Analysis and Interpretation of Social Data (303-DL-20)

Instructors

Austin Abernethy Stimpson Jenkins

Meeting Info

Online: TBA

Overview of class

This course will introduce students to the techniques, concepts, and terminology used in the quantitative analysis of social data. The course will be grounded in practical exercises in which students will analyze real-world data on health disparities, police interactions with the public, party politics, and other important social issues. Students will learn how to find and access public-use social data and how to import, clean, merge, summarize, and visualize such data in the programming language R. Students will also learn the fundamental principles of probability underlying inferential statistics, or the process of using sample data to make inferences about broader populations. Finally, students will become fluent in the language of statistics, enabling them to discuss their own research in a precise and professional manner and to understand and evaluate quantitative research done by others. Prerequisite: none.

This course is conducted completely online. A technology fee will be added to tuition.

Registration Requirements

This course is limited to School of Professional Studies students only. Undergraduate students in other schools at Northwestern are not permitted to enroll in this course.

Prerequisites: none

Learning Objectives

By the end of this course, students will be able to:

Recognize, define, and properly use the basic terminology of statistics
Find and download public-use social data from government agencies and scholarly archives
Import, clean, merge, summarize and visualize social data in R
Use sample data to construct confidence intervals for population parameters
Conduct basic hypothesis tests, including for differences in two proportions, differences in two or more means, the significance of regression coefficients, and overall model significance
Interpret regression models and compare them by fit and parsimony
Identify and address common problems in quantitative analysis, including sampling bias, omitted variable bias, and violations of model assumptions

Class Attributes

Asynchronous:Remote class-no scheduled mtg time