Analysis and Interpretation of Social Data (303-0-20)

Instructors

Beth Redbird
Beth Redbird studies Native American Inequality and how boundaries impact wellbeing. She teaches Native studies and Inequality. She also has a cute dog that comes to class (and is way more popular than she is).

Meeting Info

555 Clark B03: Mon, Wed 12:30PM - 1:50PM

Overview of class

This course introduces statistics and data analysis for the social sciences, focusing on understanding, interpreting, and deploying data and statistical analysis to understand the social world.

The course begins without numbers, encouraging students to be critical and analytical of the data they encounter every day. Using examples from policy, journalism, and the election, students will practice reading, interpreting, and critiquing empirical analyses.

After gaining familiarity with the reasoning underlying data analysis, the second part of the course will introduce basic statistical analysis. Students will collect, analyze, and interpret data in an area of their interest. The goal is for students to critically engage with statistical topics - to understand the strengths, weaknesses, assumptions, and contributions of statistics to scientific understanding and exploration.

Finally, the course will explore how computation is remaking modern social understanding. Though a focus on machine learning and neural networks, students will explore the contribution of data to human knowledge, while also gaining insight on why such methods pose serious challenges to human well-being.

While not a programming course, students will do exercises and homework using free tools, such as google sheets and the statistical software "R". Labs will be focused on gaining proficiency with these tools.

Learning Objectives

By the end of the course, students should be able to:
• Identity and explain the primary conclusions, assumptions, and scope conditions of analyses used in policy and journalism discussions.
• Articulate the meaning and significance of scientific findings.
• Discuss the validity and accuracy, and challenges therein, of scientific measures.
• Identify the populations, parameters, samples and statistics from a study.
• Critique, evaluate, and re-imagine data plots and figures to effectively convey conclusions.
• Interpret, explain, and articulate the underlying logic of summary statistics, correlations, hypothesis tests, linear regressions, and learning models.
• Conduct analysis of data, including summary statistics, correlations, hypothesis tests, and linear regression using a statistical software.

Teaching Method

Class + Discussion section

Evaluation Method

Weekly assignments and final project

Class Materials (Required)

All materials for this course will be made available on Canvas - no purchase necessary.

Class Attributes

Empirical and Deductive Reasoning Foundational Dis

Enrollment Requirements

Enrollment Requirements: Pre-registration is reserved for Sociology Majors and Minors.

Associated Classes

LAB - University Hall 121: Wed 5:00PM - 5:50PM