Matthew Guzman

Matthew Guzman is PhD student in Education Policy whose primary research interests are the economics of education, drivers of student achievement in secondary education, and competition as a driver of education outcomes.

This is my second year as a TA at ICPSR. First and foremost, I love teaching statistics. I was really excited to be part of ICPSR to work with students at the ground level, working on the nuts and bolts of statistics, regression, and experimental design.

ICPSR is my favorite community to come to every summer. It's a lot of like-minded people who are working on really interesting things, and a lot of their research is really inspiring and gets me curious about new things to learn. It's a wonderful experience and a great group of people to be around.

Pushing research methods forward

I would say the Summer Program is a meeting of the minds. It's a lot of people from different disciplines, but all are tangentially interested in similar problems and trying to push methods and research forward. This is a very supportive community. Whatever problem you have, whatever you're working on, somebody here wants to help you look at it a different way, and to try to look at this angle or learn this new method to address it in some way you haven't tried before.

This year all the TAs are in our own TA office, which is really fun because I overhear people from different courses helping people with different types of problems and solving problems in different ways. I'm TA-ing for Regression II, but there's also the Machine Learning guys, the Data Visualization guys, and the Measurement and Scaling TAs too. It's really interesting because the Machine Learning guys are thinking about big data sets and algorithms, trying to get inside the black box of how a machine learning model would produce a result. I'm looking at the nuts and bolts: the differences in means, trying to answer this question using traditional methods. The Scaling and Measurement people, they're thinking, let's take your problem and reframe it in a way that we might be able to use this other data set. It's great that we're all there and able to share ideas.

Applying what we’ve learned

In the beginning a lot of students feel like they're drinking out of a fire hose. A lot of it is like, "How do I do this problem, how do I finish this homework?" But towards the end of the program, I feel like students really start to focus on their own research problems and questions. We're nearing the end and students are coming to office hours not to ask, "How do I run this regression?" They say, "This is my research, this is what my dissertation is on. How do you think I can use these methods to solve my problems?" That's the most fun. It's great to talk to them and be able to suggest some other things they should think about, as well as some other courses they might think of taking next year.

Who should attend?

I can't think of a single graduate student I've met who wouldn’t benefit from being here. If you're starting from ground zero, or even if you have a PhD in statistics, there's something new every year. This is really where people are talking about the newest, cutting-edge ideas. So I would recommend it to anyone interested in quantitative methods. I think there's something for you here.

"The Summer Program is a meeting of the minds."