economics from Northwestern University, she began working in technology, where she picked up programming skills to apply to behavioral analysis and visual design. Her work has been nominated for the Information is Beautiful Awards and can be found onwww.robinwe.is. Robin now works in UX and organizes the Quantified Self Meetup in Chicago, providing an outlet for others to share their measured, introspective endeavors.
