Spring 2027

Design and Analysis of Psychological Experiments II

PSYCH 710 is an advanced course in experimental design and analysis focused on within-subjects designs, mixed models, power analysis, mediation, contrast analysis, and related statistical methods.

offering recorded4 credits
Recorded instructors · Spring 2027 Markus Brauer4.2/5

Summary

1 / 3

Recent recorded grades — Spring 2024: 3.79 GPA, 87.5% A/AB (n=40 letter grades); Spring 2025: 3.67 GPA, 76.3% A/AB (n=38 letter grades); Spring 2026: 3.74 GPA, 87.1% A/AB (n=31 letter grades).

What you’ll be able to do

  • Master analysis of a single and multiple dichotomous within-subject predictors (within-subjects ANOVA)

  • Master analysis of a single and multiple continuous within-subject predictors (mixed models)

  • Master analysis of indirect effects in within-subjects designs (mediation)

  • Master analysis of categorical predictors with 3 or more levels (contrast analysis)

  • Master power analysis

  • Master analysis of dichotomous outcome variables (logistic regression

Source: UW Guide

Grade history

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average GPA
letter grades
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Prerequisites

Course map

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PSYCH 710

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Professors

Spring 2027

Markus Brauer

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.84/5 from 19 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

RMP profile ↗ · All captured review dates; profile matched by name.

Recent recorded grades — Spring 2020: 3.83 GPA, 100.0% A/AB (n=24 letter grades); Spring 2022: 3.81 GPA, 90.5% A/AB (n=21 letter grades); Spring 2026: 3.74 GPA, 87.1% A/AB (n=31 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

MARKUS BRAUER is recorded teaching in Spring 2007, Fall 2011, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2022, Spring 2026. 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 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LAB 301Classroom Instruction0 / 160
LEC 001Classroom Instruction0 / 480
LAB 302Classroom Instruction0 / 160
LAB 303Classroom Instruction0 / 160

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Recent recorded grades — Spring 2024: 3.79 GPA, 87.5% A/AB (n=40 letter grades); Spring 2025: 3.67 GPA, 76.3% A/AB (n=38 letter grades); Spring 2026: 3.74 GPA, 87.1% A/AB (n=31 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Within-subjects ANOVA and analysis of categorical within-subject predictors.
  • Mixed models and linear mixed-effects modeling for repeated measures.
  • Power analysis for experimental designs.
  • Mediation analysis in within-subjects designs.
  • Contrast analysis for categorical predictors with three or more levels.
  • Logistic regression for dichotomous outcomes.
  • Signal detection theory.
  • Multilevel modeling, restricted maximum likelihood, exploratory factor analysis, and missing-data methods.

Skills

  • Fit and interpret within-subjects ANOVA for dichotomous within-subject predictors.
  • Specify and evaluate mixed models for continuous within-subject predictors.
  • Analyze indirect effects in within-subjects designs using mediation methods.
  • Construct and interpret contrasts for categorical predictors with three or more levels.
  • Conduct power analysis for planned experiments.
  • Model dichotomous outcome variables using logistic regression.
  • Apply signal detection theory to perception or decision-making data.
  • Use exploratory factor analysis and address missing data in applied analyses.

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
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F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

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.

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Selected offering source records
PSYCH 710 · Spring 2027

Design and Analysis of Psychological Experiments II

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:820:016502",
    "course_id": "PSYCH 710",
    "course_uid": "course_d5eb02e4f4bcae9fed26defe",
    "term_id": "1274",
    "source_course_id": "016502",
    "source_subject_id": "820",
    "title": "Design and Analysis of Psychological Experiments II",
    "credits_min": 4,
    "credits_max": 4,
    "typically_offered": "Occasionally"
  }
]
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Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "93d9ab1177aef02b8fc9e82eac98d3fbe07c34f525fb95f2899bd437d90b8fbb",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}