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Location: Philadelphia, Pennsylvania
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Lab Instructors (Course Assistants) for MSSP 8970: Applied Linear Modeling
The MS in Social Policy Program seeks 5 lab instructors for MSSP 8970: Applied Linear Modeling. Each Lab Instructor must be available for one of the lecture times + one of the weekly lab sections, below.
Lecture Times:
Weekly Labs:
Course description: This course deals with how to critically and responsibly model real-world data to answer social science, education, and social policy-related questions, using the framework of the general linear model. Linear modeling (which, in statistics, is synonymous with regression analysis) is the workhorse of much of quantitative social science and, despite its enormous flaws and powerful limitations (which this course will also cover!), it remains an important tool to understand and be able to use.
The course builds up multiple regression from correlation and bi-variate regression, and then covers categorical independent variables, nonlinear transformations and polynomial terms, diagnostic checks, model-building and model iteration, interaction effects, mediation analysis, and logistic regression. Mathematical (e.g., Gauss-Markov) assumptions are covered but the emphasis is on deeper epistemic assumptions and more immediate practical limitations. While not covered in detail, pointers will be given to techniques for specific types of data (especially multilevel modeling for nested data) and to important modern developments (especially structural causal modeling, non-parametrics, and machine learning).
Throughout, the course will return to and emphasize critiques of linear modeling, to encourage students to be able to use (or choose not to use and oppose) regression analysis rigorously, critically, and responsibly. The course will be taught using R. This course includes an introduction to R. Background in R or in programming is not strictly necessary for this course, but it is helpful. Prerequisite: MSSP 6300 Quantitative Reasoning/Social Statistics, or another Introductory graduate statistics course.
Requirements: Mastery of multiple regression and linear modeling with R. Ideally, some research experience in a policy-related field using R as demonstrated by relevant degrees, classes taken, and/or professional experience. Lab instructors independently run a discussion section. Lab Instructor duties include the following:
Manage the Canvas site Design lab sessions (based on prior lab materials, if available) Instruct lab sessions Develop problem sets and lab assignments Grade assignments & class participation Contribute to the design of some of the assignments Answer students' questions about assignments, course logistics, course content, and grading. The questions will be answered on email and/or Ed Discussion, as determined by the instructor. Difficult questions are escalated to the instructor. Attend lectures or, by permission of the instructor, reading the relevant course materials instead of attending lectures.
Lab Instructors (Course Assistants) can expect to work approximately 7-10 hours/week (max 100hrs total) and are paid $50/hour up to $5,000 over the course of the semester.
To apply, please send the following to
msspprogram@sp2.upenn.edu:
To submit your video:
Salary offers are made based on the candidate's qualifications, experience, skills, and education as they directly relate to the requirements of the position, as well as internal and market factors and grade profile.
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