STATISTICS FOR SOCIOLOGISTS II

Prerequisites & course information

Requisites: C&E SOC/SOC 360 , ECON 310 , PSYCH 210 , MATH/STAT 310 , STAT 301 , or graduate/professional standing Course Designation: Core GenEd - Natural Science & Wellness + Lab Level - Advanced L&S Credit - L&S Liberal Arts and Science Grad 50% - Counts toward 50% graduate coursework requirement Honors - Accelerated Honors (!) Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Prepare oneself to conduct one's own research using linear and logistic regression analysis and secondary data sources, at a level befitting beginning research assistants on faculty research projects. Audience: Both Grad & Undergrad 2. Find, download, and format social science data suitable for analysis. Audience: Both Grad & Undergrad 3. Make, interpret, and critique advanced specifications involving dummy variables, interactions, and nonlinear transformations. Audience: Both Grad & Undergrad 4. Use diagnostics for various threats to model fit and interpretation. Audience: Both Grad & Undergrad 5. Establish a solid foundation for studying the many methods that are extensions of linear regression (including logistic regression). Audience: Both Grad & Undergrad 6. Achieve a deep understanding of ordinary least squares regression (OLS). Audience: Both Grad & Undergrad 7. Become expert at presenting quantitative results from OLS models in a substantively meaningful and accessible way. Audience: Both Grad & Undergrad 8. Understand and critique the ways in which OLS is used in the field. Audience: Both Grad & Undergrad 9. Prepare oneself to write an article-length description of an independent research project that uses linear or logistic regression. Audience: Graduate Learning Outcomes: 1. Prepare oneself to conduct one's own research using linear and logistic regression analysis and secondary data sources, at a level befitting beginning research assistants on faculty research projects. Audience: Both Grad & Undergrad 2. Find, download, and format social science data suitable for analysis. Audience: Both Grad & Undergrad 3. Make, interpret, and critique advanced specifications involving dummy variables, interactions, and nonlinear transformations. Audience: Both Grad & Undergrad 4. Use diagnostics for various threats to model fit and interpretation. Audience: Both Grad & Undergrad 5. Establish a solid foundation for studying the many methods that are extensions of linear regression (including logistic regression). Audience: Both Grad & Undergrad 6. Achieve a deep understanding of ordinary least squares regression (OLS). Audience: Both Grad & Undergrad 7. Become expert at presenting quantitative results from OLS models in a substantively meaningful and accessible way. Audience: Both Grad & Undergrad 8. Understand and critique the ways in which OLS is used in the field. Audience: Both Grad & Undergrad 9. Prepare oneself to write an article-length description of an independent research project that uses linear or logistic regression. Audience: Graduate