Lab Instructors (Course Assistants) for MSSP 8970: Applied Linear Modeling
University of Pennsylvania

Job Info

---

University Overview

The University of Pennsylvania, the largest private employer in Philadelphia, is a world-renowned leader in education, research, and innovation. This historic, Ivy League school consistently ranks among the top 10 universities in the annual U.S. News & World Report survey. Penn has 12 highly-regarded schools that provide opportunities for undergraduate, graduate and continuing education, all influenced by Penn's distinctive interdisciplinary approach to scholarship and learning. As an employer Penn has been ranked nationally on many occasions with the most recent award from Forbes who named Penn one of America's Best Large Employers in 2023.

Penn offers a unique working environment within the city of Philadelphia. The University is situated on a beautiful urban campus, with easy access to a range of educational, cultural, and recreational activities. With its historical significance and landmarks, lively cultural offerings, and wide variety of atmospheres, Philadelphia is the perfect place to call home for work and play.

Posted Job TitleLab Instructors (Course Assistants) for MSSP 8970: Applied Linear ModelingJob Profile TitleTemporary Employee - Non-ExemptJob Description SummaryJob Description

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:

• Section 401: Tuesdays, 10:15am-12:15pm
• Section 402: Tuesdays, 5:15pm-7:45pm

Weekly Labs:

• 410: Wednesdays, 10:30-11:30am
• 411: Wednesdays, 1:00-2:00pm
• 412: Wednesdays, 3:30-4:30pm
• 413: Thursdays, 12:45-1:45pm
• 414: Thursdays, 1:45-2:45pm

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:

• Your CV/resume
• A brief email/cover letter detailing your relevant experience
• A list of your availability for lecture and lab sections
• Submit a 5min video of you teaching how to: 1) import a simple two-variable data set into R; and 2) generate a scatterplot with a registration line. Upload the video via Zoom (instructions below).

To submit your video:

• Record your video on Zoom. Start a new meeting + click on record. Record to the cloud.
• Once your video is complete, Zoom will send you an email.
• To share the video with MSSP, please see Zoom instructions on how to "How to share, download, and delete cloud recordings" at: https://support.zoom.com/hc/en/article?id=zm_kb&sysparm_article=KB0067567
  (you will have to scroll down the page a bit for the relevant section).
  

Job Location - City, StatePhiladelphia, PennsylvaniaDepartment / SchoolSchool of Social Policy and PracticePay Range$7.25 - $50.00 Hourly Rate

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.

Affirmative Action Statement
Penn adheres to a policy that prohibits discrimination on the basis of race, color, sex, sexual orientation, gender identity, religion, creed, national or ethnic origin, citizenship status, age, disability, veteran status, or any other legally protected class.Special Requirements
Background check may be required after a conditional job offer is made. Consideration of the background check will be tailored to the requirements of the job.

---