About this course: You may never be sure whether you have an effective user experience until you have tested it with users. In this course, you’ll learn how to design user-centered experiments, how to run such experiments, and how to analyze data from these experiments in order to evaluate and validate user experiences. You will work through real-world examples of experiments from the fields of UX, IxD, and HCI, understanding issues in experiment design and analysis. You will analyze multiple data sets using recipes given to you in the R statistical programming language -- no prior programming experience is assumed or required, but you will be required to read, understand, and modify code snippets provided to you. By the end of the course, you will be able to knowledgeably design, run, and analyze your own experiments that give statistical weight to your designs.
Who is this class for: This class is at the level of advanced undergraduates or first-year graduate students in fields related to human-computer interaction, computer science, interaction design, user experience design, user-centered design, or human-technology behavior studies.
Created by: University of California, San Diego
Taught by: Scott Klemmer, Associate Professor
Cognitive Science & Computer Science
Taught by: Jacob O. Wobbrock, Associate Professor
The Information School
| Basic Info | Course 7 of 8 in the Interaction Design Specialization |
| Commitment | 9 weeks, 3-4 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Basic Experiment Design Concepts
In this module, you will learn basic concepts relevant to the design and analysis of experiments, including mean comparisons, variance, statistical significance, practical significance, sampling, inclusion and exclusion criteria, and informed consent. You’ll also learn to think of an experiment in terms of its participants, apparatus, procedure, and design & analysis. This module covers lecture videos 1-2.
2 videos, 1 reading
- Video: 01. What You Will Learn in this Course
- Reading: All Course Materials
- Video: 02. Basic Experiment Design Concepts
Graded: Understanding the Basics
WEEK 2
Tests of Proportions
In this module, you will learn how to analyze user preferences (or other tallies) using tests of proportions. You will also get up and running with R and RStudio. Topics covered include independent and dependent variables, variable types, exploratory data analysis, p-values, asymptotic tests, exact tests, one-sample tests, two-sample tests, Chi-Square test, G-test, Fisher’s exact test, binomial test, multinomial test, post hoc tests, and pairwise comparisons. This module covers lecture videos 3-9.
7 videos
- Video: 03. Description of a Study of User Preferences
- Video: 04. Getting Started with R and RStudio
- Video: 05. Exploring Data and a First Test of Proportions
- Video: 06. Understanding and Reporting Your First Statistical Test
- Video: 07. Exact Tests, Asymptotic Tests, and the Binomial Test
- Video: 08. More One-Sample Tests of Proportions
- Video: 09. Two-Sample Tests of Proportions
Graded: Understanding Tests of Proportions
Graded: Doing Tests of Proportions
WEEK 3
The T-Test
In this module, you will learn how to design and analyze a simple website A/B test. Topics include measurement error, independent variables as factors, factor levels, between-subjects factors, within-subjects factors, dependent variables as responses, response types, balanced designs, and how to report a t-test. You will perform your first analysis of variance in the form of an independent-samples t-test. This module covers lecture videos 10-11.
2 videos
- Video: 10. Experiment Design Concepts in a Simple A/B Test
- Video: 11. Analyzing a Simple A/B Test with a T-Test
Graded: Understanding Experiment Designs
Graded: Doing Independent-Samples T-Tests
WEEK 4
Validity in Design and Analysis
In this module, you will learn about how to ensure that your data is valid through the design of experiments, and that your analyses are valid by understanding and testing for their assumptions. Topics include how to achieve experimental control, confounds, ecological validity, the three assumptions of ANOVA, data distributions, residuals, normality, homoscedasticity, parametric versus nonparametric tests, the Shapiro-Wilk test, the Kolmogorov-Smirnov test, Levene’s test, the Brown-Forsythe test, and the Mann-Whitney U test. This module covers lecture videos 12-15.
4 videos
- Video: 12. Designing for Experimental Control
- Video: 13. Data Assumptions and Distributions
- Video: 14. Testing for ANOVA Assumptions
- Video: 15. Mann-Whitney, a Nonparametric T-Test
Graded: Understanding Validity
Graded: Doing Tests of Assumptions
WEEK 5
One-Factor Between-Subjects Experiments
In this module, you will learn about one-factor between-subjects experiments. The experiment examined will be a between-subjects study of task completion time with various programming tools. You will understand and analyze data from two-level factors and three-level factors using the independent-samples t-test, Mann-Whitney U test, one-way ANOVA, and Kruskal-Wallis test. You will learn how to report an F-test. You will also understand omnibus tests and how they relate to post hoc pairwise comparisons with adjustments for multiple comparisons. This module covers lecture videos 16-18.
