Working Smarter, Not Harder: Inter-Disciplinary Methods for the Age of Analytics
Summary:
Increasingly large and complex data sets, fewer resources, and short timelines pose unique challenges to researchers. To over come obstacles of scale, complexity, and velocity, we propose two techniques — data triangulation and process scaling. We will explain how we’ve used these techniques, discuss lessons learned, & through interactive exercises, participants will learn how to apply these techniques to their own work.
Detailed description:
Class attendees will learn how to use these techniques by completing an observational pre-exercise as well as working with quantitative data, designing a study applying data triangulation, and experimenting with process scaling by conducting an in-workshop mini-analysis.
In the first part of the workshop, we will show participants how all data is, in a way, wrong. Specifically, no single method provides all necessary information, and even if perfect information existed, it would be impossible to process. Oftentimes, 6 weeks is all you have: shrinking timelines and budgets mean researchers no longer have the luxury of spending months and years collecting and analyzing data.
In the second part of the workshop, we will discuss how we address these challenges at Google by triangulating quantitative and qualitative data at scale. Participants review their pre-workshop observational study field notes, as well as previously provided quantitative data to create, and execute a data triangulation research design.