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

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Posted Job Title

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

Job Profile Title

Temporary Employee - Non-Exempt

Job Description Summary

Job 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 cour