3 videos
- Video: 16. Description of a Study for a Oneway ANOVA
- Video: 17. Analyzing and Reporting a Oneway ANOVA
- Video: 18. Kruskal-Wallis, a Nonparametric Oneway ANOVA
Graded: Understanding Oneway Designs
Graded: Doing Oneway ANOVAs
WEEK 6
One-Factor Within-Subjects Experiments
In this module, you will learn about one-factor within-subjects experiments, also known as repeated measures designs. The experiment examined will be a within-subjects study of subjects searching for contacts in a smartphone contacts manager, including the analysis of times, errors, and effort Likert-type scale ratings. You will learn counterbalancing strategies to avoid carryover effects, including full counterbalancing, Latin Squares, and balanced Latin Squares. You will understand and analyze data from two-level factors and three-level factors using the paired-samples t-test, Wilcoxon signed-rank test, oneway repeated measures ANOVA, and Friedman test. This module covers lecture videos 19-23.
5 videos
- Video: 19. Description of a Study for a Oneway Repeated Measures ANOVA
- Video: 20. Counterbalancing Repeated Measures Factors
- Video: 21. Long-Format and Wide-Format Data Tables
- Video: 22. The Paired T-Test and Wilcoxon Signed-Rank Test
- Video: 23. Analyzing a Repeated Measures ANOVA and Friedman Test
Graded: Understanding Oneway Repeated Measures Designs
Graded: Doing Oneway Repeated Measures ANOVAs
WEEK 7
Factorial Experiment Designs
In this module, you will learn about experiments with multiple factors and factorial ANOVAs. The experiment examined will be text entry performance on different smartphone keyboards while sitting, standing, and walking. Topics include mixed factorial designs, interaction effects, factorial ANOVAs, and the Aligned Rank Transform as a nonparametric factorial ANOVA. This module covers lecture videos 24-27.
4 videos
- Video: 24. Description of a Study for a Factorial ANOVA
- Video: 25. Understanding Interaction Effects
- Video: 26. Analyzing a Factorial ANOVA
- Video: 27. The ART, a Nonparametric Factorial ANOVA
Graded: Understanding Factorial Designs
Graded: Doing Factorial ANOVAs
WEEK 8
Generalizing the Response
In this module, you will learn about analyses for non-normal or non-numeric responses for between-subjects experiments using Generalized Linear Models (GLM). We will revisit three previous experiments and analyze them using generalized models. Topics include a review of response distributions, nominal logistic regression, ordinal logistic regression, and Poisson regression. This module covers lecture videos 28-29.
2 videos
- Video: 28. Introduction to Generalized Linear Models
- Video: 29. Analyzing Three Generalized Linear Models
Graded: Understanding Generalized Linear Models
Graded: Doing Generalized Linear Models
WEEK 9
The Power of Mixed Effects Models
In this module, you will learn about mixed effects models, specifically Linear Mixed Models (LMM) and Generalized Linear Mixed Models (GLMM). We will revisit our prior experiment on text entry performance on smartphones but this time, keeping every single measurement trial as part of the analysis. The full set of analyses covered in this course will also be reviewed. This module covers lecture videos 30-33.
4 videos
- Video: 30. Introduction to Mixed Effects Models
- Video: 31. Analyzing a Linear Mixed Model
- Video: 32. Analyzing a Generalized Linear Mixed Model
- Video: 33. Course in Review
Graded: Understanding Mixed Effects Models
Graded: Doing Mixed Effects Models
FAQs
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UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
Pricing
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
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Ratings and Reviews
Very useful!!!
great course
Have to watch three times to keep up because it is really hard for people without former experiences... Anyway it is worth it
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b
Great course. The title is speaking and I found what I was looking for.
Great balance between theory (not much) and application (a bit more).
I used different statistical tools in different situation,
everything in R (you mast have a bit of knowledge of it).
Recommended.