Last December, I publicly announced a new project with Justin Ortagus of the University of Florida and Kelly Rosinger of Pennsylvania State University that would collect data on the details of states’ performance-based funding (PBF) systems. We have spent the last nine months diving even deeper into policy documents and obscure corners of the Internet as well as talking with state higher education officials to build our dataset. Now is a good chance to come up for air for a few minutes and provide an update on our project and our status going forward.
First, I’m happy to share that data collection is moving along pretty well. We gave a presentation at the State Higher Education Executives Officers Association’s annual policy conference in Boston in early August and were also able to make some great connections with people from more states at the conference. We are getting close to having a solid first draft of a 20-plus year dataset on state-level policies, and are working hard to build institution-level datasets for each state. As we discuss in the slide deck, our painstaking data collection process is leading us to question some of the prior typologies of performance funding systems. We will have more to share on that in the coming months, but going back to get data on early PBF systems is quite illuminating.
Second, our initial announcement about the project included a one-year, $204,528 grant from the William T. Grant Foundation to fund our data collection efforts. We recently received $373,590 in funding from Arnold Ventures and the Joyce Foundation to extend the project through mid-2021. This will allow us to build a project website, analyze the data, and disseminate results to policymakers and the public.
Finally, we have learned an incredible amount about data collection over the last couple of years working together as a team. (And I couldn’t ask for better colleagues!) One thing that we learned is that there is little guidance to researchers on how to collect the types of detailed data needed to provide useful information to the field. We decided to write up a how-to guide on data collection and analyses, and I’m pleased to share our new article on the topic in AERA Open. In this article (which is fully open access), we share some tips and tricks for collecting data (the Wayback Machine might as well be a member of our research team at this point), as well as how to do difference-in-differences analyses with continuous treatment variables. Hopefully, this article will encourage other researchers to launch similar data collection efforts while helping them avoid some of the missteps that we made early in our project.
Stay tuned for future updates on our project, as we will have some exciting new research to share throughout the next few years!