Resources

Measuring Progress to Move Forward toward Equity

This workshop for the 2018 UW Women in Science and Engineering (WiSE) conference was designed to engage people in high school, college and early STEM careers in a conversation that results in greater understanding of best mentoring, internship, and sponsor practices for achieving STEM career goals faster and easier by strategically connecting and building mutually beneficial relationships with excellent professionals along the way. Click here for slides, a list of additional resources, and a table comparing the three professional experiences from that workshop.

Measuring Progress to Move Forward toward Equity

There are four elements necessary to harness the power of data to solve complex problems like making our courts more fair: 1) program and systems change mapping, 2) people and organizational learning, 3) the collective impact approach to solving complex problems, and 4)  evidence based leadership. Click here to explore resources that will help you solve complex problems.

 

Do Better with Data

Data science has the potential to help us solve the most complex problems of our age, but only if it supports meaningful measurement, research and evaluation that people use to learn and continuously improve. Click here to explore resources that will help you solve complex problems.

AEA STEM evaluation repository

The American Evaluation Association’s STEM Topical Interest Group is building a repository of STEM evaluation resources. The repository aims to consolidate new knowledge from STEM evaluation experts in AEA, while also identifying and linking to existing repositories to avoid duplication of effort. To contribute, click here.

Data system readiness tool

data2insight has developed a tool to help organizations determine whether they are ready to implement a new data system. To view the tool, click here.

Display Platforms for Quantitative Information by Stephen Few

Many people come to data2insight because they are interested in effectively communicating data. This article by Stephen Few is a great place to start in thinking through the complexities involved in doing so. Read it now.