Statistics for Sociologists II
Statistics for Sociologists II covers applied linear regression modeling for social scientists, including diagnostics and corrections, using Stata and/or SAS for dataset creation and analysis.
Summary
Past student reviews · John Logan
Workload was heavy, with one student reporting 4–6 hours per homework assignment and another estimating 20 hours weekly; some terms had no exams, only quizzes and substantial assignments.
What you’ll be able to do
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.
Find, download, and format social science data suitable for analysis.
Make, interpret, and critique advanced specifications involving dummy variables, interactions, and nonlinear transformations.
Use diagnostics for various threats to model fit and interpretation.
Establish a solid foundation for studying the many methods that are extensions of linear regression (including logistic regression).
Achieve a deep understanding of ordinary least squares regression (OLS).
Become expert at presenting quantitative results from OLS models in a substantively meaningful and accessible way.
Understand and critique the ways in which OLS is used in the field.
Prepare oneself to write an article-length description of an independent research project that uses linear or logistic regression.
Source: UW Guide 1· UW Guide 2
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapC&E SOC/SOC 360,ECON 310,PSYCH 210,MATH/STAT 310,STAT 301, or graduate/professional standing
- C&ESOC/SOC 360
- ECON 310
- PSYCH 210
- MATH/STAT 310
- STAT 301
- graduate/professional standing
“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Any of
- C&ESOC/SOC 360
- ECON 310
- PSYCH 210
- MATH/STAT 310
- STAT 301
- graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
ERIC GRODSKY is recorded teaching in Fall 2016, Fall 2018, Fall 2020, Fall 2021. Recorded history may be incomplete and does not establish a future schedule.
JOHN LOGAN is recorded teaching in Fall 2006, Spring 2007, Fall 2007, Spring 2008, Fall 2008, Spring 2009, Fall 2009, Spring 2010, Fall 2011, Spring 2012, Fall 2012, Fall 2013, Spring 2014, Fall 2014, Fall 2015, Spring 2016, Spring 2017, Fall 2017, Spring 2018, Spring 2019, Spring 2020. Recorded history may be incomplete and does not establish a future schedule.
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
Student experience
the class
Past student reviews · Eric Grodsky
The class is difficult, with recent feedback describing Eric Grodsky's lectures as engaging and concept-focused, while an earlier student found his teaching unclear and unhelpful.
Recent recorded grades — Fall 2023: 3.30 GPA, 65.8% A/AB (n=38 letter grades); Fall 2024: 3.50 GPA, 79.3% A/AB (n=29 letter grades); Fall 2025: 2.89 GPA, 50.0% A/AB (n=14 letter grades).
difficulty & workload
Past student reviews · John Logan
Workload was heavy, with one student reporting 4–6 hours per homework assignment and another estimating 20 hours weekly; some terms had no exams, only quizzes and substantial assignments.
Past student reviews · John Logan
Students had mixed experiences with clarity: one found the textbook and TA sufficient to earn good grades despite unclear lectures, while another cited disorganized grading and poor instruction as detrimental to their performance.
Topics
- Linear regression specifications: bivariate/multiple regression, dummy variables, interactions, nonlinear relationships, indirect effects, omitted variable bias, outliers, heteroskedasticity, multicollinearity.
- Diagnostics and corrections for regression model fit and interpretation.
- Dataset creation and analysis using Stata and/or SAS.
Skills
- Apply bivariate and multiple regression, dummy variables, interactions, nonlinear relationships, and diagnostics for model threats.
- Use Stata and/or SAS for dataset creation and analysis.
- Find, download, and format social science data suitable for analysis.
Grades
Latest available · Fall 2025— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Fall 2025
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
No offering records for the selected term.
Raw records
[]
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "68c36e86dce11a7ffc9383917b666fe5229e816eeaa3ff588c549251ec2ad794",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}