Ross Jacobucci
For Ross Jacobucci, quantitative psychology is a chance to invent, to improvise — and to create new tools to answer complex questions.
As a new assistant professor in the Department of Psychology, he wants his students to see that side of it, too.
“When I was able to start developing my own methods and inventing new ways to do things in graduate school, that was when quantitative psychology became really fascinating and fun,” he said. “I think students are much more interested in the quantitative side once they realize everything they can do with it.”
Jacobucci, who joined Arts and Letters in the fall after receiving his Ph.D. from the University of Southern California, specializes in structural equation modeling and data mining.
It is an exciting time to be in the field, he said, when advances in computing and technology are revolutionizing data analysis.
“For a long time, researchers were very limited in the types of questions they could ask,” Jacobucci said. “Now, we are exploring how to leverage the increasing data capacity we have. My research focuses on providing a mathematical framework to help people examine really intricate relationships involving a lot of variables.”
“I really push for students to find datasets out in the real world and not just use something I hand them. I want them to have that ownership so that they can start to see how these tools will allow them to be more productive later in their research.”
Peculiar data
While data mining has been used in a wide variety of disciplines in science, business, and engineering, psychological data has its own challenges, Jacobucci said.
“Humans are just messy, and it’s hard to measure things directly,” he said. “So, I am working to figure out how we incorporate methods from other fields in a way that takes into account — and takes advantage of — the peculiarities of psychological data.”
One of Jacobucci’s current projects is a data-centric approach to creating thresholds that help determine whether someone is diagnosed with a psychological disorder — and, therefore, whether the person is eligible to receive services or treatment.
“For something like depression, there’s not always a clear boundary where someone either has depression or they don’t. It’s continuous,” he said. “But when it comes down to it, someone has to make a decision about whether a patient meets the criteria or not. So, we’ve been trying to use data mining and some new types of algorithms to create optimal cutoffs for disorders.”
Practical data
Jacobucci was drawn to Notre Dame because of the strength of its quantitative psychology program — and how much it is valued by the department and the University.
“I am so lucky to be at Notre Dame, which has the largest quantitative program in the U.S.,” he said, “and to be in a supportive environment where quantitative psychology is viewed as an important area of research and where we know our program will continue to be valued.”
Last fall, Jacobucci taught an undergraduate introductory statistics course, and this semester he is teaching graduate-level statistics.
In both classes, he works to show students the practical applications of what they’re learning and encourages them to seek out their own datasets and to ask their own research questions.
“I really push for students to find datasets out in the real world and not just use something I hand them,” he said. “I want them to have that ownership so that they can start to see how these tools will allow them to be more productive later in their research.”
