Introduction to Quantitative Methods in Social Science
Introduction to quantitative methods in social science focusing on empirical consumer science research, data structures, and bivariate/multivariate linear regression models.
Summary
Recent recorded grades — Fall 2024: 3.86 GPA, 100.0% A/AB (n=7 letter grades); Fall 2025: 3.35 GPA, 50.0% A/AB (n=10 letter grades).
What you’ll be able to do
Ascertain differences in data structures and key statistical properties to understand types of quantitative data used in consumer science research
Master technical proficiency in linear regression and statistical inference by applying these techniques to real-world consumer data
Critically assess estimates derived from linear models commonly used in consumer research by evaluating the accuracy, precision, and relevance of model estimates to draw meaningful insights from empirical analyses
Discern the suitability of model assumptions for different empirical challenges in consumer research by evaluating assumptions and selecting and modifying models to best fit the specific characteristics of the data and the nature of the research question
Source: UW Guide
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapGraduate/professional standing
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- Graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
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Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
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Student experience
the class
Recent recorded grades — Fall 2024: 3.86 GPA, 100.0% A/AB (n=7 letter grades); Fall 2025: 3.35 GPA, 50.0% A/AB (n=10 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Empirical consumer science research methods
- Data structures and statistical properties
- Bivariate and multivariate linear regression models
Skills
- Identify data structures and statistical properties in consumer science research
- Apply linear regression and statistical inference to real-world consumer data
- Assess accuracy, precision, and relevance of linear model estimates
- Evaluate model assumptions and select or modify models for specific data
Grades
Latest available · Fall 2025— not enough history to project Spring 2027.
Grade distribution · % of letter grades
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Through Fall 2025
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
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Catalog & offerings
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Grade history
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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
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"output_id": "70e1c1365b24d7fdf7b332abb95d3d5910fbea3caa08da438582b3bbab7cf1b9",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}