Ten Reasons Not to Measure Impact—and What to Do Instead
Ten Reasons Not to Measure Impact—and What to Do Instead, a Stanford Social Innovation Review article, simplified the task of improving data collection and analysis with a three-question test. The author emphasized that if your organization cannot answer yes to at least one of the following questions, then your organization probably should not be collecting data. 1) Can and will the (cost-effectively collected) data help manage the day-to-day operations or design decisions for your program? 2) Are the data useful for accountability, to verify that the organization is doing what it said it would do? 3) Will your organization commit to using the data and make investments in organizational structures necessary to do so?
The Business Case for Home Visiting
This PEW Research Center document on The Business Case for Home Visiting emphasizes the compelling evidence that home visitation promotes learning and success, and ultimately why it matters to business leaders.
How to Support Your Data Interpretations
We All Count develops tools, case studies, practices, and systems to improve equity in data science. This information is combined to create the Data Equity Framework, a living, feedback-responsive system for addressing data project equity which is continually updated, added to, and refined.
Visualizing Small Datasets
Stephanie Evergreen's short blog post breaks down visualizing small datasets.
Urban Institute’s Style Guide
Use this data visualization style guide to create a uniform look and feel to all of Urban’s charts and graphs. This site contains guidelines that are in line with data visualization best practices and proven design principles. It also eliminates the burden of design and color decisions when creating charts.