{"id":569,"date":"2020-08-10T10:22:19","date_gmt":"2020-08-10T15:22:19","guid":{"rendered":"https:\/\/wp.stolaf.edu\/cir\/?page_id=569"},"modified":"2023-06-07T08:13:09","modified_gmt":"2023-06-07T13:13:09","slug":"projects-for-2020-21","status":"publish","type":"page","link":"https:\/\/wp.stolaf.edu\/cir\/projects-for-2020-21\/","title":{"rendered":"Projects for 2020-21"},"content":{"rendered":"

Title: Discerning structure in random geometric point processes<\/em><\/strong>
\nDomain Expert: Matthew Wright (MSCS)
\nStats\/DS Mentor: Jaime Davila
\nCIR Fellows: Prabesh Paudel, Ken Wang, Julie Yuldasheva<\/p>\n

Description: Random processes often produce small-scale variability amid large-scale structure. For random geometric processes, this large-scale structure can be studied with a combination of geometry, topology, and statistics. For example, the study of mathematical graphs\u00a0generated by random processes provides insight into how graph-theoretic properties depend on the underlying randomness, with applications to real-world networks. As another example, the study of configurations of random geometric shapes provides limit theorems for domain coverage problems, with applications to materials science.<\/p>\n

This project will study random geometric point processes from the perspective of filtrations: parametrized families of geometric objects constructed from a set of random points. Filtrations are essential constructions in the field of topological data analysis, in which data is indexed by one or more parameters. Students will write code to generate random geometric data according to one or more probability models. Students will construct filtrations from the data and investigate geometric and topological features. Students will then compute statistical properties of these features, with reference to the underlying probability model. This work will provide new insight into unexplored questions at the intersection of geometry, topology, and statistics.<\/p>\n

Title: Staying on the Hill: modeling student retention<\/strong> <\/em>
\nDomain Experts: Chris Chapp (Political Science) and Lauren Feiler (IE&A)
\nStats\/DS Mentor: Paul Roback
\nCIR Fellows: Martha Barnard, Lindsey Jansen, Kate Rudberg<\/p>\n

Description: Colleges and universities have a vested interest in understanding why some students leave before graduation or transfer to other institutions. An ability to predict retention patterns gives the college information about how to best serve its students and fulfill its mission. To this end, St. Olaf\u2019s Retention and Student Success Committee has spent several years piloting new programs to reduce transfer rates. While we have learned a great deal about why some students leave (and while St. Olaf\u2019s overall retention rate is good compared to many of our peer institutions), an ability to predict retention remains a vexing problem.<\/p>\n

This project will draw on several data sources to predict student retention. First, we have access to student demographic data and data related to academic preparedness and academic success at St. Olaf. Second, we plan to use a \u201ctext as data\u201d approach to examine student writing and see if patterns in open-ended writing about St. Olaf is predictive of retention. We will utilize several statistical tools, potentially including cluster analysis, propensity score matching, machine learning, and rare event modelling. Students working on this project should have a willingness to explore new tools as well as an ability to explain potentially complex findings to a broad audience. This project will not only help the college better serve future Oles, it will contribute to research in higher education aimed at improving student success.<\/p>\n

Title: Leaks in the classical music pipeline: where are the women soloists?<\/strong><\/em>
\nDomain Expert: Sara Clifton (MSCS)
\nStats\/DS Mentor: Sharon Lane-Getaz
\nCIR Fellows: Carly Dammann, Ella Koenig, David McGowan<\/p>\n

Description: Women are poorly represented at the highest levels of most professions; the debate over the major causes of this phenomenon remains contentious and unresolved. In an attempt to understand the underlying causes of women\u2019s underrepresentation, we will explore classical music performance as a case study.
\nClassical soloists are an elite group of professional musicians that tour the world, performing with the best orchestras in existence. These rock stars of the classical world \u2013 think Yo-Yo Ma, Joshua Bell, Lang Lang \u2013 often have one thing in common. They are mostly men. Using a large dataset of performances from the world\u2019s major orchestras from their inceptions until now, we will explore how and why the representation of women soloists has changed over time. Some open questions include (1) why the pipeline for solo performance is so leaky for women, (2) why women are better represented in solo violin than solo piano, (3) how culture and society affect women musicians of different nationalities, and (4) how the soloist lifestyle influences men and women differently.<\/p>\n

This project would be ideal for students with an interest in statistics\/data science and one or more of the following fields: classical music, psychology, sociology, gender studies, and\/or economics.\u00a0 Experience with R is required. It would be beneficial, but not necessary, to have experience with time series analysis, data scraping, data cleaning, machine learning (particularly gender inference from name), and\/or data visualization.\u00a0 The raw data is one or more Excel spreadsheet(s) for each of the major world orchestras (around two dozen) back to the founding of each orchestra (about 100-150 years). For each orchestra, we have information on each performance (e.g., date, venue, type of event, conductor, pieces played). For each piece featuring a soloist, we have the composer, type of piece (usually a concerto), period of the piece, duration, and soloist name. For each soloist, conductor and composer, we have the instrument (for soloist), inferred gender, date of birth, and nationality. The data will be cleaned and processed using R.<\/p>\n

Title: The effect of doctors\u2019 care coordination style on multiple chronic condition patients: evidence from primary care physician retirements and relocations<\/strong> <\/em>
\nDomain Expert: Ashley Hodgson (Economics)
\nStats\/DS Mentor: Laura Boehm Vock
\nCIR Fellows: Noah Hillman, Sarah Rodman, William Wei<\/p>\n

Description: This project will look at the way that doctor \u201cexits\u201d (retirements or relocations) impact multiple chronic condition patients who are forced to switch to a new doctor.\u00a0 When a patient\u2019s primary care doctor exits, that patient is likely to experience a sudden and sustained change in the way they receive health care, according to differences in the way the old and new doctors practice medicine (Fadlon and Van Parys, 2020).\u00a0 One group of patients who is understudied relative to their need for and use of the health care system is multiple chronic condition patients, patients with numerous ongoing medical concerns such as diabetes, asthma, chronic depression, heart disease, etc.\u00a0 Forty-two percent of Americans have multiple chronic conditions, and this group makes up a higher share of those interacting with the health care system, spending about 65% of health care dollars.\u00a0 Yet controlled medical studies generally exclude this population from trials of medical treatments due to the complexity they would add to the analysis.\u00a0 One of the most important parts of health care for multiple chronic condition patients is the coordination of their care across doctors with different specialties.\u00a0 Because primary care doctors play an important role in coordinating care, doctor exits are a good opportunity to observe how patients fare when they have a sudden change in the style of coordinating care.<\/p>\n

We will use a large data set of Medicare patients from 2016 and 2017.\u00a0 We will focus our analysis on the questions relating to care coordination for multiple chronic condition patients, particularly before and after primary care doctor exits:<\/p>\n