3 min read
Lynne was good at math from an early age. Although she decided to major in biology in college and take the pre-med path, she continued taking math courses out of curiosity and enjoyment. Following graduation, Lynne taught middle school math and discovered that although she loved teaching, she missed the higher level math she had studied in college. She bounced around a little, also working for an actuarial firm, where she discovered her fascination for statistics and continued to be interested in math. When Lynne started graduate school she decided on a joint path studing statistics and public policy, focusing on issues of modeling interventions and evaluations of educational policy (her interest in application of statistics is ongoing). Ultimately, Lynne became convinced that we don't have good measures of things that we're trying to improve, and was motivated to study measurement in the hopes of rectifying this problem.
Lynne's current research arises from the fact that inequities (including racial issues and the gender gap) exist in many contexts and is driven by her desire to undertand these inequities. Why do they exist? What kind of interventions might be effective? Are pre-existing factors a stronger determinant of success in the labor force than intervention programs? Lynne has studied individuals that come to a job with the same set of skills to see whether inequities still exist. Throughout, her interest in how we measure skills and other factors like personality has remained at the forefront.
At Swarthmore, Lynne involves students in her research in multiple ways. Primarily, they work more on application than on theoretical issues. Two specific directions have involved her students evaluating the success of a program as part of a class project, and assessing the impact of the inside/out course on the students that participate. Her ultimate goal is for students to walk away valuing their experience with research, appreciating good data and how difficult it is to find (as well as the fact that research is not done in an ideal world), learning alternative methods and thinking outside the box, all in addition to concrete skills with coding, statistics, and writing.
Collaboration plays a strong role in Lynne's work, as statistics is arguably the most interdisciplinary discipline and that statisticians are often not collecting their own data but relying on others to collect good data that they then analyze. In her experience a good collaboration involves participants being clear on the task at hand, on assigned and expected roles, and on delineating tasks that all are accomplishing. Regular meetings via phone, Skype, and in person are critical for this. Bad collaborations (as we've all had them) tend to be characterized by no clear agenda, unclear roles and expectations, and sometimes unexpected events (e.g., collaborators not using their time well, not getting tenure). Ultimately, setting a clear set of tasks is important.
Lynne loves teaching at Swarthmore, at all levels - from service courses (Stat 011) to higher level classes. She believes that the teaching allows for more actual data analysis, and tries to provide her students with more reality through use and access to real, consequential data rather than canned datasets.